diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.1 b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.1
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.11 b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.11
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.13 b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.13
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.14 b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.14
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.15 b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.15
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.24 b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.24
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.28 b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.28
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.29 b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.29
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.30 b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.30
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.4 b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.4
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.5 b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.5
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.6 b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.6
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.8 b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.8
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.9 b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/done.2641097.9
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/stats.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/stats.log
new file mode 100644
index 0000000000000000000000000000000000000000..bc6399c689963ee504911d8139215e85982e1205
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/stats.log
@@ -0,0 +1 @@
+Submitted batch job 2133206
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/stats.sh b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/stats.sh
new file mode 100644
index 0000000000000000000000000000000000000000..76bcbf3b4099790ec2f29e05ae2b311c268323b5
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/stats.sh
@@ -0,0 +1,29 @@
+#!/bin/bash
+cd /scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+. ./path.sh
+( echo '#' Running on `hostname`
+  echo '#' Started at `date`
+  set | grep SLURM | while read line; do echo "# $line"; done
+  echo -n '# '; cat <<EOF
+python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.${SLURM_ARRAY_TASK_ID}.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.${SLURM_ARRAY_TASK_ID}.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.${SLURM_ARRAY_TASK_ID} --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+EOF
+) >exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.$SLURM_ARRAY_TASK_ID.log
+if [ "$CUDA_VISIBLE_DEVICES" == "NoDevFiles" ]; then
+  ( echo CUDA_VISIBLE_DEVICES set to NoDevFiles, unsetting it... 
+  )>>exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.$SLURM_ARRAY_TASK_ID.log
+  unset CUDA_VISIBLE_DEVICES
+fi
+time1=`date +"%s"`
+ ( python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.${SLURM_ARRAY_TASK_ID}.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.${SLURM_ARRAY_TASK_ID}.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.${SLURM_ARRAY_TASK_ID} --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text  ) &>>exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.$SLURM_ARRAY_TASK_ID.log
+ret=$?
+sync || true
+time2=`date +"%s"`
+echo '#' Accounting: begin_time=$time1 >>exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.$SLURM_ARRAY_TASK_ID.log
+echo '#' Accounting: end_time=$time2 >>exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.$SLURM_ARRAY_TASK_ID.log
+echo '#' Accounting: time=$(($time2-$time1)) threads=1 >>exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.$SLURM_ARRAY_TASK_ID.log
+echo '#' Finished at `date` with status $ret >>exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.$SLURM_ARRAY_TASK_ID.log
+[ $ret -eq 137 ] && exit 100;
+touch exp/asr_stats_multilingual_1h_lid_1h/logdir/q/done.3643963.$SLURM_ARRAY_TASK_ID
+exit $[$ret ? 1 : 0]
+## submitted with:
+# sbatch --export=PATH  -p cpu --account=bbjs-delta-cpu --time 48:00:00 --mem-per-cpu 4g  --open-mode=append -e exp/asr_stats_multilingual_1h_lid_1h/logdir/q/stats.log -o exp/asr_stats_multilingual_1h_lid_1h/logdir/q/stats.log --array 1-32 /scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1/exp/asr_stats_multilingual_1h_lid_1h/logdir/q/stats.sh >>exp/asr_stats_multilingual_1h_lid_1h/logdir/q/stats.log 2>&1
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1.log
new file mode 100644
index 0000000000000000000000000000000000000000..aa841fceeeda3d5713c508cd40707e68ce0134c7
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1.log
@@ -0,0 +1,157 @@
+# Running on cn001.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:14 CDT 2023
+# SLURMD_NODENAME=cn001
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=1
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133207
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133207
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn001
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn001
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3660391
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn001
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.1.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.1.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.1 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.1.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.1.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.1 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn001] 2023-07-06 08:52:51,877 (asr:475) INFO: Vocabulary size: 6454
+[cn001] 2023-07-06 08:52:53,975 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1214) INFO: Scheduler: None
+[cn001] 2023-07-06 08:52:53,983 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.1/config.yaml
+[cn001] 2023-07-06 08:52:54,084 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.1', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 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'尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', 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'喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.1.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.1.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn001] 2023-07-06 08:53:08,704 (collect_stats:106) INFO: Niter: 100
+[cn001] 2023-07-06 08:53:14,169 (collect_stats:106) INFO: Niter: 200
+[cn001] 2023-07-06 08:53:19,464 (collect_stats:106) INFO: Niter: 300
+[cn001] 2023-07-06 08:53:25,319 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651414
+# Accounting: end_time=1688651639
+# Accounting: time=225 threads=1
+# Finished at Thu Jul 6 08:53:59 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..53e263f86fd1b8a4c19a575e647fb522c91d0540
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.1
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.1.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.1.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
+- ໂ
+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
+- 기
+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
+- 前
+- ዊ
+- Ҟ
+- 世
+- ญ
+- イ
+- 公
+- ໜ
+- え
+- ト
+- ၏
+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
+- ္
+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
+- ッ
+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
+- 西
+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
+- 加
+- 教
+- 國
+- ඩ
+- 道
+- 市
+- 表
+- 立
+- 而
+- 食
+- ޒ
+- Ψ
+- 動
+- ័
+- ¡
+- 尔
+- ዜ
+- 亚
+- 化
+- 先
+- 性
+- 形
+- 里
+- 後
+- ሜ
+- ፊ
+- ቹ
+- 力
+- ド
+- 呢
+- 佢
+- …
+- Ṛ
+- ഏ
+- ሱ
+- 比
+- 高
+- 起
+- 球
+- ዙ
+- 拉
+- 后
+- 体
+- 月
+- 但
+- 話
+- 적
+- Ӡ
+- Ӳ
+- 克
+- 使
+- Ჯ
+- 부
+- 心
+- ੌ
+- ੜ
+- ‌
+- 们
+- 更
+- 正
+- 라
+- 보
+- ቦ
+- ዶ
+- 美
+- 也
+- 南
+- タ
+- ஜ
+- ජ
+- ሙ
+- ፕ
+- 現
+- ૃ
+- 일
+- ຜ
+- ਯ
+- ఖ
+- 口
+- 因
+- ロ
+- ൃ
+- ೃ
+- 式
+- ޝ
+- Ẓ
+- 했
+- ቤ
+- 十
+- 区
+- み
+- Ķ
+- 나
+- ۍ
+- ఏ
+- ං
+- 外
+- 海
+- 重
+- 天
+- 語
+- ۰
+- ៀ
+- 전
+- ඇ
+- 全
+- 被
+- 面
+- ឆ
+- 것
+- ධ
+- ፋ
+- 字
+- 发
+- ऑ
+- テ
+- 頭
+- ዱ
+- 身
+- 果
+- ஏ
+- ඒ
+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
+- カ
+- 無
+- 場
+- 來
+- 啲
+- ඔ
+- ඉ
+- ቲ
+- ጅ
+- 直
+- 德
+- 些
+- 或
+- 開
+- 게
+- 주
+- ቴ
+- ቁ
+- ሶ
+- 期
+- ろ
+- Ṱ
+- ኔ
+- ቋ
+- ኩ
+- 阿
+- 提
+- 音
+- ジ
+- 電
+- ऱ
+- 상
+- ጽ
+- ቆ
+- 动
+- 民
+- 然
+- 做
+- 金
+- フ
+- シ
+- 만
+- උ
+- ޕ
+- ク
+- ፣
+- ०
+- १
+- 解
+- 真
+- 空
+- 機
+- コ
+- Ჭ
+- 冇
+- ฐ
+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
+- 工
+- 英
+- 代
+- Ģ
+- ූ
+- ዩ
+- ̃
+- 北
+- 如
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+- ൺ
+- 정
+- 제
+- ਝ
+- ፓ
+- ·
+- 伊
+- 非
+- ៏
+- ጡ
+- ጫ
+- ቂ
+- 何
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+- ጨ
+- ሻ
+- 及
+- 属
+- 原
+- 這
+- 트
+- ሸ
+- Џ
+- 城
+- 影
+- 次
+- 安
+- べ
+- Ჰ
+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
+- 었
+- ਏ
+- ౌ
+- ኦ
+- 建
+- 政
+- 巴
+- Ɓ
+- 々
+- 識
+- 種
+- 여
+- ڀ
+- ኙ
+- 过
+- 制
+- 始
+- 数
+- 社
+- 死
+- ޢ
+- 車
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+- ଝ
+- ๆ
+- 장
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+- 光
+- 四
+- 由
+- 著
+- ः
+- 彼
+- 點
+- ೌ
+- 되
+- ໆ
+- ሴ
+- 想
+- 任
+- 当
+- 站
+- 感
+- 住
+- 未
+- 旅
+- 持
+- バ
+- 問
+- 學
+- ۱
+- ૌ
+- 할
+- 와
+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
+- 已
+- 造
+- 管
+- 必
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+- ィ
+- パ
+- Ǧ
+- 미
+- 유
+- 위
+- 면
+- 원
+- ච
+- ጸ
+- 对
+- 很
+- 史
+- 客
+- 考
+- ャ
+- オ
+- ഓ
+- ዓ
+- ሃ
+- 동
+- 물
+- ආ
+- ቷ
+- ኪ
+- ঃ
+- 称
+- 活
+- 落
+- へ
+- マ
+- ニ
+- ょ
+- 記
+- 発
+- ഠ
+- 發
+- 咗
+- ឋ
+- 국
+- 现
+- 兰
+- 基
+- 平
+- 保
+- 火
+- 情
+- ほ
+- 気
+- ୌ
+- ٠
+- 거
+- 們
+- ෂ
+- ኞ
+- ፎ
+- ጃ
+- ঝ
+- ڤ
+- 经
+- 信
+- 布
+- 思
+- 打
+- 結
+- ฮ
+- 嘢
+- ̇
+- 우
+- ໝ
+- ፖ
+- 量
+- 曾
+- 车
+- 品
+- 速
+- 件
+- 於
+- ۲
+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
+- 」
+- 石
+- 交
+- 司
+- チ
+- ず
+- 題
+- 對
+- ஞ
+- 睇
+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
+- 五
+- ޗ
+- ュ
+- ଵ
+- ১
+- 세
+- 구
+- 경
+- 비
+- ။
+- ዴ
+- 士
+- 县
+- 台
+- 业
+- 林
+- 存
+- 着
+- 集
+- 足
+- 它
+- じ
+- ム
+- 観
+- ഘ
+- 經
+- 邊
+- ஷ
+- 、
+- ጎ
+- 계
+- ဇ
+- ਛ
+- ጂ
+- ጭ
+- 场
+- 故
+- 型
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+- 告
+- 星
+- 等
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+- 공
+- 며
+- 간
+- ኳ
+- ኘ
+- Œ
+- 镇
+- 罗
+- 长
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+init: null
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+ctc_conf:
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+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
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+noise_db_range: '13_15'
+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/train/batch_keys
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--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/train/feats_lengths_stats.npz
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index 0000000000000000000000000000000000000000..aab80277994e1031645ab2a1e75877f5a9fd812c
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/train/feats_lengths_stats.npz
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+version https://git-lfs.github.com/spec/v1
+oid sha256:a2e13e694437499595a18ddc09d1fd15fbd9b05303faa6e7ab4152a7801aa514
+size 778
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/train/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..770eda9aff49842c8a97bb456754bd936ce05195
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/valid/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..ab736dea1c18137d6d8ece7723712fd9ec2b95de
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1/valid/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10.log
new file mode 100644
index 0000000000000000000000000000000000000000..2de21e1cb37d441c7dd53448889aaa28c7beed9c
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10.log
@@ -0,0 +1,157 @@
+# Running on cn001.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:13 CDT 2023
+# SLURMD_NODENAME=cn001
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=10
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133216
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133216
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn001
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn001
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3660347
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn001
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.10.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.10.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.10 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.10.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.10.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.10 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn001] 2023-07-06 08:52:51,877 (asr:475) INFO: Vocabulary size: 6454
+[cn001] 2023-07-06 08:52:53,975 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn001] 2023-07-06 08:52:53,976 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1214) INFO: Scheduler: None
+[cn001] 2023-07-06 08:52:53,995 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.10/config.yaml
+[cn001] 2023-07-06 08:52:54,134 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.10', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', 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'尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', 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'喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.10.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.10.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn001] 2023-07-06 08:53:06,874 (collect_stats:106) INFO: Niter: 100
+[cn001] 2023-07-06 08:53:14,338 (collect_stats:106) INFO: Niter: 200
+[cn001] 2023-07-06 08:53:19,409 (collect_stats:106) INFO: Niter: 300
+[cn001] 2023-07-06 08:53:24,180 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651413
+# Accounting: end_time=1688651639
+# Accounting: time=226 threads=1
+# Finished at Thu Jul 6 08:53:59 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b94be58f4a81d64f827959506df245bbb0b77bf5
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.10
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.10.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.10.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
+- ໂ
+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
+- 기
+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
+- 前
+- ዊ
+- Ҟ
+- 世
+- ญ
+- イ
+- 公
+- ໜ
+- え
+- ト
+- ၏
+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
+- ္
+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
+- ッ
+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
+- 西
+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
+- 加
+- 教
+- 國
+- ඩ
+- 道
+- 市
+- 表
+- 立
+- 而
+- 食
+- ޒ
+- Ψ
+- 動
+- ័
+- ¡
+- 尔
+- ዜ
+- 亚
+- 化
+- 先
+- 性
+- 形
+- 里
+- 後
+- ሜ
+- ፊ
+- ቹ
+- 力
+- ド
+- 呢
+- 佢
+- …
+- Ṛ
+- ഏ
+- ሱ
+- 比
+- 高
+- 起
+- 球
+- ዙ
+- 拉
+- 后
+- 体
+- 月
+- 但
+- 話
+- 적
+- Ӡ
+- Ӳ
+- 克
+- 使
+- Ჯ
+- 부
+- 心
+- ੌ
+- ੜ
+- ‌
+- 们
+- 更
+- 正
+- 라
+- 보
+- ቦ
+- ዶ
+- 美
+- 也
+- 南
+- タ
+- ஜ
+- ජ
+- ሙ
+- ፕ
+- 現
+- ૃ
+- 일
+- ຜ
+- ਯ
+- ఖ
+- 口
+- 因
+- ロ
+- ൃ
+- ೃ
+- 式
+- ޝ
+- Ẓ
+- 했
+- ቤ
+- 十
+- 区
+- み
+- Ķ
+- 나
+- ۍ
+- ఏ
+- ං
+- 外
+- 海
+- 重
+- 天
+- 語
+- ۰
+- ៀ
+- 전
+- ඇ
+- 全
+- 被
+- 面
+- ឆ
+- 것
+- ධ
+- ፋ
+- 字
+- 发
+- ऑ
+- テ
+- 頭
+- ዱ
+- 身
+- 果
+- ஏ
+- ඒ
+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
+- カ
+- 無
+- 場
+- 來
+- 啲
+- ඔ
+- ඉ
+- ቲ
+- ጅ
+- 直
+- 德
+- 些
+- 或
+- 開
+- 게
+- 주
+- ቴ
+- ቁ
+- ሶ
+- 期
+- ろ
+- Ṱ
+- ኔ
+- ቋ
+- ኩ
+- 阿
+- 提
+- 音
+- ジ
+- 電
+- ऱ
+- 상
+- ጽ
+- ቆ
+- 动
+- 民
+- 然
+- 做
+- 金
+- フ
+- シ
+- 만
+- උ
+- ޕ
+- ク
+- ፣
+- ०
+- १
+- 解
+- 真
+- 空
+- 機
+- コ
+- Ჭ
+- 冇
+- ฐ
+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
+- 工
+- 英
+- 代
+- Ģ
+- ූ
+- ዩ
+- ̃
+- 北
+- 如
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+- ൺ
+- 정
+- 제
+- ਝ
+- ፓ
+- ·
+- 伊
+- 非
+- ៏
+- ጡ
+- ጫ
+- ቂ
+- 何
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+- ጨ
+- ሻ
+- 及
+- 属
+- 原
+- 這
+- 트
+- ሸ
+- Џ
+- 城
+- 影
+- 次
+- 安
+- べ
+- Ჰ
+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
+- 었
+- ਏ
+- ౌ
+- ኦ
+- 建
+- 政
+- 巴
+- Ɓ
+- 々
+- 識
+- 種
+- 여
+- ڀ
+- ኙ
+- 过
+- 制
+- 始
+- 数
+- 社
+- 死
+- ޢ
+- 車
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+- ଝ
+- ๆ
+- 장
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+- 光
+- 四
+- 由
+- 著
+- ः
+- 彼
+- 點
+- ೌ
+- 되
+- ໆ
+- ሴ
+- 想
+- 任
+- 当
+- 站
+- 感
+- 住
+- 未
+- 旅
+- 持
+- バ
+- 問
+- 學
+- ۱
+- ૌ
+- 할
+- 와
+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
+- 已
+- 造
+- 管
+- 必
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+- ィ
+- パ
+- Ǧ
+- 미
+- 유
+- 위
+- 면
+- 원
+- ච
+- ጸ
+- 对
+- 很
+- 史
+- 客
+- 考
+- ャ
+- オ
+- ഓ
+- ዓ
+- ሃ
+- 동
+- 물
+- ආ
+- ቷ
+- ኪ
+- ঃ
+- 称
+- 活
+- 落
+- へ
+- マ
+- ニ
+- ょ
+- 記
+- 発
+- ഠ
+- 發
+- 咗
+- ឋ
+- 국
+- 现
+- 兰
+- 基
+- 平
+- 保
+- 火
+- 情
+- ほ
+- 気
+- ୌ
+- ٠
+- 거
+- 們
+- ෂ
+- ኞ
+- ፎ
+- ጃ
+- ঝ
+- ڤ
+- 经
+- 信
+- 布
+- 思
+- 打
+- 結
+- ฮ
+- 嘢
+- ̇
+- 우
+- ໝ
+- ፖ
+- 量
+- 曾
+- 车
+- 品
+- 速
+- 件
+- 於
+- ۲
+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
+- 」
+- 石
+- 交
+- 司
+- チ
+- ず
+- 題
+- 對
+- ஞ
+- 睇
+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
+- 五
+- ޗ
+- ュ
+- ଵ
+- ১
+- 세
+- 구
+- 경
+- 비
+- ။
+- ዴ
+- 士
+- 县
+- 台
+- 业
+- 林
+- 存
+- 着
+- 集
+- 足
+- 它
+- じ
+- ム
+- 観
+- ഘ
+- 經
+- 邊
+- ஷ
+- 、
+- ጎ
+- 계
+- ဇ
+- ਛ
+- ጂ
+- ጭ
+- 场
+- 故
+- 型
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+- 告
+- 星
+- 等
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+- 공
+- 며
+- 간
+- ኳ
+- ኘ
+- Œ
+- 镇
+- 罗
+- 长
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+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
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+noise_db_range: '13_15'
+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
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+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/train/batch_keys
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--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/train/feats_lengths_stats.npz
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index 0000000000000000000000000000000000000000..48d2e0e8481ec7037168f3ee823b30c3760fbe6f
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/train/feats_lengths_stats.npz
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+version https://git-lfs.github.com/spec/v1
+oid sha256:ca9f22b7f310ff4aa83168e122fa8938016d1313d9e0b426571d0946bd9757b8
+size 778
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/train/stats_keys
@@ -0,0 +1,2 @@
+feats
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..dc8969c94411b1af4dbf1f8ca873e0b30f493507
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/train/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
+feats_lengths
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..2650a1211801ebc0e4a264072b8e5c96e65ede34
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.10/valid/text_shape
@@ -0,0 +1,760 @@
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+cv_kab_000984 30
+cv_kab_000985 30
+cv_kab_000989 22
+cv_kab_000993 63
+cv_kab_000997 8
+cv_kab_001001 7
+cv_kab_001005 31
+cv_kab_001009 8
+cv_kab_001013 27
+cv_kab_000986 17
+cv_kab_000990 22
+cv_kab_000994 25
+cv_kab_000998 6
+cv_kab_001002 56
+cv_kab_001006 46
+cv_kab_001010 6
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+cv_kab_000987 14
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+cv_kab_000995 40
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+cv_kab_000988 23
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+cv_kab_000996 25
+cv_kab_001000 6
+cv_kab_001004 16
+cv_kab_001008 5
+cv_kab_001012 26
+cv_kab_001016 26
+cv_kab_001017 40
+cv_kab_001021 84
+cv_kab_001025 26
+cv_kab_001029 23
+cv_kat_000003 52
+cv_kat_000007 7
+cv_kat_000011 6
+cv_kat_000015 5
+cv_kab_001018 12
+cv_kab_001022 23
+cv_kab_001026 27
+cv_kat_000000 6
+cv_kat_000004 6
+cv_kat_000008 24
+cv_kat_000012 6
+cv_kat_000016 6
+cv_kab_001019 17
+cv_kab_001023 25
+cv_kab_001027 22
+cv_kat_000001 6
+cv_kat_000005 32
+cv_kat_000009 62
+cv_kat_000013 6
+cv_kat_000017 6
+cv_kab_001020 38
+cv_kab_001024 25
+cv_kab_001028 22
+cv_kat_000002 6
+cv_kat_000006 6
+cv_kat_000010 23
+cv_kat_000014 6
+cv_kat_000018 6
+cv_kat_000019 7
+cv_kat_000023 7
+cv_kat_000027 6
+cv_kat_000031 6
+cv_kat_000035 7
+cv_kat_000039 6
+cv_kat_000043 6
+cv_kat_000047 6
+cv_kat_000020 6
+cv_kat_000024 5
+cv_kat_000028 6
+cv_kat_000032 6
+cv_kat_000036 7
+cv_kat_000040 6
+cv_kat_000044 6
+cv_kat_000048 7
+cv_kat_000021 6
+cv_kat_000025 7
+cv_kat_000029 7
+cv_kat_000033 6
+cv_kat_000037 6
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+cv_kat_000045 5
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+cv_kat_000026 7
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+cv_kat_000059 6
+cv_kat_000063 5
+cv_kat_000067 68
+cv_kat_000071 6
+cv_kat_000075 6
+cv_kat_000079 98
+cv_kat_000052 6
+cv_kat_000056 6
+cv_kat_000060 6
+cv_kat_000064 7
+cv_kat_000068 62
+cv_kat_000072 5
+cv_kat_000076 6
+cv_kat_000080 44
+cv_kat_000053 6
+cv_kat_000057 6
+cv_kat_000061 7
+cv_kat_000065 7
+cv_kat_000069 33
+cv_kat_000073 7
+cv_kat_000077 5
+cv_kat_000081 108
+cv_kat_000054 6
+cv_kat_000058 6
+cv_kat_000062 6
+cv_kat_000066 58
+cv_kat_000070 5
+cv_kat_000074 6
+cv_kat_000078 45
+cv_kat_000082 5
+cv_kat_000083 6
+cv_kat_000087 6
+cv_kat_000091 7
+cv_kat_000095 5
+cv_kat_000099 7
+cv_kat_000103 5
+cv_kat_000107 48
+cv_kat_000111 7
+cv_kat_000084 6
+cv_kat_000088 6
+cv_kat_000092 6
+cv_kat_000096 6
+cv_kat_000100 6
+cv_kat_000104 6
+cv_kat_000108 44
+cv_kat_000112 6
+cv_kat_000085 7
+cv_kat_000089 6
+cv_kat_000093 5
+cv_kat_000097 5
+cv_kat_000101 6
+cv_kat_000105 7
+cv_kat_000109 60
+cv_kat_000113 5
+cv_kat_000086 5
+cv_kat_000090 6
+cv_kat_000094 6
+cv_kat_000098 5
+cv_kat_000102 5
+cv_kat_000106 33
+cv_kat_000110 5
+cv_kat_000114 5
+cv_kat_000115 6
+cv_kat_000119 7
+cv_kat_000123 110
+cv_kat_000127 5
+cv_kat_000131 6
+cv_kat_000135 87
+cv_kat_000139 94
+cv_kat_000143 6
+cv_kat_000116 6
+cv_kat_000120 6
+cv_kat_000124 124
+cv_kat_000128 6
+cv_kat_000132 5
+cv_kat_000136 84
+cv_kat_000140 45
+cv_kat_000144 7
+cv_kat_000117 6
+cv_kat_000121 6
+cv_kat_000125 20
+cv_kat_000129 6
+cv_kat_000133 5
+cv_kat_000137 86
+cv_kat_000141 29
+cv_kat_000145 5
+cv_kat_000118 7
+cv_kat_000122 41
+cv_kat_000126 6
+cv_kat_000130 6
+cv_kat_000134 75
+cv_kat_000138 25
+cv_kat_000142 6
+cv_kat_000146 53
+cv_kat_000147 81
+cv_kat_000151 6
+cv_kat_000155 6
+cv_kat_000159 6
+cv_kat_000163 7
+cv_kat_000167 7
+cv_kaz_000743 44
+cv_kaz_000747 43
+cv_kat_000148 40
+cv_kat_000152 6
+cv_kat_000156 6
+cv_kat_000160 5
+cv_kat_000164 6
+cv_kat_000168 5
+cv_kaz_000744 29
+cv_kaz_000748 41
+cv_kat_000149 35
+cv_kat_000153 5
+cv_kat_000157 6
+cv_kat_000161 6
+cv_kat_000165 7
+cv_kaz_000741 55
+cv_kaz_000745 58
+cv_kaz_000749 21
+cv_kat_000150 6
+cv_kat_000154 6
+cv_kat_000158 6
+cv_kat_000162 6
+cv_kat_000166 6
+cv_kaz_000742 71
+cv_kaz_000746 21
+cv_kaz_000750 49
+cv_kaz_000751 45
+cv_kaz_000755 52
+cv_kaz_000759 36
+cv_kaz_000763 43
+cv_kaz_000767 29
+cv_kaz_000771 28
+cv_kaz_000775 33
+cv_kaz_000779 37
+cv_kaz_000752 34
+cv_kaz_000756 45
+cv_kaz_000760 42
+cv_kaz_000764 34
+cv_kaz_000768 72
+cv_kaz_000772 33
+cv_kaz_000776 45
+cv_kaz_000780 50
+cv_kaz_000753 39
+cv_kaz_000757 48
+cv_kaz_000761 44
+cv_kaz_000765 40
+cv_kaz_000769 47
+cv_kaz_000773 54
+cv_kaz_000777 46
+cv_kaz_000781 44
+cv_kaz_000754 22
+cv_kaz_000758 32
+cv_kaz_000762 43
+cv_kaz_000766 37
+cv_kaz_000770 34
+cv_kaz_000774 59
+cv_kaz_000778 30
+cv_kaz_000782 50
+cv_kaz_000783 23
+cv_kaz_000787 23
+cv_kaz_000791 23
+cv_kaz_000795 47
+cv_kaz_000799 32
+cv_kaz_000803 31
+cv_kaz_000807 40
+cv_kaz_000811 44
+cv_kaz_000784 48
+cv_kaz_000788 25
+cv_kaz_000792 45
+cv_kaz_000796 23
+cv_kaz_000800 49
+cv_kaz_000804 45
+cv_kaz_000808 38
+cv_kaz_000812 44
+cv_kaz_000785 43
+cv_kaz_000789 85
+cv_kaz_000793 40
+cv_kaz_000797 59
+cv_kaz_000801 26
+cv_kaz_000805 19
+cv_kaz_000809 22
+cv_kaz_000813 44
+cv_kaz_000786 40
+cv_kaz_000790 45
+cv_kaz_000794 35
+cv_kaz_000798 22
+cv_kaz_000802 40
+cv_kaz_000806 24
+cv_kaz_000810 40
+cv_kaz_000814 29
+cv_kaz_000815 49
+cv_kaz_000819 46
+cv_kaz_000823 44
+cv_kaz_000827 51
+cv_kaz_000831 46
+cv_kaz_000835 25
+cv_kaz_000839 33
+cv_kaz_000843 75
+cv_kaz_000816 23
+cv_kaz_000820 37
+cv_kaz_000824 21
+cv_kaz_000828 29
+cv_kaz_000832 26
+cv_kaz_000836 34
+cv_kaz_000840 53
+cv_kaz_000844 37
+cv_kaz_000817 45
+cv_kaz_000821 61
+cv_kaz_000825 33
+cv_kaz_000829 38
+cv_kaz_000833 33
+cv_kaz_000837 25
+cv_kaz_000841 35
+cv_kaz_000845 27
+cv_kaz_000818 47
+cv_kaz_000822 49
+cv_kaz_000826 63
+cv_kaz_000830 49
+cv_kaz_000834 28
+cv_kaz_000838 40
+cv_kaz_000842 31
+cv_kin_000656 21
+cv_kin_000657 34
+cv_kin_000661 36
+cv_kin_000665 40
+cv_kin_000669 46
+cv_kin_000673 54
+cv_kin_000677 78
+cv_kin_000681 74
+cv_kin_000685 14
+cv_kin_000658 48
+cv_kin_000662 59
+cv_kin_000666 44
+cv_kin_000670 44
+cv_kin_000674 60
+cv_kin_000678 59
+cv_kin_000682 69
+cv_kin_000686 99
+cv_kin_000659 38
+cv_kin_000663 45
+cv_kin_000667 70
+cv_kin_000671 34
+cv_kin_000675 88
+cv_kin_000679 80
+cv_kin_000683 18
+cv_kin_000687 111
+cv_kin_000660 62
+cv_kin_000664 37
+cv_kin_000668 28
+cv_kin_000672 60
+cv_kin_000676 79
+cv_kin_000680 87
+cv_kin_000684 101
+cv_kin_000688 134
+cv_kin_000689 108
+cv_kin_000693 58
+cv_kin_000697 25
+cv_kin_000701 56
+cv_kin_000705 43
+cv_kin_000709 82
+cv_kin_000713 37
+cv_kin_000717 25
+cv_kin_000690 81
+cv_kin_000694 43
+cv_kin_000698 40
+cv_kin_000702 90
+cv_kin_000706 49
+cv_kin_000710 22
+cv_kin_000714 18
+cv_kin_000718 27
+cv_kin_000691 18
+cv_kin_000695 28
+cv_kin_000699 34
+cv_kin_000703 25
+cv_kin_000707 64
+cv_kin_000711 26
+cv_kin_000715 11
+cv_kin_000719 61
+cv_kin_000692 32
+cv_kin_000696 64
+cv_kin_000700 28
+cv_kin_000704 7
+cv_kin_000708 15
+cv_kin_000712 75
+cv_kin_000716 25
+cv_kin_000720 47
+cv_kin_000721 37
+cv_kin_000725 53
+cv_kin_000729 32
+cv_kin_000733 35
+cv_kin_000737 29
+cv_kin_000741 54
+cv_kin_000745 62
+cv_kin_000749 34
+cv_kin_000722 31
+cv_kin_000726 47
+cv_kin_000730 31
+cv_kin_000734 29
+cv_kin_000738 32
+cv_kin_000742 23
+cv_kin_000746 74
+cv_kin_000750 31
+cv_kin_000723 78
+cv_kin_000727 18
+cv_kin_000731 22
+cv_kin_000735 36
+cv_kin_000739 19
+cv_kin_000743 35
+cv_kin_000747 33
+cv_kin_000751 43
+cv_kin_000724 37
+cv_kin_000728 30
+cv_kin_000732 48
+cv_kin_000736 18
+cv_kin_000740 64
+cv_kin_000744 29
+cv_kin_000748 29
+cv_kin_000752 69
+cv_kin_000753 73
+cv_kin_000757 29
+cv_kin_000761 47
+cv_kin_000765 54
+cv_kir_000852 5
+cv_kir_000856 5
+cv_kir_000860 6
+cv_kir_000864 54
+cv_kin_000754 18
+cv_kin_000758 111
+cv_kin_000762 54
+cv_kin_000766 117
+cv_kir_000853 5
+cv_kir_000857 5
+cv_kir_000861 4
+cv_kir_000865 23
+cv_kin_000755 22
+cv_kin_000759 37
+cv_kin_000763 122
+cv_kir_000850 7
+cv_kir_000854 7
+cv_kir_000858 6
+cv_kir_000862 60
+cv_kir_000866 56
+cv_kin_000756 21
+cv_kin_000760 56
+cv_kin_000764 9
+cv_kir_000851 5
+cv_kir_000855 6
+cv_kir_000859 6
+cv_kir_000863 37
+cv_kir_000867 64
+cv_kir_000868 60
+cv_kir_000872 53
+cv_kir_000876 66
+cv_kir_000880 67
+cv_kir_000884 49
+cv_kir_000888 38
+cv_kir_000869 66
+cv_kir_000873 55
+cv_kir_000877 29
+cv_kir_000881 30
+cv_kir_000885 63
+cv_kir_000889 46
+cv_kir_000870 43
+cv_kir_000874 54
+cv_kir_000878 48
+cv_kir_000882 31
+cv_kir_000886 46
+cv_kir_000890 38
+cv_kir_000871 44
+cv_kir_000875 54
+cv_kir_000879 38
+cv_kir_000883 47
+cv_kir_000887 54
+cv_kir_000891 45
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11.log
new file mode 100644
index 0000000000000000000000000000000000000000..5ec1ce52576d98c5afabbda93384740e52086487
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11.log
@@ -0,0 +1,157 @@
+# Running on cn001.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:14 CDT 2023
+# SLURMD_NODENAME=cn001
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=11
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133217
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133217
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn001
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn001
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3660392
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn001
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.11.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.11.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.11 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.11.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.11.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.11 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn001] 2023-07-06 08:52:51,877 (asr:475) INFO: Vocabulary size: 6454
+[cn001] 2023-07-06 08:52:53,975 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn001] 2023-07-06 08:52:53,978 (abs_task:1214) INFO: Scheduler: None
+[cn001] 2023-07-06 08:52:53,988 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.11/config.yaml
+[cn001] 2023-07-06 08:52:54,093 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.11', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', '行', 'Ļ', '[som]', 'భ', 'ሆ', '[fil]', 'Ӣ', 'き', 'Ӧ', 'ሉ', 'ʼ', 'ณ', '[oci]', 'Ҷ', 'ڳ', 'ഡ', '[kam]', 'Ღ', 'ݨ', '을', 'ஆ', 'Ƙ', 'ਉ', 'ቶ', 'ຮ', 'ካ', 'ቅ', 'औ', 'ଂ', 'ណ', '의', 'ణ', 'Փ', 'ਈ', 'ګ', 'హ', 'ौ', 'ໍ', 'ಭ', 'ಎ', '地', 'ఆ', 'ん', '하', 'ਖ', '自', '上', 'ፍ', 'ਡ', '可', 'ዮ', '生', '[ibo]', 'ٻ', 'さ', 'ູ', '多', 'ޓ', 'ភ', '니', '不', 'ढ', 'ઈ', 'Ň', 'ഥ', 'ศ', '要', 'Ժ', 'ී', 'ۋ', 'ො', 'ሮ', '年', '到', 'Ძ', 'ซ', '۾', '你', '로', 'ጋ', '[lug]', 'ঞ', '国', 'ផ', '個', 'ฉ', '和', 'ஒ', 'ខ', '[mri]', '他', 'ଠ', 'ຼ', 'ঙ', 'ޅ', '唔', 'ຳ', 'ځ', '៉', 'ថ', '가', '[lin]', 'Ճ', '은', 'ធ', '੍', 'Đ', '嘅', '出', 'ټ', 'Ĕ', 'ス', '지', '下', 'ୂ', 'ޑ', 'Ɗ', 'ຸ', 'ઉ', 'ញ', 'ሁ', 'ޖ', '”', '用', 'ሩ', 'ޔ', '为', '了', 'ຖ', 'එ', 'ጣ', 'ઇ', '日', 'ޯ', 'よ', '고', 'ኛ', 'ሊ', '得', 'ൈ', 'ಒ', 'ፈ', '好', '同', '成', '分', 'そ', 'ธ', 'Ҭ', 'Ğ', 'Ṭ', 'ዲ', 'Њ', 'ڏ', '۽', '物', 'ޫ', 'Ჩ', 'Ӯ', '都', 'け', '个', 'ഫ', 'Ō', 'ዘ', '方', 'ډ', 'Ḍ', '学', 'Օ', 'だ', '能', 'ڊ', '部', '仔', 'ภ', 'థ', 'ओ', 'Җ', 'ษ', 'ಉ', 'ឹ', '서', 'ਫ', 'Ï', 'ル', 'ୱ', 'ਧ', 'ቱ', '就', 'Ű', 'ళ', '斯', 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'尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', 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'喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.11.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.11.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn001] 2023-07-06 08:53:06,959 (collect_stats:106) INFO: Niter: 100
+[cn001] 2023-07-06 08:53:13,457 (collect_stats:106) INFO: Niter: 200
+[cn001] 2023-07-06 08:53:18,724 (collect_stats:106) INFO: Niter: 300
+[cn001] 2023-07-06 08:53:24,306 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651414
+# Accounting: end_time=1688651639
+# Accounting: time=225 threads=1
+# Finished at Thu Jul 6 08:53:59 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a2c3f646bc875095689b7866ce8d3a5bbe1753f2
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.11
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.11.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.11.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
+- ໂ
+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
+- 기
+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
+- 前
+- ዊ
+- Ҟ
+- 世
+- ญ
+- イ
+- 公
+- ໜ
+- え
+- ト
+- ၏
+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
+- ္
+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
+- ッ
+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
+- 西
+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
+- 加
+- 教
+- 國
+- ඩ
+- 道
+- 市
+- 表
+- 立
+- 而
+- 食
+- ޒ
+- Ψ
+- 動
+- ័
+- ¡
+- 尔
+- ዜ
+- 亚
+- 化
+- 先
+- 性
+- 形
+- 里
+- 後
+- ሜ
+- ፊ
+- ቹ
+- 力
+- ド
+- 呢
+- 佢
+- …
+- Ṛ
+- ഏ
+- ሱ
+- 比
+- 高
+- 起
+- 球
+- ዙ
+- 拉
+- 后
+- 体
+- 月
+- 但
+- 話
+- 적
+- Ӡ
+- Ӳ
+- 克
+- 使
+- Ჯ
+- 부
+- 心
+- ੌ
+- ੜ
+- ‌
+- 们
+- 更
+- 正
+- 라
+- 보
+- ቦ
+- ዶ
+- 美
+- 也
+- 南
+- タ
+- ஜ
+- ජ
+- ሙ
+- ፕ
+- 現
+- ૃ
+- 일
+- ຜ
+- ਯ
+- ఖ
+- 口
+- 因
+- ロ
+- ൃ
+- ೃ
+- 式
+- ޝ
+- Ẓ
+- 했
+- ቤ
+- 十
+- 区
+- み
+- Ķ
+- 나
+- ۍ
+- ఏ
+- ං
+- 外
+- 海
+- 重
+- 天
+- 語
+- ۰
+- ៀ
+- 전
+- ඇ
+- 全
+- 被
+- 面
+- ឆ
+- 것
+- ධ
+- ፋ
+- 字
+- 发
+- ऑ
+- テ
+- 頭
+- ዱ
+- 身
+- 果
+- ஏ
+- ඒ
+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
+- カ
+- 無
+- 場
+- 來
+- 啲
+- ඔ
+- ඉ
+- ቲ
+- ጅ
+- 直
+- 德
+- 些
+- 或
+- 開
+- 게
+- 주
+- ቴ
+- ቁ
+- ሶ
+- 期
+- ろ
+- Ṱ
+- ኔ
+- ቋ
+- ኩ
+- 阿
+- 提
+- 音
+- ジ
+- 電
+- ऱ
+- 상
+- ጽ
+- ቆ
+- 动
+- 民
+- 然
+- 做
+- 金
+- フ
+- シ
+- 만
+- උ
+- ޕ
+- ク
+- ፣
+- ०
+- १
+- 解
+- 真
+- 空
+- 機
+- コ
+- Ჭ
+- 冇
+- ฐ
+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
+- 工
+- 英
+- 代
+- Ģ
+- ූ
+- ዩ
+- ̃
+- 北
+- 如
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+- ൺ
+- 정
+- 제
+- ਝ
+- ፓ
+- ·
+- 伊
+- 非
+- ៏
+- ጡ
+- ጫ
+- ቂ
+- 何
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+- ጨ
+- ሻ
+- 及
+- 属
+- 原
+- 這
+- 트
+- ሸ
+- Џ
+- 城
+- 影
+- 次
+- 安
+- べ
+- Ჰ
+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
+- 었
+- ਏ
+- ౌ
+- ኦ
+- 建
+- 政
+- 巴
+- Ɓ
+- 々
+- 識
+- 種
+- 여
+- ڀ
+- ኙ
+- 过
+- 制
+- 始
+- 数
+- 社
+- 死
+- ޢ
+- 車
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+- ଝ
+- ๆ
+- 장
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+- 光
+- 四
+- 由
+- 著
+- ः
+- 彼
+- 點
+- ೌ
+- 되
+- ໆ
+- ሴ
+- 想
+- 任
+- 当
+- 站
+- 感
+- 住
+- 未
+- 旅
+- 持
+- バ
+- 問
+- 學
+- ۱
+- ૌ
+- 할
+- 와
+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
+- 已
+- 造
+- 管
+- 必
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+- ィ
+- パ
+- Ǧ
+- 미
+- 유
+- 위
+- 면
+- 원
+- ච
+- ጸ
+- 对
+- 很
+- 史
+- 客
+- 考
+- ャ
+- オ
+- ഓ
+- ዓ
+- ሃ
+- 동
+- 물
+- ආ
+- ቷ
+- ኪ
+- ঃ
+- 称
+- 活
+- 落
+- へ
+- マ
+- ニ
+- ょ
+- 記
+- 発
+- ഠ
+- 發
+- 咗
+- ឋ
+- 국
+- 现
+- 兰
+- 基
+- 平
+- 保
+- 火
+- 情
+- ほ
+- 気
+- ୌ
+- ٠
+- 거
+- 們
+- ෂ
+- ኞ
+- ፎ
+- ጃ
+- ঝ
+- ڤ
+- 经
+- 信
+- 布
+- 思
+- 打
+- 結
+- ฮ
+- 嘢
+- ̇
+- 우
+- ໝ
+- ፖ
+- 量
+- 曾
+- 车
+- 品
+- 速
+- 件
+- 於
+- ۲
+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
+- 」
+- 石
+- 交
+- 司
+- チ
+- ず
+- 題
+- 對
+- ஞ
+- 睇
+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
+- 五
+- ޗ
+- ュ
+- ଵ
+- ১
+- 세
+- 구
+- 경
+- 비
+- ။
+- ዴ
+- 士
+- 县
+- 台
+- 业
+- 林
+- 存
+- 着
+- 集
+- 足
+- 它
+- じ
+- ム
+- 観
+- ഘ
+- 經
+- 邊
+- ஷ
+- 、
+- ጎ
+- 계
+- ဇ
+- ਛ
+- ጂ
+- ጭ
+- 场
+- 故
+- 型
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+- 告
+- 星
+- 等
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+- 공
+- 며
+- 간
+- ኳ
+- ኘ
+- Œ
+- 镇
+- 罗
+- 长
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+init: null
+input_size: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
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+noise_db_range: '13_15'
+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/train/batch_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/train/feats_lengths_stats.npz
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index 0000000000000000000000000000000000000000..9299792738347f0e83f244006f9d7ce9c33f8bab
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/train/feats_lengths_stats.npz
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+version https://git-lfs.github.com/spec/v1
+oid sha256:312bb561232f5662079db98222d0dbef5acf03d3411e0a6e466c85a7e52feb43
+size 778
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/train/stats_keys
@@ -0,0 +1,2 @@
+feats
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..852c237b28d83c24f0704636b393a4880a1d8580
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/train/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..c1fdd4e534b76c2fff65c6f4fecba1a92e6e5978
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.11/valid/text_shape
@@ -0,0 +1,760 @@
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+cv_lav_001096 20
+cv_lav_001100 39
+cv_lga_000584 69
+cv_lga_000588 51
+cv_lga_000592 20
+cv_lav_001085 53
+cv_lav_001089 64
+cv_lav_001093 23
+cv_lav_001097 38
+cv_lav_001101 26
+cv_lga_000585 22
+cv_lga_000589 41
+cv_lga_000593 30
+cv_lav_001086 28
+cv_lav_001090 23
+cv_lav_001094 36
+cv_lav_001098 31
+cv_lav_001102 64
+cv_lga_000586 47
+cv_lga_000590 60
+cv_lga_000594 35
+cv_lav_001087 25
+cv_lav_001091 19
+cv_lav_001095 35
+cv_lav_001099 58
+cv_lga_000583 30
+cv_lga_000587 75
+cv_lga_000591 35
+cv_lga_000595 26
+cv_lga_000596 46
+cv_lga_000600 70
+cv_lga_000604 62
+cv_lga_000608 63
+cv_lga_000612 29
+cv_lga_000616 69
+cv_lga_000620 43
+cv_lga_000624 81
+cv_lga_000597 39
+cv_lga_000601 49
+cv_lga_000605 74
+cv_lga_000609 51
+cv_lga_000613 30
+cv_lga_000617 47
+cv_lga_000621 76
+cv_lga_000625 36
+cv_lga_000598 50
+cv_lga_000602 45
+cv_lga_000606 126
+cv_lga_000610 67
+cv_lga_000614 78
+cv_lga_000618 58
+cv_lga_000622 32
+cv_lga_000626 66
+cv_lga_000599 27
+cv_lga_000603 64
+cv_lga_000607 68
+cv_lga_000611 32
+cv_lga_000615 44
+cv_lga_000619 71
+cv_lga_000623 41
+cv_lga_000627 77
+cv_lga_000628 48
+cv_lga_000632 56
+cv_lga_000636 43
+cv_lga_000640 37
+cv_lga_000644 80
+cv_lga_000648 55
+cv_lga_000652 83
+cv_lga_000656 58
+cv_lga_000629 36
+cv_lga_000633 68
+cv_lga_000637 71
+cv_lga_000641 48
+cv_lga_000645 45
+cv_lga_000649 49
+cv_lga_000653 73
+cv_lga_000657 82
+cv_lga_000630 47
+cv_lga_000634 69
+cv_lga_000638 70
+cv_lga_000642 43
+cv_lga_000646 55
+cv_lga_000650 78
+cv_lga_000654 61
+cv_lga_000658 69
+cv_lga_000631 36
+cv_lga_000635 34
+cv_lga_000639 54
+cv_lga_000643 67
+cv_lga_000647 43
+cv_lga_000651 26
+cv_lga_000655 52
+cv_lga_000659 59
+cv_lga_000660 41
+cv_lga_000664 69
+cv_lga_000668 69
+cv_lga_000672 35
+cv_lga_000676 29
+cv_lga_000680 42
+cv_lga_000684 61
+cv_lga_000688 92
+cv_lga_000661 76
+cv_lga_000665 32
+cv_lga_000669 45
+cv_lga_000673 38
+cv_lga_000677 63
+cv_lga_000681 100
+cv_lga_000685 36
+cv_lga_000689 30
+cv_lga_000662 50
+cv_lga_000666 30
+cv_lga_000670 40
+cv_lga_000674 81
+cv_lga_000678 68
+cv_lga_000682 91
+cv_lga_000686 81
+cv_lga_000690 49
+cv_lga_000663 45
+cv_lga_000667 74
+cv_lga_000671 58
+cv_lga_000675 37
+cv_lga_000679 51
+cv_lga_000683 59
+cv_lga_000687 113
+cv_lit_000655 31
+cv_lit_000656 73
+cv_lit_000660 74
+cv_lit_000664 56
+cv_lit_000668 52
+cv_lit_000672 39
+cv_lit_000676 100
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+cv_lit_000721 80
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+cv_mal_000001 99
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12.log
new file mode 100644
index 0000000000000000000000000000000000000000..a7a0c87297860fd18d8c4363e80f00469cc722dd
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12.log
@@ -0,0 +1,157 @@
+# Running on cn001.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:14 CDT 2023
+# SLURMD_NODENAME=cn001
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=12
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133218
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133218
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn001
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn001
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3660390
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn001
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.12.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.12.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.12 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.12.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.12.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.12 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn001] 2023-07-06 08:52:51,877 (asr:475) INFO: Vocabulary size: 6454
+[cn001] 2023-07-06 08:52:53,975 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn001] 2023-07-06 08:52:53,978 (abs_task:1214) INFO: Scheduler: None
+[cn001] 2023-07-06 08:52:53,984 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.12/config.yaml
+[cn001] 2023-07-06 08:52:54,089 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.12', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', 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'處', '።', '况', '几', 'ಃ', '름', '순', '환', '특', '월', '눈', '험', '석', '근', '션', '받', 'ഔ', 'ဤ', 'ዷ', 'ቼ', 'ጌ', 'ጄ', '̆', '权', '柱', '无', '粗', '町', '舰', '护', '曼', '浮', '环', '汗', '众', '论', '辖', '铁', '固', '坏', '申', '毕', '跨', '树', '田', '腹', '血', '假', '妻', '授', '丹', '卫', '读', '术', '礼', '姆', '藏', '祖', '《', '》', '寄', '泡', '绝', '承', '际', '疫', '序', '弟', '遭', '延', '威', '修', '排', '雅', '摩', '掌', 'ऋ', '鏡', '勉', '働', 'ワ', '収', '閉', '費', 'ぬ', '距', '笑', '詞', '終', '退', '製', '訪', '査', '宿', '順', '寮', '龜', '轉', '錄', '腦', '營', '範', '雲', '裡', '割', '刻', '衛', '鐘', '估', '囉', '陷', '耐', '繼', '訊', '架', '٣', '飞', '午', '۴', 'ォ', 'ಠ', '됩', '활', '충', '돌', '응', '못', '승', '추', '감', '질', '픽', '련', '혀', '난', '목', 'ఛ', 'ෞ', 'ጩ', 'ሷ', 'ዥ', 'ቭ', 'ـ', '粉', '茨', '证', '停', '湖', '转', '茶', '范', '君', '升', '液', '略', '杜', '戏', '艺', '脱', '龙', '录', '籍', '蝶', '童', '刑', '荷', '讲', '企', '阅', '适', '岩', '若', '席', '徒', '佳', '卵', '夜', '仕', '慢', 'づ', '断', '従', '側', '憶', '左', '課', '殺', '残', '録', '説', '筆', '攏', '坑', '織', '囝', '觀', '討', '烏', '咧', '權', '尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', '喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.12.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.12.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn001] 2023-07-06 08:53:06,820 (collect_stats:106) INFO: Niter: 100
+[cn001] 2023-07-06 08:53:13,871 (collect_stats:106) INFO: Niter: 200
+[cn001] 2023-07-06 08:53:19,394 (collect_stats:106) INFO: Niter: 300
+[cn001] 2023-07-06 08:53:24,616 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651414
+# Accounting: end_time=1688651639
+# Accounting: time=225 threads=1
+# Finished at Thu Jul 6 08:53:59 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..caf5190b5bc883b375f3eab934daefba3b2b25d5
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.12
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.12.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.12.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
+- ໂ
+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
+- 기
+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
+- 前
+- ዊ
+- Ҟ
+- 世
+- ญ
+- イ
+- 公
+- ໜ
+- え
+- ト
+- ၏
+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
+- ္
+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
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+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
+- 西
+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
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+- 教
+- 國
+- ඩ
+- 道
+- 市
+- 表
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+- 而
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+- ¡
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+- 里
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+- …
+- Ṛ
+- ഏ
+- ሱ
+- 比
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+- 起
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+- 拉
+- 后
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+- Ӳ
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+- Ჯ
+- 부
+- 心
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+- ੜ
+- ‌
+- 们
+- 更
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+- 라
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+- ዶ
+- 美
+- 也
+- 南
+- タ
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+- ජ
+- ሙ
+- ፕ
+- 現
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+- ਯ
+- ఖ
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+- ೃ
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+- Ẓ
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+- ቤ
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+- ං
+- 外
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+- ៀ
+- 전
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+- 全
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+- ፋ
+- 字
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+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
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+- 無
+- 場
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+- 啲
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+- ጅ
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+- 或
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+- ቁ
+- ሶ
+- 期
+- ろ
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+- ቋ
+- ኩ
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+- ޕ
+- ク
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+- ०
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+- 機
+- コ
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+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
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+- ՝
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+- ృ
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+- ·
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+- 影
+- 次
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+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
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+- ౌ
+- ኦ
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+- Ɓ
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+- ೌ
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+- 任
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+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
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+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
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+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
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+normalize: utterance_mvn
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+model: espnet
+model_conf:
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+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
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+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
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+version: '202211'
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/train/batch_keys
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+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/train/feats_lengths_stats.npz
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+oid sha256:ee04c4bf04d61377a93218a5ced0790ef52e65788e3a1353fb290e08ea8c50f5
+size 778
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/train/stats_keys
@@ -0,0 +1,2 @@
+feats
+feats_lengths
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..1574099609867169f0ef1f54b57cf685885f4b79
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/train/text_shape
@@ -0,0 +1,3955 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
+feats_lengths
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..17321948dc5e3866dea4919c992a52c667ed1109
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.12/valid/text_shape
@@ -0,0 +1,760 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13.log
new file mode 100644
index 0000000000000000000000000000000000000000..efa14e95711afa26fa6c255276aa874602be7123
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13.log
@@ -0,0 +1,157 @@
+# Running on cn001.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:13 CDT 2023
+# SLURMD_NODENAME=cn001
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=13
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133219
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133219
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn001
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn001
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3660363
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn001
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.13.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.13.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.13 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.13.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.13.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.13 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn001] 2023-07-06 08:52:51,877 (asr:475) INFO: Vocabulary size: 6454
+[cn001] 2023-07-06 08:52:53,975 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn001] 2023-07-06 08:52:53,976 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1214) INFO: Scheduler: None
+[cn001] 2023-07-06 08:52:54,000 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.13/config.yaml
+[cn001] 2023-07-06 08:52:54,104 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.13', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', '行', 'Ļ', '[som]', 'భ', 'ሆ', '[fil]', 'Ӣ', 'き', 'Ӧ', 'ሉ', 'ʼ', 'ณ', '[oci]', 'Ҷ', 'ڳ', 'ഡ', '[kam]', 'Ღ', 'ݨ', '을', 'ஆ', 'Ƙ', 'ਉ', 'ቶ', 'ຮ', 'ካ', 'ቅ', 'औ', 'ଂ', 'ណ', '의', 'ణ', 'Փ', 'ਈ', 'ګ', 'హ', 'ौ', 'ໍ', 'ಭ', 'ಎ', '地', 'ఆ', 'ん', '하', 'ਖ', '自', '上', 'ፍ', 'ਡ', '可', 'ዮ', '生', '[ibo]', 'ٻ', 'さ', 'ູ', '多', 'ޓ', 'ភ', '니', '不', 'ढ', 'ઈ', 'Ň', 'ഥ', 'ศ', '要', 'Ժ', 'ී', 'ۋ', 'ො', 'ሮ', '年', '到', 'Ძ', 'ซ', '۾', '你', '로', 'ጋ', '[lug]', 'ঞ', '国', 'ផ', '個', 'ฉ', '和', 'ஒ', 'ខ', '[mri]', '他', 'ଠ', 'ຼ', 'ঙ', 'ޅ', '唔', 'ຳ', 'ځ', '៉', 'ថ', '가', '[lin]', 'Ճ', '은', 'ធ', '੍', 'Đ', '嘅', '出', 'ټ', 'Ĕ', 'ス', '지', '下', 'ୂ', 'ޑ', 'Ɗ', 'ຸ', 'ઉ', 'ញ', 'ሁ', 'ޖ', '”', '用', 'ሩ', 'ޔ', '为', '了', 'ຖ', 'එ', 'ጣ', 'ઇ', '日', 'ޯ', 'よ', '고', 'ኛ', 'ሊ', '得', 'ൈ', 'ಒ', 'ፈ', '好', '同', '成', '分', 'そ', 'ธ', 'Ҭ', 'Ğ', 'Ṭ', 'ዲ', 'Њ', 'ڏ', '۽', '物', 'ޫ', 'Ჩ', 'Ӯ', '都', 'け', '个', 'ഫ', 'Ō', 'ዘ', '方', 'ډ', 'Ḍ', '学', 'Օ', 'だ', '能', 'ڊ', '部', '仔', 'ภ', 'థ', 'ओ', 'Җ', 'ษ', 'ಉ', 'ឹ', '서', 'ਫ', 'Ï', 'ル', 'ୱ', 'ਧ', 'ቱ', '就', 'Ű', 'ళ', '斯', '作', 'ໂ', 'ණ', 'ጵ', '사', 'ఎ', 'ဘ', 'ఈ', '있', '時', 'ॅ', '한', '기', 'ଘ', 'ଫ', 'ڄ', 'Ď', 'ऊ', 'ఒ', 'ሎ', 'ቃ', 'ೈ', 'ْ', 'ຢ', 'Ҩ', '前', 'ዊ', 'Ҟ', '世', 'ญ', 'イ', '公', 'ໜ', 'え', 'ト', '၏', 'ৌ', 'Ḥ', 'ሲ', '家', 'ៃ', 'څ', '会', 'ಈ', 'Ẽ', 'ア', 'ち', '子', 'ઝ', '를', '္', 'ሞ', '事', '本', '最', 'ຽ', 'ङ', '會', 'ଓ', 'ਭ', 'ሽ', 'ኮ', '之', '己', '스', 'ဝ', '入', 'ළ', 'ዝ', '于', '理', 'ဟ', 'ڙ', 'Ҵ', '名', '新', '法', 'ୃ', 'ዛ', 'ቻ', 'ラ', 'ઘ', '수', '습', 'ਥ', '所', '界', 'つ', 'ஸ', 'ఫ', 'ቢ', 'ৎ', 'わ', '過', 'ഈ', '으', '来', 'ဂ', '定', '利', '者', 'お', '為', 'Ɛ', '아', 'ຶ', 'ဒ', 'ጉ', 'ኖ', '度', '常', 'ഖ', 'ឡ', '도', 'Ḓ', '去', '其', '意', 'ኑ', '这', '时', '通', 'Ֆ', '리', '어', 'ቡ', '合', 'ञ', '間', 'ଞ', 'Ќ', 'ږ', '知', 'ሬ', '手', 'ኤ', '¿', 'や', 'ך', '៊', '인', 'ዚ', 'ؤ', '들', '科', 'ど', 'ッ', 'ば', 'ಖ', 'ឺ', '시', 'ಫ', '文', '主', 'め', '見', 'ฟ', '해', 'ሀ', '州', '水', '小', '目', 'ଢ', 'ශ', 'ኒ', 'ሌ', 'ጊ', '三', '西', '特', '그', '第', '山', '대', '자', '„', '明', '位', 'せ', 'ښ', 'ጀ', '“', '二', '路', 'リ', '加', '教', '國', 'ඩ', '道', '市', '表', '立', '而', '食', 'ޒ', 'Ψ', '動', '័', '¡', '尔', 'ዜ', '亚', '化', '先', '性', '形', '里', '後', 'ሜ', 'ፊ', 'ቹ', '力', 'ド', '呢', '佢', '…', 'Ṛ', 'ഏ', 'ሱ', '比', '高', '起', '球', 'ዙ', '拉', '后', '体', '月', '但', '話', '적', 'Ӡ', 'Ӳ', '克', '使', 'Ჯ', '부', '心', 'ੌ', 'ੜ', '\u200c', '们', '更', '正', '라', '보', 'ቦ', 'ዶ', '美', '也', '南', 'タ', 'ஜ', 'ජ', 'ሙ', 'ፕ', '現', 'ૃ', '일', 'ຜ', 'ਯ', 'ఖ', '口', '因', 'ロ', 'ൃ', 'ೃ', '式', 'ޝ', 'Ẓ', '했', 'ቤ', '十', '区', 'み', 'Ķ', '나', 'ۍ', 'ఏ', 'ං', '外', '海', '重', '天', '語', '۰', 'ៀ', '전', 'ඇ', '全', '被', '面', 'ឆ', '것', 'ධ', 'ፋ', '字', '发', 'ऑ', 'テ', '頭', 'ዱ', '身', '果', 'ஏ', 'ඒ', 'ෝ', 'ኝ', 'ٍ', '今', '示', 'カ', '無', '場', '來', '啲', 'ඔ', 'ඉ', 'ቲ', 'ጅ', '直', '德', '些', '或', '開', '게', '주', 'ቴ', 'ቁ', 'ሶ', '期', 'ろ', 'Ṱ', 'ኔ', 'ቋ', 'ኩ', '阿', '提', '音', 'ジ', '電', 'ऱ', '상', 'ጽ', 'ቆ', '动', '民', '然', '做', '金', 'フ', 'シ', '만', 'උ', 'ޕ', 'ク', '፣', '०', '१', '解', '真', '空', '機', 'コ', 'Ჭ', '冇', 'ฐ', 'ף', 'ڌ', 'ሄ', 'レ', '講', '៍', '种', '少', '点', '此', '与', '相', '受', '言', '՝', 'ઠ', 'ဉ', 'ృ', '内', '工', '英', '代', 'Ģ', 'ූ', 'ዩ', '̃', '北', '如', '流', 'ൺ', '정', '제', 'ਝ', 'ፓ', '·', '伊', '非', '៏', 'ጡ', 'ጫ', 'ቂ', '何', '၎', 'ጨ', 'ሻ', '及', '属', '原', '這', '트', 'ሸ', 'Џ', '城', '影', '次', '安', 'べ', 'Ჰ', 'ൗ', '哋', 'ૈ', 'Ṅ', '과', '었', 'ਏ', 'ౌ', 'ኦ', '建', '政', '巴', 'Ɓ', '々', '識', '種', '여', 'ڀ', 'ኙ', '过', '制', '始', '数', '社', '死', 'ޢ', '車', '長', 'ଝ', 'ๆ', '장', '至', '光', '四', '由', '著', 'ः', '彼', '點', 'ೌ', '되', 'ໆ', 'ሴ', '想', '任', '当', '站', '感', '住', '未', '旅', '持', 'バ', '問', '學', '۱', 'ૌ', '할', '와', '문', 'ဗ', 'ਘ', 'ඳ', 'Ḽ', '已', '造', '管', '必', 'ऐ', 'ィ', 'パ', 'Ǧ', '미', '유', '위', '면', '원', 'ච', 'ጸ', '对', '很', '史', '客', '考', 'ャ', 'オ', 'ഓ', 'ዓ', 'ሃ', '동', '물', 'ආ', 'ቷ', 'ኪ', 'ঃ', '称', '活', '落', 'へ', 'マ', 'ニ', 'ょ', '記', '発', 'ഠ', '發', '咗', 'ឋ', '국', '现', '兰', '基', '平', '保', '火', '情', 'ほ', '気', 'ୌ', '٠', '거', '們', 'ෂ', 'ኞ', 'ፎ', 'ጃ', 'ঝ', 'ڤ', '经', '信', '布', '思', '打', '結', 'ฮ', '嘢', '̇', '우', 'ໝ', 'ፖ', '量', '曾', '车', '品', '速', '件', '於', '۲', 'ٺ', 'Ҿ', 'Ҽ', '太', '「', '」', '石', '交', '司', 'チ', 'ず', '題', '對', 'ஞ', '睇', '噉', '모', '소', '२', 'Ѓ', '赛', '该', '格', '查', '五', 'ޗ', 'ュ', 'ଵ', '১', '세', '구', '경', '비', '။', 'ዴ', '士', '县', '台', '业', '林', '存', '着', '集', '足', '它', 'じ', 'ム', '観', 'ഘ', '經', '邊', 'ஷ', '、', 'ጎ', '계', 'ဇ', 'ਛ', 'ጂ', 'ጭ', '场', '故', '型', '系', '告', '星', '等', 'サ', '입', '공', '며', '간', 'ኳ', 'ኘ', 'Œ', '镇', '罗', '长', '指', '程', '反', '接', '看', 'ウ', 'ブ', '體', '幾', '樣', '러', '요', '르', '၍', '军', '印', '向', '改', '治', '只', '書', 'メ', '味', 'プ', '網', 'ዕ', '২', 'ಘ', '년', '오', 'ਠ', 'Ǹ', '回', '王', '周', '像', 'ޙ', '強', '報', '試', 'ฝ', '০', '°', 'ៗ', '된', '식', '방', '개', '성', 'ڇ', 'ෑ', 'ጦ', 'ጐ', 'ጆ', 'ঢ', '间', '包', '供', '座', '母', '老', '神', 'Ỹ', 'グ', '중', 'ਐ', 'භ', '将', '从', '机', '马', '近', '私', '院', '取', '又', '限', 'デ', 'ェ', 'ネ', '達', '矛', '対', '業', '並', 'ઊ', '않', '화', '발', '조', 'ጓ', 'ጻ', 'Ӷ', '进', '达', '战', '再', '队', '半', '省', '街', 'ޚ', '望', 'キ', '盾', '認', '員', '•', '當', 'ฤ', 'ಏ', '합', '선', '프', '분', 'ຟ', 'ፒ', '列', '河', '尼', '联', '电', '村', '支', '府', '野', '早', '病', '族', '六', '花', '游', '说', '切', '屋', 'ナ', '買', '実', 'ஓ', 'ஹ', '관', '연', '데', '३', 'ਓ', 'ፉ', '否', 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'될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', '喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.13.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.13.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn001] 2023-07-06 08:53:05,305 (collect_stats:106) INFO: Niter: 100
+[cn001] 2023-07-06 08:53:10,266 (collect_stats:106) INFO: Niter: 200
+[cn001] 2023-07-06 08:53:17,312 (collect_stats:106) INFO: Niter: 300
+[cn001] 2023-07-06 08:53:22,408 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651413
+# Accounting: end_time=1688651639
+# Accounting: time=226 threads=1
+# Finished at Thu Jul 6 08:53:59 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8d263bca99f104cc9ab4d244440d10f28b61c256
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.13
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
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+valid_shape_file:
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+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
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+optim: adam
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+    weight_decay: 1.0e-06
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+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
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+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
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+    apply_time_mask: true
+    time_mask_width_ratio_range:
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+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
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+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/train/batch_keys
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--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/train/feats_lengths_stats.npz
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index 0000000000000000000000000000000000000000..7ed291a89002fa8d1be9800143bd4a74286c8df9
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+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/train/feats_lengths_stats.npz
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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e263cf8b191ce3494c4d3386babba0d3b7d5f7571dc7d30c13cd9f5a3b728a6
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/train/speech_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/train/speech_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/train/stats_keys
@@ -0,0 +1,2 @@
+feats
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..0eb4568a7501f86fda32426f7abf0e35dda7eed0
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/train/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
+feats_lengths
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..7685378647919d7ca055a5f3d7c4c1f9fc9dc971
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.13/valid/text_shape
@@ -0,0 +1,760 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14.log
new file mode 100644
index 0000000000000000000000000000000000000000..3b5d18fc8f2c4419d6e94768ede5d4913f904a57
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14.log
@@ -0,0 +1,157 @@
+# Running on cn061.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:16 CDT 2023
+# SLURMD_NODENAME=cn061
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=14
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133220
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133220
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn061
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn061
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3272359
+# SLURM_TOPOLOGY_ADDR=ss00.ss03.cn061
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.14.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.14.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.14 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.14.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.14.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.14 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn061] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn061] 2023-07-06 08:52:54,019 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn061] 2023-07-06 08:52:54,021 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn061] 2023-07-06 08:52:54,021 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn061] 2023-07-06 08:52:54,021 (abs_task:1214) INFO: Scheduler: None
+[cn061] 2023-07-06 08:52:54,025 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.14/config.yaml
+[cn061] 2023-07-06 08:52:54,147 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.14', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', '行', 'Ļ', '[som]', 'భ', 'ሆ', '[fil]', 'Ӣ', 'き', 'Ӧ', 'ሉ', 'ʼ', 'ณ', '[oci]', 'Ҷ', 'ڳ', 'ഡ', '[kam]', 'Ღ', 'ݨ', '을', 'ஆ', 'Ƙ', 'ਉ', 'ቶ', 'ຮ', 'ካ', 'ቅ', 'औ', 'ଂ', 'ណ', '의', 'ణ', 'Փ', 'ਈ', 'ګ', 'హ', 'ौ', 'ໍ', 'ಭ', 'ಎ', '地', 'ఆ', 'ん', '하', 'ਖ', '自', '上', 'ፍ', 'ਡ', '可', 'ዮ', '生', '[ibo]', 'ٻ', 'さ', 'ູ', '多', 'ޓ', 'ភ', '니', '不', 'ढ', 'ઈ', 'Ň', 'ഥ', 'ศ', '要', 'Ժ', 'ී', 'ۋ', 'ො', 'ሮ', '年', '到', 'Ძ', 'ซ', '۾', '你', '로', 'ጋ', '[lug]', 'ঞ', '国', 'ផ', '個', 'ฉ', '和', 'ஒ', 'ខ', '[mri]', '他', 'ଠ', 'ຼ', 'ঙ', 'ޅ', '唔', 'ຳ', 'ځ', '៉', 'ថ', '가', '[lin]', 'Ճ', '은', 'ធ', '੍', 'Đ', '嘅', '出', 'ټ', 'Ĕ', 'ス', '지', '下', 'ୂ', 'ޑ', 'Ɗ', 'ຸ', 'ઉ', 'ញ', 'ሁ', 'ޖ', '”', '用', 'ሩ', 'ޔ', '为', '了', 'ຖ', 'එ', 'ጣ', 'ઇ', '日', 'ޯ', 'よ', '고', 'ኛ', 'ሊ', '得', 'ൈ', 'ಒ', 'ፈ', '好', '同', '成', '分', 'そ', 'ธ', 'Ҭ', 'Ğ', 'Ṭ', 'ዲ', 'Њ', 'ڏ', '۽', '物', 'ޫ', 'Ჩ', 'Ӯ', '都', 'け', '个', 'ഫ', 'Ō', 'ዘ', '方', 'ډ', 'Ḍ', '学', 'Օ', 'だ', '能', 'ڊ', '部', '仔', 'ภ', 'థ', 'ओ', 'Җ', 'ษ', 'ಉ', 'ឹ', '서', 'ਫ', 'Ï', 'ル', 'ୱ', 'ਧ', 'ቱ', '就', 'Ű', 'ళ', '斯', '作', 'ໂ', 'ණ', 'ጵ', '사', 'ఎ', 'ဘ', 'ఈ', '있', '時', 'ॅ', '한', '기', 'ଘ', 'ଫ', 'ڄ', 'Ď', 'ऊ', 'ఒ', 'ሎ', 'ቃ', 'ೈ', 'ْ', 'ຢ', 'Ҩ', '前', 'ዊ', 'Ҟ', '世', 'ญ', 'イ', '公', 'ໜ', 'え', 'ト', '၏', 'ৌ', 'Ḥ', 'ሲ', '家', 'ៃ', 'څ', '会', 'ಈ', 'Ẽ', 'ア', 'ち', '子', 'ઝ', '를', '္', 'ሞ', '事', '本', '最', 'ຽ', 'ङ', '會', 'ଓ', 'ਭ', 'ሽ', 'ኮ', '之', '己', '스', 'ဝ', '入', 'ළ', 'ዝ', '于', '理', 'ဟ', 'ڙ', 'Ҵ', '名', '新', '法', 'ୃ', 'ዛ', 'ቻ', 'ラ', 'ઘ', '수', '습', 'ਥ', '所', '界', 'つ', 'ஸ', 'ఫ', 'ቢ', 'ৎ', 'わ', '過', 'ഈ', '으', '来', 'ဂ', '定', '利', '者', 'お', '為', 'Ɛ', '아', 'ຶ', 'ဒ', 'ጉ', 'ኖ', '度', '常', 'ഖ', 'ឡ', '도', 'Ḓ', '去', '其', '意', 'ኑ', '这', '时', '通', 'Ֆ', '리', '어', 'ቡ', '合', 'ञ', '間', 'ଞ', 'Ќ', 'ږ', '知', 'ሬ', '手', 'ኤ', '¿', 'や', 'ך', '៊', '인', 'ዚ', 'ؤ', '들', '科', 'ど', 'ッ', 'ば', 'ಖ', 'ឺ', '시', 'ಫ', '文', '主', 'め', '見', 'ฟ', '해', 'ሀ', '州', '水', '小', '目', 'ଢ', 'ශ', 'ኒ', 'ሌ', 'ጊ', '三', '西', '特', '그', '第', '山', '대', '자', '„', '明', '位', 'せ', 'ښ', 'ጀ', '“', '二', '路', 'リ', '加', '教', '國', 'ඩ', '道', '市', '表', '立', '而', '食', 'ޒ', 'Ψ', '動', '័', '¡', '尔', 'ዜ', '亚', '化', '先', '性', '形', '里', '後', 'ሜ', 'ፊ', 'ቹ', '力', 'ド', '呢', '佢', '…', 'Ṛ', 'ഏ', 'ሱ', '比', '高', '起', '球', 'ዙ', '拉', '后', '体', '月', '但', '話', '적', 'Ӡ', 'Ӳ', '克', '使', 'Ჯ', '부', '心', 'ੌ', 'ੜ', '\u200c', '们', '更', '正', '라', '보', 'ቦ', 'ዶ', '美', '也', '南', 'タ', 'ஜ', 'ජ', 'ሙ', 'ፕ', '現', 'ૃ', '일', 'ຜ', 'ਯ', 'ఖ', '口', '因', 'ロ', 'ൃ', 'ೃ', '式', 'ޝ', 'Ẓ', '했', 'ቤ', '十', '区', 'み', 'Ķ', '나', 'ۍ', 'ఏ', 'ං', '外', '海', '重', '天', '語', '۰', 'ៀ', '전', 'ඇ', '全', '被', '面', 'ឆ', '것', 'ධ', 'ፋ', '字', '发', 'ऑ', 'テ', '頭', 'ዱ', '身', '果', 'ஏ', 'ඒ', 'ෝ', 'ኝ', 'ٍ', '今', '示', 'カ', '無', '場', '來', '啲', 'ඔ', 'ඉ', 'ቲ', 'ጅ', '直', '德', '些', '或', '開', '게', '주', 'ቴ', 'ቁ', 'ሶ', '期', 'ろ', 'Ṱ', 'ኔ', 'ቋ', 'ኩ', '阿', '提', '音', 'ジ', '電', 'ऱ', '상', 'ጽ', 'ቆ', '动', '民', '然', '做', '金', 'フ', 'シ', '만', 'උ', 'ޕ', 'ク', '፣', '०', '१', '解', '真', '空', '機', 'コ', 'Ჭ', '冇', 'ฐ', 'ף', 'ڌ', 'ሄ', 'レ', '講', '៍', '种', '少', '点', '此', '与', '相', '受', '言', '՝', 'ઠ', 'ဉ', 'ృ', '内', '工', '英', '代', 'Ģ', 'ූ', 'ዩ', '̃', '北', '如', '流', 'ൺ', '정', '제', 'ਝ', 'ፓ', '·', '伊', '非', '៏', 'ጡ', 'ጫ', 'ቂ', '何', '၎', 'ጨ', 'ሻ', '及', '属', '原', '這', '트', 'ሸ', 'Џ', '城', '影', '次', '安', 'べ', 'Ჰ', 'ൗ', '哋', 'ૈ', 'Ṅ', '과', '었', 'ਏ', 'ౌ', 'ኦ', '建', '政', '巴', 'Ɓ', '々', '識', '種', '여', 'ڀ', 'ኙ', '过', '制', '始', '数', '社', '死', 'ޢ', '車', '長', 'ଝ', 'ๆ', '장', '至', '光', '四', '由', '著', 'ः', '彼', '點', 'ೌ', '되', 'ໆ', 'ሴ', '想', '任', '当', '站', '感', '住', '未', '旅', '持', 'バ', '問', '學', '۱', 'ૌ', '할', '와', '문', 'ဗ', 'ਘ', 'ඳ', 'Ḽ', '已', '造', '管', '必', 'ऐ', 'ィ', 'パ', 'Ǧ', '미', '유', '위', '면', '원', 'ච', 'ጸ', '对', '很', '史', '客', '考', 'ャ', 'オ', 'ഓ', 'ዓ', 'ሃ', '동', '물', 'ආ', 'ቷ', 'ኪ', 'ঃ', '称', '活', '落', 'へ', 'マ', 'ニ', 'ょ', '記', '発', 'ഠ', '發', '咗', 'ឋ', '국', '现', '兰', '基', '平', '保', '火', '情', 'ほ', '気', 'ୌ', '٠', '거', '們', 'ෂ', 'ኞ', 'ፎ', 'ጃ', 'ঝ', 'ڤ', '经', '信', '布', '思', '打', '結', 'ฮ', '嘢', '̇', '우', 'ໝ', 'ፖ', '量', '曾', '车', '品', '速', '件', '於', '۲', 'ٺ', 'Ҿ', 'Ҽ', '太', '「', '」', '石', '交', '司', 'チ', 'ず', '題', '對', 'ஞ', '睇', '噉', '모', '소', '२', 'Ѓ', '赛', '该', '格', '查', '五', 'ޗ', 'ュ', 'ଵ', '১', '세', '구', '경', '비', '။', 'ዴ', '士', '县', '台', '业', '林', '存', '着', '集', '足', '它', 'じ', 'ム', '観', 'ഘ', '經', '邊', 'ஷ', '、', 'ጎ', '계', 'ဇ', 'ਛ', 'ጂ', 'ጭ', '场', '故', '型', '系', '告', '星', '等', 'サ', '입', '공', '며', '간', 'ኳ', 'ኘ', 'Œ', '镇', '罗', '长', '指', '程', '反', '接', '看', 'ウ', 'ブ', '體', '幾', '樣', '러', '요', '르', '၍', '军', '印', '向', '改', '治', '只', '書', 'メ', '味', 'プ', '網', 'ዕ', '২', 'ಘ', '년', '오', 'ਠ', 'Ǹ', '回', '王', '周', '像', 'ޙ', '強', '報', '試', 'ฝ', '০', '°', 'ៗ', '된', '식', '방', '개', '성', 'ڇ', 'ෑ', 'ጦ', 'ጐ', 'ጆ', 'ঢ', '间', '包', '供', '座', '母', '老', '神', 'Ỹ', 'グ', '중', 'ਐ', 'භ', '将', '从', '机', '马', '近', '私', '院', '取', '又', '限', 'デ', 'ェ', 'ネ', '達', '矛', '対', '業', '並', 'ઊ', '않', '화', '발', '조', 'ጓ', 'ጻ', 'Ӷ', '进', '达', '战', '再', '队', '半', '省', '街', 'ޚ', '望', 'キ', '盾', '認', '員', '•', '當', 'ฤ', 'ಏ', '합', '선', '프', '분', 'ຟ', 'ፒ', '列', '河', '尼', '联', '电', '村', '支', '府', '野', '早', '病', '族', '六', '花', '游', '说', '切', '屋', 'ナ', '買', '実', 'ஓ', 'ஹ', '관', '연', '데', '३', 'ਓ', 'ፉ', '否', 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'依', '边', '豆', '背', '视', '侵', '瓜', '攻', '博', '注', '映', '拼', '染', 'ペ', 'ノ', '習', 'モ', 'ゃ', '判', 'ギ', '単', '興', '華', '経', '候', '鳥', '條', '傳', '師', '號', '陽', 'ฏ', '呀', '設', '擊', '워', '많', '현', '종', '매', '본', '버', '민', '၌', 'ዌ', 'ኗ', '括', '党', '广', '诺', '冬', '江', '彩', '沙', '均', '移', '曲', '展', '团', '附', '巨', '效', '话', '响', '寺', '华', '署', '冷', '皮', '项', '容', '恐', '亦', '麻', '埃', '仍', '施', '版', '么', '问', '玩', '晚', '甘', '曜', '菌', 'Ŷ', 'ޞ', 'ハ', 'ざ', '覚', '艇', '護', '洗', 'ゆ', '適', '優', '評', '領', '驚', 'ゴ', '暴', '焦', '臺', '輸', '怪', '資', '龍', '萬', 'ଈ', '請', '續', 'ឥ', '។', '언', '랑', '임', '및', '예', '표', '움', '독', 'ຝ', '၊', 'ෘ', 'ቪ', 'ዬ', 'ፐ', 'ঢ়', '许', '沃', '泰', '增', '佛', '莱', '写', '湾', '苏', '亡', '单', '纪', '武', '溪', '堂', '室', '骨', '房', '含', '幅', '健', '条', '张', '付', '费', '缺', '照', '叶', '楚', '皆', '勒', '捕', '毒', '犯', '症', '乘', '耳', '孩', '肉', '温', '婚', '響', '右', '歩', 'ぞ', '態', '飲', '売', '様', '露', '遠', '導', '勢', '監', '陣', '雞', '濟', '矣', '臭', '朋', '紅', '維', '測', '๊', 'ฬ', '咪', '淨', '幫', 'ኃ', '٥', '졌', '져', '곳', '손', '직', '초', '단', '출', '티', '건', '집', '림', '북', '산', '레', '형', '새', '증', '讓', 'ඹ', 'ጹ', 'ቧ', 'ቬ', '؛', '税', '抵', '盛', '鱼', '敢', '强', '隆', '符', '门', '规', '办', '暖', '标', '义', '永', '整', '风', '却', '叫', '破', '端', '瑞', '习', '刺', '牙', '宣', '题', '按', '阳', '微', '园', '拿', '菊', '什', '担', '决', '赤', '突', '蒂', '仁', '随', '甚', 'ޏ', 'ﷲ', '葉', '震', 'ヒ', '聞', '飯', '創', '応', '危', '協', '証', '楽', '僕', 'ヴ', '補', '広', '親', '讀', '總', '塗', '樂', '閣', '橋', '裝', '喎', '訴', '傾', '：', 'ঊ', '٤', '٨', '۶', '۳', '須', '최', '래', '급', '페', '준', '온', '격', '항', '였', '알', '탈', '너', '၁', 'Ŋ', '歷', 'ኡ', 'ጿ', 'ጁ', 'ሾ', 'ঈ', '围', '追', '添', '划', '森', '师', '银', '陆', '伯', '冠', '级', '帕', '混', '戈', '厚', '见', '央', '价', '酸', '哲', '验', '批', '亲', '测', '狗', '质', '弗', '零', '医', '试', '防', '议', '川', '汉', '散', '束', '宗', '唯', '欧', '抗', '轻', '免', '皇', '患', '疑', '努', '坐', '爆', '途', '迫', '帰', 'ザ', '痛', '願', 'ヤ', '苦', '構', '紙', '糖', '価', '極', '殊', '続', '納', '戦', '簡', '減', '敗', 'ഐ', '屎', '粒', '鬼', '蘭', '眾', '辦', '隻', '圖', '參', '輕', '夠', '賽', '處', '።', '况', '几', 'ಃ', '름', '순', '환', '특', '월', '눈', '험', '석', '근', '션', '받', 'ഔ', 'ဤ', 'ዷ', 'ቼ', 'ጌ', 'ጄ', '̆', '权', '柱', '无', '粗', '町', '舰', '护', '曼', '浮', '环', '汗', '众', '论', '辖', '铁', '固', '坏', '申', '毕', '跨', '树', '田', '腹', '血', '假', '妻', '授', '丹', '卫', '读', '术', '礼', '姆', '藏', '祖', '《', '》', '寄', '泡', '绝', '承', '际', '疫', '序', '弟', '遭', '延', '威', '修', '排', '雅', '摩', '掌', 'ऋ', '鏡', '勉', '働', 'ワ', '収', '閉', '費', 'ぬ', '距', '笑', '詞', '終', '退', '製', '訪', '査', '宿', '順', '寮', '龜', '轉', '錄', '腦', '營', '範', '雲', '裡', '割', '刻', '衛', '鐘', '估', '囉', '陷', '耐', '繼', '訊', '架', '٣', '飞', '午', '۴', 'ォ', 'ಠ', '됩', '활', '충', '돌', '응', '못', '승', '추', '감', '질', '픽', '련', '혀', '난', '목', 'ఛ', 'ෞ', 'ጩ', 'ሷ', 'ዥ', 'ቭ', 'ـ', '粉', '茨', '证', '停', '湖', '转', '茶', '范', '君', '升', '液', '略', '杜', '戏', '艺', '脱', '龙', '录', '籍', '蝶', '童', '刑', '荷', '讲', '企', '阅', '适', '岩', '若', '席', '徒', '佳', '卵', '夜', '仕', '慢', 'づ', '断', '従', '側', '憶', '左', '課', '殺', '残', '録', '説', '筆', '攏', '坑', '織', '囝', '觀', '討', '烏', '咧', '權', '尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', '喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.14.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.14.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn061] 2023-07-06 08:53:07,896 (collect_stats:106) INFO: Niter: 100
+[cn061] 2023-07-06 08:53:22,246 (collect_stats:106) INFO: Niter: 200
+[cn061] 2023-07-06 08:53:29,613 (collect_stats:106) INFO: Niter: 300
+[cn061] 2023-07-06 08:53:48,860 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651416
+# Accounting: end_time=1688651671
+# Accounting: time=255 threads=1
+# Finished at Thu Jul 6 08:54:31 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..603947ca61aeb8c56655547d686c8a2f99d46589
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.14
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.14.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.14.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
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+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
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+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
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+- 出
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+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
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+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
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+- ኛ
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+- ಒ
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+- 好
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+- 成
+- 分
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+- Ҭ
+- Ğ
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+- 物
+- ޫ
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+- Ӯ
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+- け
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+- 方
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+- 学
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+- ڊ
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+- థ
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+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
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+- ୱ
+- ਧ
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+- 斯
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+- ጵ
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+- ဘ
+- ఈ
+- 있
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+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
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+- Ḥ
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+- څ
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+- 事
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+- ኮ
+- 之
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+- 入
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+- 于
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+- 名
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+- ዛ
+- ቻ
+- ラ
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+- 수
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+- 所
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+- つ
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+- ఫ
+- ቢ
+- ৎ
+- わ
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+- ഈ
+- 으
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+- お
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+- ဒ
+- ጉ
+- ኖ
+- 度
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+- ഖ
+- ឡ
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+- Ֆ
+- 리
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+- 合
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+- 間
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+- ږ
+- 知
+- ሬ
+- 手
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+- ¿
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+- ៊
+- 인
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+- ؤ
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+- ¡
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+- 亚
+- 化
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+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
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+bpemodel: null
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+aux_ctc_tasks: []
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+frontend: default
+frontend_conf:
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+    fs: 16k
+specaug: specaug
+specaug_conf:
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+    time_warp_mode: bicubic
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+preencoder_conf: {}
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+encoder_conf:
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+    attention_dropout_rate: 0.1
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+    normalize_before: true
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+required:
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/train/stats_keys
@@ -0,0 +1,2 @@
+feats
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..3092cb4de340bcef53c3ccba5f32df91910648b3
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/train/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/valid/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/valid/batch_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
+feats_lengths
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..0249796df94da8b7e2ebc1973ddce79294832c0e
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.14/valid/text_shape
@@ -0,0 +1,760 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15.log
new file mode 100644
index 0000000000000000000000000000000000000000..5c6f62f6a0a05487eaaf797aac331b67f640b3fa
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15.log
@@ -0,0 +1,157 @@
+# Running on cn061.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:16 CDT 2023
+# SLURMD_NODENAME=cn061
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=15
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133221
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133221
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn061
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn061
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3272357
+# SLURM_TOPOLOGY_ADDR=ss00.ss03.cn061
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.15.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.15.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.15 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.15.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.15.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.15 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn061] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn061] 2023-07-06 08:52:54,019 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn061] 2023-07-06 08:52:54,021 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn061] 2023-07-06 08:52:54,021 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn061] 2023-07-06 08:52:54,021 (abs_task:1214) INFO: Scheduler: None
+[cn061] 2023-07-06 08:52:54,025 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.15/config.yaml
+[cn061] 2023-07-06 08:52:54,144 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.15', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', '行', 'Ļ', '[som]', 'భ', 'ሆ', '[fil]', 'Ӣ', 'き', 'Ӧ', 'ሉ', 'ʼ', 'ณ', '[oci]', 'Ҷ', 'ڳ', 'ഡ', '[kam]', 'Ღ', 'ݨ', '을', 'ஆ', 'Ƙ', 'ਉ', 'ቶ', 'ຮ', 'ካ', 'ቅ', 'औ', 'ଂ', 'ណ', '의', 'ణ', 'Փ', 'ਈ', 'ګ', 'హ', 'ौ', 'ໍ', 'ಭ', 'ಎ', '地', 'ఆ', 'ん', '하', 'ਖ', '自', '上', 'ፍ', 'ਡ', '可', 'ዮ', '生', '[ibo]', 'ٻ', 'さ', 'ູ', '多', 'ޓ', 'ភ', '니', '不', 'ढ', 'ઈ', 'Ň', 'ഥ', 'ศ', '要', 'Ժ', 'ී', 'ۋ', 'ො', 'ሮ', '年', '到', 'Ძ', 'ซ', '۾', '你', '로', 'ጋ', '[lug]', 'ঞ', '国', 'ផ', '個', 'ฉ', '和', 'ஒ', 'ខ', '[mri]', '他', 'ଠ', 'ຼ', 'ঙ', 'ޅ', '唔', 'ຳ', 'ځ', '៉', 'ថ', '가', '[lin]', 'Ճ', '은', 'ធ', '੍', 'Đ', '嘅', '出', 'ټ', 'Ĕ', 'ス', '지', '下', 'ୂ', 'ޑ', 'Ɗ', 'ຸ', 'ઉ', 'ញ', 'ሁ', 'ޖ', '”', '用', 'ሩ', 'ޔ', '为', '了', 'ຖ', 'එ', 'ጣ', 'ઇ', '日', 'ޯ', 'よ', '고', 'ኛ', 'ሊ', '得', 'ൈ', 'ಒ', 'ፈ', '好', '同', '成', '分', 'そ', 'ธ', 'Ҭ', 'Ğ', 'Ṭ', 'ዲ', 'Њ', 'ڏ', '۽', '物', 'ޫ', 'Ჩ', 'Ӯ', '都', 'け', '个', 'ഫ', 'Ō', 'ዘ', '方', 'ډ', 'Ḍ', '学', 'Օ', 'だ', '能', 'ڊ', '部', '仔', 'ภ', 'థ', 'ओ', 'Җ', 'ษ', 'ಉ', 'ឹ', '서', 'ਫ', 'Ï', 'ル', 'ୱ', 'ਧ', 'ቱ', '就', 'Ű', 'ళ', '斯', '作', 'ໂ', 'ණ', 'ጵ', '사', 'ఎ', 'ဘ', 'ఈ', '있', '時', 'ॅ', '한', '기', 'ଘ', 'ଫ', 'ڄ', 'Ď', 'ऊ', 'ఒ', 'ሎ', 'ቃ', 'ೈ', 'ْ', 'ຢ', 'Ҩ', '前', 'ዊ', 'Ҟ', '世', 'ญ', 'イ', '公', 'ໜ', 'え', 'ト', '၏', 'ৌ', 'Ḥ', 'ሲ', '家', 'ៃ', 'څ', '会', 'ಈ', 'Ẽ', 'ア', 'ち', '子', 'ઝ', '를', '္', 'ሞ', '事', '本', '最', 'ຽ', 'ङ', '會', 'ଓ', 'ਭ', 'ሽ', 'ኮ', '之', '己', '스', 'ဝ', '入', 'ළ', 'ዝ', '于', '理', 'ဟ', 'ڙ', 'Ҵ', '名', '新', '法', 'ୃ', 'ዛ', 'ቻ', 'ラ', 'ઘ', '수', '습', 'ਥ', '所', '界', 'つ', 'ஸ', 'ఫ', 'ቢ', 'ৎ', 'わ', '過', 'ഈ', '으', '来', 'ဂ', '定', '利', '者', 'お', '為', 'Ɛ', '아', 'ຶ', 'ဒ', 'ጉ', 'ኖ', '度', '常', 'ഖ', 'ឡ', '도', 'Ḓ', '去', '其', '意', 'ኑ', '这', '时', '通', 'Ֆ', '리', '어', 'ቡ', '合', 'ञ', '間', 'ଞ', 'Ќ', 'ږ', '知', 'ሬ', '手', 'ኤ', '¿', 'や', 'ך', '៊', '인', 'ዚ', 'ؤ', '들', '科', 'ど', 'ッ', 'ば', 'ಖ', 'ឺ', '시', 'ಫ', '文', '主', 'め', '見', 'ฟ', '해', 'ሀ', '州', '水', '小', '目', 'ଢ', 'ශ', 'ኒ', 'ሌ', 'ጊ', '三', '西', '特', '그', '第', '山', '대', '자', '„', '明', '位', 'せ', 'ښ', 'ጀ', '“', '二', '路', 'リ', '加', '教', '國', 'ඩ', '道', '市', '表', '立', '而', '食', 'ޒ', 'Ψ', '動', '័', '¡', '尔', 'ዜ', '亚', '化', '先', '性', '形', '里', '後', 'ሜ', 'ፊ', 'ቹ', '力', 'ド', '呢', '佢', '…', 'Ṛ', 'ഏ', 'ሱ', '比', '高', '起', '球', 'ዙ', '拉', '后', '体', '月', '但', '話', '적', 'Ӡ', 'Ӳ', '克', '使', 'Ჯ', '부', '心', 'ੌ', 'ੜ', '\u200c', '们', '更', '正', '라', '보', 'ቦ', 'ዶ', '美', '也', '南', 'タ', 'ஜ', 'ජ', 'ሙ', 'ፕ', '現', 'ૃ', '일', 'ຜ', 'ਯ', 'ఖ', '口', '因', 'ロ', 'ൃ', 'ೃ', '式', 'ޝ', 'Ẓ', '했', 'ቤ', '十', '区', 'み', 'Ķ', '나', 'ۍ', 'ఏ', 'ං', '外', '海', '重', '天', '語', '۰', 'ៀ', '전', 'ඇ', '全', '被', '面', 'ឆ', '것', 'ධ', 'ፋ', '字', '发', 'ऑ', 'テ', '頭', 'ዱ', '身', '果', 'ஏ', 'ඒ', 'ෝ', 'ኝ', 'ٍ', '今', '示', 'カ', '無', '場', '來', '啲', 'ඔ', 'ඉ', 'ቲ', 'ጅ', '直', '德', '些', '或', '開', '게', '주', 'ቴ', 'ቁ', 'ሶ', '期', 'ろ', 'Ṱ', 'ኔ', 'ቋ', 'ኩ', '阿', '提', '音', 'ジ', '電', 'ऱ', '상', 'ጽ', 'ቆ', '动', '民', '然', '做', '金', 'フ', 'シ', '만', 'උ', 'ޕ', 'ク', '፣', '०', '१', '解', '真', '空', '機', 'コ', 'Ჭ', '冇', 'ฐ', 'ף', 'ڌ', 'ሄ', 'レ', '講', '៍', '种', '少', '点', '此', '与', '相', '受', '言', '՝', 'ઠ', 'ဉ', 'ృ', '内', '工', '英', '代', 'Ģ', 'ූ', 'ዩ', '̃', '北', '如', '流', 'ൺ', '정', '제', 'ਝ', 'ፓ', '·', '伊', '非', '៏', 'ጡ', 'ጫ', 'ቂ', '何', '၎', 'ጨ', 'ሻ', '及', '属', '原', '這', '트', 'ሸ', 'Џ', '城', '影', '次', '安', 'べ', 'Ჰ', 'ൗ', '哋', 'ૈ', 'Ṅ', '과', '었', 'ਏ', 'ౌ', 'ኦ', '建', '政', '巴', 'Ɓ', '々', '識', '種', '여', 'ڀ', 'ኙ', '过', '制', '始', '数', '社', '死', 'ޢ', '車', '長', 'ଝ', 'ๆ', '장', '至', '光', '四', '由', '著', 'ः', '彼', '點', 'ೌ', '되', 'ໆ', 'ሴ', '想', '任', '当', '站', '感', '住', '未', '旅', '持', 'バ', '問', '學', '۱', 'ૌ', '할', '와', '문', 'ဗ', 'ਘ', 'ඳ', 'Ḽ', '已', '造', '管', '必', 'ऐ', 'ィ', 'パ', 'Ǧ', '미', '유', '위', '면', '원', 'ච', 'ጸ', '对', '很', '史', '客', '考', 'ャ', 'オ', 'ഓ', 'ዓ', 'ሃ', '동', '물', 'ආ', 'ቷ', 'ኪ', 'ঃ', '称', '活', '落', 'へ', 'マ', 'ニ', 'ょ', '記', '発', 'ഠ', '發', '咗', 'ឋ', '국', '现', '兰', '基', '平', '保', '火', '情', 'ほ', '気', 'ୌ', '٠', '거', '們', 'ෂ', 'ኞ', 'ፎ', 'ጃ', 'ঝ', 'ڤ', '经', '信', '布', '思', '打', '結', 'ฮ', '嘢', '̇', '우', 'ໝ', 'ፖ', '量', '曾', '车', '品', '速', '件', '於', '۲', 'ٺ', 'Ҿ', 'Ҽ', '太', '「', '」', '石', '交', '司', 'チ', 'ず', '題', '對', 'ஞ', '睇', '噉', '모', '소', '२', 'Ѓ', '赛', '该', '格', '查', '五', 'ޗ', 'ュ', 'ଵ', '১', '세', '구', '경', '비', '။', 'ዴ', '士', '县', '台', '业', '林', '存', '着', '集', '足', '它', 'じ', 'ム', '観', 'ഘ', '經', '邊', 'ஷ', '、', 'ጎ', '계', 'ဇ', 'ਛ', 'ጂ', 'ጭ', '场', '故', '型', '系', '告', '星', '等', 'サ', '입', '공', '며', '간', 'ኳ', 'ኘ', 'Œ', '镇', '罗', '长', '指', '程', '反', '接', '看', 'ウ', 'ブ', '體', '幾', '樣', '러', '요', '르', '၍', '军', '印', '向', '改', '治', '只', '書', 'メ', '味', 'プ', '網', 'ዕ', '২', 'ಘ', '년', '오', 'ਠ', 'Ǹ', '回', '王', '周', '像', 'ޙ', '強', '報', '試', 'ฝ', '০', '°', 'ៗ', '된', '식', '방', '개', '성', 'ڇ', 'ෑ', 'ጦ', 'ጐ', 'ጆ', 'ঢ', '间', '包', '供', '座', '母', '老', '神', 'Ỹ', 'グ', '중', 'ਐ', 'භ', '将', '从', '机', '马', '近', '私', '院', '取', '又', '限', 'デ', 'ェ', 'ネ', '達', '矛', '対', '業', '並', 'ઊ', '않', '화', '발', '조', 'ጓ', 'ጻ', 'Ӷ', '进', '达', '战', '再', '队', '半', '省', '街', 'ޚ', '望', 'キ', '盾', '認', '員', '•', '當', 'ฤ', 'ಏ', '합', '선', '프', '분', 'ຟ', 'ፒ', '列', '河', '尼', '联', '电', '村', '支', '府', '野', '早', '病', '族', '六', '花', '游', '说', '切', '屋', 'ナ', '買', '実', 'ஓ', 'ஹ', '관', '연', '데', '३', 'ਓ', 'ፉ', '否', 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'依', '边', '豆', '背', '视', '侵', '瓜', '攻', '博', '注', '映', '拼', '染', 'ペ', 'ノ', '習', 'モ', 'ゃ', '判', 'ギ', '単', '興', '華', '経', '候', '鳥', '條', '傳', '師', '號', '陽', 'ฏ', '呀', '設', '擊', '워', '많', '현', '종', '매', '본', '버', '민', '၌', 'ዌ', 'ኗ', '括', '党', '广', '诺', '冬', '江', '彩', '沙', '均', '移', '曲', '展', '团', '附', '巨', '效', '话', '响', '寺', '华', '署', '冷', '皮', '项', '容', '恐', '亦', '麻', '埃', '仍', '施', '版', '么', '问', '玩', '晚', '甘', '曜', '菌', 'Ŷ', 'ޞ', 'ハ', 'ざ', '覚', '艇', '護', '洗', 'ゆ', '適', '優', '評', '領', '驚', 'ゴ', '暴', '焦', '臺', '輸', '怪', '資', '龍', '萬', 'ଈ', '請', '續', 'ឥ', '។', '언', '랑', '임', '및', '예', '표', '움', '독', 'ຝ', '၊', 'ෘ', 'ቪ', 'ዬ', 'ፐ', 'ঢ়', '许', '沃', '泰', '增', '佛', '莱', '写', '湾', '苏', '亡', '单', '纪', '武', '溪', '堂', '室', '骨', '房', '含', '幅', '健', '条', '张', '付', '费', '缺', '照', '叶', '楚', '皆', '勒', '捕', '毒', '犯', '症', '乘', '耳', '孩', '肉', '温', '婚', '響', '右', '歩', 'ぞ', '態', '飲', '売', '様', '露', '遠', '導', '勢', '監', '陣', '雞', '濟', '矣', '臭', '朋', '紅', '維', '測', '๊', 'ฬ', '咪', '淨', '幫', 'ኃ', '٥', '졌', '져', '곳', '손', '직', '초', '단', '출', '티', '건', '집', '림', '북', '산', '레', '형', '새', '증', '讓', 'ඹ', 'ጹ', 'ቧ', 'ቬ', '؛', '税', '抵', '盛', '鱼', '敢', '强', '隆', '符', '门', '规', '办', '暖', '标', '义', '永', '整', '风', '却', '叫', '破', '端', '瑞', '习', '刺', '牙', '宣', '题', '按', '阳', '微', '园', '拿', '菊', '什', '担', '决', '赤', '突', '蒂', '仁', '随', '甚', 'ޏ', 'ﷲ', '葉', '震', 'ヒ', '聞', '飯', '創', '応', '危', '協', '証', '楽', '僕', 'ヴ', '補', '広', '親', '讀', '總', '塗', '樂', '閣', '橋', '裝', '喎', '訴', '傾', '：', 'ঊ', '٤', '٨', '۶', '۳', '須', '최', '래', '급', '페', '준', '온', '격', '항', '였', '알', '탈', '너', '၁', 'Ŋ', '歷', 'ኡ', 'ጿ', 'ጁ', 'ሾ', 'ঈ', '围', '追', '添', '划', '森', '师', '银', '陆', '伯', '冠', '级', '帕', '混', '戈', '厚', '见', '央', '价', '酸', '哲', '验', '批', '亲', '测', '狗', '质', '弗', '零', '医', '试', '防', '议', '川', '汉', '散', '束', '宗', '唯', '欧', '抗', '轻', '免', '皇', '患', '疑', '努', '坐', '爆', '途', '迫', '帰', 'ザ', '痛', '願', 'ヤ', '苦', '構', '紙', '糖', '価', '極', '殊', '続', '納', '戦', '簡', '減', '敗', 'ഐ', '屎', '粒', '鬼', '蘭', '眾', '辦', '隻', '圖', '參', '輕', '夠', '賽', '處', '።', '况', '几', 'ಃ', '름', '순', '환', '특', '월', '눈', '험', '석', '근', '션', '받', 'ഔ', 'ဤ', 'ዷ', 'ቼ', 'ጌ', 'ጄ', '̆', '权', '柱', '无', '粗', '町', '舰', '护', '曼', '浮', '环', '汗', '众', '论', '辖', '铁', '固', '坏', '申', '毕', '跨', '树', '田', '腹', '血', '假', '妻', '授', '丹', '卫', '读', '术', '礼', '姆', '藏', '祖', '《', '》', '寄', '泡', '绝', '承', '际', '疫', '序', '弟', '遭', '延', '威', '修', '排', '雅', '摩', '掌', 'ऋ', '鏡', '勉', '働', 'ワ', '収', '閉', '費', 'ぬ', '距', '笑', '詞', '終', '退', '製', '訪', '査', '宿', '順', '寮', '龜', '轉', '錄', '腦', '營', '範', '雲', '裡', '割', '刻', '衛', '鐘', '估', '囉', '陷', '耐', '繼', '訊', '架', '٣', '飞', '午', '۴', 'ォ', 'ಠ', '됩', '활', '충', '돌', '응', '못', '승', '추', '감', '질', '픽', '련', '혀', '난', '목', 'ఛ', 'ෞ', 'ጩ', 'ሷ', 'ዥ', 'ቭ', 'ـ', '粉', '茨', '证', '停', '湖', '转', '茶', '范', '君', '升', '液', '略', '杜', '戏', '艺', '脱', '龙', '录', '籍', '蝶', '童', '刑', '荷', '讲', '企', '阅', '适', '岩', '若', '席', '徒', '佳', '卵', '夜', '仕', '慢', 'づ', '断', '従', '側', '憶', '左', '課', '殺', '残', '録', '説', '筆', '攏', '坑', '織', '囝', '觀', '討', '烏', '咧', '權', '尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', '喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.15.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.15.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn061] 2023-07-06 08:53:07,954 (collect_stats:106) INFO: Niter: 100
+[cn061] 2023-07-06 08:53:21,735 (collect_stats:106) INFO: Niter: 200
+[cn061] 2023-07-06 08:53:29,837 (collect_stats:106) INFO: Niter: 300
+[cn061] 2023-07-06 08:53:49,605 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651416
+# Accounting: end_time=1688651671
+# Accounting: time=255 threads=1
+# Finished at Thu Jul 6 08:54:31 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..641b919e39e6ec0d77b50e005b4193515bea6e63
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.15
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.15.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.15.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
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+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
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+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
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+- 出
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+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
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+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
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+- ኛ
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+- ಒ
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+- 好
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+- 成
+- 分
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+- Ҭ
+- Ğ
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+- 物
+- ޫ
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+- Ӯ
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+- け
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+- 方
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+- 学
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+- ڊ
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+- థ
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+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
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+- ୱ
+- ਧ
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+- 斯
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+- ጵ
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+- ဘ
+- ఈ
+- 있
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+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
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+- Ḥ
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+- څ
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+- 事
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+- ኮ
+- 之
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+- 入
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+- 于
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+- 名
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+- ዛ
+- ቻ
+- ラ
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+- 수
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+- 所
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+- つ
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+- ఫ
+- ቢ
+- ৎ
+- わ
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+- ഈ
+- 으
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+- お
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+- ဒ
+- ጉ
+- ኖ
+- 度
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+- ഖ
+- ឡ
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+- Ֆ
+- 리
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+- 合
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+- 間
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+- ږ
+- 知
+- ሬ
+- 手
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+- ¿
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+- ៊
+- 인
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+- ؤ
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+- ¡
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+- 亚
+- 化
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+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
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+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
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+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
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+    apply_time_mask: true
+    time_mask_width_ratio_range:
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+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
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+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
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+version: '202211'
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+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15/train/feats_lengths_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15/train/stats_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..8d3768c48510d338a3f5446613d0f9ba6c95146d
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
+feats_lengths
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..90a625ef8964141d2c2593f6424d859a78b3129c
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.15/valid/text_shape
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+cv_tam_000711 30
+cv_tat_000002 38
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+cv_tam_000712 48
+cv_tat_000003 23
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+cv_tat_000011 35
+cv_tat_000015 28
+cv_tam_000701 32
+cv_tam_000705 48
+cv_tam_000709 40
+cv_tat_000000 14
+cv_tat_000004 19
+cv_tat_000008 55
+cv_tat_000012 14
+cv_tat_000016 17
+cv_tat_000017 88
+cv_tat_000021 19
+cv_tat_000025 15
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+cv_tat_000033 41
+cv_tat_000037 47
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+cv_tat_000045 70
+cv_tat_000018 28
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+cv_tat_000030 62
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+cv_tat_000038 53
+cv_tat_000042 37
+cv_tat_000046 17
+cv_tat_000019 20
+cv_tat_000023 35
+cv_tat_000027 14
+cv_tat_000031 16
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+cv_tat_000039 12
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+cv_tat_000047 18
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+cv_tat_000024 32
+cv_tat_000028 31
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+cv_tat_000076 36
+cv_tat_000080 58
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+cv_tat_000089 40
+cv_tat_000093 32
+cv_tat_000097 32
+cv_tat_000101 28
+cv_tat_000105 7
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+cv_tat_000082 74
+cv_tat_000086 15
+cv_tat_000090 23
+cv_tat_000094 66
+cv_tat_000098 83
+cv_tat_000102 29
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+cv_tat_000110 36
+cv_tat_000083 55
+cv_tat_000087 22
+cv_tat_000091 50
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+cv_tat_000103 20
+cv_tat_000107 5
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16.log
new file mode 100644
index 0000000000000000000000000000000000000000..db6e3e248b20b292fb29bb1f8258b2b602d8f2ab
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16.log
@@ -0,0 +1,157 @@
+# Running on cn061.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:16 CDT 2023
+# SLURMD_NODENAME=cn061
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=16
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133222
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133222
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn061
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn061
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3272358
+# SLURM_TOPOLOGY_ADDR=ss00.ss03.cn061
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.16.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.16.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.16 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.16.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.16.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.16 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn061] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn061] 2023-07-06 08:52:54,019 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn061] 2023-07-06 08:52:54,021 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn061] 2023-07-06 08:52:54,021 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn061] 2023-07-06 08:52:54,021 (abs_task:1214) INFO: Scheduler: None
+[cn061] 2023-07-06 08:52:54,025 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.16/config.yaml
+[cn061] 2023-07-06 08:52:54,139 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.16', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', '行', 'Ļ', '[som]', 'భ', 'ሆ', '[fil]', 'Ӣ', 'き', 'Ӧ', 'ሉ', 'ʼ', 'ณ', '[oci]', 'Ҷ', 'ڳ', 'ഡ', '[kam]', 'Ღ', 'ݨ', '을', 'ஆ', 'Ƙ', 'ਉ', 'ቶ', 'ຮ', 'ካ', 'ቅ', 'औ', 'ଂ', 'ណ', '의', 'ణ', 'Փ', 'ਈ', 'ګ', 'హ', 'ौ', 'ໍ', 'ಭ', 'ಎ', '地', 'ఆ', 'ん', '하', 'ਖ', '自', '上', 'ፍ', 'ਡ', '可', 'ዮ', '生', '[ibo]', 'ٻ', 'さ', 'ູ', '多', 'ޓ', 'ភ', '니', '不', 'ढ', 'ઈ', 'Ň', 'ഥ', 'ศ', '要', 'Ժ', 'ී', 'ۋ', 'ො', 'ሮ', '年', '到', 'Ძ', 'ซ', '۾', '你', '로', 'ጋ', '[lug]', 'ঞ', '国', 'ផ', '個', 'ฉ', '和', 'ஒ', 'ខ', '[mri]', '他', 'ଠ', 'ຼ', 'ঙ', 'ޅ', '唔', 'ຳ', 'ځ', '៉', 'ថ', '가', '[lin]', 'Ճ', '은', 'ធ', '੍', 'Đ', '嘅', '出', 'ټ', 'Ĕ', 'ス', '지', '下', 'ୂ', 'ޑ', 'Ɗ', 'ຸ', 'ઉ', 'ញ', 'ሁ', 'ޖ', '”', '用', 'ሩ', 'ޔ', '为', '了', 'ຖ', 'එ', 'ጣ', 'ઇ', '日', 'ޯ', 'よ', '고', 'ኛ', 'ሊ', '得', 'ൈ', 'ಒ', 'ፈ', '好', '同', '成', '分', 'そ', 'ธ', 'Ҭ', 'Ğ', 'Ṭ', 'ዲ', 'Њ', 'ڏ', '۽', '物', 'ޫ', 'Ჩ', 'Ӯ', '都', 'け', '个', 'ഫ', 'Ō', 'ዘ', '方', 'ډ', 'Ḍ', '学', 'Օ', 'だ', '能', 'ڊ', '部', '仔', 'ภ', 'థ', 'ओ', 'Җ', 'ษ', 'ಉ', 'ឹ', '서', 'ਫ', 'Ï', 'ル', 'ୱ', 'ਧ', 'ቱ', '就', 'Ű', 'ళ', '斯', '作', 'ໂ', 'ණ', 'ጵ', '사', 'ఎ', 'ဘ', 'ఈ', '있', '時', 'ॅ', '한', '기', 'ଘ', 'ଫ', 'ڄ', 'Ď', 'ऊ', 'ఒ', 'ሎ', 'ቃ', 'ೈ', 'ْ', 'ຢ', 'Ҩ', '前', 'ዊ', 'Ҟ', '世', 'ญ', 'イ', '公', 'ໜ', 'え', 'ト', '၏', 'ৌ', 'Ḥ', 'ሲ', '家', 'ៃ', 'څ', '会', 'ಈ', 'Ẽ', 'ア', 'ち', '子', 'ઝ', '를', '္', 'ሞ', '事', '本', '最', 'ຽ', 'ङ', '會', 'ଓ', 'ਭ', 'ሽ', 'ኮ', '之', '己', '스', 'ဝ', '入', 'ළ', 'ዝ', '于', '理', 'ဟ', 'ڙ', 'Ҵ', '名', '新', '法', 'ୃ', 'ዛ', 'ቻ', 'ラ', 'ઘ', '수', '습', 'ਥ', '所', '界', 'つ', 'ஸ', 'ఫ', 'ቢ', 'ৎ', 'わ', '過', 'ഈ', '으', '来', 'ဂ', '定', '利', '者', 'お', '為', 'Ɛ', '아', 'ຶ', 'ဒ', 'ጉ', 'ኖ', '度', '常', 'ഖ', 'ឡ', '도', 'Ḓ', '去', '其', '意', 'ኑ', '这', '时', '通', 'Ֆ', '리', '어', 'ቡ', '合', 'ञ', '間', 'ଞ', 'Ќ', 'ږ', '知', 'ሬ', '手', 'ኤ', '¿', 'や', 'ך', '៊', '인', 'ዚ', 'ؤ', '들', '科', 'ど', 'ッ', 'ば', 'ಖ', 'ឺ', '시', 'ಫ', '文', '主', 'め', '見', 'ฟ', '해', 'ሀ', '州', '水', '小', '目', 'ଢ', 'ශ', 'ኒ', 'ሌ', 'ጊ', '三', '西', '特', '그', '第', '山', '대', '자', '„', '明', '位', 'せ', 'ښ', 'ጀ', '“', '二', '路', 'リ', '加', '教', '國', 'ඩ', '道', '市', '表', '立', '而', '食', 'ޒ', 'Ψ', '動', '័', 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'될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', 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'乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.16.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.16.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn061] 2023-07-06 08:53:08,282 (collect_stats:106) INFO: Niter: 100
+[cn061] 2023-07-06 08:53:22,952 (collect_stats:106) INFO: Niter: 200
+[cn061] 2023-07-06 08:53:30,262 (collect_stats:106) INFO: Niter: 300
+[cn061] 2023-07-06 08:53:49,265 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651416
+# Accounting: end_time=1688651671
+# Accounting: time=255 threads=1
+# Finished at Thu Jul 6 08:54:31 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5af1cfdc5c075603717dfb2147d1297cc2b4d585
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.16
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.16.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.16.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
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+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
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+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
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+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
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+- Ҟ
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+- イ
+- 公
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+- え
+- ト
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+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
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+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
+- ッ
+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
+- 西
+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
+- 加
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+- 國
+- ඩ
+- 道
+- 市
+- 表
+- 立
+- 而
+- 食
+- ޒ
+- Ψ
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+- ័
+- ¡
+- 尔
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+- 亚
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+- 里
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+- ቹ
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+- …
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+- ഏ
+- ሱ
+- 比
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+- 起
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+- 拉
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+- Ӡ
+- Ӳ
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+- 부
+- 心
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+- ੜ
+- ‌
+- 们
+- 更
+- 正
+- 라
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+- ቦ
+- ዶ
+- 美
+- 也
+- 南
+- タ
+- ஜ
+- ජ
+- ሙ
+- ፕ
+- 現
+- ૃ
+- 일
+- ຜ
+- ਯ
+- ఖ
+- 口
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+- ロ
+- ൃ
+- ೃ
+- 式
+- ޝ
+- Ẓ
+- 했
+- ቤ
+- 十
+- 区
+- み
+- Ķ
+- 나
+- ۍ
+- ఏ
+- ං
+- 外
+- 海
+- 重
+- 天
+- 語
+- ۰
+- ៀ
+- 전
+- ඇ
+- 全
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+- 面
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+- 것
+- ධ
+- ፋ
+- 字
+- 发
+- ऑ
+- テ
+- 頭
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+- 身
+- 果
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+- ඒ
+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
+- カ
+- 無
+- 場
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+- 啲
+- ඔ
+- ඉ
+- ቲ
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+- ろ
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+- ኩ
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+- ऱ
+- 상
+- ጽ
+- ቆ
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+- 만
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+- ޕ
+- ク
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+- ०
+- १
+- 解
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+- コ
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+- 冇
+- ฐ
+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
+- 工
+- 英
+- 代
+- Ģ
+- ූ
+- ዩ
+- ̃
+- 北
+- 如
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+- ൺ
+- 정
+- 제
+- ਝ
+- ፓ
+- ·
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+- ጡ
+- ጫ
+- ቂ
+- 何
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+- ጨ
+- ሻ
+- 及
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+- 原
+- 這
+- 트
+- ሸ
+- Џ
+- 城
+- 影
+- 次
+- 安
+- べ
+- Ჰ
+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
+- 었
+- ਏ
+- ౌ
+- ኦ
+- 建
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+- Ɓ
+- 々
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+- 種
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+- ڀ
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+- 过
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+- ޢ
+- 車
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+- 點
+- ೌ
+- 되
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+- ሴ
+- 想
+- 任
+- 当
+- 站
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+- 未
+- 旅
+- 持
+- バ
+- 問
+- 學
+- ۱
+- ૌ
+- 할
+- 와
+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
+- 已
+- 造
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+- 必
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+- ィ
+- パ
+- Ǧ
+- 미
+- 유
+- 위
+- 면
+- 원
+- ච
+- ጸ
+- 对
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+- 史
+- 客
+- 考
+- ャ
+- オ
+- ഓ
+- ዓ
+- ሃ
+- 동
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+- ආ
+- ቷ
+- ኪ
+- ঃ
+- 称
+- 活
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+- へ
+- マ
+- ニ
+- ょ
+- 記
+- 発
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+- 發
+- 咗
+- ឋ
+- 국
+- 现
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+- 保
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+- 情
+- ほ
+- 気
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+- ٠
+- 거
+- 們
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+- ኞ
+- ፎ
+- ጃ
+- ঝ
+- ڤ
+- 经
+- 信
+- 布
+- 思
+- 打
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+- 嘢
+- ̇
+- 우
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+- ፖ
+- 量
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+- 车
+- 品
+- 速
+- 件
+- 於
+- ۲
+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
+- 」
+- 石
+- 交
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+- チ
+- ず
+- 題
+- 對
+- ஞ
+- 睇
+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
+- 五
+- ޗ
+- ュ
+- ଵ
+- ১
+- 세
+- 구
+- 경
+- 비
+- ။
+- ዴ
+- 士
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+- 林
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+- 着
+- 集
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+- 它
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+- ഘ
+- 經
+- 邊
+- ஷ
+- 、
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+- 계
+- ဇ
+- ਛ
+- ጂ
+- ጭ
+- 场
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+- ኘ
+- Œ
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+- 接
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+- 러
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+- ၍
+- 军
+- 印
+- 向
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+- メ
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+- プ
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+- ২
+- ಘ
+- 년
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+- ਠ
+- Ǹ
+- 回
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+- 周
+- 像
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+- 強
+- 報
+- 試
+- ฝ
+- ০
+- °
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+- 된
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+- ڇ
+- ෑ
+- ጦ
+- ጐ
+- ጆ
+- ঢ
+- 间
+- 包
+- 供
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+- グ
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+- ェ
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+- 발
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+- ጓ
+- ጻ
+- Ӷ
+- 进
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+- 战
+- 再
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+- 半
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+- 街
+- ޚ
+- 望
+- キ
+- 盾
+- 認
+- 員
+- •
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+- ฤ
+- ಏ
+- 합
+- 선
+- 프
+- 분
+- ຟ
+- ፒ
+- 列
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+- 联
+- 电
+- 村
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+- ナ
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+- 데
+- ३
+- ਓ
+- ፉ
+- 否
+- 演
+- 图
+- 夫
+- 放
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+- ）
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+- ሦ
+- ኣ
+- "\x93"
+- "\x94"
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+- 遣
+- 册
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+- 拦
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+- 尝
+- ％
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+- 陨
+- 咎
+- 箭
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+- 钢
+- 键
+- 锐
+- 啸
+- 纹
+- 磷
+- 硫
+- 骚
+- 晓
+- 培
+- 骗
+- 疗
+- 锻
+- 炼
+- 喘
+- 陡
+- 谨
+- 梵
+- 鲜
+- 纯
+- 粹
+- 厉
+- 汁
+- 夺
+- 丢
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+- 陶
+- 旨
+- 聪
+- 审
+- 铃
+- 璀
+- 璨
+- 垒
+- 劫
+- 荣
+- 惊
+- 叹
+- 绵
+- 攀
+- 损
+- 肿
+- 瘤
+- 龄
+- 齐
+- 鞭
+- 缭
+- 穷
+- 哺
+- 粘
+- 宏
+- 倾
+- 财
+- 纸
+- 荐
+- 媚
+- 滥
+- 嘲
+- 泄
+- 盘
+- 廷
+- 抢
+- 敛
+- 猎
+- 苹
+- 笔
+- 愤
+- 慨
+- 罄
+- 茵
+- 碍
+- 舱
+- 励
+- 雕
+- 酋
+- 镑
+- 暂
+- 疽
+- 卓
+- 咨
+- 饮
+- 笨
+- 闲
+- 逛
+- §
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+- <sos/eos>
+init: null
+input_size: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/train/batch_keys
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index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/train/feats_lengths_stats.npz
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index 0000000000000000000000000000000000000000..745a657eb988b163c8ff7b1750e065e01b0da450
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/train/feats_lengths_stats.npz
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+version https://git-lfs.github.com/spec/v1
+oid sha256:cc5bbae99bec820fc445a78a750a3e3d57d6e58ac21281eac342bc3e8be96f82
+size 778
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/train/stats_keys
@@ -0,0 +1,2 @@
+feats
+feats_lengths
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..b01b2247cb9c0d7aeb649522d9f4683638a5e93a
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/train/text_shape
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index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
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index 0000000000000000000000000000000000000000..a73c22d811eee901a87b01994641ecde85125935
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/valid/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/valid/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/valid/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..fb576a4e2aee21fd0f422d318ae16109f268bfa2
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.16/valid/text_shape
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+cv_uig_000641 52
+cv_uig_000645 67
+cv_uig_000649 81
+cv_uig_000650 88
+cv_uig_000654 64
+cv_uig_000658 75
+cv_ukr_000671 31
+cv_ukr_000675 60
+cv_ukr_000679 89
+cv_ukr_000683 24
+cv_ukr_000687 64
+cv_uig_000651 103
+cv_uig_000655 39
+cv_uig_000659 75
+cv_ukr_000672 15
+cv_ukr_000676 35
+cv_ukr_000680 34
+cv_ukr_000684 34
+cv_ukr_000688 42
+cv_uig_000652 85
+cv_uig_000656 76
+cv_uig_000660 62
+cv_ukr_000673 33
+cv_ukr_000677 51
+cv_ukr_000681 35
+cv_ukr_000685 46
+cv_ukr_000689 64
+cv_uig_000653 81
+cv_uig_000657 43
+cv_uig_000661 60
+cv_ukr_000674 88
+cv_ukr_000678 21
+cv_ukr_000682 48
+cv_ukr_000686 35
+cv_ukr_000690 31
+cv_ukr_000691 65
+cv_ukr_000695 52
+cv_ukr_000699 67
+cv_ukr_000703 106
+cv_ukr_000707 37
+cv_ukr_000711 59
+cv_ukr_000715 50
+cv_ukr_000719 25
+cv_ukr_000692 72
+cv_ukr_000696 37
+cv_ukr_000700 16
+cv_ukr_000704 65
+cv_ukr_000708 12
+cv_ukr_000712 15
+cv_ukr_000716 91
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+cv_ukr_000693 72
+cv_ukr_000697 22
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+cv_ukr_000713 109
+cv_ukr_000717 23
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+cv_ukr_000694 78
+cv_ukr_000698 76
+cv_ukr_000702 47
+cv_ukr_000706 91
+cv_ukr_000710 97
+cv_ukr_000714 50
+cv_ukr_000718 42
+cv_ukr_000722 53
+cv_ukr_000723 47
+cv_ukr_000727 86
+cv_ukr_000731 30
+cv_ukr_000735 67
+cv_ukr_000739 58
+cv_ukr_000743 14
+cv_ukr_000747 22
+cv_ukr_000751 58
+cv_ukr_000724 25
+cv_ukr_000728 60
+cv_ukr_000732 53
+cv_ukr_000736 40
+cv_ukr_000740 61
+cv_ukr_000744 38
+cv_ukr_000748 27
+cv_ukr_000752 74
+cv_ukr_000725 63
+cv_ukr_000729 80
+cv_ukr_000733 9
+cv_ukr_000737 31
+cv_ukr_000741 91
+cv_ukr_000745 73
+cv_ukr_000749 42
+cv_ukr_000753 25
+cv_ukr_000726 51
+cv_ukr_000730 66
+cv_ukr_000734 38
+cv_ukr_000738 47
+cv_ukr_000742 61
+cv_ukr_000746 29
+cv_ukr_000750 35
+cv_ukr_000754 75
+cv_ukr_000755 29
+cv_ukr_000759 43
+cv_ukr_000763 16
+cv_ukr_000767 64
+cv_ukr_000771 85
+cv_ukr_000775 26
+cv_ukr_000779 84
+cv_urd_000810 20
+cv_ukr_000756 31
+cv_ukr_000760 32
+cv_ukr_000764 17
+cv_ukr_000768 35
+cv_ukr_000772 44
+cv_ukr_000776 30
+cv_ukr_000780 79
+cv_urd_000811 50
+cv_ukr_000757 22
+cv_ukr_000761 22
+cv_ukr_000765 24
+cv_ukr_000769 32
+cv_ukr_000773 28
+cv_ukr_000777 24
+cv_ukr_000781 73
+cv_urd_000812 24
+cv_ukr_000758 44
+cv_ukr_000762 52
+cv_ukr_000766 19
+cv_ukr_000770 39
+cv_ukr_000774 115
+cv_ukr_000778 57
+cv_urd_000809 26
+cv_urd_000813 15
+cv_urd_000814 32
+cv_urd_000818 63
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+cv_urd_000941 71
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+cv_urd_000950 23
+cv_uzb_000650 93
+cv_uzb_000654 66
+cv_uzb_000658 49
+cv_uzb_000662 31
+cv_uzb_000666 31
+cv_urd_000943 37
+cv_urd_000947 21
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+cv_uzb_000664 38
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+cv_uzb_000661 61
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+cv_uzb_000683 61
+cv_uzb_000687 90
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+cv_uzb_000695 63
+cv_uzb_000699 37
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+cv_uzb_000676 58
+cv_uzb_000680 39
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+cv_uzb_000688 72
+cv_uzb_000692 54
+cv_uzb_000696 56
+cv_uzb_000700 47
+cv_uzb_000673 61
+cv_uzb_000677 30
+cv_uzb_000681 24
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+cv_uzb_000689 46
+cv_uzb_000693 93
+cv_uzb_000697 32
+cv_uzb_000701 33
+cv_uzb_000702 34
+cv_uzb_000706 78
+cv_uzb_000710 63
+cv_uzb_000714 32
+cv_uzb_000718 45
+cv_uzb_000722 57
+cv_uzb_000703 35
+cv_uzb_000707 26
+cv_uzb_000711 67
+cv_uzb_000715 18
+cv_uzb_000719 50
+cv_uzb_000723 64
+cv_uzb_000704 56
+cv_uzb_000708 93
+cv_uzb_000712 44
+cv_uzb_000716 85
+cv_uzb_000720 22
+cv_uzb_000724 34
+cv_uzb_000705 75
+cv_uzb_000709 40
+cv_uzb_000713 77
+cv_uzb_000717 69
+cv_uzb_000721 37
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17.log
new file mode 100644
index 0000000000000000000000000000000000000000..8265c4920c13979dd9ee96deb67ae59508fc1fa8
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17.log
@@ -0,0 +1,157 @@
+# Running on cn061.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:16 CDT 2023
+# SLURMD_NODENAME=cn061
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=17
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133223
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133223
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn061
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn061
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3272356
+# SLURM_TOPOLOGY_ADDR=ss00.ss03.cn061
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.17.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.17.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.17 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.17.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.17.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.17 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn061] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn061] 2023-07-06 08:52:54,019 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn061] 2023-07-06 08:52:54,021 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn061] 2023-07-06 08:52:54,021 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn061] 2023-07-06 08:52:54,021 (abs_task:1214) INFO: Scheduler: None
+[cn061] 2023-07-06 08:52:54,025 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.17/config.yaml
+[cn061] 2023-07-06 08:52:54,139 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.17', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 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'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 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'尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', 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'喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.17.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.17.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn061] 2023-07-06 08:53:08,227 (collect_stats:106) INFO: Niter: 100
+[cn061] 2023-07-06 08:53:24,163 (collect_stats:106) INFO: Niter: 200
+[cn061] 2023-07-06 08:53:48,966 (collect_stats:106) INFO: Niter: 300
+[cn061] 2023-07-06 08:54:00,798 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651416
+# Accounting: end_time=1688651671
+# Accounting: time=255 threads=1
+# Finished at Thu Jul 6 08:54:31 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..664174db52daa9395ef8bb49969a0931e865121f
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.17
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.17.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.17.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
+- ໂ
+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
+- 기
+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
+- 前
+- ዊ
+- Ҟ
+- 世
+- ญ
+- イ
+- 公
+- ໜ
+- え
+- ト
+- ၏
+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
+- ္
+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
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+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
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+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
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+- 教
+- 國
+- ඩ
+- 道
+- 市
+- 表
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+- 而
+- 食
+- ޒ
+- Ψ
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+- ¡
+- 尔
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+- 性
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+- 里
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+- ቹ
+- 力
+- ド
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+- 佢
+- …
+- Ṛ
+- ഏ
+- ሱ
+- 比
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+- 起
+- 球
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+- 拉
+- 后
+- 体
+- 月
+- 但
+- 話
+- 적
+- Ӡ
+- Ӳ
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+- 使
+- Ჯ
+- 부
+- 心
+- ੌ
+- ੜ
+- ‌
+- 们
+- 更
+- 正
+- 라
+- 보
+- ቦ
+- ዶ
+- 美
+- 也
+- 南
+- タ
+- ஜ
+- ජ
+- ሙ
+- ፕ
+- 現
+- ૃ
+- 일
+- ຜ
+- ਯ
+- ఖ
+- 口
+- 因
+- ロ
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+- ೃ
+- 式
+- ޝ
+- Ẓ
+- 했
+- ቤ
+- 十
+- 区
+- み
+- Ķ
+- 나
+- ۍ
+- ఏ
+- ං
+- 外
+- 海
+- 重
+- 天
+- 語
+- ۰
+- ៀ
+- 전
+- ඇ
+- 全
+- 被
+- 面
+- ឆ
+- 것
+- ධ
+- ፋ
+- 字
+- 发
+- ऑ
+- テ
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+- 身
+- 果
+- ஏ
+- ඒ
+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
+- カ
+- 無
+- 場
+- 來
+- 啲
+- ඔ
+- ඉ
+- ቲ
+- ጅ
+- 直
+- 德
+- 些
+- 或
+- 開
+- 게
+- 주
+- ቴ
+- ቁ
+- ሶ
+- 期
+- ろ
+- Ṱ
+- ኔ
+- ቋ
+- ኩ
+- 阿
+- 提
+- 音
+- ジ
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+- 상
+- ጽ
+- ቆ
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+- 做
+- 金
+- フ
+- シ
+- 만
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+- ޕ
+- ク
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+- ०
+- १
+- 解
+- 真
+- 空
+- 機
+- コ
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+- 冇
+- ฐ
+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
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+- 英
+- 代
+- Ģ
+- ූ
+- ዩ
+- ̃
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+- 제
+- ਝ
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+- ·
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+- ጫ
+- ቂ
+- 何
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+- ጨ
+- ሻ
+- 及
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+- 原
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+- ሸ
+- Џ
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+- 影
+- 次
+- 安
+- べ
+- Ჰ
+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
+- 었
+- ਏ
+- ౌ
+- ኦ
+- 建
+- 政
+- 巴
+- Ɓ
+- 々
+- 識
+- 種
+- 여
+- ڀ
+- ኙ
+- 过
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+- 数
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+- 車
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+- 點
+- ೌ
+- 되
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+- ሴ
+- 想
+- 任
+- 当
+- 站
+- 感
+- 住
+- 未
+- 旅
+- 持
+- バ
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+- 學
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+- ૌ
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+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
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+- 必
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+- ィ
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+- 미
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+- 원
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+- ጸ
+- 对
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+- 史
+- 客
+- 考
+- ャ
+- オ
+- ഓ
+- ዓ
+- ሃ
+- 동
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+- ආ
+- ቷ
+- ኪ
+- ঃ
+- 称
+- 活
+- 落
+- へ
+- マ
+- ニ
+- ょ
+- 記
+- 発
+- ഠ
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+- ឋ
+- 국
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+- 兰
+- 基
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+- ほ
+- 気
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+- ٠
+- 거
+- 們
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+- ኞ
+- ፎ
+- ጃ
+- ঝ
+- ڤ
+- 经
+- 信
+- 布
+- 思
+- 打
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+- 嘢
+- ̇
+- 우
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+- ፖ
+- 量
+- 曾
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+- 品
+- 速
+- 件
+- 於
+- ۲
+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
+- 」
+- 石
+- 交
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+- チ
+- ず
+- 題
+- 對
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+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
+- 五
+- ޗ
+- ュ
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+- ১
+- 세
+- 구
+- 경
+- 비
+- ။
+- ዴ
+- 士
+- 县
+- 台
+- 业
+- 林
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+- 着
+- 集
+- 足
+- 它
+- じ
+- ム
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+- ഘ
+- 經
+- 邊
+- ஷ
+- 、
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+- ጭ
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+- 간
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+- ኘ
+- Œ
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+- 罗
+- 长
+- 指
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+init: null
+input_size: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/train/feats_lengths_stats.npz
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--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/train/feats_lengths_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/train/stats_keys
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index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/train/text_shape
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index 0000000000000000000000000000000000000000..c7d76735c994cacb3f49fd3202e50a4f49923e0f
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..6df626f4980d306139397a55129bbcd0456abbee
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.17/valid/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18.log
new file mode 100644
index 0000000000000000000000000000000000000000..7998a3ad54b709b4c2fb655826da6d26657a140a
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18.log
@@ -0,0 +1,157 @@
+# Running on cn002.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:13 CDT 2023
+# SLURMD_NODENAME=cn002
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=18
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133224
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133224
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn002
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn002
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=860810
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn002
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.18.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.18.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.18 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.18.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.18.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.18 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn002] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn002] 2023-07-06 08:52:53,974 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn002] 2023-07-06 08:52:53,976 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn002] 2023-07-06 08:52:53,976 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn002] 2023-07-06 08:52:53,976 (abs_task:1214) INFO: Scheduler: None
+[cn002] 2023-07-06 08:52:53,984 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.18/config.yaml
+[cn002] 2023-07-06 08:52:54,088 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.18', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 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'尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', 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'喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.18.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.18.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn002] 2023-07-06 08:53:11,767 (collect_stats:106) INFO: Niter: 100
+[cn002] 2023-07-06 08:53:23,500 (collect_stats:106) INFO: Niter: 200
+[cn002] 2023-07-06 08:53:35,010 (collect_stats:106) INFO: Niter: 300
+[cn002] 2023-07-06 08:53:46,083 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651413
+# Accounting: end_time=1688651652
+# Accounting: time=239 threads=1
+# Finished at Thu Jul 6 08:54:12 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8ff80c25862cc0174bae7c987eac5b42a6c5f02e
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.18
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.18.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.18.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
+- ໂ
+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
+- 기
+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
+- 前
+- ዊ
+- Ҟ
+- 世
+- ญ
+- イ
+- 公
+- ໜ
+- え
+- ト
+- ၏
+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
+- ္
+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
+- ッ
+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
+- 西
+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
+- 加
+- 教
+- 國
+- ඩ
+- 道
+- 市
+- 表
+- 立
+- 而
+- 食
+- ޒ
+- Ψ
+- 動
+- ័
+- ¡
+- 尔
+- ዜ
+- 亚
+- 化
+- 先
+- 性
+- 形
+- 里
+- 後
+- ሜ
+- ፊ
+- ቹ
+- 力
+- ド
+- 呢
+- 佢
+- …
+- Ṛ
+- ഏ
+- ሱ
+- 比
+- 高
+- 起
+- 球
+- ዙ
+- 拉
+- 后
+- 体
+- 月
+- 但
+- 話
+- 적
+- Ӡ
+- Ӳ
+- 克
+- 使
+- Ჯ
+- 부
+- 心
+- ੌ
+- ੜ
+- ‌
+- 们
+- 更
+- 正
+- 라
+- 보
+- ቦ
+- ዶ
+- 美
+- 也
+- 南
+- タ
+- ஜ
+- ජ
+- ሙ
+- ፕ
+- 現
+- ૃ
+- 일
+- ຜ
+- ਯ
+- ఖ
+- 口
+- 因
+- ロ
+- ൃ
+- ೃ
+- 式
+- ޝ
+- Ẓ
+- 했
+- ቤ
+- 十
+- 区
+- み
+- Ķ
+- 나
+- ۍ
+- ఏ
+- ං
+- 外
+- 海
+- 重
+- 天
+- 語
+- ۰
+- ៀ
+- 전
+- ඇ
+- 全
+- 被
+- 面
+- ឆ
+- 것
+- ධ
+- ፋ
+- 字
+- 发
+- ऑ
+- テ
+- 頭
+- ዱ
+- 身
+- 果
+- ஏ
+- ඒ
+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
+- カ
+- 無
+- 場
+- 來
+- 啲
+- ඔ
+- ඉ
+- ቲ
+- ጅ
+- 直
+- 德
+- 些
+- 或
+- 開
+- 게
+- 주
+- ቴ
+- ቁ
+- ሶ
+- 期
+- ろ
+- Ṱ
+- ኔ
+- ቋ
+- ኩ
+- 阿
+- 提
+- 音
+- ジ
+- 電
+- ऱ
+- 상
+- ጽ
+- ቆ
+- 动
+- 民
+- 然
+- 做
+- 金
+- フ
+- シ
+- 만
+- උ
+- ޕ
+- ク
+- ፣
+- ०
+- १
+- 解
+- 真
+- 空
+- 機
+- コ
+- Ჭ
+- 冇
+- ฐ
+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
+- 工
+- 英
+- 代
+- Ģ
+- ූ
+- ዩ
+- ̃
+- 北
+- 如
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+- ൺ
+- 정
+- 제
+- ਝ
+- ፓ
+- ·
+- 伊
+- 非
+- ៏
+- ጡ
+- ጫ
+- ቂ
+- 何
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+- ጨ
+- ሻ
+- 及
+- 属
+- 原
+- 這
+- 트
+- ሸ
+- Џ
+- 城
+- 影
+- 次
+- 安
+- べ
+- Ჰ
+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
+- 었
+- ਏ
+- ౌ
+- ኦ
+- 建
+- 政
+- 巴
+- Ɓ
+- 々
+- 識
+- 種
+- 여
+- ڀ
+- ኙ
+- 过
+- 制
+- 始
+- 数
+- 社
+- 死
+- ޢ
+- 車
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+- ଝ
+- ๆ
+- 장
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+- 光
+- 四
+- 由
+- 著
+- ः
+- 彼
+- 點
+- ೌ
+- 되
+- ໆ
+- ሴ
+- 想
+- 任
+- 当
+- 站
+- 感
+- 住
+- 未
+- 旅
+- 持
+- バ
+- 問
+- 學
+- ۱
+- ૌ
+- 할
+- 와
+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
+- 已
+- 造
+- 管
+- 必
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+- ィ
+- パ
+- Ǧ
+- 미
+- 유
+- 위
+- 면
+- 원
+- ච
+- ጸ
+- 对
+- 很
+- 史
+- 客
+- 考
+- ャ
+- オ
+- ഓ
+- ዓ
+- ሃ
+- 동
+- 물
+- ආ
+- ቷ
+- ኪ
+- ঃ
+- 称
+- 活
+- 落
+- へ
+- マ
+- ニ
+- ょ
+- 記
+- 発
+- ഠ
+- 發
+- 咗
+- ឋ
+- 국
+- 现
+- 兰
+- 基
+- 平
+- 保
+- 火
+- 情
+- ほ
+- 気
+- ୌ
+- ٠
+- 거
+- 們
+- ෂ
+- ኞ
+- ፎ
+- ጃ
+- ঝ
+- ڤ
+- 经
+- 信
+- 布
+- 思
+- 打
+- 結
+- ฮ
+- 嘢
+- ̇
+- 우
+- ໝ
+- ፖ
+- 量
+- 曾
+- 车
+- 品
+- 速
+- 件
+- 於
+- ۲
+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
+- 」
+- 石
+- 交
+- 司
+- チ
+- ず
+- 題
+- 對
+- ஞ
+- 睇
+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
+- 五
+- ޗ
+- ュ
+- ଵ
+- ১
+- 세
+- 구
+- 경
+- 비
+- ။
+- ዴ
+- 士
+- 县
+- 台
+- 业
+- 林
+- 存
+- 着
+- 集
+- 足
+- 它
+- じ
+- ム
+- 観
+- ഘ
+- 經
+- 邊
+- ஷ
+- 、
+- ጎ
+- 계
+- ဇ
+- ਛ
+- ጂ
+- ጭ
+- 场
+- 故
+- 型
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+- 告
+- 星
+- 等
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+- 공
+- 며
+- 간
+- ኳ
+- ኘ
+- Œ
+- 镇
+- 罗
+- 长
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+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
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+aux_ctc_tasks: []
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+frontend: default
+frontend_conf:
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+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
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+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
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+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
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+preencoder_conf: {}
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+encoder_conf:
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+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
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+decoder_conf: {}
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+preprocessor_conf: {}
+required:
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+version: '202211'
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18/train/batch_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18/train/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..b6394ea964bfa5ade1627e1ad82c879fae6a807d
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.18/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..abc947eaa97aad22f00002431e3b48bf04141476
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19.log
new file mode 100644
index 0000000000000000000000000000000000000000..1760274b3a06389ad33f7bf48e86995ed67741b0
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19.log
@@ -0,0 +1,157 @@
+# Running on cn002.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:13 CDT 2023
+# SLURMD_NODENAME=cn002
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=19
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133225
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133225
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn002
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn002
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=860852
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn002
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.19.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.19.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.19 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.19.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.19.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.19 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn002] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn002] 2023-07-06 08:52:53,974 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn002] 2023-07-06 08:52:53,976 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn002] 2023-07-06 08:52:53,976 (abs_task:1214) INFO: Scheduler: None
+[cn002] 2023-07-06 08:52:53,987 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.19/config.yaml
+[cn002] 2023-07-06 08:52:54,090 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.19', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', '行', 'Ļ', '[som]', 'భ', 'ሆ', '[fil]', 'Ӣ', 'き', 'Ӧ', 'ሉ', 'ʼ', 'ณ', '[oci]', 'Ҷ', 'ڳ', 'ഡ', '[kam]', 'Ღ', 'ݨ', '을', 'ஆ', 'Ƙ', 'ਉ', 'ቶ', 'ຮ', 'ካ', 'ቅ', 'औ', 'ଂ', 'ណ', '의', 'ణ', 'Փ', 'ਈ', 'ګ', 'హ', 'ौ', 'ໍ', 'ಭ', 'ಎ', '地', 'ఆ', 'ん', '하', 'ਖ', '自', '上', 'ፍ', 'ਡ', '可', 'ዮ', '生', '[ibo]', 'ٻ', 'さ', 'ູ', '多', 'ޓ', 'ភ', '니', '不', 'ढ', 'ઈ', 'Ň', 'ഥ', 'ศ', '要', 'Ժ', 'ී', 'ۋ', 'ො', 'ሮ', '年', '到', 'Ძ', 'ซ', '۾', '你', '로', 'ጋ', '[lug]', 'ঞ', '国', 'ផ', '個', 'ฉ', '和', 'ஒ', 'ខ', '[mri]', '他', 'ଠ', 'ຼ', 'ঙ', 'ޅ', '唔', 'ຳ', 'ځ', '៉', 'ថ', '가', '[lin]', 'Ճ', '은', 'ធ', '੍', 'Đ', '嘅', '出', 'ټ', 'Ĕ', 'ス', '지', '下', 'ୂ', 'ޑ', 'Ɗ', 'ຸ', 'ઉ', 'ញ', 'ሁ', 'ޖ', '”', '用', 'ሩ', 'ޔ', '为', '了', 'ຖ', 'එ', 'ጣ', 'ઇ', '日', 'ޯ', 'よ', '고', 'ኛ', 'ሊ', '得', 'ൈ', 'ಒ', 'ፈ', '好', '同', '成', '分', 'そ', 'ธ', 'Ҭ', 'Ğ', 'Ṭ', 'ዲ', 'Њ', 'ڏ', '۽', '物', 'ޫ', 'Ჩ', 'Ӯ', '都', 'け', '个', 'ഫ', 'Ō', 'ዘ', '方', 'ډ', 'Ḍ', '学', 'Օ', 'だ', '能', 'ڊ', '部', '仔', 'ภ', 'థ', 'ओ', 'Җ', 'ษ', 'ಉ', 'ឹ', '서', 'ਫ', 'Ï', 'ル', 'ୱ', 'ਧ', 'ቱ', '就', 'Ű', 'ళ', '斯', '作', 'ໂ', 'ණ', 'ጵ', '사', 'ఎ', 'ဘ', 'ఈ', '있', '時', 'ॅ', '한', '기', 'ଘ', 'ଫ', 'ڄ', 'Ď', 'ऊ', 'ఒ', 'ሎ', 'ቃ', 'ೈ', 'ْ', 'ຢ', 'Ҩ', '前', 'ዊ', 'Ҟ', '世', 'ญ', 'イ', '公', 'ໜ', 'え', 'ト', '၏', 'ৌ', 'Ḥ', 'ሲ', '家', 'ៃ', 'څ', '会', 'ಈ', 'Ẽ', 'ア', 'ち', '子', 'ઝ', '를', '္', 'ሞ', '事', '本', '最', 'ຽ', 'ङ', '會', 'ଓ', 'ਭ', 'ሽ', 'ኮ', '之', '己', '스', 'ဝ', '入', 'ළ', 'ዝ', '于', '理', 'ဟ', 'ڙ', 'Ҵ', '名', '新', '法', 'ୃ', 'ዛ', 'ቻ', 'ラ', 'ઘ', '수', '습', 'ਥ', '所', '界', 'つ', 'ஸ', 'ఫ', 'ቢ', 'ৎ', 'わ', '過', 'ഈ', '으', '来', 'ဂ', '定', '利', '者', 'お', '為', 'Ɛ', '아', 'ຶ', 'ဒ', 'ጉ', 'ኖ', '度', '常', 'ഖ', 'ឡ', '도', 'Ḓ', '去', '其', '意', 'ኑ', '这', '时', '通', 'Ֆ', '리', '어', 'ቡ', '合', 'ञ', '間', 'ଞ', 'Ќ', 'ږ', '知', 'ሬ', '手', 'ኤ', '¿', 'や', 'ך', '៊', '인', 'ዚ', 'ؤ', '들', '科', 'ど', 'ッ', 'ば', 'ಖ', 'ឺ', '시', 'ಫ', '文', '主', 'め', '見', 'ฟ', '해', 'ሀ', '州', '水', '小', '目', 'ଢ', 'ශ', 'ኒ', 'ሌ', 'ጊ', '三', '西', '特', '그', '第', '山', '대', '자', '„', '明', '位', 'せ', 'ښ', 'ጀ', '“', '二', '路', 'リ', '加', '教', '國', 'ඩ', '道', '市', '表', '立', '而', '食', 'ޒ', 'Ψ', '動', '័', '¡', '尔', 'ዜ', '亚', '化', '先', '性', '形', '里', '後', 'ሜ', 'ፊ', 'ቹ', '力', 'ド', '呢', '佢', '…', 'Ṛ', 'ഏ', 'ሱ', '比', '高', '起', '球', 'ዙ', '拉', '后', '体', '月', '但', '話', '적', 'Ӡ', 'Ӳ', '克', '使', 'Ჯ', '부', '心', 'ੌ', 'ੜ', '\u200c', '们', '更', '正', '라', '보', 'ቦ', 'ዶ', '美', '也', '南', 'タ', 'ஜ', 'ජ', 'ሙ', 'ፕ', '現', 'ૃ', '일', 'ຜ', 'ਯ', 'ఖ', '口', '因', 'ロ', 'ൃ', 'ೃ', '式', 'ޝ', 'Ẓ', '했', 'ቤ', '十', '区', 'み', 'Ķ', '나', 'ۍ', 'ఏ', 'ං', '外', '海', '重', '天', '語', '۰', 'ៀ', '전', 'ඇ', '全', '被', '面', 'ឆ', '것', 'ධ', 'ፋ', '字', '发', 'ऑ', 'テ', '頭', 'ዱ', '身', '果', 'ஏ', 'ඒ', 'ෝ', 'ኝ', 'ٍ', '今', '示', 'カ', '無', '場', '來', '啲', 'ඔ', 'ඉ', 'ቲ', 'ጅ', '直', '德', '些', '或', '開', '게', '주', 'ቴ', 'ቁ', 'ሶ', '期', 'ろ', 'Ṱ', 'ኔ', 'ቋ', 'ኩ', '阿', '提', '音', 'ジ', '電', 'ऱ', '상', 'ጽ', 'ቆ', '动', '民', '然', '做', '金', 'フ', 'シ', '만', 'උ', 'ޕ', 'ク', '፣', '०', '१', '解', '真', '空', '機', 'コ', 'Ჭ', '冇', 'ฐ', 'ף', 'ڌ', 'ሄ', 'レ', '講', '៍', '种', '少', '点', '此', '与', '相', '受', '言', '՝', 'ઠ', 'ဉ', 'ృ', '内', '工', '英', '代', 'Ģ', 'ූ', 'ዩ', '̃', '北', '如', '流', 'ൺ', '정', '제', 'ਝ', 'ፓ', '·', '伊', '非', '៏', 'ጡ', 'ጫ', 'ቂ', '何', '၎', 'ጨ', 'ሻ', '及', '属', '原', '這', '트', 'ሸ', 'Џ', '城', '影', '次', '安', 'べ', 'Ჰ', 'ൗ', '哋', 'ૈ', 'Ṅ', '과', '었', 'ਏ', 'ౌ', 'ኦ', '建', '政', '巴', 'Ɓ', '々', '識', '種', '여', 'ڀ', 'ኙ', '过', '制', '始', '数', '社', '死', 'ޢ', '車', '長', 'ଝ', 'ๆ', '장', '至', '光', '四', '由', '著', 'ः', '彼', '點', 'ೌ', '되', 'ໆ', 'ሴ', '想', '任', '当', '站', '感', '住', '未', '旅', '持', 'バ', '問', '學', '۱', 'ૌ', '할', '와', '문', 'ဗ', 'ਘ', 'ඳ', 'Ḽ', '已', '造', '管', '必', 'ऐ', 'ィ', 'パ', 'Ǧ', '미', '유', '위', '면', '원', 'ච', 'ጸ', '对', '很', '史', '客', '考', 'ャ', 'オ', 'ഓ', 'ዓ', 'ሃ', '동', '물', 'ආ', 'ቷ', 'ኪ', 'ঃ', '称', '活', '落', 'へ', 'マ', 'ニ', 'ょ', '記', '発', 'ഠ', '發', '咗', 'ឋ', '국', '现', '兰', '基', '平', '保', '火', '情', 'ほ', '気', 'ୌ', '٠', '거', '們', 'ෂ', 'ኞ', 'ፎ', 'ጃ', 'ঝ', 'ڤ', '经', '信', '布', '思', '打', '結', 'ฮ', '嘢', '̇', '우', 'ໝ', 'ፖ', '量', '曾', '车', '品', '速', '件', '於', '۲', 'ٺ', 'Ҿ', 'Ҽ', '太', '「', '」', '石', '交', '司', 'チ', 'ず', '題', '對', 'ஞ', '睇', '噉', '모', '소', '२', 'Ѓ', '赛', '该', '格', '查', '五', 'ޗ', 'ュ', 'ଵ', '১', '세', '구', '경', '비', '။', 'ዴ', '士', '县', '台', '业', '林', '存', '着', '集', '足', '它', 'じ', 'ム', '観', 'ഘ', '經', '邊', 'ஷ', '、', 'ጎ', '계', 'ဇ', 'ਛ', 'ጂ', 'ጭ', '场', '故', '型', '系', '告', '星', '等', 'サ', '입', '공', '며', '간', 'ኳ', 'ኘ', 'Œ', '镇', '罗', '长', '指', '程', '反', '接', '看', 'ウ', 'ブ', '體', '幾', '樣', '러', '요', '르', '၍', '军', '印', '向', '改', '治', '只', '書', 'メ', '味', 'プ', '網', 'ዕ', '২', 'ಘ', '년', '오', 'ਠ', 'Ǹ', '回', '王', '周', '像', 'ޙ', '強', '報', '試', 'ฝ', '০', '°', 'ៗ', '된', '식', '방', '개', '성', 'ڇ', 'ෑ', 'ጦ', 'ጐ', 'ጆ', 'ঢ', '间', '包', '供', '座', '母', '老', '神', 'Ỹ', 'グ', '중', 'ਐ', 'භ', '将', '从', '机', '马', '近', '私', '院', '取', '又', '限', 'デ', 'ェ', 'ネ', '達', '矛', '対', '業', '並', 'ઊ', '않', '화', '발', '조', 'ጓ', 'ጻ', 'Ӷ', '进', '达', '战', '再', '队', '半', '省', '街', 'ޚ', '望', 'キ', '盾', '認', '員', '•', '當', 'ฤ', 'ಏ', '합', '선', '프', '분', 'ຟ', 'ፒ', '列', '河', '尼', '联', '电', '村', '支', '府', '野', '早', '病', '族', '六', '花', '游', '说', '切', '屋', 'ナ', '買', '実', 'ஓ', 'ஹ', '관', '연', '데', '३', 'ਓ', 'ፉ', '否', 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'依', '边', '豆', '背', '视', '侵', '瓜', '攻', '博', '注', '映', '拼', '染', 'ペ', 'ノ', '習', 'モ', 'ゃ', '判', 'ギ', '単', '興', '華', '経', '候', '鳥', '條', '傳', '師', '號', '陽', 'ฏ', '呀', '設', '擊', '워', '많', '현', '종', '매', '본', '버', '민', '၌', 'ዌ', 'ኗ', '括', '党', '广', '诺', '冬', '江', '彩', '沙', '均', '移', '曲', '展', '团', '附', '巨', '效', '话', '响', '寺', '华', '署', '冷', '皮', '项', '容', '恐', '亦', '麻', '埃', '仍', '施', '版', '么', '问', '玩', '晚', '甘', '曜', '菌', 'Ŷ', 'ޞ', 'ハ', 'ざ', '覚', '艇', '護', '洗', 'ゆ', '適', '優', '評', '領', '驚', 'ゴ', '暴', '焦', '臺', '輸', '怪', '資', '龍', '萬', 'ଈ', '請', '續', 'ឥ', '។', '언', '랑', '임', '및', '예', '표', '움', '독', 'ຝ', '၊', 'ෘ', 'ቪ', 'ዬ', 'ፐ', 'ঢ়', '许', '沃', '泰', '增', '佛', '莱', '写', '湾', '苏', '亡', '单', '纪', '武', '溪', '堂', '室', '骨', '房', '含', '幅', '健', '条', '张', '付', '费', '缺', '照', '叶', '楚', '皆', '勒', '捕', '毒', '犯', '症', '乘', '耳', '孩', '肉', '温', '婚', '響', '右', '歩', 'ぞ', '態', '飲', '売', '様', '露', '遠', '導', '勢', '監', '陣', '雞', '濟', '矣', '臭', '朋', '紅', '維', '測', '๊', 'ฬ', '咪', '淨', '幫', 'ኃ', '٥', '졌', '져', '곳', '손', '직', '초', '단', '출', '티', '건', '집', '림', '북', '산', '레', '형', '새', '증', '讓', 'ඹ', 'ጹ', 'ቧ', 'ቬ', '؛', '税', '抵', '盛', '鱼', '敢', '强', '隆', '符', '门', '规', '办', '暖', '标', '义', '永', '整', '风', '却', '叫', '破', '端', '瑞', '习', '刺', '牙', '宣', '题', '按', '阳', '微', '园', '拿', '菊', '什', '担', '决', '赤', '突', '蒂', '仁', '随', '甚', 'ޏ', 'ﷲ', '葉', '震', 'ヒ', '聞', '飯', '創', '応', '危', '協', '証', '楽', '僕', 'ヴ', '補', '広', '親', '讀', '總', '塗', '樂', '閣', '橋', '裝', '喎', '訴', '傾', '：', 'ঊ', '٤', '٨', '۶', '۳', '須', '최', '래', '급', '페', '준', '온', '격', '항', '였', '알', '탈', '너', '၁', 'Ŋ', '歷', 'ኡ', 'ጿ', 'ጁ', 'ሾ', 'ঈ', '围', '追', '添', '划', '森', '师', '银', '陆', '伯', '冠', '级', '帕', '混', '戈', '厚', '见', '央', '价', '酸', '哲', '验', '批', '亲', '测', '狗', '质', '弗', '零', '医', '试', '防', '议', '川', '汉', '散', '束', '宗', '唯', '欧', '抗', '轻', '免', '皇', '患', '疑', '努', '坐', '爆', '途', '迫', '帰', 'ザ', '痛', '願', 'ヤ', '苦', '構', '紙', '糖', '価', '極', '殊', '続', '納', '戦', '簡', '減', '敗', 'ഐ', '屎', '粒', '鬼', '蘭', '眾', '辦', '隻', '圖', '參', '輕', '夠', '賽', '處', '።', '况', '几', 'ಃ', '름', '순', '환', '특', '월', '눈', '험', '석', '근', '션', '받', 'ഔ', 'ဤ', 'ዷ', 'ቼ', 'ጌ', 'ጄ', '̆', '权', '柱', '无', '粗', '町', '舰', '护', '曼', '浮', '环', '汗', '众', '论', '辖', '铁', '固', '坏', '申', '毕', '跨', '树', '田', '腹', '血', '假', '妻', '授', '丹', '卫', '读', '术', '礼', '姆', '藏', '祖', '《', '》', '寄', '泡', '绝', '承', '际', '疫', '序', '弟', '遭', '延', '威', '修', '排', '雅', '摩', '掌', 'ऋ', '鏡', '勉', '働', 'ワ', '収', '閉', '費', 'ぬ', '距', '笑', '詞', '終', '退', '製', '訪', '査', '宿', '順', '寮', '龜', '轉', '錄', '腦', '營', '範', '雲', '裡', '割', '刻', '衛', '鐘', '估', '囉', '陷', '耐', '繼', '訊', '架', '٣', '飞', '午', '۴', 'ォ', 'ಠ', '됩', '활', '충', '돌', '응', '못', '승', '추', '감', '질', '픽', '련', '혀', '난', '목', 'ఛ', 'ෞ', 'ጩ', 'ሷ', 'ዥ', 'ቭ', 'ـ', '粉', '茨', '证', '停', '湖', '转', '茶', '范', '君', '升', '液', '略', '杜', '戏', '艺', '脱', '龙', '录', '籍', '蝶', '童', '刑', '荷', '讲', '企', '阅', '适', '岩', '若', '席', '徒', '佳', '卵', '夜', '仕', '慢', 'づ', '断', '従', '側', '憶', '左', '課', '殺', '残', '録', '説', '筆', '攏', '坑', '織', '囝', '觀', '討', '烏', '咧', '權', '尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', '喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.19.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.19.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn002] 2023-07-06 08:53:12,943 (collect_stats:106) INFO: Niter: 100
+[cn002] 2023-07-06 08:53:25,344 (collect_stats:106) INFO: Niter: 200
+[cn002] 2023-07-06 08:53:36,907 (collect_stats:106) INFO: Niter: 300
+[cn002] 2023-07-06 08:53:49,705 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651413
+# Accounting: end_time=1688651654
+# Accounting: time=241 threads=1
+# Finished at Thu Jul 6 08:54:14 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3d9088c0ec22467c3559405b42dd792bada67ec7
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.19
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.19.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.19.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
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+- Ž
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+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
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+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
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+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
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+- Ň
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+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
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+- 出
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+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
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+- ኛ
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+- ಒ
+- ፈ
+- 好
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+- 分
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+- Ҭ
+- Ğ
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+- 物
+- ޫ
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+- Ӯ
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+- け
+- 个
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+- 方
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+- 学
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+- ڊ
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+- థ
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+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
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+- ୱ
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+- 斯
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+- ጵ
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+- ఈ
+- 있
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+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
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+- ೈ
+- ْ
+- ຢ
+- Ҩ
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+- Ḥ
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+- 事
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+- 入
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+- 于
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+- 名
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+- ዛ
+- ቻ
+- ラ
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+- 수
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+- 所
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+- つ
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+- ఫ
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+- ৎ
+- わ
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+- ഈ
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+- お
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+- ဒ
+- ጉ
+- ኖ
+- 度
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+- Ֆ
+- 리
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+- 合
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+- 間
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+- ږ
+- 知
+- ሬ
+- 手
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+- ¿
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+- ៊
+- 인
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+- ؤ
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+- ば
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+- ¡
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+- 化
+- 先
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+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
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+noise_scp: null
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+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
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+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
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+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
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+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
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+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/train/batch_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/train/feats_lengths_stats.npz
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+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/train/feats_lengths_stats.npz
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+oid sha256:17ac442eafdf57cb8c08d3ac08f4520f26c7bf3b0de9ff1168b74dc2a3439461
+size 778
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/train/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..8548b486e60aa797eaaffe1a411019fc9f67c078
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/valid/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.19/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..eec7929238067d1e5fd35ae9af2b6945c0a99992
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2.log
new file mode 100644
index 0000000000000000000000000000000000000000..089006ec947b32224608df34d9f76d2cd23a518f
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2.log
@@ -0,0 +1,157 @@
+# Running on cn001.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:14 CDT 2023
+# SLURMD_NODENAME=cn001
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=2
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133208
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133208
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn001
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn001
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3660469
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn001
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.2.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.2.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.2 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.2.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.2.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.2 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn001] 2023-07-06 08:52:51,877 (asr:475) INFO: Vocabulary size: 6454
+[cn001] 2023-07-06 08:52:53,975 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn001] 2023-07-06 08:52:53,978 (abs_task:1214) INFO: Scheduler: None
+[cn001] 2023-07-06 08:52:53,987 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.2/config.yaml
+[cn001] 2023-07-06 08:52:54,088 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.2', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 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'될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', 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'乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.2.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.2.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn001] 2023-07-06 08:53:07,722 (collect_stats:106) INFO: Niter: 100
+[cn001] 2023-07-06 08:53:14,967 (collect_stats:106) INFO: Niter: 200
+[cn001] 2023-07-06 08:53:25,332 (collect_stats:106) INFO: Niter: 300
+[cn001] 2023-07-06 08:53:34,628 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651414
+# Accounting: end_time=1688651639
+# Accounting: time=225 threads=1
+# Finished at Thu Jul 6 08:53:59 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4ca3d707b953054890d33233e248eaca1cda90a6
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.2
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.2.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.2.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
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+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
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+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
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+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
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+- Ҟ
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+- イ
+- 公
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+- え
+- ト
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+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
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+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
+- ッ
+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
+- 西
+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
+- 加
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+- 國
+- ඩ
+- 道
+- 市
+- 表
+- 立
+- 而
+- 食
+- ޒ
+- Ψ
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+- ័
+- ¡
+- 尔
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+- 亚
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+- 里
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+- ቹ
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+- …
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+- ഏ
+- ሱ
+- 比
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+- 起
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+- 拉
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+- Ӡ
+- Ӳ
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+- 부
+- 心
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+- ੜ
+- ‌
+- 们
+- 更
+- 正
+- 라
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+- ቦ
+- ዶ
+- 美
+- 也
+- 南
+- タ
+- ஜ
+- ජ
+- ሙ
+- ፕ
+- 現
+- ૃ
+- 일
+- ຜ
+- ਯ
+- ఖ
+- 口
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+- ロ
+- ൃ
+- ೃ
+- 式
+- ޝ
+- Ẓ
+- 했
+- ቤ
+- 十
+- 区
+- み
+- Ķ
+- 나
+- ۍ
+- ఏ
+- ං
+- 外
+- 海
+- 重
+- 天
+- 語
+- ۰
+- ៀ
+- 전
+- ඇ
+- 全
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+- 面
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+- 것
+- ධ
+- ፋ
+- 字
+- 发
+- ऑ
+- テ
+- 頭
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+- 身
+- 果
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+- ඒ
+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
+- カ
+- 無
+- 場
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+- 啲
+- ඔ
+- ඉ
+- ቲ
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+- ろ
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+- ኩ
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+- ऱ
+- 상
+- ጽ
+- ቆ
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+- 만
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+- ޕ
+- ク
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+- ०
+- १
+- 解
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+- コ
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+- 冇
+- ฐ
+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
+- 工
+- 英
+- 代
+- Ģ
+- ූ
+- ዩ
+- ̃
+- 北
+- 如
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+- ൺ
+- 정
+- 제
+- ਝ
+- ፓ
+- ·
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+- ጡ
+- ጫ
+- ቂ
+- 何
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+- ጨ
+- ሻ
+- 及
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+- 原
+- 這
+- 트
+- ሸ
+- Џ
+- 城
+- 影
+- 次
+- 安
+- べ
+- Ჰ
+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
+- 었
+- ਏ
+- ౌ
+- ኦ
+- 建
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+- Ɓ
+- 々
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+- 種
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+- ڀ
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+- 过
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+- ޢ
+- 車
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+- 點
+- ೌ
+- 되
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+- ሴ
+- 想
+- 任
+- 当
+- 站
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+- 未
+- 旅
+- 持
+- バ
+- 問
+- 學
+- ۱
+- ૌ
+- 할
+- 와
+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
+- 已
+- 造
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+- 必
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+- ィ
+- パ
+- Ǧ
+- 미
+- 유
+- 위
+- 면
+- 원
+- ච
+- ጸ
+- 对
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+- 史
+- 客
+- 考
+- ャ
+- オ
+- ഓ
+- ዓ
+- ሃ
+- 동
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+- ආ
+- ቷ
+- ኪ
+- ঃ
+- 称
+- 活
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+- へ
+- マ
+- ニ
+- ょ
+- 記
+- 発
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+- 發
+- 咗
+- ឋ
+- 국
+- 现
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+- 保
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+- 情
+- ほ
+- 気
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+- ٠
+- 거
+- 們
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+- ኞ
+- ፎ
+- ጃ
+- ঝ
+- ڤ
+- 经
+- 信
+- 布
+- 思
+- 打
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+- 嘢
+- ̇
+- 우
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+- ፖ
+- 量
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+- 车
+- 品
+- 速
+- 件
+- 於
+- ۲
+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
+- 」
+- 石
+- 交
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+- チ
+- ず
+- 題
+- 對
+- ஞ
+- 睇
+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
+- 五
+- ޗ
+- ュ
+- ଵ
+- ১
+- 세
+- 구
+- 경
+- 비
+- ။
+- ዴ
+- 士
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+- 林
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+- 着
+- 集
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+- 它
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+- ഘ
+- 經
+- 邊
+- ஷ
+- 、
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+- 계
+- ဇ
+- ਛ
+- ጂ
+- ጭ
+- 场
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+- ኘ
+- Œ
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+- 接
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+- 러
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+- ၍
+- 军
+- 印
+- 向
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+- メ
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+- プ
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+- ২
+- ಘ
+- 년
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+- ਠ
+- Ǹ
+- 回
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+- 周
+- 像
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+- 強
+- 報
+- 試
+- ฝ
+- ০
+- °
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+- 된
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+- ڇ
+- ෑ
+- ጦ
+- ጐ
+- ጆ
+- ঢ
+- 间
+- 包
+- 供
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+- グ
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+- ェ
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+- 발
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+- ጓ
+- ጻ
+- Ӷ
+- 进
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+- 战
+- 再
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+- 半
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+- 街
+- ޚ
+- 望
+- キ
+- 盾
+- 認
+- 員
+- •
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+- ฤ
+- ಏ
+- 합
+- 선
+- 프
+- 분
+- ຟ
+- ፒ
+- 列
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+- 联
+- 电
+- 村
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+- ナ
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+- 데
+- ३
+- ਓ
+- ፉ
+- 否
+- 演
+- 图
+- 夫
+- 放
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+- ）
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+- ሦ
+- ኣ
+- "\x93"
+- "\x94"
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+- 遣
+- 册
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+- 拦
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+- 尝
+- ％
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+- 陨
+- 咎
+- 箭
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+- 钢
+- 键
+- 锐
+- 啸
+- 纹
+- 磷
+- 硫
+- 骚
+- 晓
+- 培
+- 骗
+- 疗
+- 锻
+- 炼
+- 喘
+- 陡
+- 谨
+- 梵
+- 鲜
+- 纯
+- 粹
+- 厉
+- 汁
+- 夺
+- 丢
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+- 陶
+- 旨
+- 聪
+- 审
+- 铃
+- 璀
+- 璨
+- 垒
+- 劫
+- 荣
+- 惊
+- 叹
+- 绵
+- 攀
+- 损
+- 肿
+- 瘤
+- 龄
+- 齐
+- 鞭
+- 缭
+- 穷
+- 哺
+- 粘
+- 宏
+- 倾
+- 财
+- 纸
+- 荐
+- 媚
+- 滥
+- 嘲
+- 泄
+- 盘
+- 廷
+- 抢
+- 敛
+- 猎
+- 苹
+- 笔
+- 愤
+- 慨
+- 罄
+- 茵
+- 碍
+- 舱
+- 励
+- 雕
+- 酋
+- 镑
+- 暂
+- 疽
+- 卓
+- 咨
+- 饮
+- 笨
+- 闲
+- 逛
+- §
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+- <sos/eos>
+init: null
+input_size: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/train/batch_keys
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index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/train/feats_lengths_stats.npz
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index 0000000000000000000000000000000000000000..17e02bea20939b2fe176cf1b5147a274c03a0bf6
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/train/feats_lengths_stats.npz
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+size 778
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/train/stats_keys
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index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..f72b3749bb46e2f6e1586f54c36b429c3f406938
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/valid/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/valid/batch_keys
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index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/valid/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/valid/feats_lengths_stats.npz
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index 0000000000000000000000000000000000000000..1def0a55e2a52321b0f9583bd5b4529ae1c46148
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/valid/feats_lengths_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/valid/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/valid/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/valid/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.2/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..af5ec7e2c973a6281ba93ea753bdfdc3b61b3be4
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20.log
new file mode 100644
index 0000000000000000000000000000000000000000..a28ddc15a580d7893f6c14d97cc72a77331510e6
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20.log
@@ -0,0 +1,157 @@
+# Running on cn002.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:14 CDT 2023
+# SLURMD_NODENAME=cn002
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=20
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133226
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133226
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn002
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn002
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=860921
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn002
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.20.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.20.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.20 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.20.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.20.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.20 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn002] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn002] 2023-07-06 08:52:53,973 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1214) INFO: Scheduler: None
+[cn002] 2023-07-06 08:52:53,984 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.20/config.yaml
+[cn002] 2023-07-06 08:52:54,088 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.20', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', 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'處', '።', '况', '几', 'ಃ', '름', '순', '환', '특', '월', '눈', '험', '석', '근', '션', '받', 'ഔ', 'ဤ', 'ዷ', 'ቼ', 'ጌ', 'ጄ', '̆', '权', '柱', '无', '粗', '町', '舰', '护', '曼', '浮', '环', '汗', '众', '论', '辖', '铁', '固', '坏', '申', '毕', '跨', '树', '田', '腹', '血', '假', '妻', '授', '丹', '卫', '读', '术', '礼', '姆', '藏', '祖', '《', '》', '寄', '泡', '绝', '承', '际', '疫', '序', '弟', '遭', '延', '威', '修', '排', '雅', '摩', '掌', 'ऋ', '鏡', '勉', '働', 'ワ', '収', '閉', '費', 'ぬ', '距', '笑', '詞', '終', '退', '製', '訪', '査', '宿', '順', '寮', '龜', '轉', '錄', '腦', '營', '範', '雲', '裡', '割', '刻', '衛', '鐘', '估', '囉', '陷', '耐', '繼', '訊', '架', '٣', '飞', '午', '۴', 'ォ', 'ಠ', '됩', '활', '충', '돌', '응', '못', '승', '추', '감', '질', '픽', '련', '혀', '난', '목', 'ఛ', 'ෞ', 'ጩ', 'ሷ', 'ዥ', 'ቭ', 'ـ', '粉', '茨', '证', '停', '湖', '转', '茶', '范', '君', '升', '液', '略', '杜', '戏', '艺', '脱', '龙', '录', '籍', '蝶', '童', '刑', '荷', '讲', '企', '阅', '适', '岩', '若', '席', '徒', '佳', '卵', '夜', '仕', '慢', 'づ', '断', '従', '側', '憶', '左', '課', '殺', '残', '録', '説', '筆', '攏', '坑', '織', '囝', '觀', '討', '烏', '咧', '權', '尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', '喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.20.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.20.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn002] 2023-07-06 08:53:16,224 (collect_stats:106) INFO: Niter: 100
+[cn002] 2023-07-06 08:53:33,697 (collect_stats:106) INFO: Niter: 200
+[cn002] 2023-07-06 08:53:48,249 (collect_stats:106) INFO: Niter: 300
+[cn002] 2023-07-06 08:54:03,341 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651414
+# Accounting: end_time=1688651669
+# Accounting: time=255 threads=1
+# Finished at Thu Jul 6 08:54:29 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..35bb3fb9f425b4630a345783828d3f61821b7156
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.20
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.20.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.20.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
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+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
+- ໂ
+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
+- 기
+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
+- 前
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+- Ҟ
+- 世
+- ญ
+- イ
+- 公
+- ໜ
+- え
+- ト
+- ၏
+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
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+- ሞ
+- 事
+- 本
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+- ङ
+- 會
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+- ኮ
+- 之
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+- 스
+- ဝ
+- 入
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+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
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+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
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+- 아
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+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
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+- 去
+- 其
+- 意
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+- 时
+- 通
+- Ֆ
+- 리
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+- ቡ
+- 合
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+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
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+- ば
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+- ኒ
+- ሌ
+- ጊ
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+- 자
+- „
+- 明
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+- せ
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+- “
+- 二
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+- 种
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+    reduce: true
+    ignore_nan_grad: null
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+    time_warp_window: 5
+    time_warp_mode: bicubic
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+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/train/batch_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/train/feats_lengths_stats.npz
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+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/train/feats_lengths_stats.npz
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+version https://git-lfs.github.com/spec/v1
+oid sha256:c495dff37c2e940552da96cd5d0896cb7848c6ccc47d5885951bdd6ac62b5d13
+size 778
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/train/stats_keys
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index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/train/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/valid/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.20/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..caed7d9008814df46fd82ff4ab57d607a8314722
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21.log
new file mode 100644
index 0000000000000000000000000000000000000000..19d08d13d229906ab786cbf17ca7f472ad05ae05
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21.log
@@ -0,0 +1,157 @@
+# Running on cn002.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:13 CDT 2023
+# SLURMD_NODENAME=cn002
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=21
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133227
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133227
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn002
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn002
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=860809
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn002
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.21.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.21.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.21 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.21.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.21.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.21 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn002] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn002] 2023-07-06 08:52:53,974 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn002] 2023-07-06 08:52:53,976 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn002] 2023-07-06 08:52:53,976 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn002] 2023-07-06 08:52:53,976 (abs_task:1214) INFO: Scheduler: None
+[cn002] 2023-07-06 08:52:53,982 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.21/config.yaml
+[cn002] 2023-07-06 08:52:54,086 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.21', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', '行', 'Ļ', '[som]', 'భ', 'ሆ', '[fil]', 'Ӣ', 'き', 'Ӧ', 'ሉ', 'ʼ', 'ณ', '[oci]', 'Ҷ', 'ڳ', 'ഡ', '[kam]', 'Ღ', 'ݨ', '을', 'ஆ', 'Ƙ', 'ਉ', 'ቶ', 'ຮ', 'ካ', 'ቅ', 'औ', 'ଂ', 'ណ', '의', 'ణ', 'Փ', 'ਈ', 'ګ', 'హ', 'ौ', 'ໍ', 'ಭ', 'ಎ', '地', 'ఆ', 'ん', '하', 'ਖ', '自', '上', 'ፍ', 'ਡ', '可', 'ዮ', '生', '[ibo]', 'ٻ', 'さ', 'ູ', '多', 'ޓ', 'ភ', '니', '不', 'ढ', 'ઈ', 'Ň', 'ഥ', 'ศ', '要', 'Ժ', 'ී', 'ۋ', 'ො', 'ሮ', '年', '到', 'Ძ', 'ซ', '۾', '你', '로', 'ጋ', '[lug]', 'ঞ', '国', 'ផ', '個', 'ฉ', '和', 'ஒ', 'ខ', '[mri]', '他', 'ଠ', 'ຼ', 'ঙ', 'ޅ', '唔', 'ຳ', 'ځ', '៉', 'ថ', '가', '[lin]', 'Ճ', '은', 'ធ', '੍', 'Đ', '嘅', '出', 'ټ', 'Ĕ', 'ス', '지', '下', 'ୂ', 'ޑ', 'Ɗ', 'ຸ', 'ઉ', 'ញ', 'ሁ', 'ޖ', '”', '用', 'ሩ', 'ޔ', '为', '了', 'ຖ', 'එ', 'ጣ', 'ઇ', '日', 'ޯ', 'よ', '고', 'ኛ', 'ሊ', '得', 'ൈ', 'ಒ', 'ፈ', '好', '同', '成', '分', 'そ', 'ธ', 'Ҭ', 'Ğ', 'Ṭ', 'ዲ', 'Њ', 'ڏ', '۽', '物', 'ޫ', 'Ჩ', 'Ӯ', '都', 'け', '个', 'ഫ', 'Ō', 'ዘ', '方', 'ډ', 'Ḍ', '学', 'Օ', 'だ', '能', 'ڊ', '部', '仔', 'ภ', 'థ', 'ओ', 'Җ', 'ษ', 'ಉ', 'ឹ', '서', 'ਫ', 'Ï', 'ル', 'ୱ', 'ਧ', 'ቱ', '就', 'Ű', 'ళ', '斯', '作', 'ໂ', 'ණ', 'ጵ', '사', 'ఎ', 'ဘ', 'ఈ', '있', '時', 'ॅ', '한', '기', 'ଘ', 'ଫ', 'ڄ', 'Ď', 'ऊ', 'ఒ', 'ሎ', 'ቃ', 'ೈ', 'ْ', 'ຢ', 'Ҩ', '前', 'ዊ', 'Ҟ', '世', 'ญ', 'イ', '公', 'ໜ', 'え', 'ト', '၏', 'ৌ', 'Ḥ', 'ሲ', '家', 'ៃ', 'څ', '会', 'ಈ', 'Ẽ', 'ア', 'ち', '子', 'ઝ', '를', '္', 'ሞ', '事', '本', '最', 'ຽ', 'ङ', '會', 'ଓ', 'ਭ', 'ሽ', 'ኮ', '之', '己', '스', 'ဝ', '入', 'ළ', 'ዝ', '于', '理', 'ဟ', 'ڙ', 'Ҵ', '名', '新', '法', 'ୃ', 'ዛ', 'ቻ', 'ラ', 'ઘ', '수', '습', 'ਥ', '所', '界', 'つ', 'ஸ', 'ఫ', 'ቢ', 'ৎ', 'わ', '過', 'ഈ', '으', '来', 'ဂ', '定', '利', '者', 'お', '為', 'Ɛ', '아', 'ຶ', 'ဒ', 'ጉ', 'ኖ', '度', '常', 'ഖ', 'ឡ', '도', 'Ḓ', '去', '其', '意', 'ኑ', '这', '时', '通', 'Ֆ', '리', '어', 'ቡ', '合', 'ञ', '間', 'ଞ', 'Ќ', 'ږ', '知', 'ሬ', '手', 'ኤ', '¿', 'や', 'ך', '៊', '인', 'ዚ', 'ؤ', '들', '科', 'ど', 'ッ', 'ば', 'ಖ', 'ឺ', '시', 'ಫ', '文', '主', 'め', '見', 'ฟ', '해', 'ሀ', '州', '水', '小', '目', 'ଢ', 'ශ', 'ኒ', 'ሌ', 'ጊ', '三', '西', '特', '그', '第', '山', '대', '자', '„', '明', '位', 'せ', 'ښ', 'ጀ', '“', '二', '路', 'リ', '加', '教', '國', 'ඩ', '道', '市', '表', '立', '而', '食', 'ޒ', 'Ψ', '動', '័', '¡', '尔', 'ዜ', '亚', '化', '先', '性', '形', '里', '後', 'ሜ', 'ፊ', 'ቹ', '力', 'ド', '呢', '佢', '…', 'Ṛ', 'ഏ', 'ሱ', '比', '高', '起', '球', 'ዙ', '拉', '后', '体', '月', '但', '話', '적', 'Ӡ', 'Ӳ', '克', '使', 'Ჯ', '부', '心', 'ੌ', 'ੜ', '\u200c', '们', '更', '正', '라', '보', 'ቦ', 'ዶ', '美', '也', '南', 'タ', 'ஜ', 'ජ', 'ሙ', 'ፕ', '現', 'ૃ', '일', 'ຜ', 'ਯ', 'ఖ', '口', '因', 'ロ', 'ൃ', 'ೃ', '式', 'ޝ', 'Ẓ', '했', 'ቤ', '十', '区', 'み', 'Ķ', '나', 'ۍ', 'ఏ', 'ං', '外', '海', '重', '天', '語', '۰', 'ៀ', '전', 'ඇ', '全', '被', '面', 'ឆ', '것', 'ධ', 'ፋ', '字', '发', 'ऑ', 'テ', '頭', 'ዱ', '身', '果', 'ஏ', 'ඒ', 'ෝ', 'ኝ', 'ٍ', '今', '示', 'カ', '無', '場', '來', '啲', 'ඔ', 'ඉ', 'ቲ', 'ጅ', '直', '德', '些', '或', '開', '게', '주', 'ቴ', 'ቁ', 'ሶ', '期', 'ろ', 'Ṱ', 'ኔ', 'ቋ', 'ኩ', '阿', '提', '音', 'ジ', '電', 'ऱ', '상', 'ጽ', 'ቆ', '动', '民', '然', '做', '金', 'フ', 'シ', '만', 'උ', 'ޕ', 'ク', '፣', '०', '१', '解', '真', '空', '機', 'コ', 'Ჭ', '冇', 'ฐ', 'ף', 'ڌ', 'ሄ', 'レ', '講', '៍', '种', '少', '点', '此', '与', '相', '受', '言', '՝', 'ઠ', 'ဉ', 'ృ', '内', '工', '英', '代', 'Ģ', 'ූ', 'ዩ', '̃', '北', '如', '流', 'ൺ', 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'될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', 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'乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.21.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.21.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn002] 2023-07-06 08:53:16,267 (collect_stats:106) INFO: Niter: 100
+[cn002] 2023-07-06 08:53:35,420 (collect_stats:106) INFO: Niter: 200
+[cn002] 2023-07-06 08:53:49,906 (collect_stats:106) INFO: Niter: 300
+[cn002] 2023-07-06 08:54:05,250 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651413
+# Accounting: end_time=1688651670
+# Accounting: time=257 threads=1
+# Finished at Thu Jul 6 08:54:30 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fa70ccf385811766ad737f5274f3758ad3915e3c
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.21
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.21.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.21.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
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+- Ṣ
+- ็
+- ِ
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+- ڵ
+- ށ
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+- ീ
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+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
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+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
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+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
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+- ധ
+- Ľ
+- ൂ
+- ़
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+- Խ
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+- Ғ
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+- బ
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+- Ӱ
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+- 이
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+- ழ
+- ۈ
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+- 有
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+- ূ
+- ਗ
+- Қ
+- っ
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+- ክ
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+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
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+- ޑ
+- Ɗ
+- ຸ
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+- ញ
+- ሁ
+- ޖ
+- ”
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+- ሩ
+- ޔ
+- 为
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+- ጣ
+- ઇ
+- 日
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+- よ
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+- ಒ
+- ፈ
+- 好
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+- そ
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+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
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+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
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+- ਧ
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+- ණ
+- ጵ
+- 사
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+- ဘ
+- ఈ
+- 있
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+- 한
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+- ఒ
+- ሎ
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+- Ḥ
+- ሲ
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+- ち
+- 子
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+- ኮ
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+- ዝ
+- 于
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+- ڙ
+- Ҵ
+- 名
+- 新
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+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
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+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
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+- 定
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+- 者
+- お
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+- 아
+- ຶ
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+- ጉ
+- ኖ
+- 度
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+- ഖ
+- ឡ
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+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
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+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
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+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
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+- め
+- 見
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+- ኒ
+- ሌ
+- ጊ
+- 三
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+- 그
+- 第
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+- 대
+- 자
+- „
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+- せ
+- ښ
+- ጀ
+- “
+- 二
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+- リ
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+- 表
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+- ¡
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+- Ӳ
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+- 부
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+- ੜ
+- ‌
+- 们
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+- ፕ
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+- ೃ
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+- 나
+- ۍ
+- ఏ
+- ං
+- 外
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+- ៀ
+- 전
+- ඇ
+- 全
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+- 字
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+- テ
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+- 身
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+- ኝ
+- ٍ
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+- 啲
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+- コ
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+- ף
+- ڌ
+- ሄ
+- レ
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+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
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+- 제
+- ਝ
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+- ·
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+- ጫ
+- ቂ
+- 何
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+- ጨ
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+- 及
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+- 哋
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+- 과
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+- 車
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+- 되
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+- ሴ
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+- 任
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+- 旅
+- 持
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+- 學
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+- ૌ
+- 할
+- 와
+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
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+- 造
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+- 必
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+- ィ
+- パ
+- Ǧ
+- 미
+- 유
+- 위
+- 면
+- 원
+- ච
+- ጸ
+- 对
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+- 客
+- 考
+- ャ
+- オ
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+- ዓ
+- ሃ
+- 동
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+- ආ
+- ቷ
+- ኪ
+- ঃ
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+- へ
+- マ
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+- ょ
+- 記
+- 発
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+- ٠
+- 거
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+- ኞ
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+- ጃ
+- ঝ
+- ڤ
+- 经
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+- 嘢
+- ̇
+- 우
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+- ፖ
+- 量
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+- 速
+- 件
+- 於
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+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
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+- 石
+- 交
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+- チ
+- ず
+- 題
+- 對
+- ஞ
+- 睇
+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
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+- ޗ
+- ュ
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+- ১
+- 세
+- 구
+- 경
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+- ။
+- ዴ
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+- 着
+- 集
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+- 經
+- 邊
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+- ဇ
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+- ጭ
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+- ኘ
+- Œ
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+- ২
+- ಘ
+- 년
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+- Ǹ
+- 回
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+- 像
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+- 強
+- 報
+- 試
+- ฝ
+- ০
+- °
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+- 된
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+- 방
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+- ڇ
+- ෑ
+- ጦ
+- ጐ
+- ጆ
+- ঢ
+- 间
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+- 供
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+- 발
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+- ጓ
+- ጻ
+- Ӷ
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+- キ
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+- 員
+- •
+- 當
+- ฤ
+- ಏ
+- 합
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+- 프
+- 분
+- ຟ
+- ፒ
+- 列
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+- 联
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+- 村
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+- ナ
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+- ३
+- ਓ
+- ፉ
+- 否
+- 演
+- 图
+- 夫
+- 放
+- 港
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+- 镑
+- 暂
+- 疽
+- 卓
+- 咨
+- 饮
+- 笨
+- 闲
+- 逛
+- §
+- €
+- ․
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+init: null
+input_size: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
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+aux_ctc_tasks: []
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+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/train/batch_keys
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index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
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@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/train/feats_lengths_stats.npz
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index 0000000000000000000000000000000000000000..f4e89836b209d4a390dd74cb0f9f5109f2e77d7f
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/train/feats_lengths_stats.npz
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+version https://git-lfs.github.com/spec/v1
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/train/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..f874003072ea2123adf851f60fda28c33ed97ace
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/valid/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..fbd193d0314d954ba2b0cf95d27497002f080df6
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.21/valid/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22.log
new file mode 100644
index 0000000000000000000000000000000000000000..35a65d6673f02b0edabdda8521ac5a4e703251e2
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22.log
@@ -0,0 +1,157 @@
+# Running on cn002.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:13 CDT 2023
+# SLURMD_NODENAME=cn002
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=22
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133228
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133228
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn002
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn002
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=860884
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn002
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.22.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.22.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.22 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.22.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.22.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.22 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn002] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn002] 2023-07-06 08:52:53,973 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1214) INFO: Scheduler: None
+[cn002] 2023-07-06 08:52:53,991 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.22/config.yaml
+[cn002] 2023-07-06 08:52:54,097 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.22', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', '行', 'Ļ', '[som]', 'భ', 'ሆ', '[fil]', 'Ӣ', 'き', 'Ӧ', 'ሉ', 'ʼ', 'ณ', '[oci]', 'Ҷ', 'ڳ', 'ഡ', '[kam]', 'Ღ', 'ݨ', '을', 'ஆ', 'Ƙ', 'ਉ', 'ቶ', 'ຮ', 'ካ', 'ቅ', 'औ', 'ଂ', 'ណ', '의', 'ణ', 'Փ', 'ਈ', 'ګ', 'హ', 'ौ', 'ໍ', 'ಭ', 'ಎ', '地', 'ఆ', 'ん', '하', 'ਖ', '自', '上', 'ፍ', 'ਡ', '可', 'ዮ', '生', '[ibo]', 'ٻ', 'さ', 'ູ', '多', 'ޓ', 'ភ', '니', '不', 'ढ', 'ઈ', 'Ň', 'ഥ', 'ศ', '要', 'Ժ', 'ී', 'ۋ', 'ො', 'ሮ', '年', '到', 'Ძ', 'ซ', '۾', '你', '로', 'ጋ', '[lug]', 'ঞ', '国', 'ផ', '個', 'ฉ', '和', 'ஒ', 'ខ', '[mri]', '他', 'ଠ', 'ຼ', 'ঙ', 'ޅ', '唔', 'ຳ', 'ځ', '៉', 'ថ', '가', '[lin]', 'Ճ', '은', 'ធ', '੍', 'Đ', '嘅', '出', 'ټ', 'Ĕ', 'ス', '지', '下', 'ୂ', 'ޑ', 'Ɗ', 'ຸ', 'ઉ', 'ញ', 'ሁ', 'ޖ', '”', '用', 'ሩ', 'ޔ', '为', '了', 'ຖ', 'එ', 'ጣ', 'ઇ', '日', 'ޯ', 'よ', '고', 'ኛ', 'ሊ', '得', 'ൈ', 'ಒ', 'ፈ', '好', '同', '成', '分', 'そ', 'ธ', 'Ҭ', 'Ğ', 'Ṭ', 'ዲ', 'Њ', 'ڏ', '۽', '物', 'ޫ', 'Ჩ', 'Ӯ', '都', 'け', '个', 'ഫ', 'Ō', 'ዘ', '方', 'ډ', 'Ḍ', '学', 'Օ', 'だ', '能', 'ڊ', '部', '仔', 'ภ', 'థ', 'ओ', 'Җ', 'ษ', 'ಉ', 'ឹ', '서', 'ਫ', 'Ï', 'ル', 'ୱ', 'ਧ', 'ቱ', '就', 'Ű', 'ళ', '斯', '作', 'ໂ', 'ණ', 'ጵ', '사', 'ఎ', 'ဘ', 'ఈ', '있', '時', 'ॅ', '한', '기', 'ଘ', 'ଫ', 'ڄ', 'Ď', 'ऊ', 'ఒ', 'ሎ', 'ቃ', 'ೈ', 'ْ', 'ຢ', 'Ҩ', '前', 'ዊ', 'Ҟ', '世', 'ญ', 'イ', '公', 'ໜ', 'え', 'ト', '၏', 'ৌ', 'Ḥ', 'ሲ', '家', 'ៃ', 'څ', '会', 'ಈ', 'Ẽ', 'ア', 'ち', '子', 'ઝ', '를', '္', 'ሞ', '事', '本', '最', 'ຽ', 'ङ', '會', 'ଓ', 'ਭ', 'ሽ', 'ኮ', '之', '己', '스', 'ဝ', '入', 'ළ', 'ዝ', '于', '理', 'ဟ', 'ڙ', 'Ҵ', '名', '新', '法', 'ୃ', 'ዛ', 'ቻ', 'ラ', 'ઘ', '수', '습', 'ਥ', '所', '界', 'つ', 'ஸ', 'ఫ', 'ቢ', 'ৎ', 'わ', '過', 'ഈ', '으', '来', 'ဂ', '定', '利', '者', 'お', '為', 'Ɛ', '아', 'ຶ', 'ဒ', 'ጉ', 'ኖ', '度', '常', 'ഖ', 'ឡ', '도', 'Ḓ', '去', '其', '意', 'ኑ', '这', '时', '通', 'Ֆ', '리', '어', 'ቡ', '合', 'ञ', '間', 'ଞ', 'Ќ', 'ږ', '知', 'ሬ', '手', 'ኤ', '¿', 'や', 'ך', '៊', '인', 'ዚ', 'ؤ', '들', '科', 'ど', 'ッ', 'ば', 'ಖ', 'ឺ', '시', 'ಫ', '文', '主', 'め', '見', 'ฟ', '해', 'ሀ', '州', '水', '小', '目', 'ଢ', 'ශ', 'ኒ', 'ሌ', 'ጊ', '三', '西', '特', '그', '第', '山', '대', '자', '„', '明', '位', 'せ', 'ښ', 'ጀ', '“', '二', '路', 'リ', '加', '教', '國', 'ඩ', '道', '市', '表', '立', '而', '食', 'ޒ', 'Ψ', '動', '័', '¡', '尔', 'ዜ', '亚', '化', '先', '性', '形', '里', '後', 'ሜ', 'ፊ', 'ቹ', '力', 'ド', '呢', '佢', '…', 'Ṛ', 'ഏ', 'ሱ', '比', '高', '起', '球', 'ዙ', '拉', '后', '体', '月', '但', '話', '적', 'Ӡ', 'Ӳ', '克', '使', 'Ჯ', '부', '心', 'ੌ', 'ੜ', '\u200c', '们', '更', '正', '라', '보', 'ቦ', 'ዶ', '美', '也', '南', 'タ', 'ஜ', 'ජ', 'ሙ', 'ፕ', '現', 'ૃ', '일', 'ຜ', 'ਯ', 'ఖ', '口', '因', 'ロ', 'ൃ', 'ೃ', '式', 'ޝ', 'Ẓ', '했', 'ቤ', '十', '区', 'み', 'Ķ', '나', 'ۍ', 'ఏ', 'ං', '外', '海', '重', '天', '語', '۰', 'ៀ', '전', 'ඇ', '全', '被', '面', 'ឆ', '것', 'ධ', 'ፋ', '字', '发', 'ऑ', 'テ', '頭', 'ዱ', '身', '果', 'ஏ', 'ඒ', 'ෝ', 'ኝ', 'ٍ', '今', '示', 'カ', '無', '場', '來', '啲', 'ඔ', 'ඉ', 'ቲ', 'ጅ', '直', '德', '些', '或', '開', '게', '주', 'ቴ', 'ቁ', 'ሶ', '期', 'ろ', 'Ṱ', 'ኔ', 'ቋ', 'ኩ', '阿', '提', '音', 'ジ', '電', 'ऱ', '상', 'ጽ', 'ቆ', '动', '民', '然', '做', '金', 'フ', 'シ', '만', 'උ', 'ޕ', 'ク', '፣', '०', '१', '解', '真', '空', '機', 'コ', 'Ჭ', '冇', 'ฐ', 'ף', 'ڌ', 'ሄ', 'レ', '講', '៍', '种', '少', '点', '此', '与', '相', '受', '言', '՝', 'ઠ', 'ဉ', 'ృ', '内', '工', '英', '代', 'Ģ', 'ූ', 'ዩ', '̃', '北', '如', '流', 'ൺ', '정', '제', 'ਝ', 'ፓ', '·', '伊', '非', '៏', 'ጡ', 'ጫ', 'ቂ', '何', '၎', 'ጨ', 'ሻ', '及', '属', '原', '這', '트', 'ሸ', 'Џ', '城', '影', '次', '安', 'べ', 'Ჰ', 'ൗ', '哋', 'ૈ', 'Ṅ', '과', '었', 'ਏ', 'ౌ', 'ኦ', '建', '政', '巴', 'Ɓ', '々', '識', '種', '여', 'ڀ', 'ኙ', '过', '制', '始', '数', '社', '死', 'ޢ', '車', '長', 'ଝ', 'ๆ', '장', '至', '光', '四', '由', '著', 'ः', '彼', '點', 'ೌ', '되', 'ໆ', 'ሴ', '想', '任', '当', '站', '感', '住', '未', '旅', '持', 'バ', '問', '學', '۱', 'ૌ', '할', '와', '문', 'ဗ', 'ਘ', 'ඳ', 'Ḽ', '已', '造', '管', '必', 'ऐ', 'ィ', 'パ', 'Ǧ', '미', '유', '위', '면', '원', 'ච', 'ጸ', '对', '很', '史', '客', '考', 'ャ', 'オ', 'ഓ', 'ዓ', 'ሃ', '동', '물', 'ආ', 'ቷ', 'ኪ', 'ঃ', '称', '活', '落', 'へ', 'マ', 'ニ', 'ょ', '記', '発', 'ഠ', '發', '咗', 'ឋ', '국', '现', '兰', '基', '平', '保', '火', '情', 'ほ', '気', 'ୌ', '٠', '거', '們', 'ෂ', 'ኞ', 'ፎ', 'ጃ', 'ঝ', 'ڤ', '经', '信', '布', '思', '打', '結', 'ฮ', '嘢', '̇', '우', 'ໝ', 'ፖ', '量', '曾', '车', '品', '速', '件', '於', '۲', 'ٺ', 'Ҿ', 'Ҽ', '太', '「', '」', '石', '交', '司', 'チ', 'ず', '題', '對', 'ஞ', '睇', '噉', '모', '소', '२', 'Ѓ', '赛', '该', '格', '查', '五', 'ޗ', 'ュ', 'ଵ', '১', '세', '구', '경', '비', '။', 'ዴ', '士', '县', '台', '业', '林', '存', '着', '集', '足', '它', 'じ', 'ム', '観', 'ഘ', '經', '邊', 'ஷ', '、', 'ጎ', '계', 'ဇ', 'ਛ', 'ጂ', 'ጭ', '场', '故', '型', '系', '告', '星', '等', 'サ', '입', '공', '며', '간', 'ኳ', 'ኘ', 'Œ', '镇', '罗', '长', '指', '程', '反', '接', '看', 'ウ', 'ブ', '體', '幾', '樣', '러', '요', '르', '၍', '军', '印', '向', '改', '治', '只', '書', 'メ', '味', 'プ', '網', 'ዕ', '২', 'ಘ', '년', '오', 'ਠ', 'Ǹ', '回', '王', '周', '像', 'ޙ', '強', '報', '試', 'ฝ', '০', '°', 'ៗ', '된', '식', '방', '개', '성', 'ڇ', 'ෑ', 'ጦ', 'ጐ', 'ጆ', 'ঢ', '间', '包', '供', '座', '母', '老', '神', 'Ỹ', 'グ', '중', 'ਐ', 'භ', '将', '从', '机', '马', '近', '私', '院', '取', '又', '限', 'デ', 'ェ', 'ネ', '達', '矛', '対', '業', '並', 'ઊ', '않', '화', '발', '조', 'ጓ', 'ጻ', 'Ӷ', '进', '达', '战', '再', '队', '半', '省', '街', 'ޚ', '望', 'キ', '盾', '認', '員', '•', '當', 'ฤ', 'ಏ', '합', '선', '프', '분', 'ຟ', 'ፒ', '列', '河', '尼', '联', '电', '村', '支', '府', '野', '早', '病', '族', '六', '花', '游', '说', '切', '屋', 'ナ', '買', '実', 'ஓ', 'ஹ', '관', '연', '데', '३', 'ਓ', 'ፉ', '否', 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'依', '边', '豆', '背', '视', '侵', '瓜', '攻', '博', '注', '映', '拼', '染', 'ペ', 'ノ', '習', 'モ', 'ゃ', '判', 'ギ', '単', '興', '華', '経', '候', '鳥', '條', '傳', '師', '號', '陽', 'ฏ', '呀', '設', '擊', '워', '많', '현', '종', '매', '본', '버', '민', '၌', 'ዌ', 'ኗ', '括', '党', '广', '诺', '冬', '江', '彩', '沙', '均', '移', '曲', '展', '团', '附', '巨', '效', '话', '响', '寺', '华', '署', '冷', '皮', '项', '容', '恐', '亦', '麻', '埃', '仍', '施', '版', '么', '问', '玩', '晚', '甘', '曜', '菌', 'Ŷ', 'ޞ', 'ハ', 'ざ', '覚', '艇', '護', '洗', 'ゆ', '適', '優', '評', '領', '驚', 'ゴ', '暴', '焦', '臺', '輸', '怪', '資', '龍', '萬', 'ଈ', '請', '續', 'ឥ', '។', '언', '랑', '임', '및', '예', '표', '움', '독', 'ຝ', '၊', 'ෘ', 'ቪ', 'ዬ', 'ፐ', 'ঢ়', '许', '沃', '泰', '增', '佛', '莱', '写', '湾', '苏', '亡', '单', '纪', '武', '溪', '堂', '室', '骨', '房', '含', '幅', '健', '条', '张', '付', '费', '缺', '照', '叶', '楚', '皆', '勒', '捕', '毒', '犯', '症', '乘', '耳', '孩', '肉', '温', '婚', '響', '右', '歩', 'ぞ', '態', '飲', '売', '様', '露', '遠', '導', '勢', '監', '陣', '雞', '濟', '矣', '臭', '朋', '紅', '維', '測', '๊', 'ฬ', '咪', '淨', '幫', 'ኃ', '٥', '졌', '져', '곳', '손', '직', '초', '단', '출', '티', '건', '집', '림', '북', '산', '레', '형', '새', '증', '讓', 'ඹ', 'ጹ', 'ቧ', 'ቬ', '؛', '税', '抵', '盛', '鱼', '敢', '强', '隆', '符', '门', '规', '办', '暖', '标', '义', '永', '整', '风', '却', '叫', '破', '端', '瑞', '习', '刺', '牙', '宣', '题', '按', '阳', '微', '园', '拿', '菊', '什', '担', '决', '赤', '突', '蒂', '仁', '随', '甚', 'ޏ', 'ﷲ', '葉', '震', 'ヒ', '聞', '飯', '創', '応', '危', '協', '証', '楽', '僕', 'ヴ', '補', '広', '親', '讀', '總', '塗', '樂', '閣', '橋', '裝', '喎', '訴', '傾', '：', 'ঊ', '٤', '٨', '۶', '۳', '須', '최', '래', '급', '페', '준', '온', '격', '항', '였', '알', '탈', '너', '၁', 'Ŋ', '歷', 'ኡ', 'ጿ', 'ጁ', 'ሾ', 'ঈ', '围', '追', '添', '划', '森', '师', '银', '陆', '伯', '冠', '级', '帕', '混', '戈', '厚', '见', '央', '价', '酸', '哲', '验', '批', '亲', '测', '狗', '质', '弗', '零', '医', '试', '防', '议', '川', '汉', '散', '束', '宗', '唯', '欧', '抗', '轻', '免', '皇', '患', '疑', '努', '坐', '爆', '途', '迫', '帰', 'ザ', '痛', '願', 'ヤ', '苦', '構', '紙', '糖', '価', '極', '殊', '続', '納', '戦', '簡', '減', '敗', 'ഐ', '屎', '粒', '鬼', '蘭', '眾', '辦', '隻', '圖', '參', '輕', '夠', '賽', '處', '።', '况', '几', 'ಃ', '름', '순', '환', '특', '월', '눈', '험', '석', '근', '션', '받', 'ഔ', 'ဤ', 'ዷ', 'ቼ', 'ጌ', 'ጄ', '̆', '权', '柱', '无', '粗', '町', '舰', '护', '曼', '浮', '环', '汗', '众', '论', '辖', '铁', '固', '坏', '申', '毕', '跨', '树', '田', '腹', '血', '假', '妻', '授', '丹', '卫', '读', '术', '礼', '姆', '藏', '祖', '《', '》', '寄', '泡', '绝', '承', '际', '疫', '序', '弟', '遭', '延', '威', '修', '排', '雅', '摩', '掌', 'ऋ', '鏡', '勉', '働', 'ワ', '収', '閉', '費', 'ぬ', '距', '笑', '詞', '終', '退', '製', '訪', '査', '宿', '順', '寮', '龜', '轉', '錄', '腦', '營', '範', '雲', '裡', '割', '刻', '衛', '鐘', '估', '囉', '陷', '耐', '繼', '訊', '架', '٣', '飞', '午', '۴', 'ォ', 'ಠ', '됩', '활', '충', '돌', '응', '못', '승', '추', '감', '질', '픽', '련', '혀', '난', '목', 'ఛ', 'ෞ', 'ጩ', 'ሷ', 'ዥ', 'ቭ', 'ـ', '粉', '茨', '证', '停', '湖', '转', '茶', '范', '君', '升', '液', '略', '杜', '戏', '艺', '脱', '龙', '录', '籍', '蝶', '童', '刑', '荷', '讲', '企', '阅', '适', '岩', '若', '席', '徒', '佳', '卵', '夜', '仕', '慢', 'づ', '断', '従', '側', '憶', '左', '課', '殺', '残', '録', '説', '筆', '攏', '坑', '織', '囝', '觀', '討', '烏', '咧', '權', '尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', '喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.22.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.22.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn002] 2023-07-06 08:53:16,398 (collect_stats:106) INFO: Niter: 100
+[cn002] 2023-07-06 08:53:33,263 (collect_stats:106) INFO: Niter: 200
+[cn002] 2023-07-06 08:53:46,782 (collect_stats:106) INFO: Niter: 300
+[cn002] 2023-07-06 08:54:01,774 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651413
+# Accounting: end_time=1688651667
+# Accounting: time=254 threads=1
+# Finished at Thu Jul 6 08:54:27 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..afea317ba8700f2fd226874b9f22da97bd492567
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.22
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.22.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.22.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
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+- Ž
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+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
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+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
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+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
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+- Ň
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+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
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+- 出
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+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
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+- ኛ
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+- ಒ
+- ፈ
+- 好
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+- 分
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+- Ҭ
+- Ğ
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+- 物
+- ޫ
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+- Ӯ
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+- け
+- 个
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+- 方
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+- 学
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+- ڊ
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+- థ
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+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
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+- ୱ
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+- 斯
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+- ጵ
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+- ఈ
+- 있
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+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
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+- ೈ
+- ْ
+- ຢ
+- Ҩ
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+- Ḥ
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+- 事
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+- 入
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+- 于
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+- 名
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+- ዛ
+- ቻ
+- ラ
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+- 수
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+- 所
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+- つ
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+- ఫ
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+- ৎ
+- わ
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+- ഈ
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+- お
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+- ဒ
+- ጉ
+- ኖ
+- 度
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+- Ֆ
+- 리
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+- 合
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+- 間
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+- ږ
+- 知
+- ሬ
+- 手
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+- ¿
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+- ៊
+- 인
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+- ؤ
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+- ば
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+- ¡
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+- 化
+- 先
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+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
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+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
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+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/train/batch_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/train/feats_lengths_stats.npz
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+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/train/feats_lengths_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/train/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..1532b66310d249faa036e3b5502d9eec3a55f49b
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/valid/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.22/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..c353cd6de970b6df0dc928b65df14b432b31afee
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23.log
new file mode 100644
index 0000000000000000000000000000000000000000..9d6a1af2e08ccfeb220196c1d4c897dd20b5f165
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23.log
@@ -0,0 +1,157 @@
+# Running on cn002.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:14 CDT 2023
+# SLURMD_NODENAME=cn002
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=23
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133229
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133229
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn002
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn002
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=860935
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn002
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.23.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.23.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.23 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.23.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.23.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.23 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn002] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn002] 2023-07-06 08:52:53,973 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1214) INFO: Scheduler: None
+[cn002] 2023-07-06 08:52:53,982 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.23/config.yaml
+[cn002] 2023-07-06 08:52:54,086 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.23', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', '行', 'Ļ', '[som]', 'భ', 'ሆ', '[fil]', 'Ӣ', 'き', 'Ӧ', 'ሉ', 'ʼ', 'ณ', '[oci]', 'Ҷ', 'ڳ', 'ഡ', '[kam]', 'Ღ', 'ݨ', '을', 'ஆ', 'Ƙ', 'ਉ', 'ቶ', 'ຮ', 'ካ', 'ቅ', 'औ', 'ଂ', 'ណ', '의', 'ణ', 'Փ', 'ਈ', 'ګ', 'హ', 'ौ', 'ໍ', 'ಭ', 'ಎ', '地', 'ఆ', 'ん', '하', 'ਖ', '自', '上', 'ፍ', 'ਡ', '可', 'ዮ', '生', '[ibo]', 'ٻ', 'さ', 'ູ', '多', 'ޓ', 'ភ', '니', '不', 'ढ', 'ઈ', 'Ň', 'ഥ', 'ศ', '要', 'Ժ', 'ී', 'ۋ', 'ො', 'ሮ', '年', '到', 'Ძ', 'ซ', '۾', '你', '로', 'ጋ', '[lug]', 'ঞ', '国', 'ផ', '個', 'ฉ', '和', 'ஒ', 'ខ', '[mri]', '他', 'ଠ', 'ຼ', 'ঙ', 'ޅ', '唔', 'ຳ', 'ځ', '៉', 'ថ', '가', '[lin]', 'Ճ', '은', 'ធ', '੍', 'Đ', '嘅', '出', 'ټ', 'Ĕ', 'ス', '지', '下', 'ୂ', 'ޑ', 'Ɗ', 'ຸ', 'ઉ', 'ញ', 'ሁ', 'ޖ', '”', '用', 'ሩ', 'ޔ', '为', '了', 'ຖ', 'එ', 'ጣ', 'ઇ', '日', 'ޯ', 'よ', '고', 'ኛ', 'ሊ', '得', 'ൈ', 'ಒ', 'ፈ', '好', '同', '成', '分', 'そ', 'ธ', 'Ҭ', 'Ğ', 'Ṭ', 'ዲ', 'Њ', 'ڏ', '۽', '物', 'ޫ', 'Ჩ', 'Ӯ', '都', 'け', '个', 'ഫ', 'Ō', 'ዘ', '方', 'ډ', 'Ḍ', '学', 'Օ', 'だ', '能', 'ڊ', '部', '仔', 'ภ', 'థ', 'ओ', 'Җ', 'ษ', 'ಉ', 'ឹ', '서', 'ਫ', 'Ï', 'ル', 'ୱ', 'ਧ', 'ቱ', '就', 'Ű', 'ళ', '斯', '作', 'ໂ', 'ණ', 'ጵ', '사', 'ఎ', 'ဘ', 'ఈ', '있', '時', 'ॅ', '한', '기', 'ଘ', 'ଫ', 'ڄ', 'Ď', 'ऊ', 'ఒ', 'ሎ', 'ቃ', 'ೈ', 'ْ', 'ຢ', 'Ҩ', '前', 'ዊ', 'Ҟ', '世', 'ญ', 'イ', '公', 'ໜ', 'え', 'ト', '၏', 'ৌ', 'Ḥ', 'ሲ', '家', 'ៃ', 'څ', '会', 'ಈ', 'Ẽ', 'ア', 'ち', '子', 'ઝ', '를', '္', 'ሞ', '事', '本', '最', 'ຽ', 'ङ', '會', 'ଓ', 'ਭ', 'ሽ', 'ኮ', '之', '己', '스', 'ဝ', '入', 'ළ', 'ዝ', '于', '理', 'ဟ', 'ڙ', 'Ҵ', '名', '新', '法', 'ୃ', 'ዛ', 'ቻ', 'ラ', 'ઘ', '수', '습', 'ਥ', '所', '界', 'つ', 'ஸ', 'ఫ', 'ቢ', 'ৎ', 'わ', '過', 'ഈ', '으', '来', 'ဂ', '定', '利', '者', 'お', '為', 'Ɛ', '아', 'ຶ', 'ဒ', 'ጉ', 'ኖ', '度', '常', 'ഖ', 'ឡ', '도', 'Ḓ', '去', '其', '意', 'ኑ', '这', '时', '通', 'Ֆ', '리', '어', 'ቡ', '合', 'ञ', '間', 'ଞ', 'Ќ', 'ږ', '知', 'ሬ', '手', 'ኤ', '¿', 'や', 'ך', '៊', '인', 'ዚ', 'ؤ', '들', '科', 'ど', 'ッ', 'ば', 'ಖ', 'ឺ', '시', 'ಫ', '文', '主', 'め', '見', 'ฟ', '해', 'ሀ', '州', '水', '小', '目', 'ଢ', 'ශ', 'ኒ', 'ሌ', 'ጊ', '三', '西', '特', '그', '第', '山', '대', '자', '„', '明', '位', 'せ', 'ښ', 'ጀ', '“', '二', '路', 'リ', '加', '教', '國', 'ඩ', '道', '市', '表', '立', '而', '食', 'ޒ', 'Ψ', '動', '័', 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'될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', 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'乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.23.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.23.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn002] 2023-07-06 08:53:17,634 (collect_stats:106) INFO: Niter: 100
+[cn002] 2023-07-06 08:53:33,443 (collect_stats:106) INFO: Niter: 200
+[cn002] 2023-07-06 08:53:48,404 (collect_stats:106) INFO: Niter: 300
+[cn002] 2023-07-06 08:54:03,037 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651414
+# Accounting: end_time=1688651663
+# Accounting: time=249 threads=1
+# Finished at Thu Jul 6 08:54:23 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ea78b3d73996e98aad64319dc5852fdef12c3ce5
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.23
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.23.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.23.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
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+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
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+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
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+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
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+- Ҟ
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+- イ
+- 公
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+- え
+- ト
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+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
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+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
+- ッ
+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
+- 西
+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
+- 加
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+- 國
+- ඩ
+- 道
+- 市
+- 表
+- 立
+- 而
+- 食
+- ޒ
+- Ψ
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+- ័
+- ¡
+- 尔
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+- 亚
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+- 里
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+- ቹ
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+- …
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+- ഏ
+- ሱ
+- 比
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+- 起
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+- 拉
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+- Ӡ
+- Ӳ
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+- 부
+- 心
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+- ੜ
+- ‌
+- 们
+- 更
+- 正
+- 라
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+- ቦ
+- ዶ
+- 美
+- 也
+- 南
+- タ
+- ஜ
+- ජ
+- ሙ
+- ፕ
+- 現
+- ૃ
+- 일
+- ຜ
+- ਯ
+- ఖ
+- 口
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+- ロ
+- ൃ
+- ೃ
+- 式
+- ޝ
+- Ẓ
+- 했
+- ቤ
+- 十
+- 区
+- み
+- Ķ
+- 나
+- ۍ
+- ఏ
+- ං
+- 外
+- 海
+- 重
+- 天
+- 語
+- ۰
+- ៀ
+- 전
+- ඇ
+- 全
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+- 面
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+- 것
+- ධ
+- ፋ
+- 字
+- 发
+- ऑ
+- テ
+- 頭
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+- 身
+- 果
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+- ඒ
+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
+- カ
+- 無
+- 場
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+- 啲
+- ඔ
+- ඉ
+- ቲ
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+- ろ
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+- ኩ
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+- ऱ
+- 상
+- ጽ
+- ቆ
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+- 만
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+- ޕ
+- ク
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+- ०
+- १
+- 解
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+- コ
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+- 冇
+- ฐ
+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
+- 工
+- 英
+- 代
+- Ģ
+- ූ
+- ዩ
+- ̃
+- 北
+- 如
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+- ൺ
+- 정
+- 제
+- ਝ
+- ፓ
+- ·
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+- ጡ
+- ጫ
+- ቂ
+- 何
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+- ጨ
+- ሻ
+- 及
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+- 原
+- 這
+- 트
+- ሸ
+- Џ
+- 城
+- 影
+- 次
+- 安
+- べ
+- Ჰ
+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
+- 었
+- ਏ
+- ౌ
+- ኦ
+- 建
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+- Ɓ
+- 々
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+- 種
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+- ڀ
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+- 过
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+- ޢ
+- 車
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+- 點
+- ೌ
+- 되
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+- ሴ
+- 想
+- 任
+- 当
+- 站
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+- 未
+- 旅
+- 持
+- バ
+- 問
+- 學
+- ۱
+- ૌ
+- 할
+- 와
+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
+- 已
+- 造
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+- 必
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+- ィ
+- パ
+- Ǧ
+- 미
+- 유
+- 위
+- 면
+- 원
+- ච
+- ጸ
+- 对
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+- 史
+- 客
+- 考
+- ャ
+- オ
+- ഓ
+- ዓ
+- ሃ
+- 동
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+- ආ
+- ቷ
+- ኪ
+- ঃ
+- 称
+- 活
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+- へ
+- マ
+- ニ
+- ょ
+- 記
+- 発
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+- 發
+- 咗
+- ឋ
+- 국
+- 现
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+- 保
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+- 情
+- ほ
+- 気
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+- ٠
+- 거
+- 們
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+- ኞ
+- ፎ
+- ጃ
+- ঝ
+- ڤ
+- 经
+- 信
+- 布
+- 思
+- 打
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+- 嘢
+- ̇
+- 우
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+- ፖ
+- 量
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+- 车
+- 品
+- 速
+- 件
+- 於
+- ۲
+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
+- 」
+- 石
+- 交
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+- チ
+- ず
+- 題
+- 對
+- ஞ
+- 睇
+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
+- 五
+- ޗ
+- ュ
+- ଵ
+- ১
+- 세
+- 구
+- 경
+- 비
+- ။
+- ዴ
+- 士
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+- 林
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+- 着
+- 集
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+- 它
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+- ഘ
+- 經
+- 邊
+- ஷ
+- 、
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+- 계
+- ဇ
+- ਛ
+- ጂ
+- ጭ
+- 场
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+- ኘ
+- Œ
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+- 接
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+- 러
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+- ၍
+- 军
+- 印
+- 向
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+- メ
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+- プ
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+- ২
+- ಘ
+- 년
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+- ਠ
+- Ǹ
+- 回
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+- 周
+- 像
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+- 強
+- 報
+- 試
+- ฝ
+- ০
+- °
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+- 된
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+- ڇ
+- ෑ
+- ጦ
+- ጐ
+- ጆ
+- ঢ
+- 间
+- 包
+- 供
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+- グ
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+- ェ
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+- 발
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+- ጓ
+- ጻ
+- Ӷ
+- 进
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+- 战
+- 再
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+- 半
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+- 街
+- ޚ
+- 望
+- キ
+- 盾
+- 認
+- 員
+- •
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+- ฤ
+- ಏ
+- 합
+- 선
+- 프
+- 분
+- ຟ
+- ፒ
+- 列
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+- 联
+- 电
+- 村
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+- ナ
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+- 데
+- ३
+- ਓ
+- ፉ
+- 否
+- 演
+- 图
+- 夫
+- 放
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+- ）
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+- ሦ
+- ኣ
+- "\x93"
+- "\x94"
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+- 遣
+- 册
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+- 拦
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+- 尝
+- ％
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+- 陨
+- 咎
+- 箭
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+- 钢
+- 键
+- 锐
+- 啸
+- 纹
+- 磷
+- 硫
+- 骚
+- 晓
+- 培
+- 骗
+- 疗
+- 锻
+- 炼
+- 喘
+- 陡
+- 谨
+- 梵
+- 鲜
+- 纯
+- 粹
+- 厉
+- 汁
+- 夺
+- 丢
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+- 陶
+- 旨
+- 聪
+- 审
+- 铃
+- 璀
+- 璨
+- 垒
+- 劫
+- 荣
+- 惊
+- 叹
+- 绵
+- 攀
+- 损
+- 肿
+- 瘤
+- 龄
+- 齐
+- 鞭
+- 缭
+- 穷
+- 哺
+- 粘
+- 宏
+- 倾
+- 财
+- 纸
+- 荐
+- 媚
+- 滥
+- 嘲
+- 泄
+- 盘
+- 廷
+- 抢
+- 敛
+- 猎
+- 苹
+- 笔
+- 愤
+- 慨
+- 罄
+- 茵
+- 碍
+- 舱
+- 励
+- 雕
+- 酋
+- 镑
+- 暂
+- 疽
+- 卓
+- 咨
+- 饮
+- 笨
+- 闲
+- 逛
+- §
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+init: null
+input_size: null
+ctc_conf:
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+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
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+noise_scp: null
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+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/train/batch_keys
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index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/train/feats_lengths_stats.npz
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index 0000000000000000000000000000000000000000..d58451cecceb013998b90f1e07f2b877dc2993de
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+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/train/feats_lengths_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/train/stats_keys
@@ -0,0 +1,2 @@
+feats
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..c71690d7ff32a61c6331079c2a7e8b4f30588c17
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/train/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..3793cf870146ed0d31df7e7dfd365523510eaee8
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.23/valid/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24.log
new file mode 100644
index 0000000000000000000000000000000000000000..7638f61c208d695a964a2ba94d1225a80b24d472
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24.log
@@ -0,0 +1,157 @@
+# Running on cn002.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:13 CDT 2023
+# SLURMD_NODENAME=cn002
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=24
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133230
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133230
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn002
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn002
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=860855
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn002
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.24.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.24.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.24 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.24.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.24.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.24 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn002] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn002] 2023-07-06 08:52:53,974 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn002] 2023-07-06 08:52:53,976 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn002] 2023-07-06 08:52:53,976 (abs_task:1214) INFO: Scheduler: None
+[cn002] 2023-07-06 08:52:53,987 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.24/config.yaml
+[cn002] 2023-07-06 08:52:54,093 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.24', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', 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'곳', '손', '직', '초', '단', '출', '티', '건', '집', '림', '북', '산', '레', '형', '새', '증', '讓', 'ඹ', 'ጹ', 'ቧ', 'ቬ', '؛', '税', '抵', '盛', '鱼', '敢', '强', '隆', '符', '门', '规', '办', '暖', '标', '义', '永', '整', '风', '却', '叫', '破', '端', '瑞', '习', '刺', '牙', '宣', '题', '按', '阳', '微', '园', '拿', '菊', '什', '担', '决', '赤', '突', '蒂', '仁', '随', '甚', 'ޏ', 'ﷲ', '葉', '震', 'ヒ', '聞', '飯', '創', '応', '危', '協', '証', '楽', '僕', 'ヴ', '補', '広', '親', '讀', '總', '塗', '樂', '閣', '橋', '裝', '喎', '訴', '傾', '：', 'ঊ', '٤', '٨', '۶', '۳', '須', '최', '래', '급', '페', '준', '온', '격', '항', '였', '알', '탈', '너', '၁', 'Ŋ', '歷', 'ኡ', 'ጿ', 'ጁ', 'ሾ', 'ঈ', '围', '追', '添', '划', '森', '师', '银', '陆', '伯', '冠', '级', '帕', '混', '戈', '厚', '见', '央', '价', '酸', '哲', '验', '批', '亲', '测', '狗', '质', '弗', '零', '医', '试', '防', '议', '川', '汉', '散', '束', '宗', '唯', '欧', '抗', '轻', '免', '皇', '患', '疑', '努', '坐', '爆', '途', '迫', '帰', 'ザ', '痛', '願', 'ヤ', '苦', '構', '紙', '糖', '価', '極', '殊', '続', '納', '戦', '簡', '減', '敗', 'ഐ', '屎', '粒', '鬼', '蘭', '眾', '辦', '隻', '圖', '參', '輕', '夠', '賽', '處', '።', '况', '几', 'ಃ', '름', '순', '환', '특', '월', '눈', '험', '석', '근', '션', '받', 'ഔ', 'ဤ', 'ዷ', 'ቼ', 'ጌ', 'ጄ', '̆', '权', '柱', '无', '粗', '町', '舰', '护', '曼', '浮', '环', '汗', '众', '论', '辖', '铁', '固', '坏', '申', '毕', '跨', '树', '田', '腹', '血', '假', '妻', '授', '丹', '卫', '读', '术', '礼', '姆', '藏', '祖', '《', '》', '寄', '泡', '绝', '承', '际', '疫', '序', '弟', '遭', '延', '威', '修', '排', '雅', '摩', '掌', 'ऋ', '鏡', '勉', '働', 'ワ', '収', '閉', '費', 'ぬ', '距', '笑', '詞', '終', '退', '製', '訪', '査', '宿', '順', '寮', '龜', '轉', '錄', '腦', '營', '範', '雲', '裡', '割', '刻', '衛', '鐘', '估', '囉', '陷', '耐', '繼', '訊', '架', '٣', '飞', '午', '۴', 'ォ', 'ಠ', '됩', '활', '충', '돌', '응', '못', '승', '추', '감', '질', '픽', '련', '혀', '난', '목', 'ఛ', 'ෞ', 'ጩ', 'ሷ', 'ዥ', 'ቭ', 'ـ', '粉', '茨', '证', '停', '湖', '转', '茶', '范', '君', '升', '液', '略', '杜', '戏', '艺', '脱', '龙', '录', '籍', '蝶', '童', '刑', '荷', '讲', '企', '阅', '适', '岩', '若', '席', '徒', '佳', '卵', '夜', '仕', '慢', 'づ', '断', '従', '側', '憶', '左', '課', '殺', '残', '録', '説', '筆', '攏', '坑', '織', '囝', '觀', '討', '烏', '咧', '權', '尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', '喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.24.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.24.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn002] 2023-07-06 08:53:08,970 (collect_stats:106) INFO: Niter: 100
+[cn002] 2023-07-06 08:53:15,784 (collect_stats:106) INFO: Niter: 200
+[cn002] 2023-07-06 08:53:23,432 (collect_stats:106) INFO: Niter: 300
+[cn002] 2023-07-06 08:53:32,669 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651413
+# Accounting: end_time=1688651635
+# Accounting: time=222 threads=1
+# Finished at Thu Jul 6 08:53:55 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..dc58e50d318970c09d0da1c4579f75908616fe68
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.24
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.24.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.24.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
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+- ス
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+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
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+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
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+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
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+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
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+- ୱ
+- ਧ
+- ቱ
+- 就
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+- ళ
+- 斯
+- 作
+- ໂ
+- ණ
+- ጵ
+- 사
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+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
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+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
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+- 世
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+- イ
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+- ໜ
+- え
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+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
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+- ち
+- 子
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+- 事
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+- ኮ
+- 之
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+- 스
+- ဝ
+- 入
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+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
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+- 定
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+- お
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+- 아
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+- ဒ
+- ጉ
+- ኖ
+- 度
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+- ഖ
+- ឡ
+- 도
+- Ḓ
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+- 时
+- 通
+- Ֆ
+- 리
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+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
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+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
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+- ば
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+- ኒ
+- ሌ
+- ጊ
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+- „
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+- ኝ
+- ٍ
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+- 만
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+- ޕ
+- ク
+- ፣
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+init: null
+input_size: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/train/batch_keys
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index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/train/batch_keys
@@ -0,0 +1,2 @@
+speech
+text
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/train/feats_lengths_stats.npz
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index 0000000000000000000000000000000000000000..2fa9989587314f754389197ced83cce08f5c2faa
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/train/feats_lengths_stats.npz
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+version https://git-lfs.github.com/spec/v1
+oid sha256:b190120fc58613e8a9a8c4ea3189d3a2af3a9a54f9f37ca282355ad4331a84d4
+size 778
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/train/feats_stats.npz
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--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/train/speech_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/train/speech_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..be06374721bc58df77603963b367b442d43f4133
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/valid/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..0a47b6dcbc458bc60fa5791ccbe75f8ef27d8d7c
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.24/valid/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25.log
new file mode 100644
index 0000000000000000000000000000000000000000..e60240a09d15072e5cd2fe954d3e90fa42a2720c
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25.log
@@ -0,0 +1,157 @@
+# Running on cn002.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:13 CDT 2023
+# SLURMD_NODENAME=cn002
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=25
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133231
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133231
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn002
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn002
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=860887
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn002
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.25.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.25.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.25 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.25.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.25.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.25 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn002] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn002] 2023-07-06 08:52:53,973 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1214) INFO: Scheduler: None
+[cn002] 2023-07-06 08:52:53,998 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.25/config.yaml
+[cn002] 2023-07-06 08:52:54,106 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.25', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', 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'尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', 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'喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.25.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.25.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn002] 2023-07-06 08:53:10,476 (collect_stats:106) INFO: Niter: 100
+[cn002] 2023-07-06 08:53:20,390 (collect_stats:106) INFO: Niter: 200
+[cn002] 2023-07-06 08:53:31,082 (collect_stats:106) INFO: Niter: 300
+[cn002] 2023-07-06 08:53:40,133 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651413
+# Accounting: end_time=1688651641
+# Accounting: time=228 threads=1
+# Finished at Thu Jul 6 08:54:01 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a8bb8ccbfbb1612c2e34cfc0750e798577454e75
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.25
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.25.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.25.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
+- ໂ
+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
+- 기
+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
+- 前
+- ዊ
+- Ҟ
+- 世
+- ญ
+- イ
+- 公
+- ໜ
+- え
+- ト
+- ၏
+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
+- ္
+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
+- ッ
+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
+- 西
+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
+- 加
+- 教
+- 國
+- ඩ
+- 道
+- 市
+- 表
+- 立
+- 而
+- 食
+- ޒ
+- Ψ
+- 動
+- ័
+- ¡
+- 尔
+- ዜ
+- 亚
+- 化
+- 先
+- 性
+- 形
+- 里
+- 後
+- ሜ
+- ፊ
+- ቹ
+- 力
+- ド
+- 呢
+- 佢
+- …
+- Ṛ
+- ഏ
+- ሱ
+- 比
+- 高
+- 起
+- 球
+- ዙ
+- 拉
+- 后
+- 体
+- 月
+- 但
+- 話
+- 적
+- Ӡ
+- Ӳ
+- 克
+- 使
+- Ჯ
+- 부
+- 心
+- ੌ
+- ੜ
+- ‌
+- 们
+- 更
+- 正
+- 라
+- 보
+- ቦ
+- ዶ
+- 美
+- 也
+- 南
+- タ
+- ஜ
+- ජ
+- ሙ
+- ፕ
+- 現
+- ૃ
+- 일
+- ຜ
+- ਯ
+- ఖ
+- 口
+- 因
+- ロ
+- ൃ
+- ೃ
+- 式
+- ޝ
+- Ẓ
+- 했
+- ቤ
+- 十
+- 区
+- み
+- Ķ
+- 나
+- ۍ
+- ఏ
+- ං
+- 外
+- 海
+- 重
+- 天
+- 語
+- ۰
+- ៀ
+- 전
+- ඇ
+- 全
+- 被
+- 面
+- ឆ
+- 것
+- ධ
+- ፋ
+- 字
+- 发
+- ऑ
+- テ
+- 頭
+- ዱ
+- 身
+- 果
+- ஏ
+- ඒ
+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
+- カ
+- 無
+- 場
+- 來
+- 啲
+- ඔ
+- ඉ
+- ቲ
+- ጅ
+- 直
+- 德
+- 些
+- 或
+- 開
+- 게
+- 주
+- ቴ
+- ቁ
+- ሶ
+- 期
+- ろ
+- Ṱ
+- ኔ
+- ቋ
+- ኩ
+- 阿
+- 提
+- 音
+- ジ
+- 電
+- ऱ
+- 상
+- ጽ
+- ቆ
+- 动
+- 民
+- 然
+- 做
+- 金
+- フ
+- シ
+- 만
+- උ
+- ޕ
+- ク
+- ፣
+- ०
+- १
+- 解
+- 真
+- 空
+- 機
+- コ
+- Ჭ
+- 冇
+- ฐ
+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
+- 工
+- 英
+- 代
+- Ģ
+- ූ
+- ዩ
+- ̃
+- 北
+- 如
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+- ൺ
+- 정
+- 제
+- ਝ
+- ፓ
+- ·
+- 伊
+- 非
+- ៏
+- ጡ
+- ጫ
+- ቂ
+- 何
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+- ጨ
+- ሻ
+- 及
+- 属
+- 原
+- 這
+- 트
+- ሸ
+- Џ
+- 城
+- 影
+- 次
+- 安
+- べ
+- Ჰ
+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
+- 었
+- ਏ
+- ౌ
+- ኦ
+- 建
+- 政
+- 巴
+- Ɓ
+- 々
+- 識
+- 種
+- 여
+- ڀ
+- ኙ
+- 过
+- 制
+- 始
+- 数
+- 社
+- 死
+- ޢ
+- 車
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+- ଝ
+- ๆ
+- 장
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+- 光
+- 四
+- 由
+- 著
+- ः
+- 彼
+- 點
+- ೌ
+- 되
+- ໆ
+- ሴ
+- 想
+- 任
+- 当
+- 站
+- 感
+- 住
+- 未
+- 旅
+- 持
+- バ
+- 問
+- 學
+- ۱
+- ૌ
+- 할
+- 와
+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
+- 已
+- 造
+- 管
+- 必
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+- ィ
+- パ
+- Ǧ
+- 미
+- 유
+- 위
+- 면
+- 원
+- ච
+- ጸ
+- 对
+- 很
+- 史
+- 客
+- 考
+- ャ
+- オ
+- ഓ
+- ዓ
+- ሃ
+- 동
+- 물
+- ආ
+- ቷ
+- ኪ
+- ঃ
+- 称
+- 活
+- 落
+- へ
+- マ
+- ニ
+- ょ
+- 記
+- 発
+- ഠ
+- 發
+- 咗
+- ឋ
+- 국
+- 现
+- 兰
+- 基
+- 平
+- 保
+- 火
+- 情
+- ほ
+- 気
+- ୌ
+- ٠
+- 거
+- 們
+- ෂ
+- ኞ
+- ፎ
+- ጃ
+- ঝ
+- ڤ
+- 经
+- 信
+- 布
+- 思
+- 打
+- 結
+- ฮ
+- 嘢
+- ̇
+- 우
+- ໝ
+- ፖ
+- 量
+- 曾
+- 车
+- 品
+- 速
+- 件
+- 於
+- ۲
+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
+- 」
+- 石
+- 交
+- 司
+- チ
+- ず
+- 題
+- 對
+- ஞ
+- 睇
+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
+- 五
+- ޗ
+- ュ
+- ଵ
+- ১
+- 세
+- 구
+- 경
+- 비
+- ။
+- ዴ
+- 士
+- 县
+- 台
+- 业
+- 林
+- 存
+- 着
+- 集
+- 足
+- 它
+- じ
+- ム
+- 観
+- ഘ
+- 經
+- 邊
+- ஷ
+- 、
+- ጎ
+- 계
+- ဇ
+- ਛ
+- ጂ
+- ጭ
+- 场
+- 故
+- 型
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+- 告
+- 星
+- 等
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+- 공
+- 며
+- 간
+- ኳ
+- ኘ
+- Œ
+- 镇
+- 罗
+- 长
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+ctc_conf:
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+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/batch_keys
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index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/batch_keys
@@ -0,0 +1,2 @@
+speech
+text
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/feats_lengths_stats.npz
new file mode 100644
index 0000000000000000000000000000000000000000..9b559fe7bedd941c42026bcccf21dd7c577dce63
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/feats_lengths_stats.npz
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+version https://git-lfs.github.com/spec/v1
+oid sha256:0b46e6b94b6a2e40ab13f1ec886e97593dacc50a8321f172a428a3fe61ebb032
+size 778
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/feats_stats.npz
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--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/feats_stats.npz
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+version https://git-lfs.github.com/spec/v1
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+size 1402
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/speech_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/speech_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..26a0a51ea56f2f9f33adf17cf25707954a6bc5f6
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/train/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/valid/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/valid/batch_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/valid/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..8b471b0f4d1c29e445816148babd12c79f10c117
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.25/valid/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26.log
new file mode 100644
index 0000000000000000000000000000000000000000..7125375a2391cde02b5e6f6166c8990b159d431a
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26.log
@@ -0,0 +1,157 @@
+# Running on cn002.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:13 CDT 2023
+# SLURMD_NODENAME=cn002
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=26
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133232
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133232
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn002
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn002
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=860883
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn002
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.26.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.26.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.26 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.26.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.26.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.26 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn002] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn002] 2023-07-06 08:52:53,974 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn002] 2023-07-06 08:52:53,976 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn002] 2023-07-06 08:52:53,976 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn002] 2023-07-06 08:52:53,976 (abs_task:1214) INFO: Scheduler: None
+[cn002] 2023-07-06 08:52:53,985 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.26/config.yaml
+[cn002] 2023-07-06 08:52:54,095 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.26', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', 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'處', '።', '况', '几', 'ಃ', '름', '순', '환', '특', '월', '눈', '험', '석', '근', '션', '받', 'ഔ', 'ဤ', 'ዷ', 'ቼ', 'ጌ', 'ጄ', '̆', '权', '柱', '无', '粗', '町', '舰', '护', '曼', '浮', '环', '汗', '众', '论', '辖', '铁', '固', '坏', '申', '毕', '跨', '树', '田', '腹', '血', '假', '妻', '授', '丹', '卫', '读', '术', '礼', '姆', '藏', '祖', '《', '》', '寄', '泡', '绝', '承', '际', '疫', '序', '弟', '遭', '延', '威', '修', '排', '雅', '摩', '掌', 'ऋ', '鏡', '勉', '働', 'ワ', '収', '閉', '費', 'ぬ', '距', '笑', '詞', '終', '退', '製', '訪', '査', '宿', '順', '寮', '龜', '轉', '錄', '腦', '營', '範', '雲', '裡', '割', '刻', '衛', '鐘', '估', '囉', '陷', '耐', '繼', '訊', '架', '٣', '飞', '午', '۴', 'ォ', 'ಠ', '됩', '활', '충', '돌', '응', '못', '승', '추', '감', '질', '픽', '련', '혀', '난', '목', 'ఛ', 'ෞ', 'ጩ', 'ሷ', 'ዥ', 'ቭ', 'ـ', '粉', '茨', '证', '停', '湖', '转', '茶', '范', '君', '升', '液', '略', '杜', '戏', '艺', '脱', '龙', '录', '籍', '蝶', '童', '刑', '荷', '讲', '企', '阅', '适', '岩', '若', '席', '徒', '佳', '卵', '夜', '仕', '慢', 'づ', '断', '従', '側', '憶', '左', '課', '殺', '残', '録', '説', '筆', '攏', '坑', '織', '囝', '觀', '討', '烏', '咧', '權', '尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', '喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.26.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.26.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn002] 2023-07-06 08:53:08,604 (collect_stats:106) INFO: Niter: 100
+[cn002] 2023-07-06 08:53:17,053 (collect_stats:106) INFO: Niter: 200
+[cn002] 2023-07-06 08:53:25,013 (collect_stats:106) INFO: Niter: 300
+[cn002] 2023-07-06 08:53:34,440 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651413
+# Accounting: end_time=1688651637
+# Accounting: time=224 threads=1
+# Finished at Thu Jul 6 08:53:57 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..bde04dff09e37b7490e0f1d71f75929864161b21
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.26
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.26.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.26.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
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+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
+- ໂ
+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
+- 기
+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
+- 前
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+- Ҟ
+- 世
+- ญ
+- イ
+- 公
+- ໜ
+- え
+- ト
+- ၏
+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
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+- ሞ
+- 事
+- 本
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+- ङ
+- 會
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+- ኮ
+- 之
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+- 스
+- ဝ
+- 入
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+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
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+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
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+- 아
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+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
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+- 去
+- 其
+- 意
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+- 时
+- 通
+- Ֆ
+- 리
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+- ቡ
+- 合
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+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
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+- ば
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+- ኒ
+- ሌ
+- ጊ
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+- 자
+- „
+- 明
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+- せ
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+- “
+- 二
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+- 种
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+init: null
+input_size: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/train/batch_keys
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index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/train/feats_lengths_stats.npz
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index 0000000000000000000000000000000000000000..cf857c4a252a96bfe5c4556508e0e3214d83779b
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/train/feats_lengths_stats.npz
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+version https://git-lfs.github.com/spec/v1
+oid sha256:2406855ef7fbf938300ad2c1a8ce17a49cfdbd2b93dbba289798eb3f477d5353
+size 778
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/train/feats_stats.npz
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--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/train/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..d073fef9f2257619950bf23ab1ae977b6461327b
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/train/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/valid/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..26a2bb0c5660b51f7ed31a926c4626544353b4ff
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.26/valid/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27.log
new file mode 100644
index 0000000000000000000000000000000000000000..108d6c8f4ec306a8c7316877f790290b5b5e0855
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27.log
@@ -0,0 +1,157 @@
+# Running on cn002.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:13 CDT 2023
+# SLURMD_NODENAME=cn002
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=27
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133233
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133233
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn002
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn002
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=860820
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn002
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.27.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.27.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.27 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.27.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.27.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.27 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn002] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn002] 2023-07-06 08:52:53,973 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1214) INFO: Scheduler: None
+[cn002] 2023-07-06 08:52:53,982 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.27/config.yaml
+[cn002] 2023-07-06 08:52:54,086 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.27', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', '行', 'Ļ', '[som]', 'భ', 'ሆ', '[fil]', 'Ӣ', 'き', 'Ӧ', 'ሉ', 'ʼ', 'ณ', '[oci]', 'Ҷ', 'ڳ', 'ഡ', '[kam]', 'Ღ', 'ݨ', '을', 'ஆ', 'Ƙ', 'ਉ', 'ቶ', 'ຮ', 'ካ', 'ቅ', 'औ', 'ଂ', 'ណ', '의', 'ణ', 'Փ', 'ਈ', 'ګ', 'హ', 'ौ', 'ໍ', 'ಭ', 'ಎ', '地', 'ఆ', 'ん', '하', 'ਖ', '自', '上', 'ፍ', 'ਡ', '可', 'ዮ', '生', '[ibo]', 'ٻ', 'さ', 'ູ', '多', 'ޓ', 'ភ', '니', '不', 'ढ', 'ઈ', 'Ň', 'ഥ', 'ศ', '要', 'Ժ', 'ී', 'ۋ', 'ො', 'ሮ', '年', '到', 'Ძ', 'ซ', '۾', '你', '로', 'ጋ', '[lug]', 'ঞ', '国', 'ផ', '個', 'ฉ', '和', 'ஒ', 'ខ', '[mri]', '他', 'ଠ', 'ຼ', 'ঙ', 'ޅ', '唔', 'ຳ', 'ځ', '៉', 'ថ', '가', '[lin]', 'Ճ', '은', 'ធ', '੍', 'Đ', '嘅', '出', 'ټ', 'Ĕ', 'ス', '지', '下', 'ୂ', 'ޑ', 'Ɗ', 'ຸ', 'ઉ', 'ញ', 'ሁ', 'ޖ', '”', '用', 'ሩ', 'ޔ', '为', '了', 'ຖ', 'එ', 'ጣ', 'ઇ', '日', 'ޯ', 'よ', '고', 'ኛ', 'ሊ', '得', 'ൈ', 'ಒ', 'ፈ', '好', '同', '成', '分', 'そ', 'ธ', 'Ҭ', 'Ğ', 'Ṭ', 'ዲ', 'Њ', 'ڏ', '۽', '物', 'ޫ', 'Ჩ', 'Ӯ', '都', 'け', '个', 'ഫ', 'Ō', 'ዘ', '方', 'ډ', 'Ḍ', '学', 'Օ', 'だ', '能', 'ڊ', '部', '仔', 'ภ', 'థ', 'ओ', 'Җ', 'ษ', 'ಉ', 'ឹ', '서', 'ਫ', 'Ï', 'ル', 'ୱ', 'ਧ', 'ቱ', '就', 'Ű', 'ళ', '斯', '作', 'ໂ', 'ණ', 'ጵ', '사', 'ఎ', 'ဘ', 'ఈ', '있', '時', 'ॅ', '한', '기', 'ଘ', 'ଫ', 'ڄ', 'Ď', 'ऊ', 'ఒ', 'ሎ', 'ቃ', 'ೈ', 'ْ', 'ຢ', 'Ҩ', '前', 'ዊ', 'Ҟ', '世', 'ญ', 'イ', '公', 'ໜ', 'え', 'ト', '၏', 'ৌ', 'Ḥ', 'ሲ', '家', 'ៃ', 'څ', '会', 'ಈ', 'Ẽ', 'ア', 'ち', '子', 'ઝ', '를', '္', 'ሞ', '事', '本', '最', 'ຽ', 'ङ', '會', 'ଓ', 'ਭ', 'ሽ', 'ኮ', '之', '己', '스', 'ဝ', '入', 'ළ', 'ዝ', '于', '理', 'ဟ', 'ڙ', 'Ҵ', '名', '新', '法', 'ୃ', 'ዛ', 'ቻ', 'ラ', 'ઘ', '수', '습', 'ਥ', '所', '界', 'つ', 'ஸ', 'ఫ', 'ቢ', 'ৎ', 'わ', '過', 'ഈ', '으', '来', 'ဂ', '定', '利', '者', 'お', '為', 'Ɛ', '아', 'ຶ', 'ဒ', 'ጉ', 'ኖ', '度', '常', 'ഖ', 'ឡ', '도', 'Ḓ', '去', '其', '意', 'ኑ', '这', '时', '通', 'Ֆ', '리', '어', 'ቡ', '合', 'ञ', '間', 'ଞ', 'Ќ', 'ږ', '知', 'ሬ', '手', 'ኤ', '¿', 'や', 'ך', '៊', '인', 'ዚ', 'ؤ', '들', '科', 'ど', 'ッ', 'ば', 'ಖ', 'ឺ', '시', 'ಫ', '文', '主', 'め', '見', 'ฟ', '해', 'ሀ', '州', '水', '小', '目', 'ଢ', 'ශ', 'ኒ', 'ሌ', 'ጊ', '三', '西', '特', '그', '第', '山', '대', '자', '„', '明', '位', 'せ', 'ښ', 'ጀ', '“', '二', '路', 'リ', '加', '教', '國', 'ඩ', '道', '市', '表', '立', '而', '食', 'ޒ', 'Ψ', '動', '័', '¡', '尔', 'ዜ', '亚', '化', '先', '性', '形', '里', '後', 'ሜ', 'ፊ', 'ቹ', '力', 'ド', '呢', '佢', '…', 'Ṛ', 'ഏ', 'ሱ', '比', '高', '起', '球', 'ዙ', '拉', '后', '体', '月', '但', '話', '적', 'Ӡ', 'Ӳ', '克', '使', 'Ჯ', '부', '心', 'ੌ', 'ੜ', '\u200c', '们', '更', '正', '라', '보', 'ቦ', 'ዶ', '美', '也', '南', 'タ', 'ஜ', 'ජ', 'ሙ', 'ፕ', '現', 'ૃ', '일', 'ຜ', 'ਯ', 'ఖ', '口', '因', 'ロ', 'ൃ', 'ೃ', '式', 'ޝ', 'Ẓ', '했', 'ቤ', '十', '区', 'み', 'Ķ', '나', 'ۍ', 'ఏ', 'ං', '外', '海', '重', '天', '語', '۰', 'ៀ', '전', 'ඇ', '全', '被', '面', 'ឆ', '것', 'ධ', 'ፋ', '字', '发', 'ऑ', 'テ', '頭', 'ዱ', '身', '果', 'ஏ', 'ඒ', 'ෝ', 'ኝ', 'ٍ', '今', '示', 'カ', '無', '場', '來', '啲', 'ඔ', 'ඉ', 'ቲ', 'ጅ', '直', '德', '些', '或', '開', '게', '주', 'ቴ', 'ቁ', 'ሶ', '期', 'ろ', 'Ṱ', 'ኔ', 'ቋ', 'ኩ', '阿', '提', '音', 'ジ', '電', 'ऱ', '상', 'ጽ', 'ቆ', '动', '民', '然', '做', '金', 'フ', 'シ', '만', 'උ', 'ޕ', 'ク', '፣', '०', '१', '解', '真', '空', '機', 'コ', 'Ჭ', '冇', 'ฐ', 'ף', 'ڌ', 'ሄ', 'レ', '講', '៍', '种', '少', '点', '此', '与', '相', '受', '言', '՝', 'ઠ', 'ဉ', 'ృ', '内', '工', '英', '代', 'Ģ', 'ූ', 'ዩ', '̃', '北', '如', '流', 'ൺ', '정', '제', 'ਝ', 'ፓ', '·', '伊', '非', '៏', 'ጡ', 'ጫ', 'ቂ', '何', '၎', 'ጨ', 'ሻ', '及', '属', '原', '這', '트', 'ሸ', 'Џ', '城', '影', '次', '安', 'べ', 'Ჰ', 'ൗ', '哋', 'ૈ', 'Ṅ', '과', '었', 'ਏ', 'ౌ', 'ኦ', '建', '政', '巴', 'Ɓ', '々', '識', '種', '여', 'ڀ', 'ኙ', '过', '制', '始', '数', '社', '死', 'ޢ', '車', '長', 'ଝ', 'ๆ', '장', '至', '光', '四', '由', '著', 'ः', '彼', '點', 'ೌ', '되', 'ໆ', 'ሴ', '想', '任', '当', '站', '感', '住', '未', '旅', '持', 'バ', '問', '學', '۱', 'ૌ', '할', '와', '문', 'ဗ', 'ਘ', 'ඳ', 'Ḽ', '已', '造', '管', '必', 'ऐ', 'ィ', 'パ', 'Ǧ', '미', '유', '위', '면', '원', 'ච', 'ጸ', '对', '很', '史', '客', '考', 'ャ', 'オ', 'ഓ', 'ዓ', 'ሃ', '동', '물', 'ආ', 'ቷ', 'ኪ', 'ঃ', '称', '活', '落', 'へ', 'マ', 'ニ', 'ょ', '記', '発', 'ഠ', '發', '咗', 'ឋ', '국', '现', '兰', '基', '平', '保', '火', '情', 'ほ', '気', 'ୌ', '٠', '거', '們', 'ෂ', 'ኞ', 'ፎ', 'ጃ', 'ঝ', 'ڤ', '经', '信', '布', '思', '打', '結', 'ฮ', '嘢', '̇', '우', 'ໝ', 'ፖ', '量', '曾', '车', '品', '速', '件', '於', '۲', 'ٺ', 'Ҿ', 'Ҽ', '太', '「', '」', '石', '交', '司', 'チ', 'ず', '題', '對', 'ஞ', '睇', '噉', '모', '소', '२', 'Ѓ', '赛', '该', '格', '查', '五', 'ޗ', 'ュ', 'ଵ', '১', '세', '구', '경', '비', '။', 'ዴ', '士', '县', '台', '业', '林', '存', '着', '集', '足', '它', 'じ', 'ム', '観', 'ഘ', '經', '邊', 'ஷ', '、', 'ጎ', '계', 'ဇ', 'ਛ', 'ጂ', 'ጭ', '场', '故', '型', '系', '告', '星', '等', 'サ', '입', '공', '며', '간', 'ኳ', 'ኘ', 'Œ', '镇', '罗', '长', '指', '程', '反', '接', '看', 'ウ', 'ブ', '體', '幾', '樣', '러', '요', '르', '၍', '军', '印', '向', '改', '治', '只', '書', 'メ', '味', 'プ', '網', 'ዕ', '২', 'ಘ', '년', '오', 'ਠ', 'Ǹ', '回', '王', '周', '像', 'ޙ', '強', '報', '試', 'ฝ', '০', '°', 'ៗ', '된', '식', '방', '개', '성', 'ڇ', 'ෑ', 'ጦ', 'ጐ', 'ጆ', 'ঢ', '间', '包', '供', '座', '母', '老', '神', 'Ỹ', 'グ', '중', 'ਐ', 'භ', '将', '从', '机', '马', '近', '私', '院', '取', '又', '限', 'デ', 'ェ', 'ネ', '達', '矛', '対', '業', '並', 'ઊ', '않', '화', '발', '조', 'ጓ', 'ጻ', 'Ӷ', '进', '达', '战', '再', '队', '半', '省', '街', 'ޚ', '望', 'キ', '盾', '認', '員', '•', '當', 'ฤ', 'ಏ', '합', '선', '프', '분', 'ຟ', 'ፒ', '列', '河', '尼', '联', '电', '村', '支', '府', '野', '早', '病', '族', '六', '花', '游', '说', '切', '屋', 'ナ', '買', '実', 'ஓ', 'ஹ', '관', '연', '데', '३', 'ਓ', 'ፉ', '否', 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'依', '边', '豆', '背', '视', '侵', '瓜', '攻', '博', '注', '映', '拼', '染', 'ペ', 'ノ', '習', 'モ', 'ゃ', '判', 'ギ', '単', '興', '華', '経', '候', '鳥', '條', '傳', '師', '號', '陽', 'ฏ', '呀', '設', '擊', '워', '많', '현', '종', '매', '본', '버', '민', '၌', 'ዌ', 'ኗ', '括', '党', '广', '诺', '冬', '江', '彩', '沙', '均', '移', '曲', '展', '团', '附', '巨', '效', '话', '响', '寺', '华', '署', '冷', '皮', '项', '容', '恐', '亦', '麻', '埃', '仍', '施', '版', '么', '问', '玩', '晚', '甘', '曜', '菌', 'Ŷ', 'ޞ', 'ハ', 'ざ', '覚', '艇', '護', '洗', 'ゆ', '適', '優', '評', '領', '驚', 'ゴ', '暴', '焦', '臺', '輸', '怪', '資', '龍', '萬', 'ଈ', '請', '續', 'ឥ', '។', '언', '랑', '임', '및', '예', '표', '움', '독', 'ຝ', '၊', 'ෘ', 'ቪ', 'ዬ', 'ፐ', 'ঢ়', '许', '沃', '泰', '增', '佛', '莱', '写', '湾', '苏', '亡', '单', '纪', '武', '溪', '堂', '室', '骨', '房', '含', '幅', '健', '条', '张', '付', '费', '缺', '照', '叶', '楚', '皆', '勒', '捕', '毒', '犯', '症', '乘', '耳', '孩', '肉', '温', '婚', '響', '右', '歩', 'ぞ', '態', '飲', '売', '様', '露', '遠', '導', '勢', '監', '陣', '雞', '濟', '矣', '臭', '朋', '紅', '維', '測', '๊', 'ฬ', '咪', '淨', '幫', 'ኃ', '٥', '졌', '져', '곳', '손', '직', '초', '단', '출', '티', '건', '집', '림', '북', '산', '레', '형', '새', '증', '讓', 'ඹ', 'ጹ', 'ቧ', 'ቬ', '؛', '税', '抵', '盛', '鱼', '敢', '强', '隆', '符', '门', '规', '办', '暖', '标', '义', '永', '整', '风', '却', '叫', '破', '端', '瑞', '习', '刺', '牙', '宣', '题', '按', '阳', '微', '园', '拿', '菊', '什', '担', '决', '赤', '突', '蒂', '仁', '随', '甚', 'ޏ', 'ﷲ', '葉', '震', 'ヒ', '聞', '飯', '創', '応', '危', '協', '証', '楽', '僕', 'ヴ', '補', '広', '親', '讀', '總', '塗', '樂', '閣', '橋', '裝', '喎', '訴', '傾', '：', 'ঊ', '٤', '٨', '۶', '۳', '須', '최', '래', '급', '페', '준', '온', '격', '항', '였', '알', '탈', '너', '၁', 'Ŋ', '歷', 'ኡ', 'ጿ', 'ጁ', 'ሾ', 'ঈ', '围', '追', '添', '划', '森', '师', '银', '陆', '伯', '冠', '级', '帕', '混', '戈', '厚', '见', '央', '价', '酸', '哲', '验', '批', '亲', '测', '狗', '质', '弗', '零', '医', '试', '防', '议', '川', '汉', '散', '束', '宗', '唯', '欧', '抗', '轻', '免', '皇', '患', '疑', '努', '坐', '爆', '途', '迫', '帰', 'ザ', '痛', '願', 'ヤ', '苦', '構', '紙', '糖', '価', '極', '殊', '続', '納', '戦', '簡', '減', '敗', 'ഐ', '屎', '粒', '鬼', '蘭', '眾', '辦', '隻', '圖', '參', '輕', '夠', '賽', '處', '።', '况', '几', 'ಃ', '름', '순', '환', '특', '월', '눈', '험', '석', '근', '션', '받', 'ഔ', 'ဤ', 'ዷ', 'ቼ', 'ጌ', 'ጄ', '̆', '权', '柱', '无', '粗', '町', '舰', '护', '曼', '浮', '环', '汗', '众', '论', '辖', '铁', '固', '坏', '申', '毕', '跨', '树', '田', '腹', '血', '假', '妻', '授', '丹', '卫', '读', '术', '礼', '姆', '藏', '祖', '《', '》', '寄', '泡', '绝', '承', '际', '疫', '序', '弟', '遭', '延', '威', '修', '排', '雅', '摩', '掌', 'ऋ', '鏡', '勉', '働', 'ワ', '収', '閉', '費', 'ぬ', '距', '笑', '詞', '終', '退', '製', '訪', '査', '宿', '順', '寮', '龜', '轉', '錄', '腦', '營', '範', '雲', '裡', '割', '刻', '衛', '鐘', '估', '囉', '陷', '耐', '繼', '訊', '架', '٣', '飞', '午', '۴', 'ォ', 'ಠ', '됩', '활', '충', '돌', '응', '못', '승', '추', '감', '질', '픽', '련', '혀', '난', '목', 'ఛ', 'ෞ', 'ጩ', 'ሷ', 'ዥ', 'ቭ', 'ـ', '粉', '茨', '证', '停', '湖', '转', '茶', '范', '君', '升', '液', '略', '杜', '戏', '艺', '脱', '龙', '录', '籍', '蝶', '童', '刑', '荷', '讲', '企', '阅', '适', '岩', '若', '席', '徒', '佳', '卵', '夜', '仕', '慢', 'づ', '断', '従', '側', '憶', '左', '課', '殺', '残', '録', '説', '筆', '攏', '坑', '織', '囝', '觀', '討', '烏', '咧', '權', '尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', '喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.27.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.27.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn002] 2023-07-06 08:53:18,060 (collect_stats:106) INFO: Niter: 100
+[cn002] 2023-07-06 08:53:34,225 (collect_stats:106) INFO: Niter: 200
+[cn002] 2023-07-06 08:53:40,539 (collect_stats:106) INFO: Niter: 300
+[cn002] 2023-07-06 08:53:46,764 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651413
+# Accounting: end_time=1688651645
+# Accounting: time=232 threads=1
+# Finished at Thu Jul 6 08:54:05 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..bd9bb2b7210b86cf4f13c330cf184aa527c6c443
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.27
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.27.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.27.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
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+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
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+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
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+- 出
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+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
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+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
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+- ኛ
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+- ಒ
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+- 好
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+- 成
+- 分
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+- Ҭ
+- Ğ
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+- 物
+- ޫ
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+- Ӯ
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+- け
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+- 方
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+- 学
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+- ڊ
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+- థ
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+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
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+- ୱ
+- ਧ
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+- 斯
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+- ጵ
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+- ဘ
+- ఈ
+- 있
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+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
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+- Ḥ
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+- څ
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+- 事
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+- ኮ
+- 之
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+- 入
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+- 于
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+- 名
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+- ዛ
+- ቻ
+- ラ
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+- 수
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+- 所
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+- つ
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+- ఫ
+- ቢ
+- ৎ
+- わ
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+- ഈ
+- 으
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+- お
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+- ဒ
+- ጉ
+- ኖ
+- 度
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+- ഖ
+- ឡ
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+- Ֆ
+- 리
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+- 合
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+- 間
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+- ږ
+- 知
+- ሬ
+- 手
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+- ¿
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+- ៊
+- 인
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+- ؤ
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+- ¡
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+- 亚
+- 化
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+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
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+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
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+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/train/batch_keys
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+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/train/feats_lengths_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/train/stats_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..b285160c84dafac61ad763d0cc1d8a37e15a5b67
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/valid/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/valid/batch_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/valid/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..1208ed2e4d7af10b2526fffa5888716a115c07ed
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.27/valid/text_shape
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+mls_por_000267 159
+mls_por_000271 259
+mls_por_000275 207
+mls_spa_000249 149
+mls_spa_000253 190
+mls_por_000256 248
+mls_por_000260 127
+mls_por_000264 182
+mls_por_000268 256
+mls_por_000272 253
+mls_spa_000246 193
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+mls_spa_000267 279
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+mls_spa_000275 296
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+mls_spa_000276 197
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+mls_spa_000270 222
+mls_spa_000274 182
+mls_spa_000278 163
+mls_spa_000282 86
+mls_spa_000286 263
+nchlt_afr_001168 32
+nchlt_afr_001172 33
+nchlt_afr_001176 17
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+nchlt_afr_001170 18
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+nchlt_afr_001182 42
+nchlt_afr_001186 20
+nchlt_afr_001190 16
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+nchlt_afr_001171 17
+nchlt_afr_001175 20
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+nchlt_afr_001187 21
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+nchlt_afr_001200 23
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+nchlt_afr_001212 25
+nchlt_afr_001216 21
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+nchlt_afr_001228 22
+nchlt_afr_001201 21
+nchlt_afr_001205 22
+nchlt_afr_001209 17
+nchlt_afr_001213 50
+nchlt_afr_001217 28
+nchlt_afr_001221 22
+nchlt_afr_001225 27
+nchlt_afr_001229 20
+nchlt_afr_001202 30
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+nchlt_afr_001210 50
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+nchlt_afr_001226 32
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+nchlt_afr_001235 17
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+nchlt_afr_001243 20
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+nchlt_afr_001251 31
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+nchlt_afr_001259 16
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+nchlt_afr_001268 25
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+nchlt_afr_001265 28
+nchlt_afr_001269 40
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+nchlt_afr_001291 10
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+nchlt_afr_001311 22
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+nchlt_afr_001319 27
+nchlt_afr_001323 34
+nchlt_afr_001327 16
+nchlt_afr_001328 14
+nchlt_afr_001332 26
+nchlt_afr_001336 34
+nchlt_afr_001340 29
+nchlt_afr_001344 20
+nchlt_afr_001348 16
+nchlt_afr_001352 23
+nchlt_afr_001356 14
+nchlt_afr_001329 17
+nchlt_afr_001333 23
+nchlt_afr_001337 19
+nchlt_afr_001341 24
+nchlt_afr_001345 35
+nchlt_afr_001349 26
+nchlt_afr_001353 30
+nchlt_afr_001357 25
+nchlt_afr_001330 17
+nchlt_afr_001334 27
+nchlt_afr_001338 34
+nchlt_afr_001342 33
+nchlt_afr_001346 31
+nchlt_afr_001350 22
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+nchlt_afr_001335 37
+nchlt_afr_001339 33
+nchlt_afr_001343 21
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+nchlt_afr_001351 25
+nchlt_afr_001355 29
+nchlt_eng_001363 27
+nchlt_eng_001364 21
+nchlt_eng_001368 27
+nchlt_eng_001372 27
+nchlt_eng_001376 25
+nchlt_eng_001380 23
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+nchlt_eng_001392 30
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+nchlt_eng_001377 24
+nchlt_eng_001381 18
+nchlt_eng_001385 19
+nchlt_eng_001389 28
+nchlt_eng_001393 27
+nchlt_eng_001366 31
+nchlt_eng_001370 22
+nchlt_eng_001374 26
+nchlt_eng_001378 27
+nchlt_eng_001382 18
+nchlt_eng_001386 43
+nchlt_eng_001390 15
+nchlt_eng_001394 36
+nchlt_eng_001367 22
+nchlt_eng_001371 23
+nchlt_eng_001375 30
+nchlt_eng_001379 17
+nchlt_eng_001383 21
+nchlt_eng_001387 29
+nchlt_eng_001391 21
+nchlt_eng_001395 17
+nchlt_eng_001396 20
+nchlt_eng_001400 14
+nchlt_eng_001404 15
+nchlt_eng_001408 28
+nchlt_eng_001412 25
+nchlt_eng_001416 21
+nchlt_eng_001420 14
+nchlt_eng_001424 18
+nchlt_eng_001397 29
+nchlt_eng_001401 23
+nchlt_eng_001405 24
+nchlt_eng_001409 27
+nchlt_eng_001413 26
+nchlt_eng_001417 18
+nchlt_eng_001421 17
+nchlt_eng_001425 33
+nchlt_eng_001398 17
+nchlt_eng_001402 10
+nchlt_eng_001406 27
+nchlt_eng_001410 20
+nchlt_eng_001414 24
+nchlt_eng_001418 26
+nchlt_eng_001422 22
+nchlt_eng_001426 6
+nchlt_eng_001399 24
+nchlt_eng_001403 34
+nchlt_eng_001407 25
+nchlt_eng_001411 32
+nchlt_eng_001415 13
+nchlt_eng_001419 26
+nchlt_eng_001423 27
+nchlt_eng_001427 27
+nchlt_eng_001428 14
+nchlt_eng_001432 19
+nchlt_eng_001436 25
+nchlt_eng_001440 18
+nchlt_eng_001444 27
+nchlt_eng_001448 27
+nchlt_eng_001452 29
+nchlt_eng_001456 22
+nchlt_eng_001429 18
+nchlt_eng_001433 31
+nchlt_eng_001437 12
+nchlt_eng_001441 20
+nchlt_eng_001445 24
+nchlt_eng_001449 18
+nchlt_eng_001453 32
+nchlt_eng_001457 22
+nchlt_eng_001430 18
+nchlt_eng_001434 19
+nchlt_eng_001438 38
+nchlt_eng_001442 22
+nchlt_eng_001446 32
+nchlt_eng_001450 24
+nchlt_eng_001454 18
+nchlt_eng_001458 30
+nchlt_eng_001431 23
+nchlt_eng_001435 5
+nchlt_eng_001439 28
+nchlt_eng_001443 26
+nchlt_eng_001447 14
+nchlt_eng_001451 26
+nchlt_eng_001455 35
+nchlt_eng_001459 22
+nchlt_eng_001460 32
+nchlt_eng_001464 24
+nchlt_eng_001468 19
+nchlt_eng_001472 22
+nchlt_eng_001476 23
+nchlt_eng_001480 15
+nchlt_eng_001461 18
+nchlt_eng_001465 34
+nchlt_eng_001469 5
+nchlt_eng_001473 9
+nchlt_eng_001477 25
+nchlt_eng_001481 17
+nchlt_eng_001462 30
+nchlt_eng_001466 24
+nchlt_eng_001470 21
+nchlt_eng_001474 10
+nchlt_eng_001478 19
+nchlt_eng_001482 28
+nchlt_eng_001463 6
+nchlt_eng_001467 22
+nchlt_eng_001471 28
+nchlt_eng_001475 28
+nchlt_eng_001479 22
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28.log
new file mode 100644
index 0000000000000000000000000000000000000000..27e21534186d4d81ec3d7ea1f717d59389c5bb5d
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28.log
@@ -0,0 +1,157 @@
+# Running on cn002.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:14 CDT 2023
+# SLURMD_NODENAME=cn002
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=28
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133234
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133234
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn002
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn002
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=860888
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn002
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.28.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.28.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.28 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.28.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.28.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.28 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn002] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn002] 2023-07-06 08:52:53,973 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1214) INFO: Scheduler: None
+[cn002] 2023-07-06 08:52:53,981 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.28/config.yaml
+[cn002] 2023-07-06 08:52:54,088 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.28', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', '行', 'Ļ', '[som]', 'భ', 'ሆ', '[fil]', 'Ӣ', 'き', 'Ӧ', 'ሉ', 'ʼ', 'ณ', '[oci]', 'Ҷ', 'ڳ', 'ഡ', '[kam]', 'Ღ', 'ݨ', '을', 'ஆ', 'Ƙ', 'ਉ', 'ቶ', 'ຮ', 'ካ', 'ቅ', 'औ', 'ଂ', 'ណ', '의', 'ణ', 'Փ', 'ਈ', 'ګ', 'హ', 'ौ', 'ໍ', 'ಭ', 'ಎ', '地', 'ఆ', 'ん', '하', 'ਖ', '自', '上', 'ፍ', 'ਡ', '可', 'ዮ', '生', '[ibo]', 'ٻ', 'さ', 'ູ', '多', 'ޓ', 'ភ', '니', '不', 'ढ', 'ઈ', 'Ň', 'ഥ', 'ศ', '要', 'Ժ', 'ී', 'ۋ', 'ො', 'ሮ', '年', '到', 'Ძ', 'ซ', '۾', '你', '로', 'ጋ', '[lug]', 'ঞ', '国', 'ផ', '個', 'ฉ', '和', 'ஒ', 'ខ', '[mri]', '他', 'ଠ', 'ຼ', 'ঙ', 'ޅ', '唔', 'ຳ', 'ځ', '៉', 'ថ', '가', '[lin]', 'Ճ', '은', 'ធ', '੍', 'Đ', '嘅', '出', 'ټ', 'Ĕ', 'ス', '지', '下', 'ୂ', 'ޑ', 'Ɗ', 'ຸ', 'ઉ', 'ញ', 'ሁ', 'ޖ', '”', '用', 'ሩ', 'ޔ', '为', '了', 'ຖ', 'එ', 'ጣ', 'ઇ', '日', 'ޯ', 'よ', '고', 'ኛ', 'ሊ', '得', 'ൈ', 'ಒ', 'ፈ', '好', '同', '成', '分', 'そ', 'ธ', 'Ҭ', 'Ğ', 'Ṭ', 'ዲ', 'Њ', 'ڏ', '۽', '物', 'ޫ', 'Ჩ', 'Ӯ', '都', 'け', '个', 'ഫ', 'Ō', 'ዘ', '方', 'ډ', 'Ḍ', '学', 'Օ', 'だ', '能', 'ڊ', '部', '仔', 'ภ', 'థ', 'ओ', 'Җ', 'ษ', 'ಉ', 'ឹ', '서', 'ਫ', 'Ï', 'ル', 'ୱ', 'ਧ', 'ቱ', '就', 'Ű', 'ళ', '斯', '作', 'ໂ', 'ණ', 'ጵ', '사', 'ఎ', 'ဘ', 'ఈ', '있', '時', 'ॅ', '한', '기', 'ଘ', 'ଫ', 'ڄ', 'Ď', 'ऊ', 'ఒ', 'ሎ', 'ቃ', 'ೈ', 'ْ', 'ຢ', 'Ҩ', '前', 'ዊ', 'Ҟ', '世', 'ญ', 'イ', '公', 'ໜ', 'え', 'ト', '၏', 'ৌ', 'Ḥ', 'ሲ', '家', 'ៃ', 'څ', '会', 'ಈ', 'Ẽ', 'ア', 'ち', '子', 'ઝ', '를', '္', 'ሞ', '事', '本', '最', 'ຽ', 'ङ', '會', 'ଓ', 'ਭ', 'ሽ', 'ኮ', '之', '己', '스', 'ဝ', '入', 'ළ', 'ዝ', '于', '理', 'ဟ', 'ڙ', 'Ҵ', '名', '新', '法', 'ୃ', 'ዛ', 'ቻ', 'ラ', 'ઘ', '수', '습', 'ਥ', '所', '界', 'つ', 'ஸ', 'ఫ', 'ቢ', 'ৎ', 'わ', '過', 'ഈ', '으', '来', 'ဂ', '定', '利', '者', 'お', '為', 'Ɛ', '아', 'ຶ', 'ဒ', 'ጉ', 'ኖ', '度', '常', 'ഖ', 'ឡ', '도', 'Ḓ', '去', '其', '意', 'ኑ', '这', '时', '通', 'Ֆ', '리', '어', 'ቡ', '合', 'ञ', '間', 'ଞ', 'Ќ', 'ږ', '知', 'ሬ', '手', 'ኤ', '¿', 'や', 'ך', '៊', '인', 'ዚ', 'ؤ', '들', '科', 'ど', 'ッ', 'ば', 'ಖ', 'ឺ', '시', 'ಫ', '文', '主', 'め', '見', 'ฟ', '해', 'ሀ', '州', '水', '小', '目', 'ଢ', 'ශ', 'ኒ', 'ሌ', 'ጊ', '三', '西', '特', '그', '第', '山', '대', '자', '„', '明', '位', 'せ', 'ښ', 'ጀ', '“', '二', '路', 'リ', '加', '教', '國', 'ඩ', '道', '市', '表', '立', '而', '食', 'ޒ', 'Ψ', '動', '័', '¡', '尔', 'ዜ', '亚', '化', '先', '性', '形', '里', '後', 'ሜ', 'ፊ', 'ቹ', '力', 'ド', '呢', '佢', '…', 'Ṛ', 'ഏ', 'ሱ', '比', '高', '起', '球', 'ዙ', '拉', '后', '体', '月', '但', '話', '적', 'Ӡ', 'Ӳ', '克', '使', 'Ჯ', '부', '心', 'ੌ', 'ੜ', '\u200c', '们', '更', '正', '라', '보', 'ቦ', 'ዶ', '美', '也', '南', 'タ', 'ஜ', 'ජ', 'ሙ', 'ፕ', '現', 'ૃ', '일', 'ຜ', 'ਯ', 'ఖ', '口', '因', 'ロ', 'ൃ', 'ೃ', '式', 'ޝ', 'Ẓ', '했', 'ቤ', '十', '区', 'み', 'Ķ', '나', 'ۍ', 'ఏ', 'ං', '外', '海', '重', '天', '語', '۰', 'ៀ', '전', 'ඇ', '全', '被', '面', 'ឆ', '것', 'ධ', 'ፋ', '字', '发', 'ऑ', 'テ', '頭', 'ዱ', '身', '果', 'ஏ', 'ඒ', 'ෝ', 'ኝ', 'ٍ', '今', '示', 'カ', '無', '場', '來', '啲', 'ඔ', 'ඉ', 'ቲ', 'ጅ', '直', '德', '些', '或', '開', '게', '주', 'ቴ', 'ቁ', 'ሶ', '期', 'ろ', 'Ṱ', 'ኔ', 'ቋ', 'ኩ', '阿', '提', '音', 'ジ', '電', 'ऱ', '상', 'ጽ', 'ቆ', '动', '民', '然', '做', '金', 'フ', 'シ', '만', 'උ', 'ޕ', 'ク', '፣', '०', '१', '解', '真', '空', '機', 'コ', 'Ჭ', '冇', 'ฐ', 'ף', 'ڌ', 'ሄ', 'レ', '講', '៍', '种', '少', '点', '此', '与', '相', '受', '言', '՝', 'ઠ', 'ဉ', 'ృ', '内', '工', '英', '代', 'Ģ', 'ූ', 'ዩ', '̃', '北', '如', '流', 'ൺ', '정', '제', 'ਝ', 'ፓ', '·', '伊', '非', '៏', 'ጡ', 'ጫ', 'ቂ', '何', '၎', 'ጨ', 'ሻ', '及', '属', '原', '這', '트', 'ሸ', 'Џ', '城', '影', '次', '安', 'べ', 'Ჰ', 'ൗ', '哋', 'ૈ', 'Ṅ', '과', '었', 'ਏ', 'ౌ', 'ኦ', '建', '政', '巴', 'Ɓ', '々', '識', '種', '여', 'ڀ', 'ኙ', '过', '制', '始', '数', '社', '死', 'ޢ', '車', '長', 'ଝ', 'ๆ', '장', '至', '光', '四', '由', '著', 'ः', '彼', '點', 'ೌ', '되', 'ໆ', 'ሴ', '想', '任', '当', '站', '感', '住', '未', '旅', '持', 'バ', '問', '學', '۱', 'ૌ', '할', '와', '문', 'ဗ', 'ਘ', 'ඳ', 'Ḽ', '已', '造', '管', '必', 'ऐ', 'ィ', 'パ', 'Ǧ', '미', '유', '위', '면', '원', 'ච', 'ጸ', '对', '很', '史', '客', '考', 'ャ', 'オ', 'ഓ', 'ዓ', 'ሃ', '동', '물', 'ආ', 'ቷ', 'ኪ', 'ঃ', '称', '活', '落', 'へ', 'マ', 'ニ', 'ょ', '記', '発', 'ഠ', '發', '咗', 'ឋ', '국', '现', '兰', '基', '平', '保', '火', '情', 'ほ', '気', 'ୌ', '٠', '거', '們', 'ෂ', 'ኞ', 'ፎ', 'ጃ', 'ঝ', 'ڤ', '经', '信', '布', '思', '打', '結', 'ฮ', '嘢', '̇', '우', 'ໝ', 'ፖ', '量', '曾', '车', '品', '速', '件', '於', '۲', 'ٺ', 'Ҿ', 'Ҽ', '太', '「', '」', '石', '交', '司', 'チ', 'ず', '題', '對', 'ஞ', '睇', '噉', '모', '소', '२', 'Ѓ', '赛', '该', '格', '查', '五', 'ޗ', 'ュ', 'ଵ', '১', '세', '구', '경', '비', '။', 'ዴ', '士', '县', '台', '业', '林', '存', '着', '集', '足', '它', 'じ', 'ム', '観', 'ഘ', '經', '邊', 'ஷ', '、', 'ጎ', '계', 'ဇ', 'ਛ', 'ጂ', 'ጭ', '场', '故', '型', '系', '告', '星', '等', 'サ', '입', '공', '며', '간', 'ኳ', 'ኘ', 'Œ', '镇', '罗', '长', '指', '程', '反', '接', '看', 'ウ', 'ブ', '體', '幾', '樣', '러', '요', '르', '၍', '军', '印', '向', '改', '治', '只', '書', 'メ', '味', 'プ', '網', 'ዕ', '২', 'ಘ', '년', '오', 'ਠ', 'Ǹ', '回', '王', '周', '像', 'ޙ', '強', '報', '試', 'ฝ', '০', '°', 'ៗ', '된', '식', '방', '개', '성', 'ڇ', 'ෑ', 'ጦ', 'ጐ', 'ጆ', 'ঢ', '间', '包', '供', '座', '母', '老', '神', 'Ỹ', 'グ', '중', 'ਐ', 'භ', '将', '从', '机', '马', '近', '私', '院', '取', '又', '限', 'デ', 'ェ', 'ネ', '達', '矛', '対', '業', '並', 'ઊ', '않', '화', '발', '조', 'ጓ', 'ጻ', 'Ӷ', '进', '达', '战', '再', '队', '半', '省', '街', 'ޚ', '望', 'キ', '盾', '認', '員', '•', '當', 'ฤ', 'ಏ', '합', '선', '프', '분', 'ຟ', 'ፒ', '列', '河', '尼', '联', '电', '村', '支', '府', '野', '早', '病', '族', '六', '花', '游', '说', '切', '屋', 'ナ', '買', '実', 'ஓ', 'ஹ', '관', '연', '데', '३', 'ਓ', 'ፉ', '否', 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'依', '边', '豆', '背', '视', '侵', '瓜', '攻', '博', '注', '映', '拼', '染', 'ペ', 'ノ', '習', 'モ', 'ゃ', '判', 'ギ', '単', '興', '華', '経', '候', '鳥', '條', '傳', '師', '號', '陽', 'ฏ', '呀', '設', '擊', '워', '많', '현', '종', '매', '본', '버', '민', '၌', 'ዌ', 'ኗ', '括', '党', '广', '诺', '冬', '江', '彩', '沙', '均', '移', '曲', '展', '团', '附', '巨', '效', '话', '响', '寺', '华', '署', '冷', '皮', '项', '容', '恐', '亦', '麻', '埃', '仍', '施', '版', '么', '问', '玩', '晚', '甘', '曜', '菌', 'Ŷ', 'ޞ', 'ハ', 'ざ', '覚', '艇', '護', '洗', 'ゆ', '適', '優', '評', '領', '驚', 'ゴ', '暴', '焦', '臺', '輸', '怪', '資', '龍', '萬', 'ଈ', '請', '續', 'ឥ', '។', '언', '랑', '임', '및', '예', '표', '움', '독', 'ຝ', '၊', 'ෘ', 'ቪ', 'ዬ', 'ፐ', 'ঢ়', '许', '沃', '泰', '增', '佛', '莱', '写', '湾', '苏', '亡', '单', '纪', '武', '溪', '堂', '室', '骨', '房', '含', '幅', '健', '条', '张', '付', '费', '缺', '照', '叶', '楚', '皆', '勒', '捕', '毒', '犯', '症', '乘', '耳', '孩', '肉', '温', '婚', '響', '右', '歩', 'ぞ', '態', '飲', '売', '様', '露', '遠', '導', '勢', '監', '陣', '雞', '濟', '矣', '臭', '朋', '紅', '維', '測', '๊', 'ฬ', '咪', '淨', '幫', 'ኃ', '٥', '졌', '져', '곳', '손', '직', '초', '단', '출', '티', '건', '집', '림', '북', '산', '레', '형', '새', '증', '讓', 'ඹ', 'ጹ', 'ቧ', 'ቬ', '؛', '税', '抵', '盛', '鱼', '敢', '强', '隆', '符', '门', '规', '办', '暖', '标', '义', '永', '整', '风', '却', '叫', '破', '端', '瑞', '习', '刺', '牙', '宣', '题', '按', '阳', '微', '园', '拿', '菊', '什', '担', '决', '赤', '突', '蒂', '仁', '随', '甚', 'ޏ', 'ﷲ', '葉', '震', 'ヒ', '聞', '飯', '創', '応', '危', '協', '証', '楽', '僕', 'ヴ', '補', '広', '親', '讀', '總', '塗', '樂', '閣', '橋', '裝', '喎', '訴', '傾', '：', 'ঊ', '٤', '٨', '۶', '۳', '須', '최', '래', '급', '페', '준', '온', '격', '항', '였', '알', '탈', '너', '၁', 'Ŋ', '歷', 'ኡ', 'ጿ', 'ጁ', 'ሾ', 'ঈ', '围', '追', '添', '划', '森', '师', '银', '陆', '伯', '冠', '级', '帕', '混', '戈', '厚', '见', '央', '价', '酸', '哲', '验', '批', '亲', '测', '狗', '质', '弗', '零', '医', '试', '防', '议', '川', '汉', '散', '束', '宗', '唯', '欧', '抗', '轻', '免', '皇', '患', '疑', '努', '坐', '爆', '途', '迫', '帰', 'ザ', '痛', '願', 'ヤ', '苦', '構', '紙', '糖', '価', '極', '殊', '続', '納', '戦', '簡', '減', '敗', 'ഐ', '屎', '粒', '鬼', '蘭', '眾', '辦', '隻', '圖', '參', '輕', '夠', '賽', '處', '።', '况', '几', 'ಃ', '름', '순', '환', '특', '월', '눈', '험', '석', '근', '션', '받', 'ഔ', 'ဤ', 'ዷ', 'ቼ', 'ጌ', 'ጄ', '̆', '权', '柱', '无', '粗', '町', '舰', '护', '曼', '浮', '环', '汗', '众', '论', '辖', '铁', '固', '坏', '申', '毕', '跨', '树', '田', '腹', '血', '假', '妻', '授', '丹', '卫', '读', '术', '礼', '姆', '藏', '祖', '《', '》', '寄', '泡', '绝', '承', '际', '疫', '序', '弟', '遭', '延', '威', '修', '排', '雅', '摩', '掌', 'ऋ', '鏡', '勉', '働', 'ワ', '収', '閉', '費', 'ぬ', '距', '笑', '詞', '終', '退', '製', '訪', '査', '宿', '順', '寮', '龜', '轉', '錄', '腦', '營', '範', '雲', '裡', '割', '刻', '衛', '鐘', '估', '囉', '陷', '耐', '繼', '訊', '架', '٣', '飞', '午', '۴', 'ォ', 'ಠ', '됩', '활', '충', '돌', '응', '못', '승', '추', '감', '질', '픽', '련', '혀', '난', '목', 'ఛ', 'ෞ', 'ጩ', 'ሷ', 'ዥ', 'ቭ', 'ـ', '粉', '茨', '证', '停', '湖', '转', '茶', '范', '君', '升', '液', '略', '杜', '戏', '艺', '脱', '龙', '录', '籍', '蝶', '童', '刑', '荷', '讲', '企', '阅', '适', '岩', '若', '席', '徒', '佳', '卵', '夜', '仕', '慢', 'づ', '断', '従', '側', '憶', '左', '課', '殺', '残', '録', '説', '筆', '攏', '坑', '織', '囝', '觀', '討', '烏', '咧', '權', '尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', '喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.28.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.28.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn002] 2023-07-06 08:53:06,623 (collect_stats:106) INFO: Niter: 100
+[cn002] 2023-07-06 08:53:11,081 (collect_stats:106) INFO: Niter: 200
+[cn002] 2023-07-06 08:53:16,374 (collect_stats:106) INFO: Niter: 300
+[cn002] 2023-07-06 08:53:21,523 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651414
+# Accounting: end_time=1688651624
+# Accounting: time=210 threads=1
+# Finished at Thu Jul 6 08:53:44 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e180feaf66fe17453e6bc71d34b012435b92dcf0
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.28
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.28.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.28.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
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+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
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+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
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+- 出
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+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
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+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
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+- ኛ
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+- ಒ
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+- 好
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+- 成
+- 分
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+- Ҭ
+- Ğ
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+- 物
+- ޫ
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+- Ӯ
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+- け
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+- 方
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+- 学
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+- ڊ
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+- థ
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+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
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+- ୱ
+- ਧ
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+- 斯
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+- ጵ
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+- ဘ
+- ఈ
+- 있
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+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
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+- Ḥ
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+- څ
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+- 事
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+- ኮ
+- 之
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+- 入
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+- 于
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+- 名
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+- ዛ
+- ቻ
+- ラ
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+- 수
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+- 所
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+- つ
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+- ఫ
+- ቢ
+- ৎ
+- わ
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+- ഈ
+- 으
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+- お
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+- ဒ
+- ጉ
+- ኖ
+- 度
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+- ഖ
+- ឡ
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+- Ֆ
+- 리
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+- 合
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+- 間
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+- ږ
+- 知
+- ሬ
+- 手
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+- ¿
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+- ៊
+- 인
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+- ؤ
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+- ¡
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+- 亚
+- 化
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+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
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+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
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+speech_volume_normalize: null
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+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
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+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
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+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
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+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
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+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
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+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/train/batch_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/train/feats_lengths_stats.npz
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+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/train/feats_lengths_stats.npz
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+size 778
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/train/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..0692bb079e653635fc545bfc655078d208e3088e
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/train/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/valid/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/valid/batch_keys
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--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/valid/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/valid/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
+feats_lengths
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..5f5db71ce452310e64f102da41196ec38d6ac7cb
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.28/valid/text_shape
@@ -0,0 +1,759 @@
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+nchlt_nso_001152 22
+nchlt_nso_001156 25
+nchlt_nso_001160 21
+nchlt_nso_001164 23
+nchlt_nso_001168 25
+nchlt_nso_001172 18
+nchlt_nso_001145 18
+nchlt_nso_001149 15
+nchlt_nso_001153 18
+nchlt_nso_001157 31
+nchlt_nso_001161 25
+nchlt_nso_001165 24
+nchlt_nso_001169 19
+nchlt_nso_001173 26
+nchlt_nso_001174 29
+nchlt_nso_001178 15
+nchlt_nso_001182 17
+nchlt_nso_001186 28
+nchlt_nso_001190 24
+nchlt_nso_001194 22
+nchlt_nso_001198 27
+nchlt_nso_001202 21
+nchlt_nso_001175 30
+nchlt_nso_001179 29
+nchlt_nso_001183 19
+nchlt_nso_001187 31
+nchlt_nso_001191 22
+nchlt_nso_001195 19
+nchlt_nso_001199 31
+nchlt_nso_001203 30
+nchlt_nso_001176 19
+nchlt_nso_001180 26
+nchlt_nso_001184 21
+nchlt_nso_001188 17
+nchlt_nso_001192 22
+nchlt_nso_001196 26
+nchlt_nso_001200 19
+nchlt_nso_001204 16
+nchlt_nso_001177 20
+nchlt_nso_001181 16
+nchlt_nso_001185 22
+nchlt_nso_001189 21
+nchlt_nso_001193 21
+nchlt_nso_001197 23
+nchlt_nso_001201 16
+nchlt_nso_001205 14
+nchlt_nso_001206 20
+nchlt_nso_001210 28
+nchlt_nso_001214 23
+nchlt_nso_001218 15
+nchlt_nso_001222 21
+nchlt_nso_001226 22
+nchlt_nso_001230 19
+nchlt_nso_001234 24
+nchlt_nso_001207 17
+nchlt_nso_001211 34
+nchlt_nso_001215 28
+nchlt_nso_001219 16
+nchlt_nso_001223 27
+nchlt_nso_001227 32
+nchlt_nso_001231 22
+nchlt_nso_001235 19
+nchlt_nso_001208 18
+nchlt_nso_001212 15
+nchlt_nso_001216 16
+nchlt_nso_001220 17
+nchlt_nso_001224 21
+nchlt_nso_001228 19
+nchlt_nso_001232 19
+nchlt_nso_001236 29
+nchlt_nso_001209 16
+nchlt_nso_001213 20
+nchlt_nso_001217 18
+nchlt_nso_001221 19
+nchlt_nso_001225 21
+nchlt_nso_001229 22
+nchlt_nso_001233 18
+nchlt_nso_001237 28
+nchlt_nso_001238 15
+nchlt_nso_001242 22
+nchlt_sot_001018 28
+nchlt_sot_001022 13
+nchlt_sot_001026 39
+nchlt_sot_001030 14
+nchlt_sot_001034 16
+nchlt_sot_001038 32
+nchlt_nso_001239 28
+nchlt_nso_001243 19
+nchlt_sot_001019 27
+nchlt_sot_001023 34
+nchlt_sot_001027 41
+nchlt_sot_001031 16
+nchlt_sot_001035 34
+nchlt_sot_001039 23
+nchlt_nso_001240 27
+nchlt_sot_001016 18
+nchlt_sot_001020 15
+nchlt_sot_001024 24
+nchlt_sot_001028 20
+nchlt_sot_001032 25
+nchlt_sot_001036 25
+nchlt_sot_001040 37
+nchlt_nso_001241 21
+nchlt_sot_001017 41
+nchlt_sot_001021 37
+nchlt_sot_001025 24
+nchlt_sot_001029 9
+nchlt_sot_001033 20
+nchlt_sot_001037 25
+nchlt_sot_001041 26
+nchlt_sot_001042 25
+nchlt_sot_001046 27
+nchlt_sot_001050 30
+nchlt_sot_001054 22
+nchlt_sot_001058 38
+nchlt_sot_001062 19
+nchlt_sot_001066 28
+nchlt_sot_001070 28
+nchlt_sot_001043 24
+nchlt_sot_001047 17
+nchlt_sot_001051 27
+nchlt_sot_001055 18
+nchlt_sot_001059 15
+nchlt_sot_001063 28
+nchlt_sot_001067 33
+nchlt_sot_001071 22
+nchlt_sot_001044 43
+nchlt_sot_001048 20
+nchlt_sot_001052 24
+nchlt_sot_001056 10
+nchlt_sot_001060 24
+nchlt_sot_001064 21
+nchlt_sot_001068 15
+nchlt_sot_001072 24
+nchlt_sot_001045 20
+nchlt_sot_001049 12
+nchlt_sot_001053 33
+nchlt_sot_001057 5
+nchlt_sot_001061 35
+nchlt_sot_001065 24
+nchlt_sot_001069 24
+nchlt_sot_001073 22
+nchlt_sot_001074 31
+nchlt_sot_001078 26
+nchlt_sot_001082 29
+nchlt_sot_001086 3
+nchlt_sot_001090 32
+nchlt_sot_001094 26
+nchlt_sot_001098 29
+nchlt_sot_001102 22
+nchlt_sot_001075 30
+nchlt_sot_001079 19
+nchlt_sot_001083 34
+nchlt_sot_001087 19
+nchlt_sot_001091 25
+nchlt_sot_001095 22
+nchlt_sot_001099 12
+nchlt_sot_001103 27
+nchlt_sot_001076 22
+nchlt_sot_001080 30
+nchlt_sot_001084 15
+nchlt_sot_001088 21
+nchlt_sot_001092 28
+nchlt_sot_001096 29
+nchlt_sot_001100 29
+nchlt_sot_001104 20
+nchlt_sot_001077 27
+nchlt_sot_001081 24
+nchlt_sot_001085 27
+nchlt_sot_001089 23
+nchlt_sot_001093 25
+nchlt_sot_001097 34
+nchlt_sot_001101 13
+nchlt_sot_001105 23
+nchlt_sot_001106 26
+nchlt_sot_001110 24
+nchlt_sot_001114 19
+nchlt_sot_001118 22
+nchlt_sot_001122 22
+nchlt_sot_001126 23
+nchlt_sot_001130 28
+nchlt_sot_001134 19
+nchlt_sot_001107 26
+nchlt_sot_001111 26
+nchlt_sot_001115 33
+nchlt_sot_001119 19
+nchlt_sot_001123 34
+nchlt_sot_001127 16
+nchlt_sot_001131 14
+nchlt_sot_001135 24
+nchlt_sot_001108 33
+nchlt_sot_001112 19
+nchlt_sot_001116 22
+nchlt_sot_001120 17
+nchlt_sot_001124 30
+nchlt_sot_001128 29
+nchlt_sot_001132 25
+nchlt_sot_001136 28
+nchlt_sot_001109 30
+nchlt_sot_001113 24
+nchlt_sot_001117 34
+nchlt_sot_001121 19
+nchlt_sot_001125 20
+nchlt_sot_001129 36
+nchlt_sot_001133 22
+nchlt_sot_001137 28
+nchlt_sot_001138 24
+nchlt_sot_001142 24
+nchlt_sot_001146 16
+nchlt_sot_001150 26
+nchlt_sot_001154 21
+nchlt_sot_001158 31
+nchlt_sot_001162 25
+nchlt_sot_001166 17
+nchlt_sot_001139 18
+nchlt_sot_001143 21
+nchlt_sot_001147 23
+nchlt_sot_001151 23
+nchlt_sot_001155 11
+nchlt_sot_001159 33
+nchlt_sot_001163 23
+nchlt_sot_001167 6
+nchlt_sot_001140 22
+nchlt_sot_001144 16
+nchlt_sot_001148 30
+nchlt_sot_001152 27
+nchlt_sot_001156 32
+nchlt_sot_001160 23
+nchlt_sot_001164 22
+nchlt_sot_001168 23
+nchlt_sot_001141 21
+nchlt_sot_001145 20
+nchlt_sot_001149 8
+nchlt_sot_001153 26
+nchlt_sot_001157 23
+nchlt_sot_001161 31
+nchlt_sot_001165 17
+nchlt_sot_001169 16
+nchlt_sot_001170 23
+nchlt_sot_001174 27
+nchlt_sot_001178 22
+nchlt_sot_001182 25
+nchlt_ssw_000764 35
+nchlt_ssw_000768 21
+nchlt_ssw_000772 37
+nchlt_ssw_000776 33
+nchlt_sot_001171 24
+nchlt_sot_001175 25
+nchlt_sot_001179 35
+nchlt_sot_001183 36
+nchlt_ssw_000765 28
+nchlt_ssw_000769 26
+nchlt_ssw_000773 27
+nchlt_ssw_000777 28
+nchlt_sot_001172 33
+nchlt_sot_001176 32
+nchlt_sot_001180 29
+nchlt_sot_001184 27
+nchlt_ssw_000766 32
+nchlt_ssw_000770 25
+nchlt_ssw_000774 33
+nchlt_ssw_000778 28
+nchlt_sot_001173 20
+nchlt_sot_001177 18
+nchlt_sot_001181 21
+nchlt_sot_001185 21
+nchlt_ssw_000767 29
+nchlt_ssw_000771 58
+nchlt_ssw_000775 32
+nchlt_ssw_000779 24
+nchlt_ssw_000780 35
+nchlt_ssw_000784 29
+nchlt_ssw_000788 39
+nchlt_ssw_000792 29
+nchlt_ssw_000796 32
+nchlt_ssw_000800 35
+nchlt_ssw_000804 24
+nchlt_ssw_000808 28
+nchlt_ssw_000781 20
+nchlt_ssw_000785 31
+nchlt_ssw_000789 23
+nchlt_ssw_000793 27
+nchlt_ssw_000797 20
+nchlt_ssw_000801 19
+nchlt_ssw_000805 37
+nchlt_ssw_000809 26
+nchlt_ssw_000782 33
+nchlt_ssw_000786 34
+nchlt_ssw_000790 45
+nchlt_ssw_000794 24
+nchlt_ssw_000798 41
+nchlt_ssw_000802 30
+nchlt_ssw_000806 33
+nchlt_ssw_000810 27
+nchlt_ssw_000783 29
+nchlt_ssw_000787 30
+nchlt_ssw_000791 40
+nchlt_ssw_000795 22
+nchlt_ssw_000799 30
+nchlt_ssw_000803 29
+nchlt_ssw_000807 30
+nchlt_ssw_000811 30
+nchlt_ssw_000812 20
+nchlt_ssw_000816 25
+nchlt_ssw_000820 21
+nchlt_ssw_000824 35
+nchlt_ssw_000828 48
+nchlt_ssw_000832 33
+nchlt_ssw_000836 35
+nchlt_ssw_000840 29
+nchlt_ssw_000813 31
+nchlt_ssw_000817 37
+nchlt_ssw_000821 23
+nchlt_ssw_000825 22
+nchlt_ssw_000829 27
+nchlt_ssw_000833 31
+nchlt_ssw_000837 24
+nchlt_ssw_000841 36
+nchlt_ssw_000814 36
+nchlt_ssw_000818 26
+nchlt_ssw_000822 40
+nchlt_ssw_000826 32
+nchlt_ssw_000830 29
+nchlt_ssw_000834 22
+nchlt_ssw_000838 28
+nchlt_ssw_000842 31
+nchlt_ssw_000815 33
+nchlt_ssw_000819 26
+nchlt_ssw_000823 38
+nchlt_ssw_000827 27
+nchlt_ssw_000831 30
+nchlt_ssw_000835 31
+nchlt_ssw_000839 40
+nchlt_ssw_000843 35
+nchlt_ssw_000844 28
+nchlt_ssw_000848 25
+nchlt_ssw_000852 40
+nchlt_ssw_000856 21
+nchlt_ssw_000860 27
+nchlt_ssw_000864 22
+nchlt_tsn_001041 42
+nchlt_tsn_001045 30
+nchlt_ssw_000845 39
+nchlt_ssw_000849 26
+nchlt_ssw_000853 33
+nchlt_ssw_000857 39
+nchlt_ssw_000861 31
+nchlt_ssw_000865 29
+nchlt_tsn_001042 8
+nchlt_tsn_001046 22
+nchlt_ssw_000846 22
+nchlt_ssw_000850 27
+nchlt_ssw_000854 25
+nchlt_ssw_000858 23
+nchlt_ssw_000862 35
+nchlt_ssw_000866 38
+nchlt_tsn_001043 33
+nchlt_tsn_001047 29
+nchlt_ssw_000847 32
+nchlt_ssw_000851 26
+nchlt_ssw_000855 28
+nchlt_ssw_000859 33
+nchlt_ssw_000863 7
+nchlt_tsn_001040 24
+nchlt_tsn_001044 27
+nchlt_tsn_001048 30
+nchlt_tsn_001049 25
+nchlt_tsn_001053 22
+nchlt_tsn_001057 30
+nchlt_tsn_001061 14
+nchlt_tsn_001065 29
+nchlt_tsn_001069 26
+nchlt_tsn_001073 35
+nchlt_tsn_001077 22
+nchlt_tsn_001050 26
+nchlt_tsn_001054 26
+nchlt_tsn_001058 20
+nchlt_tsn_001062 29
+nchlt_tsn_001066 27
+nchlt_tsn_001070 35
+nchlt_tsn_001074 23
+nchlt_tsn_001078 33
+nchlt_tsn_001051 31
+nchlt_tsn_001055 25
+nchlt_tsn_001059 32
+nchlt_tsn_001063 21
+nchlt_tsn_001067 32
+nchlt_tsn_001071 27
+nchlt_tsn_001075 26
+nchlt_tsn_001079 26
+nchlt_tsn_001052 33
+nchlt_tsn_001056 31
+nchlt_tsn_001060 35
+nchlt_tsn_001064 21
+nchlt_tsn_001068 27
+nchlt_tsn_001072 42
+nchlt_tsn_001076 26
+nchlt_tsn_001080 31
+nchlt_tsn_001081 32
+nchlt_tsn_001085 23
+nchlt_tsn_001089 35
+nchlt_tsn_001093 23
+nchlt_tsn_001097 36
+nchlt_tsn_001101 28
+nchlt_tsn_001082 28
+nchlt_tsn_001086 43
+nchlt_tsn_001090 26
+nchlt_tsn_001094 32
+nchlt_tsn_001098 20
+nchlt_tsn_001102 29
+nchlt_tsn_001083 40
+nchlt_tsn_001087 22
+nchlt_tsn_001091 28
+nchlt_tsn_001095 35
+nchlt_tsn_001099 26
+nchlt_tsn_001103 33
+nchlt_tsn_001084 44
+nchlt_tsn_001088 22
+nchlt_tsn_001092 27
+nchlt_tsn_001096 29
+nchlt_tsn_001100 24
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29.log
new file mode 100644
index 0000000000000000000000000000000000000000..71126aba08424a639589acfcdecee7c7cfee6d35
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29.log
@@ -0,0 +1,157 @@
+# Running on cn002.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:13 CDT 2023
+# SLURMD_NODENAME=cn002
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=29
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133235
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133235
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn002
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn002
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=860893
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn002
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.29.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.29.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.29 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.29.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.29.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.29 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn002] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn002] 2023-07-06 08:52:53,974 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn002] 2023-07-06 08:52:53,976 (abs_task:1214) INFO: Scheduler: None
+[cn002] 2023-07-06 08:52:53,985 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.29/config.yaml
+[cn002] 2023-07-06 08:52:54,089 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.29', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', 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'處', '።', '况', '几', 'ಃ', '름', '순', '환', '특', '월', '눈', '험', '석', '근', '션', '받', 'ഔ', 'ဤ', 'ዷ', 'ቼ', 'ጌ', 'ጄ', '̆', '权', '柱', '无', '粗', '町', '舰', '护', '曼', '浮', '环', '汗', '众', '论', '辖', '铁', '固', '坏', '申', '毕', '跨', '树', '田', '腹', '血', '假', '妻', '授', '丹', '卫', '读', '术', '礼', '姆', '藏', '祖', '《', '》', '寄', '泡', '绝', '承', '际', '疫', '序', '弟', '遭', '延', '威', '修', '排', '雅', '摩', '掌', 'ऋ', '鏡', '勉', '働', 'ワ', '収', '閉', '費', 'ぬ', '距', '笑', '詞', '終', '退', '製', '訪', '査', '宿', '順', '寮', '龜', '轉', '錄', '腦', '營', '範', '雲', '裡', '割', '刻', '衛', '鐘', '估', '囉', '陷', '耐', '繼', '訊', '架', '٣', '飞', '午', '۴', 'ォ', 'ಠ', '됩', '활', '충', '돌', '응', '못', '승', '추', '감', '질', '픽', '련', '혀', '난', '목', 'ఛ', 'ෞ', 'ጩ', 'ሷ', 'ዥ', 'ቭ', 'ـ', '粉', '茨', '证', '停', '湖', '转', '茶', '范', '君', '升', '液', '略', '杜', '戏', '艺', '脱', '龙', '录', '籍', '蝶', '童', '刑', '荷', '讲', '企', '阅', '适', '岩', '若', '席', '徒', '佳', '卵', '夜', '仕', '慢', 'づ', '断', '従', '側', '憶', '左', '課', '殺', '残', '録', '説', '筆', '攏', '坑', '織', '囝', '觀', '討', '烏', '咧', '權', '尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', '喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.29.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.29.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn002] 2023-07-06 08:53:06,299 (collect_stats:106) INFO: Niter: 100
+[cn002] 2023-07-06 08:53:11,081 (collect_stats:106) INFO: Niter: 200
+[cn002] 2023-07-06 08:53:15,769 (collect_stats:106) INFO: Niter: 300
+[cn002] 2023-07-06 08:53:21,418 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651413
+# Accounting: end_time=1688651624
+# Accounting: time=211 threads=1
+# Finished at Thu Jul 6 08:53:44 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..27cb9c9d13402e1e328dde4ec1d9f94163c37630
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.29
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.29.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.29.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
+- ໂ
+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
+- 기
+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
+- 前
+- ዊ
+- Ҟ
+- 世
+- ญ
+- イ
+- 公
+- ໜ
+- え
+- ト
+- ၏
+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
+- ္
+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
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+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
+- 西
+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
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+- 教
+- 國
+- ඩ
+- 道
+- 市
+- 表
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+- 而
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+- ¡
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+- 里
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+- …
+- Ṛ
+- ഏ
+- ሱ
+- 比
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+- 起
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+- 拉
+- 后
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+- Ӳ
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+- Ჯ
+- 부
+- 心
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+- ੜ
+- ‌
+- 们
+- 更
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+- 라
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+- ዶ
+- 美
+- 也
+- 南
+- タ
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+- ජ
+- ሙ
+- ፕ
+- 現
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+- ਯ
+- ఖ
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+- ೃ
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+- Ẓ
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+- ቤ
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+- ං
+- 外
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+- ៀ
+- 전
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+- 全
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+- ፋ
+- 字
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+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
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+- 無
+- 場
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+- 啲
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+- ጅ
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+- 或
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+- ቁ
+- ሶ
+- 期
+- ろ
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+- ቋ
+- ኩ
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+- ޕ
+- ク
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+- ०
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+- 機
+- コ
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+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
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+- ՝
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+- ృ
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+- ·
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+- 影
+- 次
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+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
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+- ౌ
+- ኦ
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+- Ɓ
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+- ೌ
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+- 任
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+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
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+noise_scp: null
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+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/train/batch_keys
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index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/train/batch_keys
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+speech
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/train/feats_lengths_stats.npz
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index 0000000000000000000000000000000000000000..e27b63e676d2a6c0900adfb534b9019fc3e1c297
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/train/feats_lengths_stats.npz
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+version https://git-lfs.github.com/spec/v1
+oid sha256:1fe4022797d182fb9b683f2376e335a283670fde604d3da77a0631cb983a6757
+size 778
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/train/feats_stats.npz
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+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/train/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..5014fb94b5b6b8b9898a19379b3a70a00c90dd14
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/train/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
+feats_lengths
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..ab72a88622cc23e8b6dc448d12f7ba61a5c7e3f7
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.29/valid/text_shape
@@ -0,0 +1,759 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3.log
new file mode 100644
index 0000000000000000000000000000000000000000..598508b9117a90037714863302147875f0b4ffad
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3.log
@@ -0,0 +1,157 @@
+# Running on cn001.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:14 CDT 2023
+# SLURMD_NODENAME=cn001
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=3
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133209
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133209
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn001
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn001
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3660439
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn001
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.3.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.3.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.3 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.3.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.3.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.3 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn001] 2023-07-06 08:52:51,877 (asr:475) INFO: Vocabulary size: 6454
+[cn001] 2023-07-06 08:52:53,975 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1214) INFO: Scheduler: None
+[cn001] 2023-07-06 08:52:53,987 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.3/config.yaml
+[cn001] 2023-07-06 08:52:54,089 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.3', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 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'尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', 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'喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.3.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.3.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn001] 2023-07-06 08:53:10,662 (collect_stats:106) INFO: Niter: 100
+[cn001] 2023-07-06 08:53:21,161 (collect_stats:106) INFO: Niter: 200
+[cn001] 2023-07-06 08:53:28,510 (collect_stats:106) INFO: Niter: 300
+[cn001] 2023-07-06 08:53:35,525 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651414
+# Accounting: end_time=1688651639
+# Accounting: time=225 threads=1
+# Finished at Thu Jul 6 08:53:59 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3425165eeefbd28816d6763d64ebea36a3bb7cec
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.3
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.3.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.3.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
+- ໂ
+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
+- 기
+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
+- 前
+- ዊ
+- Ҟ
+- 世
+- ญ
+- イ
+- 公
+- ໜ
+- え
+- ト
+- ၏
+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
+- ္
+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
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+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
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+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
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+- 教
+- 國
+- ඩ
+- 道
+- 市
+- 表
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+- 而
+- 食
+- ޒ
+- Ψ
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+- ¡
+- 尔
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+- 性
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+- 里
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+- ቹ
+- 力
+- ド
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+- 佢
+- …
+- Ṛ
+- ഏ
+- ሱ
+- 比
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+- 起
+- 球
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+- 拉
+- 后
+- 体
+- 月
+- 但
+- 話
+- 적
+- Ӡ
+- Ӳ
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+- 使
+- Ჯ
+- 부
+- 心
+- ੌ
+- ੜ
+- ‌
+- 们
+- 更
+- 正
+- 라
+- 보
+- ቦ
+- ዶ
+- 美
+- 也
+- 南
+- タ
+- ஜ
+- ජ
+- ሙ
+- ፕ
+- 現
+- ૃ
+- 일
+- ຜ
+- ਯ
+- ఖ
+- 口
+- 因
+- ロ
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+- ೃ
+- 式
+- ޝ
+- Ẓ
+- 했
+- ቤ
+- 十
+- 区
+- み
+- Ķ
+- 나
+- ۍ
+- ఏ
+- ං
+- 外
+- 海
+- 重
+- 天
+- 語
+- ۰
+- ៀ
+- 전
+- ඇ
+- 全
+- 被
+- 面
+- ឆ
+- 것
+- ධ
+- ፋ
+- 字
+- 发
+- ऑ
+- テ
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+- 身
+- 果
+- ஏ
+- ඒ
+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
+- カ
+- 無
+- 場
+- 來
+- 啲
+- ඔ
+- ඉ
+- ቲ
+- ጅ
+- 直
+- 德
+- 些
+- 或
+- 開
+- 게
+- 주
+- ቴ
+- ቁ
+- ሶ
+- 期
+- ろ
+- Ṱ
+- ኔ
+- ቋ
+- ኩ
+- 阿
+- 提
+- 音
+- ジ
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+- 상
+- ጽ
+- ቆ
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+- 做
+- 金
+- フ
+- シ
+- 만
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+- ޕ
+- ク
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+- ०
+- १
+- 解
+- 真
+- 空
+- 機
+- コ
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+- 冇
+- ฐ
+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
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+- 英
+- 代
+- Ģ
+- ූ
+- ዩ
+- ̃
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+- 제
+- ਝ
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+- ·
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+- ጫ
+- ቂ
+- 何
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+- ጨ
+- ሻ
+- 及
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+- 原
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+- ሸ
+- Џ
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+- 影
+- 次
+- 安
+- べ
+- Ჰ
+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
+- 었
+- ਏ
+- ౌ
+- ኦ
+- 建
+- 政
+- 巴
+- Ɓ
+- 々
+- 識
+- 種
+- 여
+- ڀ
+- ኙ
+- 过
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+- 数
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+- 車
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+- 點
+- ೌ
+- 되
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+- ሴ
+- 想
+- 任
+- 当
+- 站
+- 感
+- 住
+- 未
+- 旅
+- 持
+- バ
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+- 學
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+- ૌ
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+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
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+- 必
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+- ィ
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+- 미
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+- 원
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+- ጸ
+- 对
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+- 史
+- 客
+- 考
+- ャ
+- オ
+- ഓ
+- ዓ
+- ሃ
+- 동
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+- ආ
+- ቷ
+- ኪ
+- ঃ
+- 称
+- 活
+- 落
+- へ
+- マ
+- ニ
+- ょ
+- 記
+- 発
+- ഠ
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+- ឋ
+- 국
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+- 兰
+- 基
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+- ほ
+- 気
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+- ٠
+- 거
+- 們
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+- ኞ
+- ፎ
+- ጃ
+- ঝ
+- ڤ
+- 经
+- 信
+- 布
+- 思
+- 打
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+- 嘢
+- ̇
+- 우
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+- ፖ
+- 量
+- 曾
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+- 品
+- 速
+- 件
+- 於
+- ۲
+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
+- 」
+- 石
+- 交
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+- チ
+- ず
+- 題
+- 對
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+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
+- 五
+- ޗ
+- ュ
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+- ১
+- 세
+- 구
+- 경
+- 비
+- ။
+- ዴ
+- 士
+- 县
+- 台
+- 业
+- 林
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+- 着
+- 集
+- 足
+- 它
+- じ
+- ム
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+- ഘ
+- 經
+- 邊
+- ஷ
+- 、
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+- ጭ
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+- 간
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+- ኘ
+- Œ
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+- 罗
+- 长
+- 指
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+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
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+aux_ctc_tasks: []
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+frontend: default
+frontend_conf:
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+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
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+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
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+model: espnet
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+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
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+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/train/batch_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/train/feats_lengths_stats.npz
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+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/train/feats_lengths_stats.npz
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+version https://git-lfs.github.com/spec/v1
+oid sha256:e337ace31c53a9f6ff60ffc0a199b47f1e634eca0be90d765e6691863d986073
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/train/speech_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/train/speech_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/train/stats_keys
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index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/train/stats_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..d9b3875076d1e08a3d39bd974a935f770317f991
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/valid/stats_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.3/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..99beae689d039d6764410e6438aa3cf010266af5
--- /dev/null
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+NST_dan_000800 85
+NST_dan_000804 68
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+NST_dan_000812 159
+NST_dan_000816 89
+NST_nor_000686 6
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+NST_swe_000769 21
+NST_swe_000773 23
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+NST_swe_000781 22
+NST_swe_000785 14
+NST_swe_000789 13
+NST_swe_000793 65
+NST_swe_000797 123
+NST_swe_000770 45
+NST_swe_000774 49
+NST_swe_000778 31
+NST_swe_000782 11
+NST_swe_000786 50
+NST_swe_000790 18
+NST_swe_000794 115
+NST_swe_000798 124
+NST_swe_000771 8
+NST_swe_000775 17
+NST_swe_000779 17
+NST_swe_000783 72
+NST_swe_000787 47
+NST_swe_000791 26
+NST_swe_000795 107
+NST_swe_000799 12
+NST_swe_000800 28
+NST_swe_000804 52
+NST_swe_000808 51
+NST_swe_000812 13
+NST_swe_000816 46
+NST_swe_000820 17
+NST_swe_000824 37
+NST_swe_000828 11
+NST_swe_000801 34
+NST_swe_000805 36
+NST_swe_000809 9
+NST_swe_000813 73
+NST_swe_000817 92
+NST_swe_000821 88
+NST_swe_000825 15
+NST_swe_000829 42
+NST_swe_000802 39
+NST_swe_000806 19
+NST_swe_000810 23
+NST_swe_000814 37
+NST_swe_000818 19
+NST_swe_000822 55
+NST_swe_000826 50
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+NST_swe_000803 19
+NST_swe_000807 154
+NST_swe_000811 17
+NST_swe_000815 53
+NST_swe_000819 148
+NST_swe_000823 99
+NST_swe_000827 33
+NST_swe_000831 20
+NST_swe_000832 82
+NST_swe_000836 62
+NST_swe_000840 64
+NST_swe_000844 88
+NST_swe_000848 27
+NST_swe_000852 84
+NST_swe_000856 31
+NST_swe_000860 17
+NST_swe_000833 15
+NST_swe_000837 37
+NST_swe_000841 17
+NST_swe_000845 64
+NST_swe_000849 19
+NST_swe_000853 13
+NST_swe_000857 81
+NST_swe_000861 47
+NST_swe_000834 47
+NST_swe_000838 40
+NST_swe_000842 13
+NST_swe_000846 96
+NST_swe_000850 13
+NST_swe_000854 89
+NST_swe_000858 144
+NST_swe_000862 15
+NST_swe_000835 35
+NST_swe_000839 23
+NST_swe_000843 72
+NST_swe_000847 19
+NST_swe_000851 23
+NST_swe_000855 38
+NST_swe_000859 28
+NST_swe_000863 62
+NST_swe_000864 87
+NST_swe_000868 14
+NST_swe_000872 125
+cv_abk_001025 24
+cv_abk_001029 34
+cv_abk_001033 5
+cv_abk_001037 6
+cv_abk_001041 59
+NST_swe_000865 44
+NST_swe_000869 79
+NST_swe_000873 126
+cv_abk_001026 15
+cv_abk_001030 41
+cv_abk_001034 5
+cv_abk_001038 5
+cv_abk_001042 51
+NST_swe_000866 34
+NST_swe_000870 72
+NST_swe_000874 38
+cv_abk_001027 25
+cv_abk_001031 7
+cv_abk_001035 6
+cv_abk_001039 5
+cv_abk_001043 63
+NST_swe_000867 42
+NST_swe_000871 13
+NST_swe_000875 63
+cv_abk_001028 46
+cv_abk_001032 5
+cv_abk_001036 5
+cv_abk_001040 11
+cv_abk_001044 73
+cv_abk_001045 33
+cv_abk_001049 73
+cv_abk_001053 5
+cv_abk_001057 5
+cv_abk_001061 5
+cv_abk_001065 22
+cv_abk_001069 30
+cv_abk_001073 21
+cv_abk_001046 36
+cv_abk_001050 46
+cv_abk_001054 8
+cv_abk_001058 6
+cv_abk_001062 5
+cv_abk_001066 20
+cv_abk_001070 15
+cv_abk_001074 5
+cv_abk_001047 70
+cv_abk_001051 32
+cv_abk_001055 5
+cv_abk_001059 5
+cv_abk_001063 6
+cv_abk_001067 38
+cv_abk_001071 70
+cv_abk_001075 5
+cv_abk_001048 80
+cv_abk_001052 11
+cv_abk_001056 6
+cv_abk_001060 7
+cv_abk_001064 16
+cv_abk_001068 38
+cv_abk_001072 30
+cv_abk_001076 5
+cv_abk_001077 5
+cv_abk_001081 6
+cv_abk_001085 6
+cv_abk_001089 71
+cv_abk_001093 79
+cv_abk_001097 21
+cv_abk_001101 24
+cv_abk_001105 33
+cv_abk_001078 8
+cv_abk_001082 5
+cv_abk_001086 32
+cv_abk_001090 58
+cv_abk_001094 33
+cv_abk_001098 30
+cv_abk_001102 46
+cv_abk_001106 39
+cv_abk_001079 6
+cv_abk_001083 7
+cv_abk_001087 18
+cv_abk_001091 45
+cv_abk_001095 72
+cv_abk_001099 33
+cv_abk_001103 29
+cv_abk_001107 45
+cv_abk_001080 5
+cv_abk_001084 11
+cv_abk_001088 80
+cv_abk_001092 50
+cv_abk_001096 16
+cv_abk_001100 40
+cv_abk_001104 45
+cv_abk_001108 22
+cv_abk_001109 57
+cv_abk_001113 12
+cv_abk_001117 27
+cv_abk_001121 33
+cv_abk_001125 73
+cv_abk_001129 19
+cv_abk_001133 77
+cv_abk_001137 40
+cv_abk_001110 30
+cv_abk_001114 31
+cv_abk_001118 41
+cv_abk_001122 37
+cv_abk_001126 21
+cv_abk_001130 47
+cv_abk_001134 32
+cv_abk_001138 26
+cv_abk_001111 35
+cv_abk_001115 14
+cv_abk_001119 33
+cv_abk_001123 34
+cv_abk_001127 37
+cv_abk_001131 31
+cv_abk_001135 69
+cv_abk_001139 32
+cv_abk_001112 25
+cv_abk_001116 50
+cv_abk_001120 34
+cv_abk_001124 64
+cv_abk_001128 41
+cv_abk_001132 70
+cv_abk_001136 33
+cv_abk_001140 54
+cv_abk_001141 35
+cv_abk_001145 5
+cv_abk_001149 6
+cv_abk_001153 6
+cv_abk_001157 61
+cv_ara_000807 12
+cv_ara_000811 45
+cv_ara_000815 29
+cv_abk_001142 32
+cv_abk_001146 5
+cv_abk_001150 8
+cv_abk_001154 5
+cv_abk_001158 26
+cv_ara_000808 28
+cv_ara_000812 32
+cv_ara_000816 23
+cv_abk_001143 5
+cv_abk_001147 5
+cv_abk_001151 5
+cv_abk_001155 18
+cv_abk_001159 24
+cv_ara_000809 19
+cv_ara_000813 80
+cv_ara_000817 27
+cv_abk_001144 7
+cv_abk_001148 6
+cv_abk_001152 11
+cv_abk_001156 77
+cv_abk_001160 71
+cv_ara_000810 25
+cv_ara_000814 50
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+cv_ara_000819 33
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+cv_ara_000833 7
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+cv_ara_000841 22
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+cv_ara_000849 65
+cv_ara_000822 11
+cv_ara_000826 21
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+cv_ara_000834 7
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+cv_ara_000842 61
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+cv_ara_000899 16
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+cv_ara_000892 48
+cv_ara_000896 21
+cv_ara_000900 21
+cv_ara_000904 30
+cv_ara_000885 5
+cv_ara_000889 20
+cv_ara_000893 56
+cv_ara_000897 15
+cv_ara_000901 25
+cv_ara_000905 27
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+cv_ara_000898 51
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30.log
new file mode 100644
index 0000000000000000000000000000000000000000..ac794065db11c1d59a5492e2730ecf128951c6cf
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30.log
@@ -0,0 +1,157 @@
+# Running on cn002.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:14 CDT 2023
+# SLURMD_NODENAME=cn002
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=30
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133236
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133236
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn002
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn002
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=860938
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn002
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.30.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.30.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.30 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.30.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.30.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.30 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn002] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn002] 2023-07-06 08:52:53,974 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn002] 2023-07-06 08:52:53,975 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn002] 2023-07-06 08:52:53,976 (abs_task:1214) INFO: Scheduler: None
+[cn002] 2023-07-06 08:52:53,987 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.30/config.yaml
+[cn002] 2023-07-06 08:52:54,089 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.30', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', 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'處', '።', '况', '几', 'ಃ', '름', '순', '환', '특', '월', '눈', '험', '석', '근', '션', '받', 'ഔ', 'ဤ', 'ዷ', 'ቼ', 'ጌ', 'ጄ', '̆', '权', '柱', '无', '粗', '町', '舰', '护', '曼', '浮', '环', '汗', '众', '论', '辖', '铁', '固', '坏', '申', '毕', '跨', '树', '田', '腹', '血', '假', '妻', '授', '丹', '卫', '读', '术', '礼', '姆', '藏', '祖', '《', '》', '寄', '泡', '绝', '承', '际', '疫', '序', '弟', '遭', '延', '威', '修', '排', '雅', '摩', '掌', 'ऋ', '鏡', '勉', '働', 'ワ', '収', '閉', '費', 'ぬ', '距', '笑', '詞', '終', '退', '製', '訪', '査', '宿', '順', '寮', '龜', '轉', '錄', '腦', '營', '範', '雲', '裡', '割', '刻', '衛', '鐘', '估', '囉', '陷', '耐', '繼', '訊', '架', '٣', '飞', '午', '۴', 'ォ', 'ಠ', '됩', '활', '충', '돌', '응', '못', '승', '추', '감', '질', '픽', '련', '혀', '난', '목', 'ఛ', 'ෞ', 'ጩ', 'ሷ', 'ዥ', 'ቭ', 'ـ', '粉', '茨', '证', '停', '湖', '转', '茶', '范', '君', '升', '液', '略', '杜', '戏', '艺', '脱', '龙', '录', '籍', '蝶', '童', '刑', '荷', '讲', '企', '阅', '适', '岩', '若', '席', '徒', '佳', '卵', '夜', '仕', '慢', 'づ', '断', '従', '側', '憶', '左', '課', '殺', '残', '録', '説', '筆', '攏', '坑', '織', '囝', '觀', '討', '烏', '咧', '權', '尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', '喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.30.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.30.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn002] 2023-07-06 08:53:06,275 (collect_stats:106) INFO: Niter: 100
+[cn002] 2023-07-06 08:53:11,371 (collect_stats:106) INFO: Niter: 200
+[cn002] 2023-07-06 08:53:17,444 (collect_stats:106) INFO: Niter: 300
+[cn002] 2023-07-06 08:53:23,127 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651414
+# Accounting: end_time=1688651624
+# Accounting: time=210 threads=1
+# Finished at Thu Jul 6 08:53:44 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0c1c3fff3220c57ba8d98ff3996a5c3e706f2f43
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.30
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.30.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.30.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
+- ໂ
+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
+- 기
+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
+- 前
+- ዊ
+- Ҟ
+- 世
+- ญ
+- イ
+- 公
+- ໜ
+- え
+- ト
+- ၏
+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
+- ္
+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
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+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
+- 西
+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
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+- 教
+- 國
+- ඩ
+- 道
+- 市
+- 表
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+- 而
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+- ¡
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+- 里
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+- …
+- Ṛ
+- ഏ
+- ሱ
+- 比
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+- 起
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+- 拉
+- 后
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+- Ӳ
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+- Ჯ
+- 부
+- 心
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+- ੜ
+- ‌
+- 们
+- 更
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+- 라
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+- ዶ
+- 美
+- 也
+- 南
+- タ
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+- ජ
+- ሙ
+- ፕ
+- 現
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+- ਯ
+- ఖ
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+- ೃ
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+- Ẓ
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+- ቤ
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+- ං
+- 外
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+- ៀ
+- 전
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+- 全
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+- ፋ
+- 字
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+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
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+- 無
+- 場
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+- 啲
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+- ጅ
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+- 或
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+- ቁ
+- ሶ
+- 期
+- ろ
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+- ቋ
+- ኩ
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+- ޕ
+- ク
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+- ०
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+- 機
+- コ
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+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
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+- ՝
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+- ృ
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+- ·
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+- 影
+- 次
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+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
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+- ౌ
+- ኦ
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+- Ɓ
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+- ೌ
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+- 任
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+init: null
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+ctc_conf:
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+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
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+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
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+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
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+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
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+required:
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+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/train/batch_keys
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--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/train/feats_lengths_stats.npz
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index 0000000000000000000000000000000000000000..94efd095e84bddc0df7735d5298adf863e53339c
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/train/feats_lengths_stats.npz
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+version https://git-lfs.github.com/spec/v1
+oid sha256:4a87dbb173bbbdc348ae6e4c0540104828f2e4a8736b569fcd4cba55f3d60630
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/train/stats_keys
@@ -0,0 +1,2 @@
+feats
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..533b44f3532cb1c219e531c0fa77e7e639943f5a
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/train/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
+feats_lengths
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..cebcf030ee77c8e6aa4b9697b3f60df1982fd50d
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.30/valid/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31.log
new file mode 100644
index 0000000000000000000000000000000000000000..3074e6abf5959ec8473de616489c2bc90d4b49d8
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31.log
@@ -0,0 +1,157 @@
+# Running on cn062.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:13 CDT 2023
+# SLURMD_NODENAME=cn062
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=31
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133237
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133237
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn062
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn062
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=301510
+# SLURM_TOPOLOGY_ADDR=ss00.ss03.cn062
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.31.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.31.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.31 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.31.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.31.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.31 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn062] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn062] 2023-07-06 08:52:53,974 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn062] 2023-07-06 08:52:53,988 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn062] 2023-07-06 08:52:53,988 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn062] 2023-07-06 08:52:53,988 (abs_task:1214) INFO: Scheduler: None
+[cn062] 2023-07-06 08:52:53,994 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.31/config.yaml
+[cn062] 2023-07-06 08:52:54,101 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.31', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', 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'尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', 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'喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.31.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.31.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn062] 2023-07-06 08:53:06,593 (collect_stats:106) INFO: Niter: 100
+[cn062] 2023-07-06 08:53:11,546 (collect_stats:106) INFO: Niter: 200
+[cn062] 2023-07-06 08:53:18,616 (collect_stats:106) INFO: Niter: 300
+[cn062] 2023-07-06 08:53:28,543 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651413
+# Accounting: end_time=1688651633
+# Accounting: time=220 threads=1
+# Finished at Thu Jul 6 08:53:53 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..330c00b68f03f9fbc73698c9e99e0b141287bc90
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.31
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.31.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.31.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
+- ໂ
+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
+- 기
+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
+- 前
+- ዊ
+- Ҟ
+- 世
+- ญ
+- イ
+- 公
+- ໜ
+- え
+- ト
+- ၏
+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
+- ္
+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
+- ッ
+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
+- 西
+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
+- 加
+- 教
+- 國
+- ඩ
+- 道
+- 市
+- 表
+- 立
+- 而
+- 食
+- ޒ
+- Ψ
+- 動
+- ័
+- ¡
+- 尔
+- ዜ
+- 亚
+- 化
+- 先
+- 性
+- 形
+- 里
+- 後
+- ሜ
+- ፊ
+- ቹ
+- 力
+- ド
+- 呢
+- 佢
+- …
+- Ṛ
+- ഏ
+- ሱ
+- 比
+- 高
+- 起
+- 球
+- ዙ
+- 拉
+- 后
+- 体
+- 月
+- 但
+- 話
+- 적
+- Ӡ
+- Ӳ
+- 克
+- 使
+- Ჯ
+- 부
+- 心
+- ੌ
+- ੜ
+- ‌
+- 们
+- 更
+- 正
+- 라
+- 보
+- ቦ
+- ዶ
+- 美
+- 也
+- 南
+- タ
+- ஜ
+- ජ
+- ሙ
+- ፕ
+- 現
+- ૃ
+- 일
+- ຜ
+- ਯ
+- ఖ
+- 口
+- 因
+- ロ
+- ൃ
+- ೃ
+- 式
+- ޝ
+- Ẓ
+- 했
+- ቤ
+- 十
+- 区
+- み
+- Ķ
+- 나
+- ۍ
+- ఏ
+- ං
+- 外
+- 海
+- 重
+- 天
+- 語
+- ۰
+- ៀ
+- 전
+- ඇ
+- 全
+- 被
+- 面
+- ឆ
+- 것
+- ධ
+- ፋ
+- 字
+- 发
+- ऑ
+- テ
+- 頭
+- ዱ
+- 身
+- 果
+- ஏ
+- ඒ
+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
+- カ
+- 無
+- 場
+- 來
+- 啲
+- ඔ
+- ඉ
+- ቲ
+- ጅ
+- 直
+- 德
+- 些
+- 或
+- 開
+- 게
+- 주
+- ቴ
+- ቁ
+- ሶ
+- 期
+- ろ
+- Ṱ
+- ኔ
+- ቋ
+- ኩ
+- 阿
+- 提
+- 音
+- ジ
+- 電
+- ऱ
+- 상
+- ጽ
+- ቆ
+- 动
+- 民
+- 然
+- 做
+- 金
+- フ
+- シ
+- 만
+- උ
+- ޕ
+- ク
+- ፣
+- ०
+- १
+- 解
+- 真
+- 空
+- 機
+- コ
+- Ჭ
+- 冇
+- ฐ
+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
+- 工
+- 英
+- 代
+- Ģ
+- ූ
+- ዩ
+- ̃
+- 北
+- 如
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+- ൺ
+- 정
+- 제
+- ਝ
+- ፓ
+- ·
+- 伊
+- 非
+- ៏
+- ጡ
+- ጫ
+- ቂ
+- 何
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+- ጨ
+- ሻ
+- 及
+- 属
+- 原
+- 這
+- 트
+- ሸ
+- Џ
+- 城
+- 影
+- 次
+- 安
+- べ
+- Ჰ
+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
+- 었
+- ਏ
+- ౌ
+- ኦ
+- 建
+- 政
+- 巴
+- Ɓ
+- 々
+- 識
+- 種
+- 여
+- ڀ
+- ኙ
+- 过
+- 制
+- 始
+- 数
+- 社
+- 死
+- ޢ
+- 車
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+- ଝ
+- ๆ
+- 장
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+- 光
+- 四
+- 由
+- 著
+- ः
+- 彼
+- 點
+- ೌ
+- 되
+- ໆ
+- ሴ
+- 想
+- 任
+- 当
+- 站
+- 感
+- 住
+- 未
+- 旅
+- 持
+- バ
+- 問
+- 學
+- ۱
+- ૌ
+- 할
+- 와
+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
+- 已
+- 造
+- 管
+- 必
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+- ィ
+- パ
+- Ǧ
+- 미
+- 유
+- 위
+- 면
+- 원
+- ච
+- ጸ
+- 对
+- 很
+- 史
+- 客
+- 考
+- ャ
+- オ
+- ഓ
+- ዓ
+- ሃ
+- 동
+- 물
+- ආ
+- ቷ
+- ኪ
+- ঃ
+- 称
+- 活
+- 落
+- へ
+- マ
+- ニ
+- ょ
+- 記
+- 発
+- ഠ
+- 發
+- 咗
+- ឋ
+- 국
+- 现
+- 兰
+- 基
+- 平
+- 保
+- 火
+- 情
+- ほ
+- 気
+- ୌ
+- ٠
+- 거
+- 們
+- ෂ
+- ኞ
+- ፎ
+- ጃ
+- ঝ
+- ڤ
+- 经
+- 信
+- 布
+- 思
+- 打
+- 結
+- ฮ
+- 嘢
+- ̇
+- 우
+- ໝ
+- ፖ
+- 量
+- 曾
+- 车
+- 品
+- 速
+- 件
+- 於
+- ۲
+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
+- 」
+- 石
+- 交
+- 司
+- チ
+- ず
+- 題
+- 對
+- ஞ
+- 睇
+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
+- 五
+- ޗ
+- ュ
+- ଵ
+- ১
+- 세
+- 구
+- 경
+- 비
+- ။
+- ዴ
+- 士
+- 县
+- 台
+- 业
+- 林
+- 存
+- 着
+- 集
+- 足
+- 它
+- じ
+- ム
+- 観
+- ഘ
+- 經
+- 邊
+- ஷ
+- 、
+- ጎ
+- 계
+- ဇ
+- ਛ
+- ጂ
+- ጭ
+- 场
+- 故
+- 型
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+- 告
+- 星
+- 等
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+- 공
+- 며
+- 간
+- ኳ
+- ኘ
+- Œ
+- 镇
+- 罗
+- 长
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+- ኣ
+- "\x93"
+- "\x94"
+- 辈
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+- Ė
+- <sos/eos>
+init: null
+input_size: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/batch_keys
new file mode 100644
index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/batch_keys
@@ -0,0 +1,2 @@
+speech
+text
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/feats_lengths_stats.npz
new file mode 100644
index 0000000000000000000000000000000000000000..9150c591958bc8fe690f2ab2af3ae5774093fcbe
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/feats_lengths_stats.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d084fb2785b46325117d8e863874533bd7303e174013a823b17c25bffd047ed
+size 778
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/feats_stats.npz
new file mode 100644
index 0000000000000000000000000000000000000000..cd02f68f1225fc1933d795594a706c5dc53c01b8
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/feats_stats.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:56d8ed0e3c7cc69bdbabf2e4d39f4ffd82ee5c5d9b3297a9fa922e6ba1cea15c
+size 1402
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/speech_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/speech_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/stats_keys
@@ -0,0 +1,2 @@
+feats
+feats_lengths
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..e5461ab3afdd7a2233d0f3f944c781e7d158a096
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/train/text_shape
@@ -0,0 +1,3954 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
+feats_lengths
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..cd5c9f87d82ca7147c3ff24936bf6a8245dc20af
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.31/valid/text_shape
@@ -0,0 +1,759 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32.log
new file mode 100644
index 0000000000000000000000000000000000000000..49b240f7432bf90786b682442777af086b906842
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32.log
@@ -0,0 +1,157 @@
+# Running on cn062.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:13 CDT 2023
+# SLURMD_NODENAME=cn062
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=32
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133206
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133206
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn062
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn062
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=301502
+# SLURM_TOPOLOGY_ADDR=ss00.ss03.cn062
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.32.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.32.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.32 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.32.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.32.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.32 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn062] 2023-07-06 08:52:51,876 (asr:475) INFO: Vocabulary size: 6454
+[cn062] 2023-07-06 08:52:53,974 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn062] 2023-07-06 08:52:53,988 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn062] 2023-07-06 08:52:53,988 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn062] 2023-07-06 08:52:53,988 (abs_task:1214) INFO: Scheduler: None
+[cn062] 2023-07-06 08:52:53,994 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.32/config.yaml
+[cn062] 2023-07-06 08:52:54,095 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.32', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', '行', 'Ļ', '[som]', 'భ', 'ሆ', '[fil]', 'Ӣ', 'き', 'Ӧ', 'ሉ', 'ʼ', 'ณ', '[oci]', 'Ҷ', 'ڳ', 'ഡ', '[kam]', 'Ღ', 'ݨ', '을', 'ஆ', 'Ƙ', 'ਉ', 'ቶ', 'ຮ', 'ካ', 'ቅ', 'औ', 'ଂ', 'ណ', '의', 'ణ', 'Փ', 'ਈ', 'ګ', 'హ', 'ौ', 'ໍ', 'ಭ', 'ಎ', '地', 'ఆ', 'ん', '하', 'ਖ', '自', '上', 'ፍ', 'ਡ', '可', 'ዮ', '生', '[ibo]', 'ٻ', 'さ', 'ູ', '多', 'ޓ', 'ភ', '니', '不', 'ढ', 'ઈ', 'Ň', 'ഥ', 'ศ', '要', 'Ժ', 'ී', 'ۋ', 'ො', 'ሮ', '年', '到', 'Ძ', 'ซ', '۾', '你', '로', 'ጋ', '[lug]', 'ঞ', '国', 'ផ', '個', 'ฉ', '和', 'ஒ', 'ខ', '[mri]', '他', 'ଠ', 'ຼ', 'ঙ', 'ޅ', '唔', 'ຳ', 'ځ', '៉', 'ថ', '가', '[lin]', 'Ճ', '은', 'ធ', '੍', 'Đ', '嘅', '出', 'ټ', 'Ĕ', 'ス', '지', '下', 'ୂ', 'ޑ', 'Ɗ', 'ຸ', 'ઉ', 'ញ', 'ሁ', 'ޖ', '”', '用', 'ሩ', 'ޔ', '为', '了', 'ຖ', 'එ', 'ጣ', 'ઇ', '日', 'ޯ', 'よ', '고', 'ኛ', 'ሊ', '得', 'ൈ', 'ಒ', 'ፈ', '好', '同', '成', '分', 'そ', 'ธ', 'Ҭ', 'Ğ', 'Ṭ', 'ዲ', 'Њ', 'ڏ', '۽', '物', 'ޫ', 'Ჩ', 'Ӯ', '都', 'け', '个', 'ഫ', 'Ō', 'ዘ', '方', 'ډ', 'Ḍ', '学', 'Օ', 'だ', '能', 'ڊ', '部', '仔', 'ภ', 'థ', 'ओ', 'Җ', 'ษ', 'ಉ', 'ឹ', '서', 'ਫ', 'Ï', 'ル', 'ୱ', 'ਧ', 'ቱ', '就', 'Ű', 'ళ', '斯', '作', 'ໂ', 'ණ', 'ጵ', '사', 'ఎ', 'ဘ', 'ఈ', '있', '時', 'ॅ', '한', '기', 'ଘ', 'ଫ', 'ڄ', 'Ď', 'ऊ', 'ఒ', 'ሎ', 'ቃ', 'ೈ', 'ْ', 'ຢ', 'Ҩ', '前', 'ዊ', 'Ҟ', '世', 'ญ', 'イ', '公', 'ໜ', 'え', 'ト', '၏', 'ৌ', 'Ḥ', 'ሲ', '家', 'ៃ', 'څ', '会', 'ಈ', 'Ẽ', 'ア', 'ち', '子', 'ઝ', '를', '္', 'ሞ', '事', '本', '最', 'ຽ', 'ङ', '會', 'ଓ', 'ਭ', 'ሽ', 'ኮ', '之', '己', '스', 'ဝ', '入', 'ළ', 'ዝ', '于', '理', 'ဟ', 'ڙ', 'Ҵ', '名', '新', '法', 'ୃ', 'ዛ', 'ቻ', 'ラ', 'ઘ', '수', '습', 'ਥ', '所', '界', 'つ', 'ஸ', 'ఫ', 'ቢ', 'ৎ', 'わ', '過', 'ഈ', '으', '来', 'ဂ', '定', '利', '者', 'お', '為', 'Ɛ', '아', 'ຶ', 'ဒ', 'ጉ', 'ኖ', '度', '常', 'ഖ', 'ឡ', '도', 'Ḓ', '去', '其', '意', 'ኑ', '这', '时', '通', 'Ֆ', '리', '어', 'ቡ', '合', 'ञ', '間', 'ଞ', 'Ќ', 'ږ', '知', 'ሬ', '手', 'ኤ', '¿', 'や', 'ך', '៊', '인', 'ዚ', 'ؤ', '들', '科', 'ど', 'ッ', 'ば', 'ಖ', 'ឺ', '시', 'ಫ', '文', '主', 'め', '見', 'ฟ', '해', 'ሀ', '州', '水', '小', '目', 'ଢ', 'ශ', 'ኒ', 'ሌ', 'ጊ', '三', '西', '特', '그', '第', '山', '대', '자', '„', '明', '位', 'せ', 'ښ', 'ጀ', '“', '二', '路', 'リ', '加', '教', '國', 'ඩ', '道', '市', '表', '立', '而', '食', 'ޒ', 'Ψ', '動', '័', '¡', '尔', 'ዜ', '亚', '化', '先', '性', '形', '里', '後', 'ሜ', 'ፊ', 'ቹ', '力', 'ド', '呢', '佢', '…', 'Ṛ', 'ഏ', 'ሱ', '比', '高', '起', '球', 'ዙ', '拉', '后', '体', '月', '但', '話', '적', 'Ӡ', 'Ӳ', '克', '使', 'Ჯ', '부', '心', 'ੌ', 'ੜ', '\u200c', '们', '更', '正', '라', '보', 'ቦ', 'ዶ', '美', '也', '南', 'タ', 'ஜ', 'ජ', 'ሙ', 'ፕ', '現', 'ૃ', '일', 'ຜ', 'ਯ', 'ఖ', '口', '因', 'ロ', 'ൃ', 'ೃ', '式', 'ޝ', 'Ẓ', '했', 'ቤ', '十', '区', 'み', 'Ķ', '나', 'ۍ', 'ఏ', 'ං', '外', '海', '重', '天', '語', '۰', 'ៀ', '전', 'ඇ', '全', '被', '面', 'ឆ', '것', 'ධ', 'ፋ', '字', '发', 'ऑ', 'テ', '頭', 'ዱ', '身', '果', 'ஏ', 'ඒ', 'ෝ', 'ኝ', 'ٍ', '今', '示', 'カ', '無', '場', '來', '啲', 'ඔ', 'ඉ', 'ቲ', 'ጅ', '直', '德', '些', '或', '開', '게', '주', 'ቴ', 'ቁ', 'ሶ', '期', 'ろ', 'Ṱ', 'ኔ', 'ቋ', 'ኩ', '阿', '提', '音', 'ジ', '電', 'ऱ', '상', 'ጽ', 'ቆ', '动', '民', '然', '做', '金', 'フ', 'シ', '만', 'උ', 'ޕ', 'ク', '፣', '०', '१', '解', '真', '空', '機', 'コ', 'Ჭ', '冇', 'ฐ', 'ף', 'ڌ', 'ሄ', 'レ', '講', '៍', '种', '少', '点', '此', '与', '相', '受', '言', '՝', 'ઠ', 'ဉ', 'ృ', '内', '工', '英', '代', 'Ģ', 'ූ', 'ዩ', '̃', '北', '如', '流', 'ൺ', 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'될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', 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'乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.32.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.32.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn062] 2023-07-06 08:53:10,474 (collect_stats:106) INFO: Niter: 100
+[cn062] 2023-07-06 08:53:20,893 (collect_stats:106) INFO: Niter: 200
+[cn062] 2023-07-06 08:53:30,681 (collect_stats:106) INFO: Niter: 300
+[cn062] 2023-07-06 08:53:41,013 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651413
+# Accounting: end_time=1688651644
+# Accounting: time=231 threads=1
+# Finished at Thu Jul 6 08:54:04 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..43e77dc8ebd203c6b14758605b369d89a419bb48
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.32
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.32.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.32.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
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+- Ṣ
+- ็
+- ِ
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+- ڵ
+- ށ
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+- ീ
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+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
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+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
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+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
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+- ധ
+- Ľ
+- ൂ
+- ़
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+- Խ
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+- Ғ
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+- బ
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+- Ӱ
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+- 이
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+- ழ
+- ۈ
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+- 有
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+- ূ
+- ਗ
+- Қ
+- っ
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+- ክ
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+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
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+- ޑ
+- Ɗ
+- ຸ
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+- ញ
+- ሁ
+- ޖ
+- ”
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+- ሩ
+- ޔ
+- 为
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+- ጣ
+- ઇ
+- 日
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+- よ
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+- ಒ
+- ፈ
+- 好
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+- そ
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+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
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+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
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+- ਧ
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+- ණ
+- ጵ
+- 사
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+- ဘ
+- ఈ
+- 있
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+- 한
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+- ఒ
+- ሎ
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+- Ḥ
+- ሲ
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+- ち
+- 子
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+- ኮ
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+- ዝ
+- 于
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+- ڙ
+- Ҵ
+- 名
+- 新
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+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
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+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
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+- 定
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+- 者
+- お
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+- 아
+- ຶ
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+- ጉ
+- ኖ
+- 度
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+- ഖ
+- ឡ
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+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
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+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
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+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
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+- め
+- 見
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+- ኒ
+- ሌ
+- ጊ
+- 三
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+- 그
+- 第
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+- 대
+- 자
+- „
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+- せ
+- ښ
+- ጀ
+- “
+- 二
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+- リ
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+- 表
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+- ¡
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+- Ӳ
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+- 부
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+- ੜ
+- ‌
+- 们
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+- ፕ
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+- ೃ
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+- 나
+- ۍ
+- ఏ
+- ං
+- 外
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+- ៀ
+- 전
+- ඇ
+- 全
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+- 字
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+- テ
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+- 身
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+- ኝ
+- ٍ
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+- 啲
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+- コ
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+- ף
+- ڌ
+- ሄ
+- レ
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+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
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+- 제
+- ਝ
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+- ·
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+- ጫ
+- ቂ
+- 何
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+- ጨ
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+- 及
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+- 哋
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+- 과
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+- 車
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+- 되
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+- ሴ
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+- 任
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+- 旅
+- 持
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+- 學
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+- ૌ
+- 할
+- 와
+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
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+- 造
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+- 必
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+- ィ
+- パ
+- Ǧ
+- 미
+- 유
+- 위
+- 면
+- 원
+- ච
+- ጸ
+- 对
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+- 客
+- 考
+- ャ
+- オ
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+- ዓ
+- ሃ
+- 동
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+- ආ
+- ቷ
+- ኪ
+- ঃ
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+- へ
+- マ
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+- ょ
+- 記
+- 発
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+- ٠
+- 거
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+- ኞ
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+- ጃ
+- ঝ
+- ڤ
+- 经
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+- 嘢
+- ̇
+- 우
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+- ፖ
+- 量
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+- 速
+- 件
+- 於
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+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
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+- 石
+- 交
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+- チ
+- ず
+- 題
+- 對
+- ஞ
+- 睇
+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
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+- ޗ
+- ュ
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+- ১
+- 세
+- 구
+- 경
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+- ။
+- ዴ
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+- 着
+- 集
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+- 經
+- 邊
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+- ဇ
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+- ጭ
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+- ኘ
+- Œ
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+- ২
+- ಘ
+- 년
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+- Ǹ
+- 回
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+- 像
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+- 強
+- 報
+- 試
+- ฝ
+- ০
+- °
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+- 된
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+- 방
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+- ڇ
+- ෑ
+- ጦ
+- ጐ
+- ጆ
+- ঢ
+- 间
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+- 供
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+- 발
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+- ጓ
+- ጻ
+- Ӷ
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+- キ
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+- 員
+- •
+- 當
+- ฤ
+- ಏ
+- 합
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+- 프
+- 분
+- ຟ
+- ፒ
+- 列
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+- 联
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+- 村
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+- ナ
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+- ३
+- ਓ
+- ፉ
+- 否
+- 演
+- 图
+- 夫
+- 放
+- 港
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+- 镑
+- 暂
+- 疽
+- 卓
+- 咨
+- 饮
+- 笨
+- 闲
+- 逛
+- §
+- €
+- ․
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+- 翌
+- 裕
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+input_size: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32/train/stats_keys
@@ -0,0 +1,2 @@
+feats
+feats_lengths
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..c9a089f9abacc1f8cc79f9c5b2ca2d67f89d7fea
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32/train/text_shape
@@ -0,0 +1,3954 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..2583a8351bc7e48db74b3ae4dcbe26071605904b
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.32/valid/text_shape
@@ -0,0 +1,759 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4.log
new file mode 100644
index 0000000000000000000000000000000000000000..475d5a751569dfcc1d042e26ab28f4c18b9ef29e
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4.log
@@ -0,0 +1,157 @@
+# Running on cn001.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:14 CDT 2023
+# SLURMD_NODENAME=cn001
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=4
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133210
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133210
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn001
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn001
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3660433
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn001
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.4.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.4.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.4 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.4.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.4.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.4 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn001] 2023-07-06 08:52:51,877 (asr:475) INFO: Vocabulary size: 6454
+[cn001] 2023-07-06 08:52:53,975 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn001] 2023-07-06 08:52:53,976 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1214) INFO: Scheduler: None
+[cn001] 2023-07-06 08:52:53,983 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.4/config.yaml
+[cn001] 2023-07-06 08:52:54,088 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.4', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 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'尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', 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'喢', '筅', '堃', '扒', '釘', '刣', '摵', '穗', '詠', '翔', '煞', '欶', '鸝', '倩', '鯪', '鯉', '邱', '焐', '鼬', '楷', '瘴', '蒜', '濱', '亥', '潲', '蕉', '岫', '敨', '粕', '撓', '鉤', '懺', '腫', '儉', '碳', '骼', '肌', '䉪', '傱', '鵰', '屜', '挾', '熄', '癖', '歇', '啉', '匕', '噪', '杏', '襄', '摠', '芎', '欹', '棧', '嶺', '壕', '棺', '哭', '撨', '韌', '霓', '菁', '欉', '脾', '髮', '硩', '緟', '挵', '蛤', '抉', '苓', '㔂', '狡', '磯', '拳', '澀', '奅', '虯', '讕', '輦', '瑤', '櫻', '櫞', '嬈', '漚', '覕', '咯', '獸', '吠', '呂', '猶', '栽', '氾', '濫', '頇', '顢', '熔', '鏨', '戽', '鷺', '餃', '粿', '䫐', '啄', '乩', '漲', '廞', '騃', '夭', '甌', '眉', '舅', '觳', '僥', '拱', '揣', '嗲', '吭', '翹', '璇', '槍', '鶴', '荖', '喨', '骷', '髏', '姼', '詼', '坱', '筍', '壙', '湳', '蟮', '兔', '鉎', '莊', '壺', '犅', '滅', '蠓', '佮', '胛', '怙', '蔭', '紗', '隘', '掰', '壠', '暝', '洘', '陂', '櫥', '戛', '咱', '苧', '雺', '妄', '忝', '蜘', '膜', '磚', '橂', '犁', '薰', '糞', '埽', '牲', '醴', '悾', '歁', '蟳', '託', '戮', '奕', '菝', '軀', '蜢', '閃', 'ௌ', '●', 'ؓ', 'ۓ', 'Ơ', '佈', '廚', '腩', '餸', '稿', '豉', '炒', '魷', '聼', '撐', '瞄', '嘴', '枕', '仆', '渺', '搵', '虱', '乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.4.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.4.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn001] 2023-07-06 08:53:06,855 (collect_stats:106) INFO: Niter: 100
+[cn001] 2023-07-06 08:53:14,920 (collect_stats:106) INFO: Niter: 200
+[cn001] 2023-07-06 08:53:21,656 (collect_stats:106) INFO: Niter: 300
+[cn001] 2023-07-06 08:53:28,406 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651414
+# Accounting: end_time=1688651639
+# Accounting: time=225 threads=1
+# Finished at Thu Jul 6 08:53:59 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f007553b2b7c628908341e0f782410d0acb73121
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.4
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.4.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.4.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
+- ଷ
+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
+- ໂ
+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
+- 기
+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
+- 前
+- ዊ
+- Ҟ
+- 世
+- ญ
+- イ
+- 公
+- ໜ
+- え
+- ト
+- ၏
+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
+- ္
+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
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+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
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+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
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+- 教
+- 國
+- ඩ
+- 道
+- 市
+- 表
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+- 而
+- 食
+- ޒ
+- Ψ
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+- ¡
+- 尔
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+- 性
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+- 里
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+- ቹ
+- 力
+- ド
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+- 佢
+- …
+- Ṛ
+- ഏ
+- ሱ
+- 比
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+- 起
+- 球
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+- 拉
+- 后
+- 体
+- 月
+- 但
+- 話
+- 적
+- Ӡ
+- Ӳ
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+- 使
+- Ჯ
+- 부
+- 心
+- ੌ
+- ੜ
+- ‌
+- 们
+- 更
+- 正
+- 라
+- 보
+- ቦ
+- ዶ
+- 美
+- 也
+- 南
+- タ
+- ஜ
+- ජ
+- ሙ
+- ፕ
+- 現
+- ૃ
+- 일
+- ຜ
+- ਯ
+- ఖ
+- 口
+- 因
+- ロ
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+- ೃ
+- 式
+- ޝ
+- Ẓ
+- 했
+- ቤ
+- 十
+- 区
+- み
+- Ķ
+- 나
+- ۍ
+- ఏ
+- ං
+- 外
+- 海
+- 重
+- 天
+- 語
+- ۰
+- ៀ
+- 전
+- ඇ
+- 全
+- 被
+- 面
+- ឆ
+- 것
+- ධ
+- ፋ
+- 字
+- 发
+- ऑ
+- テ
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+- 身
+- 果
+- ஏ
+- ඒ
+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
+- カ
+- 無
+- 場
+- 來
+- 啲
+- ඔ
+- ඉ
+- ቲ
+- ጅ
+- 直
+- 德
+- 些
+- 或
+- 開
+- 게
+- 주
+- ቴ
+- ቁ
+- ሶ
+- 期
+- ろ
+- Ṱ
+- ኔ
+- ቋ
+- ኩ
+- 阿
+- 提
+- 音
+- ジ
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+- 상
+- ጽ
+- ቆ
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+- 做
+- 金
+- フ
+- シ
+- 만
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+- ޕ
+- ク
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+- ०
+- १
+- 解
+- 真
+- 空
+- 機
+- コ
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+- 冇
+- ฐ
+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
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+- 英
+- 代
+- Ģ
+- ූ
+- ዩ
+- ̃
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+- 제
+- ਝ
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+- ·
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+- ጫ
+- ቂ
+- 何
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+- ጨ
+- ሻ
+- 及
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+- 原
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+- ሸ
+- Џ
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+- 影
+- 次
+- 安
+- べ
+- Ჰ
+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
+- 었
+- ਏ
+- ౌ
+- ኦ
+- 建
+- 政
+- 巴
+- Ɓ
+- 々
+- 識
+- 種
+- 여
+- ڀ
+- ኙ
+- 过
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+- 数
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+- 車
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+- 點
+- ೌ
+- 되
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+- ሴ
+- 想
+- 任
+- 当
+- 站
+- 感
+- 住
+- 未
+- 旅
+- 持
+- バ
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+- 學
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+- ૌ
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+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
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+- 必
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+- ィ
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+- 미
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+- 원
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+- ጸ
+- 对
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+- 史
+- 客
+- 考
+- ャ
+- オ
+- ഓ
+- ዓ
+- ሃ
+- 동
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+- ආ
+- ቷ
+- ኪ
+- ঃ
+- 称
+- 活
+- 落
+- へ
+- マ
+- ニ
+- ょ
+- 記
+- 発
+- ഠ
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+- ឋ
+- 국
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+- 兰
+- 基
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+- ほ
+- 気
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+- ٠
+- 거
+- 們
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+- ኞ
+- ፎ
+- ጃ
+- ঝ
+- ڤ
+- 经
+- 信
+- 布
+- 思
+- 打
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+- 嘢
+- ̇
+- 우
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+- ፖ
+- 量
+- 曾
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+- 品
+- 速
+- 件
+- 於
+- ۲
+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
+- 」
+- 石
+- 交
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+- チ
+- ず
+- 題
+- 對
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+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
+- 五
+- ޗ
+- ュ
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+- ১
+- 세
+- 구
+- 경
+- 비
+- ။
+- ዴ
+- 士
+- 县
+- 台
+- 业
+- 林
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+- 着
+- 集
+- 足
+- 它
+- じ
+- ム
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+- ഘ
+- 經
+- 邊
+- ஷ
+- 、
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+- ጭ
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+- 간
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+- ኘ
+- Œ
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+- 罗
+- 长
+- 指
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+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
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+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
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+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
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+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
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+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
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+normalize: utterance_mvn
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+model: espnet
+model_conf:
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+preencoder_conf: {}
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+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
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+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
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+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/train/batch_keys
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+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/train/feats_lengths_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/train/stats_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..3a13a074128f8828258b3319692b1d8e967a9877
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
+feats_lengths
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..8e397b8bb1bfc504beed54d54ade568daca8cfa8
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.4/valid/text_shape
@@ -0,0 +1,760 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5.log
new file mode 100644
index 0000000000000000000000000000000000000000..fb6f4628bba8386526f56591d814e46df2766b44
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5.log
@@ -0,0 +1,157 @@
+# Running on cn001.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:14 CDT 2023
+# SLURMD_NODENAME=cn001
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=5
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133211
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133211
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn001
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn001
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3660434
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn001
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.5.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.5.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.5 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.5.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.5.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.5 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn001] 2023-07-06 08:52:51,877 (asr:475) INFO: Vocabulary size: 6454
+[cn001] 2023-07-06 08:52:53,975 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn001] 2023-07-06 08:52:53,978 (abs_task:1214) INFO: Scheduler: None
+[cn001] 2023-07-06 08:52:53,988 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.5/config.yaml
+[cn001] 2023-07-06 08:52:54,094 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.5', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 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'될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', 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'乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.5.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.5.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn001] 2023-07-06 08:53:06,808 (collect_stats:106) INFO: Niter: 100
+[cn001] 2023-07-06 08:53:13,918 (collect_stats:106) INFO: Niter: 200
+[cn001] 2023-07-06 08:53:21,849 (collect_stats:106) INFO: Niter: 300
+[cn001] 2023-07-06 08:53:27,764 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651414
+# Accounting: end_time=1688651639
+# Accounting: time=225 threads=1
+# Finished at Thu Jul 6 08:53:59 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..33178bbb58c83aacdfb4be2341884c26d0411484
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.5
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.5.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.5.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
+- ট
+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
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+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
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+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
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+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
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+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
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+- Ҟ
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+- イ
+- 公
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+- え
+- ト
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+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
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+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
+- ッ
+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
+- 西
+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
+- 加
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+- 國
+- ඩ
+- 道
+- 市
+- 表
+- 立
+- 而
+- 食
+- ޒ
+- Ψ
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+- ័
+- ¡
+- 尔
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+- 亚
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+- 里
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+- ቹ
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+- …
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+- ഏ
+- ሱ
+- 比
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+- 起
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+- 拉
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+- Ӡ
+- Ӳ
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+- 부
+- 心
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+- ੜ
+- ‌
+- 们
+- 更
+- 正
+- 라
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+- ቦ
+- ዶ
+- 美
+- 也
+- 南
+- タ
+- ஜ
+- ජ
+- ሙ
+- ፕ
+- 現
+- ૃ
+- 일
+- ຜ
+- ਯ
+- ఖ
+- 口
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+- ロ
+- ൃ
+- ೃ
+- 式
+- ޝ
+- Ẓ
+- 했
+- ቤ
+- 十
+- 区
+- み
+- Ķ
+- 나
+- ۍ
+- ఏ
+- ං
+- 外
+- 海
+- 重
+- 天
+- 語
+- ۰
+- ៀ
+- 전
+- ඇ
+- 全
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+- 面
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+- 것
+- ධ
+- ፋ
+- 字
+- 发
+- ऑ
+- テ
+- 頭
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+- 身
+- 果
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+- ඒ
+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
+- カ
+- 無
+- 場
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+- 啲
+- ඔ
+- ඉ
+- ቲ
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+- ろ
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+- ኩ
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+- ऱ
+- 상
+- ጽ
+- ቆ
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+- 만
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+- ޕ
+- ク
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+- ०
+- १
+- 解
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+- コ
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+- 冇
+- ฐ
+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
+- 工
+- 英
+- 代
+- Ģ
+- ූ
+- ዩ
+- ̃
+- 北
+- 如
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+- ൺ
+- 정
+- 제
+- ਝ
+- ፓ
+- ·
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+- ጡ
+- ጫ
+- ቂ
+- 何
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+- ጨ
+- ሻ
+- 及
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+- 原
+- 這
+- 트
+- ሸ
+- Џ
+- 城
+- 影
+- 次
+- 安
+- べ
+- Ჰ
+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
+- 었
+- ਏ
+- ౌ
+- ኦ
+- 建
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+- Ɓ
+- 々
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+- 種
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+- ڀ
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+- 过
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+- ޢ
+- 車
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+- 點
+- ೌ
+- 되
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+- ሴ
+- 想
+- 任
+- 当
+- 站
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+- 未
+- 旅
+- 持
+- バ
+- 問
+- 學
+- ۱
+- ૌ
+- 할
+- 와
+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
+- 已
+- 造
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+- 必
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+- ィ
+- パ
+- Ǧ
+- 미
+- 유
+- 위
+- 면
+- 원
+- ච
+- ጸ
+- 对
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+- 史
+- 客
+- 考
+- ャ
+- オ
+- ഓ
+- ዓ
+- ሃ
+- 동
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+- ආ
+- ቷ
+- ኪ
+- ঃ
+- 称
+- 活
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+- へ
+- マ
+- ニ
+- ょ
+- 記
+- 発
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+- 發
+- 咗
+- ឋ
+- 국
+- 现
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+- 保
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+- 情
+- ほ
+- 気
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+- ٠
+- 거
+- 們
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+- ኞ
+- ፎ
+- ጃ
+- ঝ
+- ڤ
+- 经
+- 信
+- 布
+- 思
+- 打
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+- 嘢
+- ̇
+- 우
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+- ፖ
+- 量
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+- 车
+- 品
+- 速
+- 件
+- 於
+- ۲
+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
+- 」
+- 石
+- 交
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+- チ
+- ず
+- 題
+- 對
+- ஞ
+- 睇
+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
+- 五
+- ޗ
+- ュ
+- ଵ
+- ১
+- 세
+- 구
+- 경
+- 비
+- ။
+- ዴ
+- 士
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+- 林
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+- 着
+- 集
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+- 它
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+- ഘ
+- 經
+- 邊
+- ஷ
+- 、
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+- 계
+- ဇ
+- ਛ
+- ጂ
+- ጭ
+- 场
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+- ኘ
+- Œ
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+- 接
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+- 러
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+- ၍
+- 军
+- 印
+- 向
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+- メ
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+- プ
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+- ২
+- ಘ
+- 년
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+- ਠ
+- Ǹ
+- 回
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+- 周
+- 像
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+- 強
+- 報
+- 試
+- ฝ
+- ০
+- °
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+- 된
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+- ڇ
+- ෑ
+- ጦ
+- ጐ
+- ጆ
+- ঢ
+- 间
+- 包
+- 供
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+- グ
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+- ェ
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+- 발
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+- ጓ
+- ጻ
+- Ӷ
+- 进
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+- 战
+- 再
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+- 半
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+- 街
+- ޚ
+- 望
+- キ
+- 盾
+- 認
+- 員
+- •
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+- ฤ
+- ಏ
+- 합
+- 선
+- 프
+- 분
+- ຟ
+- ፒ
+- 列
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+- 联
+- 电
+- 村
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+- ナ
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+- 데
+- ३
+- ਓ
+- ፉ
+- 否
+- 演
+- 图
+- 夫
+- 放
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+- ）
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+- ሦ
+- ኣ
+- "\x93"
+- "\x94"
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+- 遣
+- 册
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+- 拦
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+- 尝
+- ％
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+- 陨
+- 咎
+- 箭
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+- 钢
+- 键
+- 锐
+- 啸
+- 纹
+- 磷
+- 硫
+- 骚
+- 晓
+- 培
+- 骗
+- 疗
+- 锻
+- 炼
+- 喘
+- 陡
+- 谨
+- 梵
+- 鲜
+- 纯
+- 粹
+- 厉
+- 汁
+- 夺
+- 丢
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+- 陶
+- 旨
+- 聪
+- 审
+- 铃
+- 璀
+- 璨
+- 垒
+- 劫
+- 荣
+- 惊
+- 叹
+- 绵
+- 攀
+- 损
+- 肿
+- 瘤
+- 龄
+- 齐
+- 鞭
+- 缭
+- 穷
+- 哺
+- 粘
+- 宏
+- 倾
+- 财
+- 纸
+- 荐
+- 媚
+- 滥
+- 嘲
+- 泄
+- 盘
+- 廷
+- 抢
+- 敛
+- 猎
+- 苹
+- 笔
+- 愤
+- 慨
+- 罄
+- 茵
+- 碍
+- 舱
+- 励
+- 雕
+- 酋
+- 镑
+- 暂
+- 疽
+- 卓
+- 咨
+- 饮
+- 笨
+- 闲
+- 逛
+- §
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+- <sos/eos>
+init: null
+input_size: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/train/batch_keys
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index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/train/batch_keys
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+speech
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/train/feats_lengths_stats.npz
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index 0000000000000000000000000000000000000000..da7355aeedf545f1937baa49d020be7e271adddc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/train/feats_lengths_stats.npz
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+version https://git-lfs.github.com/spec/v1
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/train/feats_stats.npz
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--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/train/speech_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/train/speech_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/valid/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/valid/batch_keys
new file mode 100644
index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/valid/batch_keys
@@ -0,0 +1,2 @@
+speech
+text
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/valid/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/valid/feats_lengths_stats.npz
new file mode 100644
index 0000000000000000000000000000000000000000..f05014c8ba32ec7ef5fc05ea65de8b7673a25f7f
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/valid/feats_lengths_stats.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:39d11cc5e2ae2c9a3de2024caae5740db90cc0ea85bc37952b2960f4d4413ec7
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/valid/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/valid/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
+feats_lengths
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..82116af2f5651c046e4a27e2e1c53376e0064145
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.5/valid/text_shape
@@ -0,0 +1,760 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6.log
new file mode 100644
index 0000000000000000000000000000000000000000..b3bf6b61c7c3aff3b8bba476b445df5f5e7ec02c
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6.log
@@ -0,0 +1,157 @@
+# Running on cn001.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:14 CDT 2023
+# SLURMD_NODENAME=cn001
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=6
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133212
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133212
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn001
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn001
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3660404
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn001
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.6.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.6.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.6 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.6.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.6.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.6 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn001] 2023-07-06 08:52:51,877 (asr:475) INFO: Vocabulary size: 6454
+[cn001] 2023-07-06 08:52:53,975 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn001] 2023-07-06 08:52:53,978 (abs_task:1214) INFO: Scheduler: None
+[cn001] 2023-07-06 08:52:53,987 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.6/config.yaml
+[cn001] 2023-07-06 08:52:54,092 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.6', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', '行', 'Ļ', '[som]', 'భ', 'ሆ', '[fil]', 'Ӣ', 'き', 'Ӧ', 'ሉ', 'ʼ', 'ณ', '[oci]', 'Ҷ', 'ڳ', 'ഡ', '[kam]', 'Ღ', 'ݨ', '을', 'ஆ', 'Ƙ', 'ਉ', 'ቶ', 'ຮ', 'ካ', 'ቅ', 'औ', 'ଂ', 'ណ', '의', 'ణ', 'Փ', 'ਈ', 'ګ', 'హ', 'ौ', 'ໍ', 'ಭ', 'ಎ', '地', 'ఆ', 'ん', '하', 'ਖ', '自', '上', 'ፍ', 'ਡ', '可', 'ዮ', '生', '[ibo]', 'ٻ', 'さ', 'ູ', '多', 'ޓ', 'ភ', '니', '不', 'ढ', 'ઈ', 'Ň', 'ഥ', 'ศ', '要', 'Ժ', 'ී', 'ۋ', 'ො', 'ሮ', '年', '到', 'Ძ', 'ซ', '۾', '你', '로', 'ጋ', '[lug]', 'ঞ', '国', 'ផ', '個', 'ฉ', '和', 'ஒ', 'ខ', '[mri]', '他', 'ଠ', 'ຼ', 'ঙ', 'ޅ', '唔', 'ຳ', 'ځ', '៉', 'ថ', '가', '[lin]', 'Ճ', '은', 'ធ', '੍', 'Đ', '嘅', '出', 'ټ', 'Ĕ', 'ス', '지', '下', 'ୂ', 'ޑ', 'Ɗ', 'ຸ', 'ઉ', 'ញ', 'ሁ', 'ޖ', '”', '用', 'ሩ', 'ޔ', '为', '了', 'ຖ', 'එ', 'ጣ', 'ઇ', '日', 'ޯ', 'よ', '고', 'ኛ', 'ሊ', '得', 'ൈ', 'ಒ', 'ፈ', '好', '同', '成', '分', 'そ', 'ธ', 'Ҭ', 'Ğ', 'Ṭ', 'ዲ', 'Њ', 'ڏ', '۽', '物', 'ޫ', 'Ჩ', 'Ӯ', '都', 'け', '个', 'ഫ', 'Ō', 'ዘ', '方', 'ډ', 'Ḍ', '学', 'Օ', 'だ', '能', 'ڊ', '部', '仔', 'ภ', 'థ', 'ओ', 'Җ', 'ษ', 'ಉ', 'ឹ', '서', 'ਫ', 'Ï', 'ル', 'ୱ', 'ਧ', 'ቱ', '就', 'Ű', 'ళ', '斯', '作', 'ໂ', 'ණ', 'ጵ', '사', 'ఎ', 'ဘ', 'ఈ', '있', '時', 'ॅ', '한', '기', 'ଘ', 'ଫ', 'ڄ', 'Ď', 'ऊ', 'ఒ', 'ሎ', 'ቃ', 'ೈ', 'ْ', 'ຢ', 'Ҩ', '前', 'ዊ', 'Ҟ', '世', 'ญ', 'イ', '公', 'ໜ', 'え', 'ト', '၏', 'ৌ', 'Ḥ', 'ሲ', '家', 'ៃ', 'څ', '会', 'ಈ', 'Ẽ', 'ア', 'ち', '子', 'ઝ', '를', '္', 'ሞ', '事', '本', '最', 'ຽ', 'ङ', '會', 'ଓ', 'ਭ', 'ሽ', 'ኮ', '之', '己', '스', 'ဝ', '入', 'ළ', 'ዝ', '于', '理', 'ဟ', 'ڙ', 'Ҵ', '名', '新', '法', 'ୃ', 'ዛ', 'ቻ', 'ラ', 'ઘ', '수', '습', 'ਥ', '所', '界', 'つ', 'ஸ', 'ఫ', 'ቢ', 'ৎ', 'わ', '過', 'ഈ', '으', '来', 'ဂ', '定', '利', '者', 'お', '為', 'Ɛ', '아', 'ຶ', 'ဒ', 'ጉ', 'ኖ', '度', '常', 'ഖ', 'ឡ', '도', 'Ḓ', '去', '其', '意', 'ኑ', '这', '时', '通', 'Ֆ', '리', '어', 'ቡ', '合', 'ञ', '間', 'ଞ', 'Ќ', 'ږ', '知', 'ሬ', '手', 'ኤ', '¿', 'や', 'ך', '៊', '인', 'ዚ', 'ؤ', '들', '科', 'ど', 'ッ', 'ば', 'ಖ', 'ឺ', '시', 'ಫ', '文', '主', 'め', '見', 'ฟ', '해', 'ሀ', '州', '水', '小', '目', 'ଢ', 'ශ', 'ኒ', 'ሌ', 'ጊ', '三', '西', '特', '그', '第', '山', '대', '자', '„', '明', '位', 'せ', 'ښ', 'ጀ', '“', '二', '路', 'リ', '加', '教', '國', 'ඩ', '道', '市', '表', '立', '而', '食', 'ޒ', 'Ψ', '動', '័', '¡', '尔', 'ዜ', '亚', '化', '先', '性', '形', '里', '後', 'ሜ', 'ፊ', 'ቹ', '力', 'ド', '呢', '佢', '…', 'Ṛ', 'ഏ', 'ሱ', '比', '高', '起', '球', 'ዙ', '拉', '后', '体', '月', '但', '話', '적', 'Ӡ', 'Ӳ', '克', '使', 'Ჯ', '부', '心', 'ੌ', 'ੜ', '\u200c', '们', '更', '正', '라', '보', 'ቦ', 'ዶ', '美', '也', '南', 'タ', 'ஜ', 'ජ', 'ሙ', 'ፕ', '現', 'ૃ', '일', 'ຜ', 'ਯ', 'ఖ', '口', '因', 'ロ', 'ൃ', 'ೃ', '式', 'ޝ', 'Ẓ', '했', 'ቤ', '十', '区', 'み', 'Ķ', '나', 'ۍ', 'ఏ', 'ං', '外', '海', '重', '天', '語', '۰', 'ៀ', '전', 'ඇ', '全', '被', '面', 'ឆ', '것', 'ධ', 'ፋ', '字', '发', 'ऑ', 'テ', '頭', 'ዱ', '身', '果', 'ஏ', 'ඒ', 'ෝ', 'ኝ', 'ٍ', '今', '示', 'カ', '無', '場', '來', '啲', 'ඔ', 'ඉ', 'ቲ', 'ጅ', '直', '德', '些', '或', '開', '게', '주', 'ቴ', 'ቁ', 'ሶ', '期', 'ろ', 'Ṱ', 'ኔ', 'ቋ', 'ኩ', '阿', '提', '音', 'ジ', '電', 'ऱ', '상', 'ጽ', 'ቆ', '动', '民', '然', '做', '金', 'フ', 'シ', '만', 'උ', 'ޕ', 'ク', '፣', '०', '१', '解', '真', '空', '機', 'コ', 'Ჭ', '冇', 'ฐ', 'ף', 'ڌ', 'ሄ', 'レ', '講', '៍', '种', '少', '点', '此', '与', '相', '受', '言', '՝', 'ઠ', 'ဉ', 'ృ', '内', '工', '英', '代', 'Ģ', 'ූ', 'ዩ', '̃', '北', '如', '流', 'ൺ', 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'될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', 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'乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.6.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.6.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn001] 2023-07-06 08:53:07,222 (collect_stats:106) INFO: Niter: 100
+[cn001] 2023-07-06 08:53:15,496 (collect_stats:106) INFO: Niter: 200
+[cn001] 2023-07-06 08:53:21,515 (collect_stats:106) INFO: Niter: 300
+[cn001] 2023-07-06 08:53:28,249 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651414
+# Accounting: end_time=1688651639
+# Accounting: time=225 threads=1
+# Finished at Thu Jul 6 08:53:59 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..af4b5bde3c79ffb25dff0ef58e843db57ebac319
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.6
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.6.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.6.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
+- ન
+- Ө
+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
+- വ
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+- '[ita]'
+- Მ
+- ো
+- ವ
+- เ
+- ਾ
+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
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+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
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+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
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+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
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+- Ҟ
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+- イ
+- 公
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+- え
+- ト
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+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
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+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
+- ッ
+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
+- 西
+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
+- 加
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+- 國
+- ඩ
+- 道
+- 市
+- 表
+- 立
+- 而
+- 食
+- ޒ
+- Ψ
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+- ័
+- ¡
+- 尔
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+- 亚
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+- 里
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+- ቹ
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+- …
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+- ഏ
+- ሱ
+- 比
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+- 起
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+- 拉
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+- Ӡ
+- Ӳ
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+- 부
+- 心
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+- ੜ
+- ‌
+- 们
+- 更
+- 正
+- 라
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+- ቦ
+- ዶ
+- 美
+- 也
+- 南
+- タ
+- ஜ
+- ජ
+- ሙ
+- ፕ
+- 現
+- ૃ
+- 일
+- ຜ
+- ਯ
+- ఖ
+- 口
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+- ロ
+- ൃ
+- ೃ
+- 式
+- ޝ
+- Ẓ
+- 했
+- ቤ
+- 十
+- 区
+- み
+- Ķ
+- 나
+- ۍ
+- ఏ
+- ං
+- 外
+- 海
+- 重
+- 天
+- 語
+- ۰
+- ៀ
+- 전
+- ඇ
+- 全
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+- 面
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+- 것
+- ධ
+- ፋ
+- 字
+- 发
+- ऑ
+- テ
+- 頭
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+- 身
+- 果
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+- ඒ
+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
+- カ
+- 無
+- 場
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+- 啲
+- ඔ
+- ඉ
+- ቲ
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+- ろ
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+- ኩ
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+- ऱ
+- 상
+- ጽ
+- ቆ
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+- 만
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+- ޕ
+- ク
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+- ०
+- १
+- 解
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+- コ
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+- 冇
+- ฐ
+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
+- 工
+- 英
+- 代
+- Ģ
+- ූ
+- ዩ
+- ̃
+- 北
+- 如
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+- ൺ
+- 정
+- 제
+- ਝ
+- ፓ
+- ·
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+- ጡ
+- ጫ
+- ቂ
+- 何
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+- ጨ
+- ሻ
+- 及
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+- 原
+- 這
+- 트
+- ሸ
+- Џ
+- 城
+- 影
+- 次
+- 安
+- べ
+- Ჰ
+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
+- 었
+- ਏ
+- ౌ
+- ኦ
+- 建
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+- Ɓ
+- 々
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+- 種
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+- ڀ
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+- 过
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+- ޢ
+- 車
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+- 點
+- ೌ
+- 되
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+- ሴ
+- 想
+- 任
+- 当
+- 站
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+- 未
+- 旅
+- 持
+- バ
+- 問
+- 學
+- ۱
+- ૌ
+- 할
+- 와
+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
+- 已
+- 造
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+- 必
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+- ィ
+- パ
+- Ǧ
+- 미
+- 유
+- 위
+- 면
+- 원
+- ච
+- ጸ
+- 对
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+- 史
+- 客
+- 考
+- ャ
+- オ
+- ഓ
+- ዓ
+- ሃ
+- 동
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+- ආ
+- ቷ
+- ኪ
+- ঃ
+- 称
+- 活
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+- へ
+- マ
+- ニ
+- ょ
+- 記
+- 発
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+- 發
+- 咗
+- ឋ
+- 국
+- 现
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+- 保
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+- 情
+- ほ
+- 気
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+- ٠
+- 거
+- 們
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+- ኞ
+- ፎ
+- ጃ
+- ঝ
+- ڤ
+- 经
+- 信
+- 布
+- 思
+- 打
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+- 嘢
+- ̇
+- 우
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+- ፖ
+- 量
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+- 车
+- 品
+- 速
+- 件
+- 於
+- ۲
+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
+- 」
+- 石
+- 交
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+- チ
+- ず
+- 題
+- 對
+- ஞ
+- 睇
+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
+- 五
+- ޗ
+- ュ
+- ଵ
+- ১
+- 세
+- 구
+- 경
+- 비
+- ။
+- ዴ
+- 士
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+- 林
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+- 着
+- 集
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+- 它
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+- ഘ
+- 經
+- 邊
+- ஷ
+- 、
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+- 계
+- ဇ
+- ਛ
+- ጂ
+- ጭ
+- 场
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+- ኘ
+- Œ
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+- 接
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+- 러
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+- ၍
+- 军
+- 印
+- 向
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+- メ
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+- プ
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+- ২
+- ಘ
+- 년
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+- ਠ
+- Ǹ
+- 回
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+- 周
+- 像
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+- 強
+- 報
+- 試
+- ฝ
+- ০
+- °
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+- 된
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+- ڇ
+- ෑ
+- ጦ
+- ጐ
+- ጆ
+- ঢ
+- 间
+- 包
+- 供
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+- グ
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+- ェ
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+- 발
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+- ጓ
+- ጻ
+- Ӷ
+- 进
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+- 战
+- 再
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+- 半
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+- 街
+- ޚ
+- 望
+- キ
+- 盾
+- 認
+- 員
+- •
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+- ฤ
+- ಏ
+- 합
+- 선
+- 프
+- 분
+- ຟ
+- ፒ
+- 列
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+- 联
+- 电
+- 村
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+- ナ
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+- 데
+- ३
+- ਓ
+- ፉ
+- 否
+- 演
+- 图
+- 夫
+- 放
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+- ）
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+- ሦ
+- ኣ
+- "\x93"
+- "\x94"
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+- 遣
+- 册
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+- 拦
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+- 尝
+- ％
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+- 陨
+- 咎
+- 箭
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+- 钢
+- 键
+- 锐
+- 啸
+- 纹
+- 磷
+- 硫
+- 骚
+- 晓
+- 培
+- 骗
+- 疗
+- 锻
+- 炼
+- 喘
+- 陡
+- 谨
+- 梵
+- 鲜
+- 纯
+- 粹
+- 厉
+- 汁
+- 夺
+- 丢
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+- 陶
+- 旨
+- 聪
+- 审
+- 铃
+- 璀
+- 璨
+- 垒
+- 劫
+- 荣
+- 惊
+- 叹
+- 绵
+- 攀
+- 损
+- 肿
+- 瘤
+- 龄
+- 齐
+- 鞭
+- 缭
+- 穷
+- 哺
+- 粘
+- 宏
+- 倾
+- 财
+- 纸
+- 荐
+- 媚
+- 滥
+- 嘲
+- 泄
+- 盘
+- 廷
+- 抢
+- 敛
+- 猎
+- 苹
+- 笔
+- 愤
+- 慨
+- 罄
+- 茵
+- 碍
+- 舱
+- 励
+- 雕
+- 酋
+- 镑
+- 暂
+- 疽
+- 卓
+- 咨
+- 饮
+- 笨
+- 闲
+- 逛
+- §
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+- <sos/eos>
+init: null
+input_size: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/train/batch_keys
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--- /dev/null
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+text
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/train/feats_lengths_stats.npz
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--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/train/feats_lengths_stats.npz
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+size 778
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/train/stats_keys
@@ -0,0 +1,2 @@
+feats
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..dfc71a57f6c86fa18ea120b3cb86f03d44b1938b
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/train/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/valid/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/valid/batch_keys
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index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/valid/batch_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/valid/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/valid/feats_lengths_stats.npz
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index 0000000000000000000000000000000000000000..f79a74c822ab674ead45fb2715af37a25e655bfc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/valid/feats_lengths_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/valid/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/valid/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/valid/speech_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/valid/speech_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.6/valid/text_shape
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index 0000000000000000000000000000000000000000..92f42393666b3686f7bc9de25b61f1f9b402da5d
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+cv_ell_000862 17
+cv_ell_000866 33
+cv_ell_000870 32
+cv_ell_000874 14
+cv_ell_000855 63
+cv_ell_000859 64
+cv_ell_000863 23
+cv_ell_000867 20
+cv_ell_000871 53
+cv_ell_000875 27
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7.log
new file mode 100644
index 0000000000000000000000000000000000000000..80432153edbb3c6644d62c11b273e5783633f58d
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7.log
@@ -0,0 +1,157 @@
+# Running on cn001.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:13 CDT 2023
+# SLURMD_NODENAME=cn001
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=7
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133213
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133213
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn001
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn001
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3660357
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn001
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.7.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.7.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.7 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.7.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.7.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.7 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn001] 2023-07-06 08:52:51,877 (asr:475) INFO: Vocabulary size: 6454
+[cn001] 2023-07-06 08:52:53,975 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn001] 2023-07-06 08:52:53,978 (abs_task:1214) INFO: Scheduler: None
+[cn001] 2023-07-06 08:52:54,004 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.7/config.yaml
+[cn001] 2023-07-06 08:52:54,108 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.7', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', '行', 'Ļ', '[som]', 'భ', 'ሆ', '[fil]', 'Ӣ', 'き', 'Ӧ', 'ሉ', 'ʼ', 'ณ', '[oci]', 'Ҷ', 'ڳ', 'ഡ', '[kam]', 'Ღ', 'ݨ', '을', 'ஆ', 'Ƙ', 'ਉ', 'ቶ', 'ຮ', 'ካ', 'ቅ', 'औ', 'ଂ', 'ណ', '의', 'ణ', 'Փ', 'ਈ', 'ګ', 'హ', 'ौ', 'ໍ', 'ಭ', 'ಎ', '地', 'ఆ', 'ん', '하', 'ਖ', '自', '上', 'ፍ', 'ਡ', '可', 'ዮ', '生', '[ibo]', 'ٻ', 'さ', 'ູ', '多', 'ޓ', 'ភ', '니', '不', 'ढ', 'ઈ', 'Ň', 'ഥ', 'ศ', '要', 'Ժ', 'ී', 'ۋ', 'ො', 'ሮ', '年', '到', 'Ძ', 'ซ', '۾', '你', '로', 'ጋ', '[lug]', 'ঞ', '国', 'ផ', '個', 'ฉ', '和', 'ஒ', 'ខ', '[mri]', '他', 'ଠ', 'ຼ', 'ঙ', 'ޅ', '唔', 'ຳ', 'ځ', '៉', 'ថ', '가', '[lin]', 'Ճ', '은', 'ធ', '੍', 'Đ', '嘅', '出', 'ټ', 'Ĕ', 'ス', '지', '下', 'ୂ', 'ޑ', 'Ɗ', 'ຸ', 'ઉ', 'ញ', 'ሁ', 'ޖ', '”', '用', 'ሩ', 'ޔ', '为', '了', 'ຖ', 'එ', 'ጣ', 'ઇ', '日', 'ޯ', 'よ', '고', 'ኛ', 'ሊ', '得', 'ൈ', 'ಒ', 'ፈ', '好', '同', '成', '分', 'そ', 'ธ', 'Ҭ', 'Ğ', 'Ṭ', 'ዲ', 'Њ', 'ڏ', '۽', '物', 'ޫ', 'Ჩ', 'Ӯ', '都', 'け', '个', 'ഫ', 'Ō', 'ዘ', '方', 'ډ', 'Ḍ', '学', 'Օ', 'だ', '能', 'ڊ', '部', '仔', 'ภ', 'థ', 'ओ', 'Җ', 'ษ', 'ಉ', 'ឹ', '서', 'ਫ', 'Ï', 'ル', 'ୱ', 'ਧ', 'ቱ', '就', 'Ű', 'ళ', '斯', '作', 'ໂ', 'ණ', 'ጵ', '사', 'ఎ', 'ဘ', 'ఈ', '있', '時', 'ॅ', '한', '기', 'ଘ', 'ଫ', 'ڄ', 'Ď', 'ऊ', 'ఒ', 'ሎ', 'ቃ', 'ೈ', 'ْ', 'ຢ', 'Ҩ', '前', 'ዊ', 'Ҟ', '世', 'ญ', 'イ', '公', 'ໜ', 'え', 'ト', '၏', 'ৌ', 'Ḥ', 'ሲ', '家', 'ៃ', 'څ', '会', 'ಈ', 'Ẽ', 'ア', 'ち', '子', 'ઝ', '를', '္', 'ሞ', '事', '本', '最', 'ຽ', 'ङ', '會', 'ଓ', 'ਭ', 'ሽ', 'ኮ', '之', '己', '스', 'ဝ', '入', 'ළ', 'ዝ', '于', '理', 'ဟ', 'ڙ', 'Ҵ', '名', '新', '法', 'ୃ', 'ዛ', 'ቻ', 'ラ', 'ઘ', '수', '습', 'ਥ', '所', '界', 'つ', 'ஸ', 'ఫ', 'ቢ', 'ৎ', 'わ', '過', 'ഈ', '으', '来', 'ဂ', '定', '利', '者', 'お', '為', 'Ɛ', '아', 'ຶ', 'ဒ', 'ጉ', 'ኖ', '度', '常', 'ഖ', 'ឡ', '도', 'Ḓ', '去', '其', '意', 'ኑ', '这', '时', '通', 'Ֆ', '리', '어', 'ቡ', '合', 'ञ', '間', 'ଞ', 'Ќ', 'ږ', '知', 'ሬ', '手', 'ኤ', '¿', 'や', 'ך', '៊', '인', 'ዚ', 'ؤ', '들', '科', 'ど', 'ッ', 'ば', 'ಖ', 'ឺ', '시', 'ಫ', '文', '主', 'め', '見', 'ฟ', '해', 'ሀ', '州', '水', '小', '目', 'ଢ', 'ශ', 'ኒ', 'ሌ', 'ጊ', '三', '西', '特', '그', '第', '山', '대', '자', '„', '明', '位', 'せ', 'ښ', 'ጀ', '“', '二', '路', 'リ', '加', '教', '國', 'ඩ', '道', '市', '表', '立', '而', '食', 'ޒ', 'Ψ', '動', '័', '¡', '尔', 'ዜ', '亚', '化', '先', '性', '形', '里', '後', 'ሜ', 'ፊ', 'ቹ', '力', 'ド', '呢', '佢', '…', 'Ṛ', 'ഏ', 'ሱ', '比', '高', '起', '球', 'ዙ', '拉', '后', '体', '月', '但', '話', '적', 'Ӡ', 'Ӳ', '克', '使', 'Ჯ', '부', '心', 'ੌ', 'ੜ', '\u200c', '们', '更', '正', '라', '보', 'ቦ', 'ዶ', '美', '也', '南', 'タ', 'ஜ', 'ජ', 'ሙ', 'ፕ', '現', 'ૃ', '일', 'ຜ', 'ਯ', 'ఖ', '口', '因', 'ロ', 'ൃ', 'ೃ', '式', 'ޝ', 'Ẓ', '했', 'ቤ', '十', '区', 'み', 'Ķ', '나', 'ۍ', 'ఏ', 'ං', '外', '海', '重', '天', '語', '۰', 'ៀ', '전', 'ඇ', '全', '被', '面', 'ឆ', '것', 'ධ', 'ፋ', '字', '发', 'ऑ', 'テ', '頭', 'ዱ', '身', '果', 'ஏ', 'ඒ', 'ෝ', 'ኝ', 'ٍ', '今', '示', 'カ', '無', '場', '來', '啲', 'ඔ', 'ඉ', 'ቲ', 'ጅ', '直', '德', '些', '或', '開', '게', '주', 'ቴ', 'ቁ', 'ሶ', '期', 'ろ', 'Ṱ', 'ኔ', 'ቋ', 'ኩ', '阿', '提', '音', 'ジ', '電', 'ऱ', '상', 'ጽ', 'ቆ', '动', '民', '然', '做', '金', 'フ', 'シ', '만', 'උ', 'ޕ', 'ク', '፣', '०', '१', '解', '真', '空', '機', 'コ', 'Ჭ', '冇', 'ฐ', 'ף', 'ڌ', 'ሄ', 'レ', '講', '៍', '种', '少', '点', '此', '与', '相', '受', '言', '՝', 'ઠ', 'ဉ', 'ృ', '内', '工', '英', '代', 'Ģ', 'ූ', 'ዩ', '̃', '北', '如', '流', 'ൺ', '정', '제', 'ਝ', 'ፓ', '·', '伊', '非', '៏', 'ጡ', 'ጫ', 'ቂ', '何', '၎', 'ጨ', 'ሻ', '及', '属', '原', '這', '트', 'ሸ', 'Џ', '城', '影', '次', '安', 'べ', 'Ჰ', 'ൗ', '哋', 'ૈ', 'Ṅ', '과', '었', 'ਏ', 'ౌ', 'ኦ', '建', '政', '巴', 'Ɓ', '々', '識', '種', '여', 'ڀ', 'ኙ', '过', '制', '始', '数', '社', '死', 'ޢ', '車', '長', 'ଝ', 'ๆ', '장', '至', '光', '四', '由', '著', 'ः', '彼', '點', 'ೌ', '되', 'ໆ', 'ሴ', '想', '任', '当', '站', '感', '住', '未', '旅', '持', 'バ', '問', '學', '۱', 'ૌ', '할', '와', '문', 'ဗ', 'ਘ', 'ඳ', 'Ḽ', '已', '造', '管', '必', 'ऐ', 'ィ', 'パ', 'Ǧ', '미', '유', '위', '면', '원', 'ච', 'ጸ', '对', '很', '史', '客', '考', 'ャ', 'オ', 'ഓ', 'ዓ', 'ሃ', '동', '물', 'ආ', 'ቷ', 'ኪ', 'ঃ', '称', '活', '落', 'へ', 'マ', 'ニ', 'ょ', '記', '発', 'ഠ', '發', '咗', 'ឋ', '국', '现', '兰', '基', '平', '保', '火', '情', 'ほ', '気', 'ୌ', '٠', '거', '們', 'ෂ', 'ኞ', 'ፎ', 'ጃ', 'ঝ', 'ڤ', '经', '信', '布', '思', '打', '結', 'ฮ', '嘢', '̇', '우', 'ໝ', 'ፖ', '量', '曾', '车', '品', '速', '件', '於', '۲', 'ٺ', 'Ҿ', 'Ҽ', '太', '「', '」', '石', '交', '司', 'チ', 'ず', '題', '對', 'ஞ', '睇', '噉', '모', '소', '२', 'Ѓ', '赛', '该', '格', '查', 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'尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', 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train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.7.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.7.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn001] 2023-07-06 08:53:07,864 (collect_stats:106) INFO: Niter: 100
+[cn001] 2023-07-06 08:53:15,734 (collect_stats:106) INFO: Niter: 200
+[cn001] 2023-07-06 08:53:23,232 (collect_stats:106) INFO: Niter: 300
+[cn001] 2023-07-06 08:53:30,845 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651413
+# Accounting: end_time=1688651639
+# Accounting: time=226 threads=1
+# Finished at Thu Jul 6 08:53:59 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..624bc1568c505d32cae1a4778f543adb343ee79f
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.7
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.7.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.7.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
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+- I
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+- А
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+- ި
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+- റ
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+- '5'
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+- '[slv]'
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+- પ
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+- ්
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+- ಂ
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+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
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+- Ò
+- ै
+- '[nep]'
+- ท
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+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
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+- డ
+- भ
+- '[est]'
+- ާ
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+- Ӓ
+- א
+- അ
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+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
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+- ۇ
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+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
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+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
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+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
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+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
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+- ြ
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+- บ
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+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
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+- Һ
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+- ້
+- '[cym]'
+- હ
+- Ş
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+- Ը
+- '7'
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+- ಪ
+- ే
+- ක
+- Ő
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+- Შ
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+- '[fas]'
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+- መ
+- Գ
+- ត
+- ရ
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+- Ხ
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+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
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+- '[ory]'
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+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
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+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
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+- ර
+- Ც
+- ွ
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+- ଥ
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+- ደ
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+- ទ
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+- ื
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+- '[kin]'
+- Ø
+- Ị
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+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
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+- Ծ
+- な
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+- इ
+- ק
+- 一
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+- ገ
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+- Â
+- を
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+- ె
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+- '[lga]'
+- ଜ
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+- '[xty]'
+- ʻ
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+- ெ
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+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
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+- ঁ
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+- ද
+- '[jpn]'
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+- ከ
+- ଶ
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+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
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+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
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+- ැ
+- ယ
+- ဆ
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+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
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+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
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+- ګ
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+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
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+- ၆
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+- ဌ
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+- '[tur]'
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+- <sos/eos>
+init: null
+input_size: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7/train/batch_keys
new file mode 100644
index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7/train/batch_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7/train/stats_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..4fc0c13bb6f98207422382ed9273ea79a19ddd4a
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7/valid/stats_keys
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.7/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..872b4b21ff6ecc055216a6d0d5d5a68ff73ac016
--- /dev/null
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+cv_fra_000668 54
+cv_fra_000672 82
+cv_fra_000645 6
+cv_fra_000649 75
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+cv_fra_000665 50
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+cv_fra_000654 4
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+cv_fra_000666 80
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+cv_fra_000683 86
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+cv_fra_000691 27
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+cv_fra_000676 80
+cv_fra_000680 74
+cv_fra_000684 53
+cv_fra_000688 29
+cv_fra_000692 80
+cv_fra_000696 80
+cv_fra_000677 58
+cv_fra_000681 68
+cv_fra_000685 80
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+cv_fra_000682 31
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+cv_fra_000690 24
+cv_fra_000694 84
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8.log
new file mode 100644
index 0000000000000000000000000000000000000000..ede1294ff6917e56411a1e7dbce3673fd6ad5a08
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8.log
@@ -0,0 +1,157 @@
+# Running on cn001.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:14 CDT 2023
+# SLURMD_NODENAME=cn001
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=8
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133214
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133214
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn001
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn001
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3660432
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn001
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.8.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.8.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.8 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.8.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.8.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.8 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn001] 2023-07-06 08:52:51,877 (asr:475) INFO: Vocabulary size: 6454
+[cn001] 2023-07-06 08:52:53,975 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn001] 2023-07-06 08:52:53,978 (abs_task:1214) INFO: Scheduler: None
+[cn001] 2023-07-06 08:52:53,988 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.8/config.yaml
+[cn001] 2023-07-06 08:52:54,094 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.8', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', '行', 'Ļ', '[som]', 'భ', 'ሆ', '[fil]', 'Ӣ', 'き', 'Ӧ', 'ሉ', 'ʼ', 'ณ', '[oci]', 'Ҷ', 'ڳ', 'ഡ', '[kam]', 'Ღ', 'ݨ', '을', 'ஆ', 'Ƙ', 'ਉ', 'ቶ', 'ຮ', 'ካ', 'ቅ', 'औ', 'ଂ', 'ណ', '의', 'ణ', 'Փ', 'ਈ', 'ګ', 'హ', 'ौ', 'ໍ', 'ಭ', 'ಎ', '地', 'ఆ', 'ん', '하', 'ਖ', '自', '上', 'ፍ', 'ਡ', '可', 'ዮ', '生', '[ibo]', 'ٻ', 'さ', 'ູ', '多', 'ޓ', 'ភ', '니', '不', 'ढ', 'ઈ', 'Ň', 'ഥ', 'ศ', '要', 'Ժ', 'ී', 'ۋ', 'ො', 'ሮ', '年', '到', 'Ძ', 'ซ', '۾', '你', '로', 'ጋ', '[lug]', 'ঞ', '国', 'ផ', '個', 'ฉ', '和', 'ஒ', 'ខ', '[mri]', '他', 'ଠ', 'ຼ', 'ঙ', 'ޅ', '唔', 'ຳ', 'ځ', '៉', 'ថ', '가', '[lin]', 'Ճ', '은', 'ធ', '੍', 'Đ', '嘅', '出', 'ټ', 'Ĕ', 'ス', '지', '下', 'ୂ', 'ޑ', 'Ɗ', 'ຸ', 'ઉ', 'ញ', 'ሁ', 'ޖ', '”', '用', 'ሩ', 'ޔ', '为', '了', 'ຖ', 'එ', 'ጣ', 'ઇ', '日', 'ޯ', 'よ', '고', 'ኛ', 'ሊ', '得', 'ൈ', 'ಒ', 'ፈ', '好', '同', '成', '分', 'そ', 'ธ', 'Ҭ', 'Ğ', 'Ṭ', 'ዲ', 'Њ', 'ڏ', '۽', '物', 'ޫ', 'Ჩ', 'Ӯ', '都', 'け', '个', 'ഫ', 'Ō', 'ዘ', '方', 'ډ', 'Ḍ', '学', 'Օ', 'だ', '能', 'ڊ', '部', '仔', 'ภ', 'థ', 'ओ', 'Җ', 'ษ', 'ಉ', 'ឹ', '서', 'ਫ', 'Ï', 'ル', 'ୱ', 'ਧ', 'ቱ', '就', 'Ű', 'ళ', '斯', '作', 'ໂ', 'ණ', 'ጵ', '사', 'ఎ', 'ဘ', 'ఈ', '있', '時', 'ॅ', '한', '기', 'ଘ', 'ଫ', 'ڄ', 'Ď', 'ऊ', 'ఒ', 'ሎ', 'ቃ', 'ೈ', 'ْ', 'ຢ', 'Ҩ', '前', 'ዊ', 'Ҟ', '世', 'ญ', 'イ', '公', 'ໜ', 'え', 'ト', '၏', 'ৌ', 'Ḥ', 'ሲ', '家', 'ៃ', 'څ', '会', 'ಈ', 'Ẽ', 'ア', 'ち', '子', 'ઝ', '를', '္', 'ሞ', '事', '本', '最', 'ຽ', 'ङ', '會', 'ଓ', 'ਭ', 'ሽ', 'ኮ', '之', '己', '스', 'ဝ', '入', 'ළ', 'ዝ', '于', '理', 'ဟ', 'ڙ', 'Ҵ', '名', '新', '法', 'ୃ', 'ዛ', 'ቻ', 'ラ', 'ઘ', '수', '습', 'ਥ', '所', '界', 'つ', 'ஸ', 'ఫ', 'ቢ', 'ৎ', 'わ', '過', 'ഈ', '으', '来', 'ဂ', '定', '利', '者', 'お', '為', 'Ɛ', '아', 'ຶ', 'ဒ', 'ጉ', 'ኖ', '度', '常', 'ഖ', 'ឡ', '도', 'Ḓ', '去', '其', '意', 'ኑ', '这', '时', '通', 'Ֆ', '리', '어', 'ቡ', '合', 'ञ', '間', 'ଞ', 'Ќ', 'ږ', '知', 'ሬ', '手', 'ኤ', '¿', 'や', 'ך', '៊', '인', 'ዚ', 'ؤ', '들', '科', 'ど', 'ッ', 'ば', 'ಖ', 'ឺ', '시', 'ಫ', '文', '主', 'め', '見', 'ฟ', '해', 'ሀ', '州', '水', '小', '目', 'ଢ', 'ශ', 'ኒ', 'ሌ', 'ጊ', '三', '西', '特', '그', '第', '山', '대', '자', '„', '明', '位', 'せ', 'ښ', 'ጀ', '“', '二', '路', 'リ', '加', '教', '國', 'ඩ', '道', '市', '表', '立', '而', '食', 'ޒ', 'Ψ', '動', '័', '¡', '尔', 'ዜ', '亚', '化', '先', '性', '形', '里', '後', 'ሜ', 'ፊ', 'ቹ', '力', 'ド', '呢', '佢', '…', 'Ṛ', 'ഏ', 'ሱ', '比', '高', '起', '球', 'ዙ', '拉', '后', '体', '月', '但', '話', '적', 'Ӡ', 'Ӳ', '克', '使', 'Ჯ', '부', '心', 'ੌ', 'ੜ', '\u200c', '们', '更', '正', '라', '보', 'ቦ', 'ዶ', '美', '也', '南', 'タ', 'ஜ', 'ජ', 'ሙ', 'ፕ', '現', 'ૃ', '일', 'ຜ', 'ਯ', 'ఖ', '口', '因', 'ロ', 'ൃ', 'ೃ', '式', 'ޝ', 'Ẓ', '했', 'ቤ', '十', '区', 'み', 'Ķ', '나', 'ۍ', 'ఏ', 'ං', '外', '海', '重', '天', '語', '۰', 'ៀ', '전', 'ඇ', '全', '被', '面', 'ឆ', '것', 'ධ', 'ፋ', '字', '发', 'ऑ', 'テ', '頭', 'ዱ', '身', '果', 'ஏ', 'ඒ', 'ෝ', 'ኝ', 'ٍ', '今', '示', 'カ', '無', '場', '來', '啲', 'ඔ', 'ඉ', 'ቲ', 'ጅ', '直', '德', '些', '或', '開', '게', '주', 'ቴ', 'ቁ', 'ሶ', '期', 'ろ', 'Ṱ', 'ኔ', 'ቋ', 'ኩ', '阿', '提', '音', 'ジ', '電', 'ऱ', '상', 'ጽ', 'ቆ', '动', '民', '然', '做', '金', 'フ', 'シ', '만', 'උ', 'ޕ', 'ク', '፣', '०', '१', '解', '真', '空', '機', 'コ', 'Ჭ', '冇', 'ฐ', 'ף', 'ڌ', 'ሄ', 'レ', '講', '៍', '种', '少', '点', '此', '与', '相', '受', '言', '՝', 'ઠ', 'ဉ', 'ృ', '内', '工', '英', '代', 'Ģ', 'ූ', 'ዩ', '̃', '北', '如', '流', 'ൺ', '정', '제', 'ਝ', 'ፓ', '·', '伊', '非', '៏', 'ጡ', 'ጫ', 'ቂ', '何', '၎', 'ጨ', 'ሻ', '及', '属', '原', '這', '트', 'ሸ', 'Џ', '城', '影', '次', '安', 'べ', 'Ჰ', 'ൗ', '哋', 'ૈ', 'Ṅ', '과', '었', 'ਏ', 'ౌ', 'ኦ', '建', '政', '巴', 'Ɓ', '々', '識', '種', '여', 'ڀ', 'ኙ', '过', '制', '始', '数', '社', '死', 'ޢ', '車', '長', 'ଝ', 'ๆ', '장', '至', '光', '四', '由', '著', 'ः', '彼', '點', 'ೌ', '되', 'ໆ', 'ሴ', '想', '任', '当', '站', '感', '住', '未', '旅', '持', 'バ', '問', '學', '۱', 'ૌ', '할', '와', '문', 'ဗ', 'ਘ', 'ඳ', 'Ḽ', '已', '造', '管', '必', 'ऐ', 'ィ', 'パ', 'Ǧ', '미', '유', '위', '면', '원', 'ච', 'ጸ', '对', '很', '史', '客', '考', 'ャ', 'オ', 'ഓ', 'ዓ', 'ሃ', '동', '물', 'ආ', 'ቷ', 'ኪ', 'ঃ', '称', '活', '落', 'へ', 'マ', 'ニ', 'ょ', '記', '発', 'ഠ', '發', '咗', 'ឋ', '국', '现', '兰', '基', '平', '保', '火', '情', 'ほ', '気', 'ୌ', '٠', '거', '們', 'ෂ', 'ኞ', 'ፎ', 'ጃ', 'ঝ', 'ڤ', '经', '信', '布', '思', '打', '結', 'ฮ', '嘢', '̇', '우', 'ໝ', 'ፖ', '量', '曾', '车', '品', '速', '件', '於', '۲', 'ٺ', 'Ҿ', 'Ҽ', '太', '「', '」', '石', '交', '司', 'チ', 'ず', '題', '對', 'ஞ', '睇', '噉', '모', '소', '२', 'Ѓ', '赛', '该', '格', '查', 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'尿', '頓', '仙', '復', '冰', '銀', '雙', '煩', '鬆', '灣', '換', '救', '證', '戲', '輪', '鍾', '吖', '晒', '迎', '項', '跟', '奧', 'ፃ', 'ሐ', 'º', '٧', '胜', '记', '艾', '较', '遺', '紀', 'ឯ', '테', '배', '처', '등', '설', '견', '착', '브', '편', '겨', '긴', '권', '탄', '란', '네', '취', '령', '확', '츠', '립', '၅', 'ఞ', '‧', '給', '產', 'ඟ', 'ፔ', 'ጾ', '末', '贝', '扎', '毁', '材', '归', '软', '秀', '锡', '伦', '杰', '沉', '壁', '婆', '谷', '识', '拜', '职', '漫', '乡', '静', '庙', '红', '离', '给', '织', '灰', '准', '频', '蛛', '衡', '余', '虫', '拒', '池', '找', '签', '撤', '码', '坡', '寒', '靴', '俱', '督', '显', '昌', '珠', '征', '欢', '契', '男', '输', 'ޠ', 'Ạ', '嫌', '級', '忘', '辞', '冊', '抑', '疲', '触', '抽', 'ヨ', '熱', '溜', '検', '穴', 'ヘ', '泊', '獅', '夢', '倒', '肝', '階', '両', '醒', '貨', '絡', '績', '枚', '尺', '燒', '款', '疼', '溫', '斗', '蓋', '黃', '喉', '貓', '禁', '喜', '橫', '跳', '鴨', '圍', '澳', '戶', '志', '樓', '熟', '價', '樹', '殼', '團', '―', '尋', '載', '享', '啱', '肯', '訂', '齊', '針', '既', '晨', '顯', '葬', '镜', '乎', '۸', '۷', 'ઋ', '権', '児', 'ಔ', 'ឲ', '즈', '투', '런', '약', '패', '케', '향', '같', '볼', '좋', '채', '될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', 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train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.8.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.8.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn001] 2023-07-06 08:53:07,831 (collect_stats:106) INFO: Niter: 100
+[cn001] 2023-07-06 08:53:16,137 (collect_stats:106) INFO: Niter: 200
+[cn001] 2023-07-06 08:53:23,587 (collect_stats:106) INFO: Niter: 300
+[cn001] 2023-07-06 08:53:30,841 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651414
+# Accounting: end_time=1688651639
+# Accounting: time=225 threads=1
+# Finished at Thu Jul 6 08:53:59 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..55cfef217d1a2be27aa011e7362768f0ddc5a1a8
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.8
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.8.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.8.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
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+- I
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+- V
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+- О
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+- ި
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+- '5'
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+- '[slv]'
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+- પ
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+- ්
+- Ά
+- ಂ
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+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
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+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
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+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
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+- ۇ
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+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
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+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
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+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
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+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
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+- い
+- ಪ
+- ే
+- ක
+- Ő
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+- ם
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+- '[fas]'
+- ል
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+- '[uzb]'
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+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
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+- '[ory]'
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+- फ
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+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
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+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
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+- ර
+- Ც
+- ွ
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+- ଥ
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+- ደ
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+- ទ
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+- ื
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+- '[kin]'
+- Ø
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+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
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+- ທ
+- ු
+- ։
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+- Ծ
+- な
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+- ק
+- 一
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+- ገ
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+- Â
+- を
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+- ె
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+- ৈ
+- '[lga]'
+- ଜ
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+- '[xty]'
+- ʻ
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+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
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+- ঁ
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+- ද
+- '[jpn]'
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+- ከ
+- ଶ
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+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
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+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
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+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
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+- '[pan]'
+- –
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+- ැ
+- ယ
+- ဆ
+- 에
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+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
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+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
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+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
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+- ګ
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+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
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+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
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+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
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+- ខ
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+- 他
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+- <sos/eos>
+init: null
+input_size: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/train/batch_keys
new file mode 100644
index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/train/batch_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/train/stats_keys
@@ -0,0 +1,2 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..547b17f4e20f4b4f327732aca0c93776ab4a6b3b
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/train/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/valid/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/valid/batch_keys
new file mode 100644
index 0000000000000000000000000000000000000000..0b8ac772b1ff198a50c038a34c04b5b8ae186fb3
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/valid/batch_keys
@@ -0,0 +1,2 @@
+speech
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/valid/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/valid/feats_lengths_stats.npz
new file mode 100644
index 0000000000000000000000000000000000000000..5259cd32ddbdce6cfd1acccc2baeb0a6c6bf9112
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/valid/feats_lengths_stats.npz
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+version https://git-lfs.github.com/spec/v1
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
+feats_lengths
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..8b38c474a9f986516d226adab2435a9275a32031
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.8/valid/text_shape
@@ -0,0 +1,760 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9.log b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9.log
new file mode 100644
index 0000000000000000000000000000000000000000..fab726a1b4d021aafa817afca40dd9e2a83b5c4e
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9.log
@@ -0,0 +1,157 @@
+# Running on cn001.delta.ncsa.illinois.edu
+# Started at Thu Jul 6 08:50:14 CDT 2023
+# SLURMD_NODENAME=cn001
+# SLURM_ARRAY_JOB_ID=2133206
+# SLURM_ARRAY_TASK_COUNT=32
+# SLURM_ARRAY_TASK_ID=9
+# SLURM_ARRAY_TASK_MAX=32
+# SLURM_ARRAY_TASK_MIN=1
+# SLURM_ARRAY_TASK_STEP=1
+# SLURM_CLUSTER_NAME=delta
+# SLURM_CONF=/var/spool/slurmd/conf-cache/slurm.conf
+# SLURM_CPUS_ON_NODE=1
+# SLURM_EXPORT_ENV=PATH
+# SLURM_GET_USER_ENV=1
+# SLURM_GTIDS=0
+# SLURM_JOBID=2133215
+# SLURM_JOB_ACCOUNT=bbjs-delta-cpu
+# SLURM_JOB_CPUS_PER_NODE=1
+# SLURM_JOB_GID=202
+# SLURM_JOB_ID=2133215
+# SLURM_JOB_NAME=stats.sh
+# SLURM_JOB_NODELIST=cn001
+# SLURM_JOB_NUM_NODES=1
+# SLURM_JOB_PARTITION=cpu
+# SLURM_JOB_QOS=bbjs-delta-cpu
+# SLURM_JOB_UID=68258
+# SLURM_JOB_USER=chen26
+# SLURM_LOCALID=0
+# SLURM_MEM_PER_CPU=4096
+# SLURM_NNODES=1
+# SLURM_NODEID=0
+# SLURM_NODELIST=cn001
+# SLURM_NODE_ALIASES='(null)'
+# SLURM_OPEN_MODE=a
+# SLURM_PRIO_PROCESS=0
+# SLURM_PROCID=0
+# SLURM_SUBMIT_DIR=/scratch/bbjs/chen26/espnet_01_23/egs2/msuperb/asr1
+# SLURM_SUBMIT_HOST=dt-login01.delta.internal.ncsa.edu
+# SLURM_TASKS_PER_NODE=1
+# SLURM_TASK_PID=3660440
+# SLURM_TOPOLOGY_ADDR=ss00.ss01.cn001
+# SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
+# SLURM_WORKING_CLUSTER=delta:dt-sched:6817:9728:109
+# python3 -m espnet2.bin.asr_train --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.9.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.9.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.9 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text 
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/bin/python3 /scratch/bbjs/chen26/espnet_01_23/espnet2/bin/asr_train.py --collect_stats true --use_preprocessor true --bpemodel none --token_type char --token_list data/multilingual_1h_token_list/char/tokens.txt --non_linguistic_symbols data/local/nlsyms.txt --cleaner none --g2p none --train_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/train.9.scp --valid_shape_file exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.9.scp --output_dir exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.9 --config conf/tuning/train_asr_fbank_single.yaml --frontend_conf fs=16k --train_data_path_and_name_and_type dump/raw/train_1h_lid/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_1h_lid/text,text,text --valid_data_path_and_name_and_type dump/raw/dev_1h_lid/text,text,text
+[cn001] 2023-07-06 08:52:51,877 (asr:475) INFO: Vocabulary size: 6454
+[cn001] 2023-07-06 08:52:53,975 (abs_task:1209) INFO: pytorch.version=1.13.1+cu116, cuda.available=False, cudnn.version=8302, cudnn.benchmark=False, cudnn.deterministic=True
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1210) INFO: Model structure:
+ESPnetASRModel(
+  (frontend): DefaultFrontend(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=320, center=True, normalized=False, onesided=True)
+    (frontend): Frontend()
+    (logmel): LogMel(sr=16000, n_fft=1024, n_mels=80, fmin=0, fmax=8000.0, htk=False)
+  )
+  (specaug): SpecAug(
+    (time_warp): TimeWarp(window=5, mode=bicubic)
+    (freq_mask): MaskAlongAxis(mask_width_range=[0, 27], num_mask=2, axis=freq)
+    (time_mask): MaskAlongAxisVariableMaxWidth(mask_width_ratio_range=[0.0, 0.05], num_mask=10, axis=time)
+  )
+  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
+  (encoder): TransformerEncoder(
+    (embed): Conv2dSubsampling2(
+      (conv): Sequential(
+        (0): Conv2d(1, 256, kernel_size=(3, 3), stride=(2, 2))
+        (1): ReLU()
+        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))
+        (3): ReLU()
+      )
+      (out): Sequential(
+        (0): Linear(in_features=9472, out_features=256, bias=True)
+        (1): PositionalEncoding(
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+      )
+    )
+    (encoders): MultiSequential(
+      (0): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+      (1): EncoderLayer(
+        (self_attn): MultiHeadedAttention(
+          (linear_q): Linear(in_features=256, out_features=256, bias=True)
+          (linear_k): Linear(in_features=256, out_features=256, bias=True)
+          (linear_v): Linear(in_features=256, out_features=256, bias=True)
+          (linear_out): Linear(in_features=256, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+        )
+        (feed_forward): PositionwiseFeedForward(
+          (w_1): Linear(in_features=256, out_features=1024, bias=True)
+          (w_2): Linear(in_features=1024, out_features=256, bias=True)
+          (dropout): Dropout(p=0.1, inplace=False)
+          (activation): ReLU()
+        )
+        (norm1): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (norm2): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+        (dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (after_norm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)
+  )
+  (criterion_att): LabelSmoothingLoss(
+    (criterion): KLDivLoss()
+  )
+  (ctc): CTC(
+    (ctc_lo): Linear(in_features=256, out_features=6454, bias=True)
+    (ctc_loss): CTCLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetASRModel
+    Total Number of model parameters: 6.26 M
+    Number of trainable parameters: 6.26 M (100.0%)
+    Size: 25.03 MB
+    Type: torch.float32
+[cn001] 2023-07-06 08:52:53,977 (abs_task:1213) INFO: Optimizer:
+Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    differentiable: False
+    eps: 1e-08
+    foreach: None
+    fused: False
+    lr: 0.0001
+    maximize: False
+    weight_decay: 1e-06
+)
+[cn001] 2023-07-06 08:52:53,978 (abs_task:1214) INFO: Scheduler: None
+[cn001] 2023-07-06 08:52:53,988 (abs_task:1223) INFO: Saving the configuration in exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.9/config.yaml
+[cn001] 2023-07-06 08:52:54,090 (abs_task:1234) INFO: Namespace(accum_grad=4, allow_variable_data_keys=False, aux_ctc_tasks=[], aux_dropout=0.0, batch_bins=1000000, batch_size=8, batch_type='sorted', best_model_criterion=[['valid', 'loss', 'min']], bpemodel=None, chunk_length=500, chunk_shift_ratio=0.5, cleaner=None, collect_stats=True, config='conf/tuning/train_asr_fbank_single.yaml', create_graph_in_tensorboard=False, ctc_conf={'dropout_rate': 0.0, 'ctc_type': 'builtin', 'reduce': True, 'ignore_nan_grad': None, 'zero_infinity': True}, cudnn_benchmark=False, cudnn_deterministic=True, cudnn_enabled=True, debug_grad=False, decoder='rnn', decoder_conf={}, detect_anomaly=False, dist_backend='nccl', dist_init_method='env://', dist_launcher=None, dist_master_addr=None, dist_master_port=None, dist_rank=None, dist_world_size=None, distributed=False, drop_last=False, dry_run=False, early_stopping_criterion=('valid', 'loss', 'min'), encoder='transformer', encoder_conf={'output_size': 256, 'attention_heads': 8, 'linear_units': 1024, 'num_blocks': 2, 'dropout_rate': 0.1, 'positional_dropout_rate': 0.1, 'attention_dropout_rate': 0.1, 'input_layer': 'conv2d2', 'normalize_before': True}, fold_length=[], freeze_param=[], frontend='default', frontend_conf={'n_fft': 1024, 'hop_length': 320, 'fs': '16k'}, g2p=None, grad_clip=5.0, grad_clip_type=2.0, grad_noise=False, ignore_init_mismatch=False, init=None, init_param=[], input_size=None, iterator_type='sequence', joint_net_conf=None, keep_nbest_models=5, local_rank=None, log_interval=None, log_level='INFO', max_cache_fd=32, max_cache_size=0.0, max_epoch=30, model='espnet', model_conf={'ctc_weight': 1.0}, multiple_iterator=False, multiprocessing_distributed=False, nbest_averaging_interval=0, ngpu=0, no_forward_run=False, noise_apply_prob=1.0, noise_db_range='13_15', noise_scp=None, non_linguistic_symbols='data/local/nlsyms.txt', normalize='utterance_mvn', normalize_conf={}, num_att_plot=3, num_cache_chunks=1024, num_iters_per_epoch=500, num_workers=4, optim='adam', optim_conf={'lr': 0.0001, 'weight_decay': 1e-06}, output_dir='exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.9', patience=None, postencoder=None, postencoder_conf={}, preencoder=None, preencoder_conf={}, preprocessor='default', preprocessor_conf={}, pretrain_path=None, print_config=False, required=['output_dir', 'token_list'], resume=False, rir_apply_prob=1.0, rir_scp=None, scheduler=None, scheduler_conf={}, seed=0, sharded_ddp=False, short_noise_thres=0.5, sort_batch='descending', sort_in_batch='descending', specaug='specaug', specaug_conf={'apply_time_warp': True, 'time_warp_window': 5, 'time_warp_mode': 'bicubic', 'apply_freq_mask': True, 'freq_mask_width_range': [0, 27], 'num_freq_mask': 2, 'apply_time_mask': True, 'time_mask_width_ratio_range': [0.0, 0.05], 'num_time_mask': 10}, speech_volume_normalize=None, token_list=['<blank>', '<unk>', '<space>', 'A', 'E', 'I', 'N', 'O', 'T', 'S', 'R', 'L', 'U', 'D', 'M', 'K', 'H', 'G', 'C', 'P', 'B', 'А', 'V', 'Y', 'W', 'Z', 'О', 'Н', 'J', 'F', 'Е', 'И', 'Т', 'Р', 'ا', 'Л', 'С', 'К', 'Д', 'М', 'В', 'У', 'ा', 'Ы', 'É', 'ی', 'و', 'ن', 'ر', 'П', 'Г', 'Á', 'Б', 'া', '്', 'Q', 'ل', 'Í', '्', 'م', 'З', 'ت', 'د', 'र', 'Ä', 'Я', '்', 'Й', 'Ա', 'Ш', 'ে', 'Э', 'ە', 'क', 'ি', 'ک', 'े', '্', 'X', 'Х', 'Ь', 'Ч', '1', '4', 'ب', 'Ó', 'র', 'ي', 'न', 'س', 'І', 'ه', 'ন', 'त', 'ক', 'ि', 'Ა', 'ા', 'ി', '3', 'Ж', 'Ն', 'स', 'Α', 'ी', 'Ი', 'ल', '[eng]', 'ত', '0', 'य', 'ന', 'ക', '್', 'म', 'ह', '2', 'ା', 'ব', 'Ր', 'Ο', 'Ո', 'ു', 'Τ', 'Σ', 'য', 'Ե', 'Ц', 'Ö', 'ল', 'Ү', 'க', 'ത', 'Š', '်', 'ى', 'ு', 'ो', 'ৰ', 'ି', 'Ł', '్', 'À', '[nld]', 'व', 'प', 'Ε', 'Ე', 'ാ', 'ಿ', 'ે', 'Ι', 'ి', 'Ú', '୍', 'ं', 'ର', 'Č', 'স', 'Ü', 'த', 'ش', '[deu]', 'ி', 'า', 'Ν', 'ম', 'প', 'پ', 'ہ', 'ર', 'ے', 'ન', 'Ө', 'Ә', 'ా', 'Ի', 'ు', 'ق', 'ަ', 'ು', 'ج', 'യ', 'น', '્', 'Ž', 'Ს', 'Ə', 'Ւ', 'Ă', 'ರ', 'ര', 'Ọ', 'ئ', 'ದ', 'י', 'Რ', 'ز', 'Ñ', 'ް', 'ర', 'द', 'କ', 'ו', 'ಾ', 'ু', 'ع', 'ப', 'ف', 'Å', '়', 'ग', '่', 'Ā', 'న', 'Ю', 'Մ', 'Ρ', 'ી', 'େ', 'ട', 'ु', 'گ', 'ನ', 'ம', 'Ę', 'ತ', 'च', 'দ', 'Ო', 'ం', 'ร', '[fra]', 'È', 'Π', 'പ', 'ज', 'Ż', 'Κ', 'ત', 'ల', 'อ', 'ា', '[rus]', '[ben]', 'ક', '[spa]', 'મ', 'އ', '្', 'ர', 'Կ', 'ட', 'વ', 'خ', 'ን', 'ก', 'ா', '।', 'വ', 'ট', '[ita]', 'Მ', 'ো', 'ವ', 'เ', 'ਾ', 'క', 'ނ', 'হ', 'ब', 'െ', 'ں', 'Ý', 'ง', 'း', 'ެ', 'ה', 'ೆ', 'ବ', 'ಗ', '[por]', '[gle]', 'ު', 'ಲ', 'ன', 'ો', 'Ლ', 'മ', 'Ą', 'ନ', 'ല', 'வ', 'ك', 'ာ', '[pol]', 'ം', 'Μ', 'Დ', 'ม', 'ல', 'Υ', 'Ě', 'چ', '้', 'ಕ', 'Ნ', 'Η', 'ં', '9', 'ट', 'ھ', 'Տ', 'জ', 'Ბ', 'ح', 'ତ', 'ន', 'Ć', 'Î', 'ั', 'ນ', 'ू', 'າ', 'Ӹ', 'ל', 'ই', '[xho]', 'ި', 'आ', 'Յ', 'ု', '[swa]', 'ಸ', 'સ', 'গ', 'റ', 'Λ', 'श', 'ય', 'এ', 'ಯ', '[swe]', '5', 'ற', 'শ', 'Ê', 'Վ', 'ୁ', 'Ვ', 'अ', 'ण', 'Ф', 'ছ', 'സ', 'Ẹ', 'ี', 'Õ', 'ว', '[sot]', 'আ', '[tam]', 'រ', 'ት', 'వ', 'త', 'ପ', 'ਰ', '[mal]', 'ை', 'ద', 'င', 'ప', 'ێ', 'က', '[slk]', '[slv]', 'ী', '[ces]', 'પ', 'Ś', 'మ', 'Ħ', 'ണ', '[glg]', 'ച', '්', 'Ά', 'ಂ', 'ው', 'ସ', 'Ð', 'Ս', 'ย', 'િ', 'ိ', 'ମ', 'ள', 'מ', 'ய', 'চ', 'ת', 'Ί', 'ר', 'ੀ', 'Ӑ', '[afr]', 'ۆ', 'ې', 'Щ', '6', '的', '[nan]', 'ด', 'ေ', '[ron]', '[ukr]', 'Უ', 'Ř', 'ള', 'ក', '8', 'න', 'ล', '[fin]', 'Ç', 'స', 'Ό', 'ੇ', 'લ', '່', '[mar]', 'Თ', 'သ', 'ହ', 'ಮ', '[kat]', 'ड', '[cat]', '[hun]', 'Լ', 'မ', 'ુ', 'ਸ', 'Ò', 'ै', '[nep]', 'ท', 'య', 'の', 'ਿ', 'Ӗ', 'Έ', '́', 'ប', 'Ъ', 'Ì', 'ങ', 'ב', 'ਕ', 'ة', 'ਹ', 'የ', '[lav]', 'డ', 'भ', '[est]', 'ާ', 'ි', 'Ӓ', 'א', 'അ', 'ए', 'በ', 'ध', 'ש', 'أ', 'Ë', 'Ã', 'ਦ', '[nso]', 'ပ', 'ስ', 'ۇ', 'ਨ', '’', 'ກ', 'ส', 'ಳ', 'থ', 'Ց', '،', 'Հ', 'ଲ', '[kir]', 'ോ', '[ara]', 'ச', 'Ē', '̀', 'ރ', '—', 'േ', 'ந', '[eus]', '[ell]', 'တ', '[urd]', 'ভ', 'ห', 'န', '[wol]', 'थ', 'ດ', '[yor]', 'ิ', 'Ҳ', 'آ', '[bre]', 'చ', 'Ё', 'Ў', 'Ô', '[ind]', 'જ', '[ckb]', 'ਤ', 'ស', 'ต', 'Γ', 'ط', 'గ', 'ধ', 'አ', 'অ', 'ষ', 'މ', 'ค', '[hau]', '[tha]', 'ص', 'ງ', 'ተ', 'Ī', 'غ', 'ో', '[yue]', 'ြ', '[abk]', 'ކ', 'บ', 'Δ', 'Թ', 'ያ', 'ម', 'ట', 'ড', 'ਂ', 'נ', 'Გ', 'ව', '[hin]', 'ር', 'Һ', '[mlt]', '້', '[cym]', 'હ', 'Ş', 'ވ', 'Ը', '7', 'ለ', 'い', 'ಪ', 'ే', 'ක', 'Ő', 'Ụ', '[cmn]', 'Შ', 'ם', 'ល', '[bul]', '[fas]', 'ል', 'ង', 'ख', '[uzb]', 'ะ', 'અ', '့', 'መ', 'Գ', 'ត', 'ရ', 'Ț', '[asm]', 'ڪ', 'Ხ', 'ष', '[hye]', 'ເ', 'Դ', '[guj]', 'စ', 'ଦ', 'ັ', '[bel]', 'ހ', 'খ', 'ਲ', '[ven]', '[cnh]', 'အ', 'እ', '[kab]', '[jav]', 'ਵ', 'ອ', 'ය', 'ป', 'ദ', '[mon]', 'Ҫ', 'ች', 'に', 'ະ', 'ও', 'ގ', 'Ք', 'છ', 'ም', 'ข', '[tat]', 'ગ', 'ไ', 'ည', '[amh]', '[bas]', 'ൽ', 'Ω', 'し', '[sin]', 'ণ', 'Ң', 'ම', 'Ղ', 'は', 'แ', 'ත', 'る', 'ಡ', '[skr]', 'ർ', 'Է', 'ා', 'ટ', 'ଣ', '[kan]', 'จ', 'で', 'દ', 'Ტ', 'उ', 'Ή', 'ୟ', '[grn]', 'ଗ', 'Ù', 'ং', '[azz]', 'Χ', '[ssw]', 'ျ', 'ີ', '[ina]', 'Պ', 'た', 'ှ', 'ມ', '[nbl]', 'ע', 'ୋ', 'ಹ', 'Բ', 'Ť', '[chv]', 'উ', 'ಟ', 'ວ', 'ੱ', 'ੋ', 'Ĩ', 'ଏ', '[kmr]', 'શ', 'ነ', '[ory]', 'ಬ', 'と', 'ና', 'Ύ', 'ଇ', 'फ', '[nno]', '[mrj]', 'छ', 'બ', 'ಅ', 'ީ', 'ދ', 'થ', 'ೇ', 'て', '[org_jpn]', 'ண', 'ይ', 'அ', '[mhr]', 'Ș', 'ד', '[nor]', 'ތ', 'ใ', 'ခ', 'ీ', 'ර', 'Ც', 'ွ', '់', 'ଥ', 'ણ', 'ደ', 'ଟ', '[bak]', 'ទ', 'Θ', 'ٹ', 'ื', 'ח', '[kin]', 'Ø', 'Ị', 'ଆ', '[div]', 'လ', 'આ', '[myv]', 'ே', 'Ń', 'ທ', 'ු', '։', '[hrv]', 'Ծ', 'な', 'Კ', 'इ', 'ק', '一', 'ਜ', 'Ҡ', 'ລ', 'Φ', 'ገ', 'ដ', 'అ', 'Â', 'を', 'פ', 'ె', 'ސ', 'ま', 'כ', 'ສ', 'ৈ', '[lga]', 'ଜ', 'ਮ', 'Ҙ', '[xty]', 'ʻ', 'ळ', 'យ', 'Ї', 'ெ', 'す', 'ൾ', '[uig]', 'இ', 'َ', 'Ġ', '‘', 'Β', 'が', 'എ', '[sah]', 'ู', 'พ', 'ض', 'ڕ', 'ई', 'ഷ', 'ލ', '[hsb]', 'Ј', 'ස', 'ບ', 'ੰ', 'ማ', 'ላ', 'ৱ', 'ފ', 'ଅ', 'ൻ', 'ଡ', 'ุ', 'ਆ', 'ঁ', 'ិ', 'Շ', 'ס', 'Ռ', 'ද', '[jpn]', 'ذ', 'ശ', 'Ů', 'ከ', 'ଶ', 'េ', 'ช', 'Ώ', 'ோ', 'ំ', 'ଯ', 'ખ', 'Ṣ', '็', 'ِ', 'ុ', 'ୀ', 'ڵ', 'ށ', 'វ', 'ീ', 'ਪ', 'Є', 'ડ', 'ົ', 'ބ', 'ើ', 'Ţ', 'ප', 'ែ', 'ግ', 'ഗ', 'ព', 'ੈ', 'ሚ', 'ठ', 'ຍ', 'ី', '人', 'ೂ', 'ೊ', 'ባ', '਼', 'ድ', 'ແ', 'ෙ', 'ଳ', 'ල', 'ফ', 'એ', 'Ქ', 'ആ', 'ብ', 'ำ', 'Ũ', '์', 'ધ', '«', 'ේ', 'ራ', '»', 'れ', 'எ', 'ច', 'ચ', 'ረ', 'こ', 'Զ', 'ڭ', 'ຫ', 'ധ', 'Ľ', 'ൂ', '़', 'ଚ', 'Խ', 'ሰ', 'Ғ', 'ଭ', 'ג', 'బ', 'ਚ', 'か', 'ଷ', 'ປ', 'జ', 'ወ', '۔', 'Ზ', 'ಶ', 'ಚ', 'ੁ', '다', 'ီ', '是', 'Û', 'ଧ', 'Æ', 'Ӱ', 'អ', 'គ', 'إ', 'ଙ', 'ט', 'ូ', 'ث', 'ਅ', 'Წ', 'ታ', 'Ź', 'घ', 'ೀ', 'ഹ', 'ُ', '在', 'Ҕ', 'ជ', 'ಷ', 'ொ', 'Ფ', 'ິ', 'Þ', 'ଁ', 'ຂ', 'あ', 'ँ', '이', 'ໃ', 'ங', 'ழ', 'ۈ', 'Ջ', 'Პ', '有', 'ޮ', 'ূ', 'ਗ', 'Қ', 'っ', 'ਬ', 'ክ', 'ഇ', 'ଛ', 'ബ', 'Չ', 'හ', 'Ɣ', 'ಣ', 'ට', 'ື', 'ޭ', 'Ū', 'ભ', '는', 'ຕ', 'ቸ', '[heb]', 'ਇ', 'ഞ', 'ঠ', '我', 'ਟ', 'ూ', 'ജ', 'ڑ', 'ଉ', 'ઓ', 'झ', 'ળ', 'ೋ', '[mkd]', 'ー', 'ံ', 'ጥ', 'ء', '[ast]', 'ಜ', 'ਣ', 'ై', 'ੂ', 'ً', 'ഒ', '؟', 'ග', 'ဖ', 'り', 'โ', 'ါ', 'ظ', '大', 'ら', 'Ņ', 'ោ', '[isl]', 'ຄ', 'ဲ', 'Ċ', 'য়', 'ถ', '٘', 'ړ', 'ৃ', 'צ', 'も', 'Ყ', 'ٽ', 'ึ', 'う', '[ori]', 'ഭ', 'Ҥ', '[pan]', '–', 'ଖ', 'ැ', 'ယ', 'ဆ', '에', '଼', 'ૂ', '[ltz]', 'ఉ', 'Ξ', 'ン', '[tel]', 'ዳ', 'శ', 'ధ', 'ዋ', '[aze]', 'ຈ', '[nob]', 'ొ', 'ໄ', '[snd]', 'ພ', 'ಇ', 'ష', 'ژ', '[kor]', 'ዎ', '[pus]', 'ڻ', '[msa]', 'ഉ', 'ષ', 'ះ', 'ួ', 'Ԥ', 'ן', 'ហ', '[tgk]', 'ቀ', 'ៅ', '\u200d', 'ಧ', 'ຊ', 'ٿ', 'ఇ', 'ൊ', '中', 'Ζ', 'உ', 'く', 'ူ', 'ݙ', 'ூ', 'ሪ', 'ّ', 'ঘ', '[mya]', '[nya]', 'ீ', 'ထ', 'අ', 'ጠ', 'ڈ', 'ફ', '[sna]', 'ህ', 'ॉ', '[lao]', 'ผ', 'Ձ', '以', 'ഴ', 'බ', 'ሳ', 'ृ', '[orm]', 'ಆ', '[khm]', '係', 'ז', 'ಥ', 'ኢ', '行', 'Ļ', '[som]', 'భ', 'ሆ', '[fil]', 'Ӣ', 'き', 'Ӧ', 'ሉ', 'ʼ', 'ณ', '[oci]', 'Ҷ', 'ڳ', 'ഡ', '[kam]', 'Ღ', 'ݨ', '을', 'ஆ', 'Ƙ', 'ਉ', 'ቶ', 'ຮ', 'ካ', 'ቅ', 'औ', 'ଂ', 'ណ', '의', 'ణ', 'Փ', 'ਈ', 'ګ', 'హ', 'ौ', 'ໍ', 'ಭ', 'ಎ', '地', 'ఆ', 'ん', '하', 'ਖ', '自', '上', 'ፍ', 'ਡ', '可', 'ዮ', '生', '[ibo]', 'ٻ', 'さ', 'ູ', '多', 'ޓ', 'ភ', '니', '不', 'ढ', 'ઈ', 'Ň', 'ഥ', 'ศ', '要', 'Ժ', 'ී', 'ۋ', 'ො', 'ሮ', '年', '到', 'Ძ', 'ซ', '۾', '你', '로', 'ጋ', '[lug]', 'ঞ', '国', 'ផ', '個', 'ฉ', '和', 'ஒ', 'ខ', '[mri]', '他', 'ଠ', 'ຼ', 'ঙ', 'ޅ', '唔', 'ຳ', 'ځ', '៉', 'ថ', '가', '[lin]', 'Ճ', '은', 'ធ', '੍', 'Đ', '嘅', '出', 'ټ', 'Ĕ', 'ス', '지', '下', 'ୂ', 'ޑ', 'Ɗ', 'ຸ', 'ઉ', 'ញ', 'ሁ', 'ޖ', '”', '用', 'ሩ', 'ޔ', '为', '了', 'ຖ', 'එ', 'ጣ', 'ઇ', '日', 'ޯ', 'よ', '고', 'ኛ', 'ሊ', '得', 'ൈ', 'ಒ', 'ፈ', '好', '同', '成', '分', 'そ', 'ธ', 'Ҭ', 'Ğ', 'Ṭ', 'ዲ', 'Њ', 'ڏ', '۽', '物', 'ޫ', 'Ჩ', 'Ӯ', '都', 'け', '个', 'ഫ', 'Ō', 'ዘ', '方', 'ډ', 'Ḍ', '学', 'Օ', 'だ', '能', 'ڊ', '部', '仔', 'ภ', 'థ', 'ओ', 'Җ', 'ษ', 'ಉ', 'ឹ', '서', 'ਫ', 'Ï', 'ル', 'ୱ', 'ਧ', 'ቱ', '就', 'Ű', 'ళ', '斯', '作', 'ໂ', 'ණ', 'ጵ', '사', 'ఎ', 'ဘ', 'ఈ', '있', '時', 'ॅ', '한', '기', 'ଘ', 'ଫ', 'ڄ', 'Ď', 'ऊ', 'ఒ', 'ሎ', 'ቃ', 'ೈ', 'ْ', 'ຢ', 'Ҩ', '前', 'ዊ', 'Ҟ', '世', 'ญ', 'イ', '公', 'ໜ', 'え', 'ト', '၏', 'ৌ', 'Ḥ', 'ሲ', '家', 'ៃ', 'څ', '会', 'ಈ', 'Ẽ', 'ア', 'ち', '子', 'ઝ', '를', '္', 'ሞ', '事', '本', '最', 'ຽ', 'ङ', '會', 'ଓ', 'ਭ', 'ሽ', 'ኮ', '之', '己', '스', 'ဝ', '入', 'ළ', 'ዝ', '于', '理', 'ဟ', 'ڙ', 'Ҵ', '名', '新', '法', 'ୃ', 'ዛ', 'ቻ', 'ラ', 'ઘ', '수', '습', 'ਥ', '所', '界', 'つ', 'ஸ', 'ఫ', 'ቢ', 'ৎ', 'わ', '過', 'ഈ', '으', '来', 'ဂ', '定', '利', '者', 'お', '為', 'Ɛ', '아', 'ຶ', 'ဒ', 'ጉ', 'ኖ', '度', '常', 'ഖ', 'ឡ', '도', 'Ḓ', '去', '其', '意', 'ኑ', '这', '时', '通', 'Ֆ', '리', '어', 'ቡ', '合', 'ञ', '間', 'ଞ', 'Ќ', 'ږ', '知', 'ሬ', '手', 'ኤ', '¿', 'や', 'ך', '៊', '인', 'ዚ', 'ؤ', '들', '科', 'ど', 'ッ', 'ば', 'ಖ', 'ឺ', '시', 'ಫ', '文', '主', 'め', '見', 'ฟ', '해', 'ሀ', '州', '水', '小', '目', 'ଢ', 'ශ', 'ኒ', 'ሌ', 'ጊ', '三', '西', '特', '그', '第', '山', '대', '자', '„', '明', '位', 'せ', 'ښ', 'ጀ', '“', '二', '路', 'リ', '加', '教', '國', 'ඩ', '道', '市', '表', '立', '而', '食', 'ޒ', 'Ψ', '動', '័', 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'될', '엔', '업', '쿠', '머', '쇼', '베', '누', '천', '별', '칠', 'ڱ', '´', '預', 'ඊ', 'ጳ', 'ዡ', 'ዢ', 'ጧ', 'Ґ', 'ড়', '嘉', '营', '梦', '豪', '陵', '续', '馆', '敦', '旧', '态', '帮', '祝', '浩', '构', '顶', '康', '评', '藓', '丁', '吸', '姓', '句', '梯', '餐', '菲', '奖', '蕨', '呈', '预', '唱', '透', '备', '儿', '谢', '幕', '爱', '措', '穿', '股', '津', '墨', '潮', '窗', '革', '逐', '骑', '云', '够', '智', '灯', '守', '兄', '稀', '赞', '恩', '避', '终', '折', '默', '买', '玻', '远', '韦', '届', '敏', '难', '烈', '细', '借', '胎', '困', 'ޘ', '՛', '薄', '毎', '雑', '暑', 'ぼ', '眠', '弁', 'ぜ', '読', 'ふ', '賞', '尖', '増', '肺', '吐', '瞬', '宮', '覆', '処', '傷', '媒', '誰', '輩', '盤', '欠', '巡', 'ユ', '乗', '慣', '削', '歹', '尻', '膠', '掠', '奶', '齒', '嘛', '誠', '餘', '亂', '豬', '彈', '歐', '崁', '歡', '藝', '囡', '郵', '截', '癮', '黐', '麥', '趣', '燈', '驗', '屬', '擔', 'ฑ', '謝', '啫', '瞓', '絕', '嘞', '審', '儘', '租', '厄', '芬', '检', '炸', '伤', '障', 'ઍ', '£', '険', '則', 'ಓ', '굴', '량', '번', '협', '박', '변', '폭', '십', '랜', '걸', '릴', '헤', '료', '품', '절', '병', '먼', '복', '날', '킬', '접', '럽', '닌', '싸', '극', '໌', '¨', 'Ѐ', '၃', '狀', '舉', 'ඕ', 'ඬ', 'ዉ', 'ቺ', 'ዤ', 'ጢ', '[dan]', 'ঔ', 'ঋ', '乔', '鳞', '春', '崎', '伐', '户', '迷', '词', '宝', '购', '编', '淆', '庄', '岐', '莎', '胶', '庆', '郡', '逃', '满', '卢', '招', '盖', '厦', '巧', '旦', '螺', '纲', '姐', '旁', '腊', '御', '疾', '井', '剧', '兴', '值', '败', '须', '采', '答', '融', '剩', '雄', '逮', '监', '调', '爪', '专', '锦', '凌', '副', '错', '瑟', '梅', '策', '桃', '迹', '脉', '囊', '址', '霸', '优', '章', '胞', '柏', '狼', '善', '魅', 'Ệ', '[epo]', 'ٔ', '̂', '[frr]', 'ड़', '財', '齢', '宇', '農', '頃', '晩', '役', '築', '挙', '勝', '硬', '込', '俺', '図', '怖', '囲', '漢', '遇', '層', '撮', '辛', '顔', '腰', '済', '尤', '養', '椅', '芸', '軽', '昨', '操', '援', '厳', '庫', '魂', '損', '繁', '枝', '[kaz]', '桶', '鼻', '旋', '閒', '猴', '婦', '拄', '薯', '禮', '佇', '箍', '戇', '塊', '爺', '險', '褲', '諸', '蘇', '畫', '蛋', '籠', '鎮', '隔', '蟲', '徛', '桌', '柴', '肚', '磁', '溶', '峰', '懸', '專', '猫', '刀', '歲', '插', '災', '裁', '璃', '療', '揀', 'Ŕ', '๋', '[tok]', '[vie]', 'Ư', '靜', '衆', '媽', '遲', '壓', '聊', '梗', '堅', '據', '執', '污', '靚', '錯', '擁', '斷', '欣', '靠', '裂', '彎', '烤', 'ፀ', 'ሣ', '፡', 'ሔ', '[bos]', '[ceb]', '仅', '枪', '益', '聚', '袭', '仰', '[ful]', '撃', '濃', '[kea]', 'ឍ', '៧', '잘', '혹', '텔', '념', '쉽', '곡', '뉴', '존', '깨', '끝', '왔', '길', '염', '판', '팀', '침', '울', '육', '턴', '푸', '첫', '청', '망', '숙', '렇', '칸', '웨', '획', '막', '[luo]', 'ഊ', 'ॲ', '[umb]', '滿', '[zul]', 'ඥ', '[sun]', 'ః', '[tsn]', '[tos]', '[tso]', 'ፏ', 'ዟ', 'ዪ', 'ኰ', 'ኜ', '黄', '鳍', '勤', '责', '绩', '泛', '梁', '涅', '允', '继', '掉', '拆', '戴', '蚬', '翁', '墙', '虎', '扁', '藉', '塑', '伍', '封', '床', '睡', '减', '丰', '楼', '俗', '叉', '侧', '积', '厅', '丽', '筑', '桥', '拥', '咬', '碎', '溯', '筒', '摸', '妹', '祥', '请', '摄', '杉', '辐', '吧', '替', '换', '顿', '鸟', '昔', '贯', '赫', '丝', '惜', '郎', '连', '乾', '苯', '胺', '玛', '闻', '宁', '双', '狱', '订', '甜', '极', '唐', '灾', '岁', '牌', '衰', '爸', '脚', '碟', '墓', '涯', '砂', '秘', '鞋', '缘', '简', '练', '赏', '迪', '膨', '辆', '丘', '尊', '搭', '临', '惠', '棵', '启', '鹿', '麦', 'ʿ', '伝', '寝', '狭', '夕', '圧', '窓', '腕', '渡', '専', '練', '劇', '柔', '盲', '振', '団', '満', '歳', '円', '徹', '眺', '辺', 'ひ', '値', '湯', '砲', '羊', '職', '聖', '筋', '負', 'ヌ', '犬', '棚', '択', '賭', '̣', '[lit]', '劍', '臘', '舊', '串', '鐵', '貴', '遮', '媠', '煎', '頸', '彙', '涼', '餅', '埕', '啥', '暢', '崙', '陳', '膏', '埔', '宅', '麵', '睏', '鼓', '磅', '駛', '飼', '竹', '廢', '讚', '爐', '陰', '銅', '牆', '勞', '虹', '毫', '羹', '渴', '疊', '碼', '黨', '畢', 'ฆ', 'Ộ', '搜', '腳', '嚴', '圈', '伸', '兒', '啩', '咖', '啡', '蘋', '歧', '複', '隨', '卻', '綠', '慶', '赢', '势', '严', '藻', '撞', '艘', '驶', '旺', '阱', '琴', '敲', '钉', '愿', '峡', '轮', '；', '迅', '竞', '肢', '氰', 'ઐ', '־', '戻', '唆', '券', '紛', '総', '競', '衣', '罪', 'ಊ', '៖', '갖', '므', '맞', '럭', '외', '황', '냅', '객', '숨', '압', '왕', '덤', '탐', '홍', '렸', '큰', '릅', '앞', '풍', '애', '살', '평', '늘', '친', '글', '퍼', '뒤', '째', '냥', '골', '얼', '잡', '램', '끄', '익', '텐', '섬', '쟁', '슷', '엘', '잠', '택', '웹', '광', '렌', '슬', '떤', '끌', '၈', '၂', 'ဧ', 'ဩ', 'ଐ', 'ਔ', 'Ế', '顛', '廟', '稅', '騎', '鮮', '潛', 'ෛ', 'ෆ', 'ಛ', 'ኸ', 'ጼ', 'ኚ', 'ጺ', 'ቿ', 'ዠ', 'ፑ', '̈', '宋', '岭', '渐', '燥', '畔', '凡', '柄', '姜', '勇', '绪', '饼', '蔡', '负', '戟', '旗', '乌', '络', '汇', '潭', '扩', '佐', '阜', '桑', '释', '舄', '滨', '隶', '循', '贸', '劲', '协', '虑', '占', '压', '壮', '炭', '脂', '翻', '昂', '橄', '榄', '秦', '葡', '萄', '伏', '豚', '邀', '冈', '鳖', '李', '卧', '罕', '弈', '斑', '纺', '捷', '搬', '乙', '亨', '苗', '稳', '幼', '辽', '脑', '乱', '碘', '宪', '鼎', '蛙', '桂', '脊', '椎', '扮', '穆', '玉', '伟', '磨', '怀', '彦', '卷', '刷', '勋', '爵', '妃', '奏', '胡', '孝', '饭', '贵', '钱', '养', '顺', '妥', '弹', '劳', '匈', '晶', '喷', '辑', '紧', '伴', '幻', '汪', '蟹', '梭', '卒', '虽', '鹃', '拓', '鲨', 'ޟ', 'Ĉ', '綴', '・', '晴', '宙', '遅', '胸', '奪', '労', '抜', '儀', '泥', '棒', '緒', '捨', '抱', '帽', '拠', '昼', '麗', '姿', '介', '聴', '幸', '丸', '敷', '巻', '肩', '額', '掛', '怒', '賀', '押', '瓶', '訓', '編', '惑', '晡', '吊', '擘', '囥', '忠', '貢', '赴', '擲', '爿', '霧', '橐', '滾', '鹹', '誕', '衫', '鬚', '圓', '捌', '泉', '柑', '鄰', '糋', '芳', '嶼', '捲', '斤', '鼠', '賤', '咒', '嚨', '獨', '煮', '彰', '壽', '姊', '隱', '淚', '贊', '錫', '挖', '韓', '颱', '瑪', '牽', '銃', '窟', '漁', '剾', '蔗', '脈', '腔', '荒', '袋', '仝', '幣', '藍', '貼', '倖', '販', '乞', '漏', '廳', '貿', '怎', '跋', '盡', '洪', '榮', '乏', 'ଊ', 'ۂ', 'Ầ', '垃', '圾', '釋', '募', '怕', '閱', '阻', '軟', '煲', '賣', '屌', '偈', '喂', '跌', '醫', '獎', '佬', '儲', '杯', '匿', '罰', '濕', '貫', '獄', '慘', '雖', '鍵', '躍', '፥', 'ዐ', 'ቮ', 'ዣ', '٦', '择', '汽', '补', '犹', '稍', '净', '屿', '驾', '弱', '赖', '摆', '钟', '寸', '匹', '冲', '摧', '汤', '棕', '隐', '殖', '课', '炎', '舞', '诉', '览', '裔', '浓', '禽', '寻', '尚', '珀', '跑', '΄', 'Ϋ', '٪', '繰', '跡', '駐', '営', '摘', '頻', '猛', '墜', '斜', 'ゥ', '扱', '億', '採', 'ಋ', '១', '측', '죄', '럼', '끼', '캐', '떨', '규', '억', '풀', '쓸', '닙', '멘', '깥', '덮', '색', '콩', '척', '땅', '밀', '검', '욕', '밝', '냐', '괴', '킹', '녀', '웃', '락', '촉', '밴', '왜', '캠', '핑', '쓰', '혐', '슈', '빠', '켰', '컨', '롤', '슨', '참', '클', '완', '흑', '둘', '농', '큼', '싱', '례', '악', '폴', '먹', '창', '맹', '륙', '송', '렴', 'ൌ', '၆', '၄', '၉', 'ဌ', 'ဍ', 'ڦ', '[srp]', '[tur]', 'Ữ', 'Ờ', 'Ừ', 'Ḿ', '揚', '徑', '迴', '寬', '豹', '壘', '牠', '劃', '墮', '艙', '僅', '簽', '霄', '賓', 'ඝ', 'ඌ', 'ඛ', 'ඨ', 'ઃ', '೫', 'ጶ', 'ሿ', 'ጇ', 'ዦ', 'ሼ', '阴', '靖', '烧', '燃', '圆', '润', '胤', '禛', '迦', '禅', '赵', '履', '罂', '粟', '贾', '雇', '佣', '壳', '凉', '涉', '冒', '觉', '遵', '崔', '铺', '宾', '矗', '谋', '饶', '漱', '恒', '锥', '翅', '韩', '扶', '济', '鬃', '趾', '懒', '绣', '瞩', '侯', '奋', '凝', '递', '颗', '衔', '豇', '棋', '陈', '扬', '剂', '铬', '钴', '误', '蝮', '蛇', '勃', '董', '堆', '瑕', '刘', '促', '详', '逗', '萌', '轨', '帘', '竺', '葵', '阮', '盗', '霍', '厂', '撒', '询', '唇', '鸠', '阁', '帅', '闪', '黎', '销', '乃', '挑', '贪', '猜', '妳', '墅', '腺', '鞍', '擦', '烟', '氟', '锌', '妇', '坎', '狠', '迁', '矶', '芭', '档', '誉', '蜜', '薛', '陀', '驱', '废', '粮', '谦', '茱', '姑', '淖', '嘎', '仪', '炮', '绞', '臂', '抖', '阶', '驳', '装', '薪', '货', '虚', '拟', '辅', '售', '执', '愚', '蓝', '邑', '径', '脏', '逊', '瑙', '疏', '墟', '狩', '灭', '屠', '础', '驻', '渔', 'ޛ', 'Ŭ', 'Ĵ', 'ढ़', '皿', '駅', '脅', '薬', '菓', '箱', '縦', '横', 'ぱ', '謀', '携', '帯', '焼', '傘', '姉', '煙', '泣', '札', '惟', '践', '逆', '騒', '雰', '揺', '酔', '踏', '浸', '妙', '黒', '徳', '積', '飽', '吹', '臨', '払', '恨', '纏', '悟', '掃', '訳', '披', '剤', '遂', '絞', '鉛', '廊', '陥', '奴', '呆', 'Ų', 'ഃ', '鬥', '瘦', '胿', '搶', '嘔', '鈴', '劉', '醋', '趁', '罨', '墘', '噴', '縣', '覽', '倚', '恬', '稚', '臟', '嗄', '恁', '孫', '枋', '糕', '茫', '迒', '衝', '翕', '娘', '剛', '醬', '蓮', '勥', '趒', '鬍', '灘', '喝', '舌', '碧', '唌', '麭', '咳', '顧', '蝦', '癌', '燕', '糜', '撚', '藥', '昏', '櫃', '嚇', '曝', '凍', '褪', '鑼', '陪', '牢', '孤', '冕', '睭', '抾', '桮', '鹽', '喔', '鳳', '廈', '稻', '寧', '韻', '歕', '滷', '拖', '蜂', '搖', '釣', '蚵', '剪', '佗', '跩', '腸', '擋', '癱', '瘓', '薑', '汴', '絳', '柯', '淺', '椰', '喌', '濁', '痟', '廠', '臆', '伨', '炕', '恆', '姨', '晝', '颺', '懵', '懂', '攄', '悠', '挫', '框', '虛', '姚', '齣', '憂', '尪', '喬', '誦', '溝', '趖', '筊', '䆀', '湠', '叢', '拔', '摔', '縫', '扭', '寶', '辣', '逝', '贏', '夯', '趨', '煏', '嬰', '鎖', '棍', '絲', '詐', '騙', '揤', '蓬', '闊', '泳', '眵', '檢', '矮', '坵', '礦', '挽', '熊', '勘', 'ٓ', '‹', '›', 'ۃ', 'Ở', 'Ề', 'Ể', '帳', '忽', '妨', '噃', '債', '謹', '跪', '榨', '恥', '惡', '倍', '竟', '撈', '揮', '懷', '簷', '卸', '慳', '壯', '概', '攝', '猩', '乳', '啓', '鳩', '犀', '嗱', '薦', '詳', '詩', '吞', '輯', '套', '遷', '蕩', '蒲', '傻', '靈', '嗯', '爲', '滔', '。', '悶', '撳', '仗', '簿', '愈', '獻', '貌', '刪', '薩', '偷', '鵝', 'ሑ', 'ዑ', 'Ѝ', '№', '函', '灵', '瀑', '壤', '绿', '弯', '扔', '宜', '眨', '奎', '篇', '闭', '袖', '听', '胁', '敞', '秒', '蚊', '复', '剥', '额', '拘', '窑', '忧', '烦', '鲍', '块', '惯', '锅', '烹', '柬', '寨', '邻', '纷', '臣', '异', '讯', '狮', '噬', '榈', '锋', '遗', '训', '飓', '郁', '惧', '拨', '杀', '峭', '慎', '湿', '险', '宴', '伽', '恶', '针', '肠', '畅', '圭', '翰', '钳', '藤', '吃', '虐', '囚', '暹', '榜', '刚', '遥', '煤', '杂', '葱', '珍', '篮', '寂', '宠', '犒', 'ઔ', '氷', '圏', '敵', '責', '棄', '効', '頼', '購', '竜', '誘', '庁', '幹', '敬', '廃', '催', '徴', '滞', '訛', '郷', '貧', '憲', '釈', '転', '×', '襲', '勧', '艦', '礁', '～', '崩', '馳', 'ಢ', '០', '븐', '붙', '즉', '률', '싶', '갑', '링', '낮', '꽤', '느', '줄', '흥', '쌓', '탕', '엄', '쳐', '플', '잔', '틴', '덜', '얇', '책', '헨', '즌', '훈', '컴', '퓨', '얻', '힘', '엽', '찰', '좌', '즐', '암', '옵', '렵', '콜', '뜰', '혁', '축', '깃', '핀', '쿄', '옮', '곤', '켜', '펼', '짧', '빵', '펜', '술', '묘', '찬', '켓', '겠', '톱', '냈', '혼', '윤', '낙', '템', '칼', '릭', '났', '씨', '징', '폐', '넘', '허', '께', '잉', '덴', '팬', '꽃', '희', '냄', '뜨', '훨', '씬', '략', 'Љ', 'ဈ', 'ଔ', 'ଋ', '‑', 'Ự', 'Ứ', '鬧', '紐', '諾', '毀', '罹', '．', '嘆', '鯊', '奔', '啟', '屈', '懼', '扯', '擇', '莉', '茲', '倫', '繪', '攜', '麼', '鋼', '汀', '釀', 'ඤ', '૦', '೭', '೧', 'ఔ', 'Ǎ', 'ቩ', 'ጱ', 'ጔ', 'ኲ', 'ኟ', 'ጬ', '恰', '涩', '睛', '矾', '矿', '惩', '罚', '樟', '畿', '挥', '厕', '屏', '贤', '铉', '鹰', '佑', '婿', '湍', '槽', '斋', '硕', '橡', '氧', '讣', '捩', '滩', '崛', '舟', '层', '鬣', '腭', '臼', '齿', '橙', '译', '仓', '绑', '肖', '纽', '皋', '堰', '傅', '偶', '谁', '褐', '弘', '壬', '戌', '颁', '褒', '崇', '郑', '醉', '献', '鲷', '苞', '莆', '描', '顾', '喀', '曰', '垓', '疵', '轴', '胖', '躯', '絮', '魁', '嘘', '豁', '弛', '豫', '芝', '丙', '哌', '酯', '抓', '牻', '氨', '唑', '芮', '琶', '淮', '琉', '熙', '浙', '泽', '秋', '酮', '酰', '氯', '溴', '镁', '蟾', '塘', '沼', '栖', '幡', '漠', '累', '耀', '徽', '滇', '缅', '颅', '窦', '叔', '坊', '扫', '淘', '汰', '嗜', '晕', '眩', '叛', '郿', '樱', '枫', '桐', '茎', '腿', '弃', '煜', '坷', '甩', '载', '垂', '鸦', '酵', '涸', '肋', '逢', '萸', '蔻', '琳', '钮', '耗', '疆', '巷', '贳', '颜', '痹', '摊', '牵', '夹', '庚', '膀', '颤', '慈', '呵', '寿', '雾', '涂', '抹', '涨', '胀', '浦', '沽', '碑', '叠', '凑', '荧', '杨', '肃', '综', '堪', '肤', '姹', '紫', '嫣', '劣', '蠢', '沤', '尧', '丞', '笼', '捞', '迈', '艰', '曹', '甸', '邓', '沐', '孟', '俯', '诞', '沱', '凤', '凰', '筼', '筜', '攸', '诃', '匠', '伪', '贡', 'ﷺ', 'Ĝ', 'Ḡ', 'Ƴ', 'ज़', 'ʹ', '〇', '郊', '賛', '肥', '曇', 'ぺ', '忙', '浴', '塁', '悲', '偉', '縮', '暮', '隅', '曖', '昧', '懐', '戸', '繕', '浜', '汚', '軒', '邪', '殻', '歓', '瞑', 'ぷ', '慌', '壇', '枠', '栄', '炊', '柢', '頑', '畳', '（', '）', '桁', '仮', 'ゼ', '沈', '肘', '狙', '悔', '償', '輝', '捉', '純', '粋', '穏', '痴', '剣', '懇', '僧', '巫', '拝', '馴', '缶', '脇', '貯', '俳', '丈', '蔵', '棟', 'ぽ', '楊', '隷', '拡', '‟', '⅛', 'ഢ', '─', '餌', '紡', '瀾', '僫', '捅', '峇', '爽', '舖', '剔', '蛣', '蛚', '銘', '昶', '攑', '潘', '碗', '遛', '扲', '魏', '膵', '澹', '酥', '溼', '踮', '蚶', '頷', '辭', '湧', '莒', '兜', '駁', '鸚', '苴', '偏', '拭', '摃', '寇', '痀', '吳', '剉', '喊', '綏', '朴', '滄', '硨', '磲', '豐', '鄞', '嗽', '霜', '抿', '捋', '繏', '絚', '擴', '巢', '梘', '叨', '恙', '崑', '癀', '砰', '旭', '畝', '夥', '煌', '檨', '卑', '躘', '扴', '繫', '擉', '燦', '燙', '疱', '耙', '擺', '凱', '壢', '翠', '驛', '鹼', '秤', '錘', '貪', '曉', '捎', '罟', '揻', '扞', '泅', '菇', '旒', '菱', '芙', '蓉', '甕', '匯', '頁', '埒', '梨', '蔣', '渭', '霞', '輾', '蕊', '芋', '爁', 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'乸', '痕', '圳', '淪', '佔', '逳', '誤', '冧', '勁', '鄭', '墊', '厭', '禾', '叭', '糊', '踩', '糟', '窩', '挺', '惰', '鏈', '矇', '嘈', '雌', '佩', '唉', '嗌', '遍', '窮', '雜', '瀏', '嘩', '恭', '鑊', '撩', '怨', '譜', '咋', '厲', '嬲', '瘟', '俊', '俏', '攞', '攬', '顏', '撇', '訣', '勳', '芥', '籽', '攪', '宵', '拗', '撬', '廁', '勿', '篼', '揦', '膽', '嘥', '酬', '吟', '齋', '擾', '踐', '謂', '娛', '罩', '矩', '穀', '睬', '擒', '掹', '蜎', '鱷', '噏', '閑', '笪', '辜', '趙', '穎', '趕', '嫲', '閂', '逼', '爛', '娥', '紹', '媳', '琪', '呃', '拐', '柳', '踴', '忍', 'ፆ', '፤', 'ሦ', 'ኣ', '\x93', '\x94', '辈', '奉', '赁', '丧', '赶', '腐', '遣', '册', '帐', '匣', '填', '顷', '漂', '杆', '兼', '诊', '挪', '宽', '钻', '拦', '仇', '颠', '彻', '尝', '％', '访', '碰', '坠', '陨', '咎', '箭', '饪', '钢', '键', '锐', '啸', '纹', '磷', '硫', '骚', '晓', '培', '骗', '疗', '锻', '炼', '喘', '陡', '谨', '梵', '鲜', '纯', '粹', '厉', '汁', '夺', '丢', '厌', '猿', '陶', '旨', '聪', '审', '铃', '璀', '璨', '垒', '劫', '荣', '惊', '叹', '绵', '攀', '损', '肿', '瘤', '龄', '齐', '鞭', '缭', '穷', '哺', '粘', '宏', '倾', '财', '纸', '荐', '媚', '滥', '嘲', '泄', '盘', '廷', '抢', '敛', '猎', '苹', '笔', '愤', '慨', '罄', '茵', '碍', '舱', '励', '雕', '酋', '镑', '暂', '疽', '卓', '咨', '饮', '笨', '闲', '逛', '§', '€', '․', '捜', '涙', '渉', '拾', '麓', '屯', '巣', '懲', '戒', '翌', '裕', '嵐', '渦', '晰', '弊', '賄', '脳', '羽', '窃', '搾', '叩', '戚', '闘', '覧', '掲', '綻', '扉', '裏', '鳴', '怯', '胆', '憾', '猟', '賊', '狂', '滝', '駆', '揃', '継', '沢', '炉', '譲', '１', '亜', '壊', 'ゾ', '仏', '唸', '隠', '宛', '診', '祉', '阪', 'Ұ', '២', '혈', '액', '팸', '돈', '춘', '꼭', '랍', '꼽', '힐', '샤', '핼', '팩', '댈', '뇨', '밤', '높', '줬', '핵', '융', '릉', '뭅', '납', '릎', '죽', '헬', '콥', '콘', '줍', '뿐', '잃', '커', '젼', '범', '헝', '롱', '뻗', '철', '녹', '귀', '겪', '묵', '낼', '셀', '햇', '빛', '센', '렀', '섯', '늄', '층', '휠', '볍', '총', '잭', '담', '낸', '읽', '앙', '싣', '썰', '랙', '맥', '닫', '백', '갈', '갇', '겁', '팡', '셔', '뒷', '랫', '윙', '졸', '겼', '맷', '옛', '빈', '벽', '딘', '낭', '벤', '캄', '놈', '갱', '답', '논', '햄', '렉', '뤄', '멀', '좀', '뭔', '텍', '쿨', '벨', '벗', '앉', '옆', '긋', '빼', '괜', '찮', '뚫', '뀌', '쇄', '뷰', '칭', '밑', '빙', '널', '튼', '샛', '룩', '셈', '캔', '좁', '욱', '둥', '폰', '앱', '찾', '짐', '텨', '씀', '탁', '쌍', '섭', '곰', '붕', '퀴', '탑', '닥', '슴', '롭', '득', '옥', '맛', '훼', '됐', '몬', '죠', '빗', '덕', '흩', '몰', '칙', '흡', '랐', '뱅', '삼', '췄', '뽑', '혔', '꿈', '၇', 'ဋ', 'ୄ', 'ª', '\xad', 'ํ', 'Ả', 'Ợ', 'Ổ', 'Ậ', 'Ủ', 'Ẫ', '倡', '珊', '涵', '銳', '萊', '峽', '寓', '哪', '慮', '脹', '檯', '吼', '嘯', '駝', '麋', '吾', '繞', '悅', '獵', '悉', '鏢', '崗', '紓', '傑', '晤', '潔', '葛', '窯', '烘', '鈔', '銜', '遙', '蹄', '勸', '﹑', '竊', '惹', '徵', '陌', '砸', '躋', '禍', '梠', '擬', '瞭', '駕', '／', '擅', '潦', '鴉', '妮', '颶', '竄', '倦', '碩', '頒', '奈', '哩', '蹈', '舒', '盒', '屆', '蕾', '詹', '旱', '裹', '巾', '豔', '裙', '壞', '飾', '盈', '夾', '輛', '拋', '謊', '刊', '魯', '漿', '殘', '迄', '弧', '觸', '緣', '盧', '剝', '潤', '穫', '鑽', '躲', '俚', '訝', '鑰', '匙', '俘', '虜', '註', 'ඓ', 'ඡ', 'ඈ', 'ඪ', 'ඵ', 'ઁ', '૧', '૯', '૭', '૮', '೯', '೨', 'ఱ', 'ఢ', '′', 'Ė', '<sos/eos>'], token_type='char', train_data_path_and_name_and_type=[('dump/raw/train_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/train_1h_lid/text', 'text', 'text')], train_dtype='float32', train_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/train.9.scp'], unused_parameters=False, use_amp=False, use_matplotlib=True, use_preprocessor=True, use_tensorboard=True, use_wandb=False, val_scheduler_criterion=('valid', 'loss'), valid_batch_bins=None, valid_batch_size=None, valid_batch_type=None, valid_data_path_and_name_and_type=[('dump/raw/dev_1h_lid/wav.scp', 'speech', 'sound'), ('dump/raw/dev_1h_lid/text', 'text', 'text')], valid_max_cache_size=None, valid_shape_file=['exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.9.scp'], version='202211', wandb_entity=None, wandb_id=None, wandb_model_log_interval=-1, wandb_name=None, wandb_project=None, write_collected_feats=False)
+/scratch/bbjs/chen26/espnet_01_23/tools/anaconda/envs/espnet_01_23/lib/python3.8/site-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
+  warnings.warn(_create_warning_msg(
+[cn001] 2023-07-06 08:53:07,826 (collect_stats:106) INFO: Niter: 100
+[cn001] 2023-07-06 08:53:17,466 (collect_stats:106) INFO: Niter: 200
+[cn001] 2023-07-06 08:53:24,461 (collect_stats:106) INFO: Niter: 300
+[cn001] 2023-07-06 08:53:31,609 (collect_stats:106) INFO: Niter: 400
+# Accounting: begin_time=1688651414
+# Accounting: end_time=1688651639
+# Accounting: time=225 threads=1
+# Finished at Thu Jul 6 08:53:59 CDT 2023 with status 0
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/config.yaml b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e2455c3c60a9b76b23366ec26f917b15fa30f271
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/config.yaml
@@ -0,0 +1,6636 @@
+config: conf/tuning/train_asr_fbank_single.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/asr_stats_multilingual_1h_lid_1h/logdir/stats.9
+ngpu: 0
+seed: 0
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: false
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: true
+write_collected_feats: false
+max_epoch: 30
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 5
+nbest_averaging_interval: 0
+grad_clip: 5.0
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 4
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+drop_last: false
+debug_grad: false
+log_interval: null
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 500
+batch_size: 8
+valid_batch_size: null
+batch_bins: 1000000
+valid_batch_bins: null
+train_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/train.9.scp
+valid_shape_file:
+- exp/asr_stats_multilingual_1h_lid_1h/logdir/valid.9.scp
+batch_type: sorted
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+-   - dump/raw/train_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_1h_lid/text
+    - text
+    - text
+valid_data_path_and_name_and_type:
+-   - dump/raw/dev_1h_lid/wav.scp
+    - speech
+    - sound
+-   - dump/raw/dev_1h_lid/text
+    - text
+    - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+    lr: 0.0001
+    weight_decay: 1.0e-06
+scheduler: null
+scheduler_conf: {}
+token_list:
+- <blank>
+- <unk>
+- <space>
+- A
+- E
+- I
+- N
+- O
+- T
+- S
+- R
+- L
+- U
+- D
+- M
+- K
+- H
+- G
+- C
+- P
+- B
+- А
+- V
+- Y
+- W
+- Z
+- О
+- Н
+- J
+- F
+- Е
+- И
+- Т
+- Р
+- ا
+- Л
+- С
+- К
+- Д
+- М
+- В
+- У
+- ा
+- Ы
+- É
+- ی
+- و
+- ن
+- ر
+- П
+- Г
+- Á
+- Б
+- া
+- ്
+- Q
+- ل
+- Í
+- ्
+- م
+- З
+- ت
+- د
+- र
+- Ä
+- Я
+- ்
+- Й
+- Ա
+- Ш
+- ে
+- Э
+- ە
+- क
+- ি
+- ک
+- े
+- ্
+- X
+- Х
+- Ь
+- Ч
+- '1'
+- '4'
+- ب
+- Ó
+- র
+- ي
+- न
+- س
+- І
+- ه
+- ন
+- त
+- ক
+- ि
+- Ა
+- ા
+- ി
+- '3'
+- Ж
+- Ն
+- स
+- Α
+- ी
+- Ი
+- ल
+- '[eng]'
+- ত
+- '0'
+- य
+- ന
+- ക
+- ್
+- म
+- ह
+- '2'
+- ା
+- ব
+- Ր
+- Ο
+- Ո
+- ു
+- Τ
+- Σ
+- য
+- Ե
+- Ц
+- Ö
+- ল
+- Ү
+- க
+- ത
+- Š
+- ်
+- ى
+- ு
+- ो
+- ৰ
+- ି
+- Ł
+- ్
+- À
+- '[nld]'
+- व
+- प
+- Ε
+- Ე
+- ാ
+- ಿ
+- ે
+- Ι
+- ి
+- Ú
+- ୍
+- ं
+- ର
+- Č
+- স
+- Ü
+- த
+- ش
+- '[deu]'
+- ி
+- า
+- Ν
+- ম
+- প
+- پ
+- ہ
+- ર
+- ے
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+- Ә
+- ా
+- Ի
+- ు
+- ق
+- ަ
+- ು
+- ج
+- യ
+- น
+- ્
+- Ž
+- Ს
+- Ə
+- Ւ
+- Ă
+- ರ
+- ര
+- Ọ
+- ئ
+- ದ
+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
+- Ю
+- Մ
+- Ρ
+- ી
+- େ
+- ട
+- ु
+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
+- ం
+- ร
+- '[fra]'
+- È
+- Π
+- പ
+- ज
+- Ż
+- Κ
+- ત
+- ల
+- อ
+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
+- ្
+- ர
+- Կ
+- ட
+- વ
+- خ
+- ን
+- ก
+- ா
+- ।
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+- '[ita]'
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+- เ
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+- క
+- ނ
+- হ
+- ब
+- െ
+- ں
+- Ý
+- ง
+- း
+- ެ
+- ה
+- ೆ
+- ବ
+- ಗ
+- '[por]'
+- '[gle]'
+- ު
+- ಲ
+- ன
+- ો
+- Ლ
+- മ
+- Ą
+- ନ
+- ല
+- வ
+- ك
+- ာ
+- '[pol]'
+- ം
+- Μ
+- Დ
+- ม
+- ல
+- Υ
+- Ě
+- چ
+- ้
+- ಕ
+- Ნ
+- Η
+- ં
+- '9'
+- ट
+- ھ
+- Տ
+- জ
+- Ბ
+- ح
+- ତ
+- ន
+- Ć
+- Î
+- ั
+- ນ
+- ू
+- າ
+- Ӹ
+- ל
+- ই
+- '[xho]'
+- ި
+- आ
+- Յ
+- ု
+- '[swa]'
+- ಸ
+- સ
+- গ
+- റ
+- Λ
+- श
+- ય
+- এ
+- ಯ
+- '[swe]'
+- '5'
+- ற
+- শ
+- Ê
+- Վ
+- ୁ
+- Ვ
+- अ
+- ण
+- Ф
+- ছ
+- സ
+- Ẹ
+- ี
+- Õ
+- ว
+- '[sot]'
+- আ
+- '[tam]'
+- រ
+- ት
+- వ
+- త
+- ପ
+- ਰ
+- '[mal]'
+- ை
+- ద
+- င
+- ప
+- ێ
+- က
+- '[slk]'
+- '[slv]'
+- ী
+- '[ces]'
+- પ
+- Ś
+- మ
+- Ħ
+- ണ
+- '[glg]'
+- ച
+- ්
+- Ά
+- ಂ
+- ው
+- ସ
+- Ð
+- Ս
+- ย
+- િ
+- ိ
+- ମ
+- ள
+- מ
+- ய
+- চ
+- ת
+- Ί
+- ר
+- ੀ
+- Ӑ
+- '[afr]'
+- ۆ
+- ې
+- Щ
+- '6'
+- 的
+- '[nan]'
+- ด
+- ေ
+- '[ron]'
+- '[ukr]'
+- Უ
+- Ř
+- ള
+- ក
+- '8'
+- න
+- ล
+- '[fin]'
+- Ç
+- స
+- Ό
+- ੇ
+- લ
+- ່
+- '[mar]'
+- Თ
+- သ
+- ହ
+- ಮ
+- '[kat]'
+- ड
+- '[cat]'
+- '[hun]'
+- Լ
+- မ
+- ુ
+- ਸ
+- Ò
+- ै
+- '[nep]'
+- ท
+- య
+- の
+- ਿ
+- Ӗ
+- Έ
+- ́
+- ប
+- Ъ
+- Ì
+- ങ
+- ב
+- ਕ
+- ة
+- ਹ
+- የ
+- '[lav]'
+- డ
+- भ
+- '[est]'
+- ާ
+- ි
+- Ӓ
+- א
+- അ
+- ए
+- በ
+- ध
+- ש
+- أ
+- Ë
+- Ã
+- ਦ
+- '[nso]'
+- ပ
+- ስ
+- ۇ
+- ਨ
+- ’
+- ກ
+- ส
+- ಳ
+- থ
+- Ց
+- ،
+- Հ
+- ଲ
+- '[kir]'
+- ോ
+- '[ara]'
+- ச
+- Ē
+- ̀
+- ރ
+- —
+- േ
+- ந
+- '[eus]'
+- '[ell]'
+- တ
+- '[urd]'
+- ভ
+- ห
+- န
+- '[wol]'
+- थ
+- ດ
+- '[yor]'
+- ิ
+- Ҳ
+- آ
+- '[bre]'
+- చ
+- Ё
+- Ў
+- Ô
+- '[ind]'
+- જ
+- '[ckb]'
+- ਤ
+- ស
+- ต
+- Γ
+- ط
+- గ
+- ধ
+- አ
+- অ
+- ষ
+- މ
+- ค
+- '[hau]'
+- '[tha]'
+- ص
+- ງ
+- ተ
+- Ī
+- غ
+- ో
+- '[yue]'
+- ြ
+- '[abk]'
+- ކ
+- บ
+- Δ
+- Թ
+- ያ
+- ម
+- ట
+- ড
+- ਂ
+- נ
+- Გ
+- ව
+- '[hin]'
+- ር
+- Һ
+- '[mlt]'
+- ້
+- '[cym]'
+- હ
+- Ş
+- ވ
+- Ը
+- '7'
+- ለ
+- い
+- ಪ
+- ే
+- ක
+- Ő
+- Ụ
+- '[cmn]'
+- Შ
+- ם
+- ល
+- '[bul]'
+- '[fas]'
+- ል
+- ង
+- ख
+- '[uzb]'
+- ะ
+- અ
+- ့
+- መ
+- Գ
+- ត
+- ရ
+- Ț
+- '[asm]'
+- ڪ
+- Ხ
+- ष
+- '[hye]'
+- ເ
+- Դ
+- '[guj]'
+- စ
+- ଦ
+- ັ
+- '[bel]'
+- ހ
+- খ
+- ਲ
+- '[ven]'
+- '[cnh]'
+- အ
+- እ
+- '[kab]'
+- '[jav]'
+- ਵ
+- ອ
+- ය
+- ป
+- ദ
+- '[mon]'
+- Ҫ
+- ች
+- に
+- ະ
+- ও
+- ގ
+- Ք
+- છ
+- ም
+- ข
+- '[tat]'
+- ગ
+- ไ
+- ည
+- '[amh]'
+- '[bas]'
+- ൽ
+- Ω
+- し
+- '[sin]'
+- ণ
+- Ң
+- ම
+- Ղ
+- は
+- แ
+- ත
+- る
+- ಡ
+- '[skr]'
+- ർ
+- Է
+- ා
+- ટ
+- ଣ
+- '[kan]'
+- จ
+- で
+- દ
+- Ტ
+- उ
+- Ή
+- ୟ
+- '[grn]'
+- ଗ
+- Ù
+- ং
+- '[azz]'
+- Χ
+- '[ssw]'
+- ျ
+- ີ
+- '[ina]'
+- Պ
+- た
+- ှ
+- ມ
+- '[nbl]'
+- ע
+- ୋ
+- ಹ
+- Բ
+- Ť
+- '[chv]'
+- উ
+- ಟ
+- ວ
+- ੱ
+- ੋ
+- Ĩ
+- ଏ
+- '[kmr]'
+- શ
+- ነ
+- '[ory]'
+- ಬ
+- と
+- ና
+- Ύ
+- ଇ
+- फ
+- '[nno]'
+- '[mrj]'
+- छ
+- બ
+- ಅ
+- ީ
+- ދ
+- થ
+- ೇ
+- て
+- '[org_jpn]'
+- ண
+- ይ
+- அ
+- '[mhr]'
+- Ș
+- ד
+- '[nor]'
+- ތ
+- ใ
+- ခ
+- ీ
+- ර
+- Ც
+- ွ
+- ់
+- ଥ
+- ણ
+- ደ
+- ଟ
+- '[bak]'
+- ទ
+- Θ
+- ٹ
+- ื
+- ח
+- '[kin]'
+- Ø
+- Ị
+- ଆ
+- '[div]'
+- လ
+- આ
+- '[myv]'
+- ே
+- Ń
+- ທ
+- ු
+- ։
+- '[hrv]'
+- Ծ
+- な
+- Კ
+- इ
+- ק
+- 一
+- ਜ
+- Ҡ
+- ລ
+- Φ
+- ገ
+- ដ
+- అ
+- Â
+- を
+- פ
+- ె
+- ސ
+- ま
+- כ
+- ສ
+- ৈ
+- '[lga]'
+- ଜ
+- ਮ
+- Ҙ
+- '[xty]'
+- ʻ
+- ळ
+- យ
+- Ї
+- ெ
+- す
+- ൾ
+- '[uig]'
+- இ
+- َ
+- Ġ
+- ‘
+- Β
+- が
+- എ
+- '[sah]'
+- ู
+- พ
+- ض
+- ڕ
+- ई
+- ഷ
+- ލ
+- '[hsb]'
+- Ј
+- ස
+- ບ
+- ੰ
+- ማ
+- ላ
+- ৱ
+- ފ
+- ଅ
+- ൻ
+- ଡ
+- ุ
+- ਆ
+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
+- ખ
+- Ṣ
+- ็
+- ِ
+- ុ
+- ୀ
+- ڵ
+- ށ
+- វ
+- ീ
+- ਪ
+- Є
+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
+- ី
+- 人
+- ೂ
+- ೊ
+- ባ
+- ਼
+- ድ
+- ແ
+- ෙ
+- ଳ
+- ල
+- ফ
+- એ
+- Ქ
+- ആ
+- ብ
+- ำ
+- Ũ
+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
+- எ
+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
+- ຫ
+- ധ
+- Ľ
+- ൂ
+- ़
+- ଚ
+- Խ
+- ሰ
+- Ғ
+- ଭ
+- ג
+- బ
+- ਚ
+- か
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+- ປ
+- జ
+- ወ
+- ۔
+- Ზ
+- ಶ
+- ಚ
+- ੁ
+- 다
+- ီ
+- 是
+- Û
+- ଧ
+- Æ
+- Ӱ
+- អ
+- គ
+- إ
+- ଙ
+- ט
+- ូ
+- ث
+- ਅ
+- Წ
+- ታ
+- Ź
+- घ
+- ೀ
+- ഹ
+- ُ
+- 在
+- Ҕ
+- ជ
+- ಷ
+- ொ
+- Ფ
+- ິ
+- Þ
+- ଁ
+- ຂ
+- あ
+- ँ
+- 이
+- ໃ
+- ங
+- ழ
+- ۈ
+- Ջ
+- Პ
+- 有
+- ޮ
+- ূ
+- ਗ
+- Қ
+- っ
+- ਬ
+- ክ
+- ഇ
+- ଛ
+- ബ
+- Չ
+- හ
+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
+- ጥ
+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
+- ظ
+- 大
+- ら
+- Ņ
+- ោ
+- '[isl]'
+- ຄ
+- ဲ
+- Ċ
+- য়
+- ถ
+- ٘
+- ړ
+- ৃ
+- צ
+- も
+- Ყ
+- ٽ
+- ึ
+- う
+- '[ori]'
+- ഭ
+- Ҥ
+- '[pan]'
+- –
+- ଖ
+- ැ
+- ယ
+- ဆ
+- 에
+- ଼
+- ૂ
+- '[ltz]'
+- ఉ
+- Ξ
+- ン
+- '[tel]'
+- ዳ
+- శ
+- ధ
+- ዋ
+- '[aze]'
+- ຈ
+- '[nob]'
+- ొ
+- ໄ
+- '[snd]'
+- ພ
+- ಇ
+- ష
+- ژ
+- '[kor]'
+- ዎ
+- '[pus]'
+- ڻ
+- '[msa]'
+- ഉ
+- ષ
+- ះ
+- ួ
+- Ԥ
+- ן
+- ហ
+- '[tgk]'
+- ቀ
+- ៅ
+- ‍
+- ಧ
+- ຊ
+- ٿ
+- ఇ
+- ൊ
+- 中
+- Ζ
+- உ
+- く
+- ူ
+- ݙ
+- ூ
+- ሪ
+- ّ
+- ঘ
+- '[mya]'
+- '[nya]'
+- ீ
+- ထ
+- අ
+- ጠ
+- ڈ
+- ફ
+- '[sna]'
+- ህ
+- ॉ
+- '[lao]'
+- ผ
+- Ձ
+- 以
+- ഴ
+- බ
+- ሳ
+- ृ
+- '[orm]'
+- ಆ
+- '[khm]'
+- 係
+- ז
+- ಥ
+- ኢ
+- 行
+- Ļ
+- '[som]'
+- భ
+- ሆ
+- '[fil]'
+- Ӣ
+- き
+- Ӧ
+- ሉ
+- ʼ
+- ณ
+- '[oci]'
+- Ҷ
+- ڳ
+- ഡ
+- '[kam]'
+- Ღ
+- ݨ
+- 을
+- ஆ
+- Ƙ
+- ਉ
+- ቶ
+- ຮ
+- ካ
+- ቅ
+- औ
+- ଂ
+- ណ
+- 의
+- ణ
+- Փ
+- ਈ
+- ګ
+- హ
+- ौ
+- ໍ
+- ಭ
+- ಎ
+- 地
+- ఆ
+- ん
+- 하
+- ਖ
+- 自
+- 上
+- ፍ
+- ਡ
+- 可
+- ዮ
+- 生
+- '[ibo]'
+- ٻ
+- さ
+- ູ
+- 多
+- ޓ
+- ភ
+- 니
+- 不
+- ढ
+- ઈ
+- Ň
+- ഥ
+- ศ
+- 要
+- Ժ
+- ී
+- ۋ
+- ො
+- ሮ
+- 年
+- 到
+- Ძ
+- ซ
+- ۾
+- 你
+- 로
+- ጋ
+- '[lug]'
+- ঞ
+- 国
+- ផ
+- 個
+- ฉ
+- 和
+- ஒ
+- ខ
+- '[mri]'
+- 他
+- ଠ
+- ຼ
+- ঙ
+- ޅ
+- 唔
+- ຳ
+- ځ
+- ៉
+- ថ
+- 가
+- '[lin]'
+- Ճ
+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
+- ୂ
+- ޑ
+- Ɗ
+- ຸ
+- ઉ
+- ញ
+- ሁ
+- ޖ
+- ”
+- 用
+- ሩ
+- ޔ
+- 为
+- 了
+- ຖ
+- එ
+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
+- 得
+- ൈ
+- ಒ
+- ፈ
+- 好
+- 同
+- 成
+- 分
+- そ
+- ธ
+- Ҭ
+- Ğ
+- Ṭ
+- ዲ
+- Њ
+- ڏ
+- ۽
+- 物
+- ޫ
+- Ჩ
+- Ӯ
+- 都
+- け
+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
+- ډ
+- Ḍ
+- 学
+- Օ
+- だ
+- 能
+- ڊ
+- 部
+- 仔
+- ภ
+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
+- Ï
+- ル
+- ୱ
+- ਧ
+- ቱ
+- 就
+- Ű
+- ళ
+- 斯
+- 作
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+- ණ
+- ጵ
+- 사
+- ఎ
+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
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+- ଘ
+- ଫ
+- ڄ
+- Ď
+- ऊ
+- ఒ
+- ሎ
+- ቃ
+- ೈ
+- ْ
+- ຢ
+- Ҩ
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+- Ҟ
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+- イ
+- 公
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+- え
+- ト
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+- ৌ
+- Ḥ
+- ሲ
+- 家
+- ៃ
+- څ
+- 会
+- ಈ
+- Ẽ
+- ア
+- ち
+- 子
+- ઝ
+- 를
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+- ሞ
+- 事
+- 本
+- 最
+- ຽ
+- ङ
+- 會
+- ଓ
+- ਭ
+- ሽ
+- ኮ
+- 之
+- 己
+- 스
+- ဝ
+- 入
+- ළ
+- ዝ
+- 于
+- 理
+- ဟ
+- ڙ
+- Ҵ
+- 名
+- 新
+- 法
+- ୃ
+- ዛ
+- ቻ
+- ラ
+- ઘ
+- 수
+- 습
+- ਥ
+- 所
+- 界
+- つ
+- ஸ
+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
+- ဂ
+- 定
+- 利
+- 者
+- お
+- 為
+- Ɛ
+- 아
+- ຶ
+- ဒ
+- ጉ
+- ኖ
+- 度
+- 常
+- ഖ
+- ឡ
+- 도
+- Ḓ
+- 去
+- 其
+- 意
+- ኑ
+- 这
+- 时
+- 通
+- Ֆ
+- 리
+- 어
+- ቡ
+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
+- 科
+- ど
+- ッ
+- ば
+- ಖ
+- ឺ
+- 시
+- ಫ
+- 文
+- 主
+- め
+- 見
+- ฟ
+- 해
+- ሀ
+- 州
+- 水
+- 小
+- 目
+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
+- 西
+- 特
+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
+- 加
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+- 國
+- ඩ
+- 道
+- 市
+- 表
+- 立
+- 而
+- 食
+- ޒ
+- Ψ
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+- ័
+- ¡
+- 尔
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+- 亚
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+- 里
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+- ቹ
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+- …
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+- ഏ
+- ሱ
+- 比
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+- 起
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+- 拉
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+- Ӡ
+- Ӳ
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+- 부
+- 心
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+- ੜ
+- ‌
+- 们
+- 更
+- 正
+- 라
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+- ቦ
+- ዶ
+- 美
+- 也
+- 南
+- タ
+- ஜ
+- ජ
+- ሙ
+- ፕ
+- 現
+- ૃ
+- 일
+- ຜ
+- ਯ
+- ఖ
+- 口
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+- ロ
+- ൃ
+- ೃ
+- 式
+- ޝ
+- Ẓ
+- 했
+- ቤ
+- 十
+- 区
+- み
+- Ķ
+- 나
+- ۍ
+- ఏ
+- ං
+- 外
+- 海
+- 重
+- 天
+- 語
+- ۰
+- ៀ
+- 전
+- ඇ
+- 全
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+- 面
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+- 것
+- ධ
+- ፋ
+- 字
+- 发
+- ऑ
+- テ
+- 頭
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+- 身
+- 果
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+- ඒ
+- ෝ
+- ኝ
+- ٍ
+- 今
+- 示
+- カ
+- 無
+- 場
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+- 啲
+- ඔ
+- ඉ
+- ቲ
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+- ろ
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+- ኩ
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+- ऱ
+- 상
+- ጽ
+- ቆ
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+- 만
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+- ޕ
+- ク
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+- ०
+- १
+- 解
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+- コ
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+- 冇
+- ฐ
+- ף
+- ڌ
+- ሄ
+- レ
+- 講
+- ៍
+- 种
+- 少
+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
+- 工
+- 英
+- 代
+- Ģ
+- ූ
+- ዩ
+- ̃
+- 北
+- 如
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+- ൺ
+- 정
+- 제
+- ਝ
+- ፓ
+- ·
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+- ጡ
+- ጫ
+- ቂ
+- 何
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+- ጨ
+- ሻ
+- 及
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+- 原
+- 這
+- 트
+- ሸ
+- Џ
+- 城
+- 影
+- 次
+- 安
+- べ
+- Ჰ
+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
+- 었
+- ਏ
+- ౌ
+- ኦ
+- 建
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+- Ɓ
+- 々
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+- 種
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+- ڀ
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+- 过
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+- ޢ
+- 車
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+- 點
+- ೌ
+- 되
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+- ሴ
+- 想
+- 任
+- 当
+- 站
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+- 未
+- 旅
+- 持
+- バ
+- 問
+- 學
+- ۱
+- ૌ
+- 할
+- 와
+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
+- 已
+- 造
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+- 必
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+- ィ
+- パ
+- Ǧ
+- 미
+- 유
+- 위
+- 면
+- 원
+- ච
+- ጸ
+- 对
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+- 史
+- 客
+- 考
+- ャ
+- オ
+- ഓ
+- ዓ
+- ሃ
+- 동
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+- ආ
+- ቷ
+- ኪ
+- ঃ
+- 称
+- 活
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+- へ
+- マ
+- ニ
+- ょ
+- 記
+- 発
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+- 發
+- 咗
+- ឋ
+- 국
+- 现
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+- 保
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+- 情
+- ほ
+- 気
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+- ٠
+- 거
+- 們
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+- ኞ
+- ፎ
+- ጃ
+- ঝ
+- ڤ
+- 经
+- 信
+- 布
+- 思
+- 打
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+- 嘢
+- ̇
+- 우
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+- ፖ
+- 量
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+- 车
+- 品
+- 速
+- 件
+- 於
+- ۲
+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
+- 」
+- 石
+- 交
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+- チ
+- ず
+- 題
+- 對
+- ஞ
+- 睇
+- 噉
+- 모
+- 소
+- २
+- Ѓ
+- 赛
+- 该
+- 格
+- 查
+- 五
+- ޗ
+- ュ
+- ଵ
+- ১
+- 세
+- 구
+- 경
+- 비
+- ။
+- ዴ
+- 士
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+- 林
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+- 着
+- 集
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+- 它
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+- ഘ
+- 經
+- 邊
+- ஷ
+- 、
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+- 계
+- ဇ
+- ਛ
+- ጂ
+- ጭ
+- 场
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+- ኘ
+- Œ
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+- 接
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+- 러
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+- ၍
+- 军
+- 印
+- 向
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+- メ
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+- プ
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+- ২
+- ಘ
+- 년
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+- ਠ
+- Ǹ
+- 回
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+- 周
+- 像
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+- 強
+- 報
+- 試
+- ฝ
+- ০
+- °
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+- 된
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+- ڇ
+- ෑ
+- ጦ
+- ጐ
+- ጆ
+- ঢ
+- 间
+- 包
+- 供
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+- グ
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+- ェ
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+- 발
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+- ጓ
+- ጻ
+- Ӷ
+- 进
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+- 战
+- 再
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+- 半
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+- 街
+- ޚ
+- 望
+- キ
+- 盾
+- 認
+- 員
+- •
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+- ฤ
+- ಏ
+- 합
+- 선
+- 프
+- 분
+- ຟ
+- ፒ
+- 列
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+- 联
+- 电
+- 村
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+- ナ
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+- 데
+- ३
+- ਓ
+- ፉ
+- 否
+- 演
+- 图
+- 夫
+- 放
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+- ）
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+- ሦ
+- ኣ
+- "\x93"
+- "\x94"
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+- 遣
+- 册
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+- 拦
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+- 尝
+- ％
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+- 陨
+- 咎
+- 箭
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+- 钢
+- 键
+- 锐
+- 啸
+- 纹
+- 磷
+- 硫
+- 骚
+- 晓
+- 培
+- 骗
+- 疗
+- 锻
+- 炼
+- 喘
+- 陡
+- 谨
+- 梵
+- 鲜
+- 纯
+- 粹
+- 厉
+- 汁
+- 夺
+- 丢
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+- 陶
+- 旨
+- 聪
+- 审
+- 铃
+- 璀
+- 璨
+- 垒
+- 劫
+- 荣
+- 惊
+- 叹
+- 绵
+- 攀
+- 损
+- 肿
+- 瘤
+- 龄
+- 齐
+- 鞭
+- 缭
+- 穷
+- 哺
+- 粘
+- 宏
+- 倾
+- 财
+- 纸
+- 荐
+- 媚
+- 滥
+- 嘲
+- 泄
+- 盘
+- 廷
+- 抢
+- 敛
+- 猎
+- 苹
+- 笔
+- 愤
+- 慨
+- 罄
+- 茵
+- 碍
+- 舱
+- 励
+- 雕
+- 酋
+- 镑
+- 暂
+- 疽
+- 卓
+- 咨
+- 饮
+- 笨
+- 闲
+- 逛
+- §
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+- <sos/eos>
+init: null
+input_size: null
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: data/local/nlsyms.txt
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+short_noise_thres: 0.5
+aux_ctc_tasks: []
+aux_dropout: 0.0
+frontend: default
+frontend_conf:
+    n_fft: 1024
+    hop_length: 320
+    fs: 16k
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 27
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_ratio_range:
+    - 0.0
+    - 0.05
+    num_time_mask: 10
+normalize: utterance_mvn
+normalize_conf: {}
+model: espnet
+model_conf:
+    ctc_weight: 1.0
+preencoder: null
+preencoder_conf: {}
+encoder: transformer
+encoder_conf:
+    output_size: 256
+    attention_heads: 8
+    linear_units: 1024
+    num_blocks: 2
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d2
+    normalize_before: true
+postencoder: null
+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/train/batch_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/train/batch_keys
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+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/train/feats_lengths_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/train/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/train/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/train/stats_keys
@@ -0,0 +1,2 @@
+feats
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..bb0ee63337da573ce9f9dfd0b10b5bf5dd2e617c
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/train/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/valid/stats_keys b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/valid/stats_keys
new file mode 100644
index 0000000000000000000000000000000000000000..8b395786dbc0bf6d417ffb91823949cfaf8116bc
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/valid/stats_keys
@@ -0,0 +1,2 @@
+feats
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..62a99635a4a7048a7cde4bf7c76e615141004c42
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.9/valid/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.1.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.1.scp
new file mode 100644
index 0000000000000000000000000000000000000000..d6a65bdc8b8c98b4891d76a38096d775d8bf9b10
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.10.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.10.scp
new file mode 100644
index 0000000000000000000000000000000000000000..8975eab94ccc3516c1d463dc6ce1dda448e6f5b1
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.11.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.11.scp
new file mode 100644
index 0000000000000000000000000000000000000000..d1559f5ab8f58a8053c4a387809262462e92ab15
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.12.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.12.scp
new file mode 100644
index 0000000000000000000000000000000000000000..d5b710a9b4f32613f1378d0e9d48c06b9e79b887
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.13.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.13.scp
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.14.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.14.scp
new file mode 100644
index 0000000000000000000000000000000000000000..014dc76132a86dfbfd56c48d2b1937c8b1128b23
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.14.scp
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.15.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.15.scp
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.16.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.16.scp
new file mode 100644
index 0000000000000000000000000000000000000000..8e94e988c7429b52137442428d3728a9f825425d
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.17.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.17.scp
new file mode 100644
index 0000000000000000000000000000000000000000..58e38606159eb42510714ee6ac4578f5fb541c60
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.17.scp
@@ -0,0 +1,3955 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.18.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.18.scp
new file mode 100644
index 0000000000000000000000000000000000000000..c350c519a45424957ae137cc163d71205dc37df0
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.19.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.19.scp
new file mode 100644
index 0000000000000000000000000000000000000000..bf4cdc0249b253203d04ed3972f249e6d479ca13
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.19.scp
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.2.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.2.scp
new file mode 100644
index 0000000000000000000000000000000000000000..6b0ef4236b47ff19a455a4488804c1b5d684df44
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.20.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.20.scp
new file mode 100644
index 0000000000000000000000000000000000000000..04e806bbb97132240c9b4612861618ff625aa6bd
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.20.scp
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.21.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.21.scp
new file mode 100644
index 0000000000000000000000000000000000000000..c1f7d7c9b11b398e4fae724541a186405272dc8f
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.22.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.22.scp
new file mode 100644
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.23.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.23.scp
new file mode 100644
index 0000000000000000000000000000000000000000..8391221d85c999883fd9591dce45a9b024cba58c
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.24.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.24.scp
new file mode 100644
index 0000000000000000000000000000000000000000..23a8f997e9d80c623baff5be39bec71ffc4bbd15
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.26.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.26.scp
new file mode 100644
index 0000000000000000000000000000000000000000..82676f6e390554292c922443890cd347b3bf13ca
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.27.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.27.scp
new file mode 100644
index 0000000000000000000000000000000000000000..60a69c639b2f34773b52fcaff8328ed510dad81b
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.28.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.28.scp
new file mode 100644
index 0000000000000000000000000000000000000000..65b60e5eed81a222f351668ae5cc2531d90de04d
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.28.scp
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.29.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.29.scp
new file mode 100644
index 0000000000000000000000000000000000000000..64dc25f1aa1aad18eca1c4adce6188e4f61c7bbe
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.3.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.3.scp
new file mode 100644
index 0000000000000000000000000000000000000000..f3e2675615c8514f62ef93f389668dde2ff21a64
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.30.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.30.scp
new file mode 100644
index 0000000000000000000000000000000000000000..56bae8e2440d6a450dead7b140297e7f79b616ee
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.31.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.31.scp
new file mode 100644
index 0000000000000000000000000000000000000000..ee4c4110931849be19eb73184e379b660dc381a7
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.31.scp
@@ -0,0 +1,3954 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.32.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.32.scp
new file mode 100644
index 0000000000000000000000000000000000000000..983aa387c5683d7bcba0cf57d715bb384e5f9556
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.4.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.4.scp
new file mode 100644
index 0000000000000000000000000000000000000000..640cf1f7e3b5ff8b8fb51d881cd2e265a71af460
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.5.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.5.scp
new file mode 100644
index 0000000000000000000000000000000000000000..9b1851621d494ae5658e5deed70137a2f543d96f
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.6.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.6.scp
new file mode 100644
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.8.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.8.scp
new file mode 100644
index 0000000000000000000000000000000000000000..3c73cfe9240ba652dd8191a353da6c02c434029a
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.9.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/train.9.scp
new file mode 100644
index 0000000000000000000000000000000000000000..292a0b1074c70b13012c093e2955830dda61b7ec
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.1.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.1.scp
new file mode 100644
index 0000000000000000000000000000000000000000..9b4863972a74115669b18be6be03d1033fef441c
--- /dev/null
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+LAD_nld_000155 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000155.flac
+LAD_nld_000156 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000156.flac
+LAD_nld_000157 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000157.flac
+LAD_nld_000158 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000158.flac
+LAD_nld_000159 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000159.flac
+LAD_nld_000160 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000160.flac
+LAD_nld_000161 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000161.flac
+LAD_nld_000162 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000162.flac
+LAD_nld_000163 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000163.flac
+LAD_nld_000164 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000164.flac
+LAD_nld_000165 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000165.flac
+LAD_nld_000166 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000166.flac
+LAD_nld_000167 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000167.flac
+LAD_nld_000168 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000168.flac
+LAD_nld_000169 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000169.flac
+LAD_nld_000170 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000170.flac
+LAD_nld_000171 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000171.flac
+LAD_nld_000172 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000172.flac
+LAD_nld_000173 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000173.flac
+LAD_nld_000174 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000174.flac
+LAD_nld_000175 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000175.flac
+LAD_nld_000176 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000176.flac
+LAD_nld_000177 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000177.flac
+LAD_nld_000178 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000178.flac
+LAD_nld_000179 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000179.flac
+LAD_nld_000180 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000180.flac
+LAD_nld_000181 dump/raw/org/dev_1h_lid/data/format.1/data_wav/LAD_nld_000181.flac
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.10.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.10.scp
new file mode 100644
index 0000000000000000000000000000000000000000..82320b1172d1d00da2821f2a2a750dc17bc6c06f
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.10.scp
@@ -0,0 +1,760 @@
+cv_jpn_000047 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_jpn_000047.flac
+cv_jpn_000048 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_jpn_000048.flac
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+cv_kin_000747 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000747.flac
+cv_kin_000748 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000748.flac
+cv_kin_000749 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000749.flac
+cv_kin_000750 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000750.flac
+cv_kin_000751 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000751.flac
+cv_kin_000752 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000752.flac
+cv_kin_000753 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000753.flac
+cv_kin_000754 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000754.flac
+cv_kin_000755 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000755.flac
+cv_kin_000756 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000756.flac
+cv_kin_000757 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000757.flac
+cv_kin_000758 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000758.flac
+cv_kin_000759 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000759.flac
+cv_kin_000760 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000760.flac
+cv_kin_000761 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000761.flac
+cv_kin_000762 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000762.flac
+cv_kin_000763 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000763.flac
+cv_kin_000764 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000764.flac
+cv_kin_000765 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000765.flac
+cv_kin_000766 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kin_000766.flac
+cv_kir_000850 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000850.flac
+cv_kir_000851 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000851.flac
+cv_kir_000852 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000852.flac
+cv_kir_000853 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000853.flac
+cv_kir_000854 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000854.flac
+cv_kir_000855 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000855.flac
+cv_kir_000856 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000856.flac
+cv_kir_000857 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000857.flac
+cv_kir_000858 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000858.flac
+cv_kir_000859 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000859.flac
+cv_kir_000860 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000860.flac
+cv_kir_000861 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000861.flac
+cv_kir_000862 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000862.flac
+cv_kir_000863 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000863.flac
+cv_kir_000864 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000864.flac
+cv_kir_000865 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000865.flac
+cv_kir_000866 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000866.flac
+cv_kir_000867 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000867.flac
+cv_kir_000868 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000868.flac
+cv_kir_000869 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000869.flac
+cv_kir_000870 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000870.flac
+cv_kir_000871 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000871.flac
+cv_kir_000872 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000872.flac
+cv_kir_000873 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000873.flac
+cv_kir_000874 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000874.flac
+cv_kir_000875 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000875.flac
+cv_kir_000876 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000876.flac
+cv_kir_000877 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000877.flac
+cv_kir_000878 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000878.flac
+cv_kir_000879 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000879.flac
+cv_kir_000880 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000880.flac
+cv_kir_000881 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000881.flac
+cv_kir_000882 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000882.flac
+cv_kir_000883 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000883.flac
+cv_kir_000884 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000884.flac
+cv_kir_000885 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000885.flac
+cv_kir_000886 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000886.flac
+cv_kir_000887 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000887.flac
+cv_kir_000888 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000888.flac
+cv_kir_000889 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000889.flac
+cv_kir_000890 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000890.flac
+cv_kir_000891 dump/raw/org/dev_1h_lid/data/format.10/data_wav/cv_kir_000891.flac
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.11.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.11.scp
new file mode 100644
index 0000000000000000000000000000000000000000..701560a911b36c68d070c022cae488dc48f935ae
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.11.scp
@@ -0,0 +1,760 @@
+cv_kir_000892 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000892.flac
+cv_kir_000893 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000893.flac
+cv_kir_000894 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000894.flac
+cv_kir_000895 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000895.flac
+cv_kir_000896 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000896.flac
+cv_kir_000897 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000897.flac
+cv_kir_000898 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000898.flac
+cv_kir_000899 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000899.flac
+cv_kir_000900 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000900.flac
+cv_kir_000901 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000901.flac
+cv_kir_000902 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000902.flac
+cv_kir_000903 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000903.flac
+cv_kir_000904 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000904.flac
+cv_kir_000905 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000905.flac
+cv_kir_000906 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000906.flac
+cv_kir_000907 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000907.flac
+cv_kir_000908 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000908.flac
+cv_kir_000909 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000909.flac
+cv_kir_000910 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000910.flac
+cv_kir_000911 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000911.flac
+cv_kir_000912 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000912.flac
+cv_kir_000913 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000913.flac
+cv_kir_000914 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000914.flac
+cv_kir_000915 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000915.flac
+cv_kir_000916 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000916.flac
+cv_kir_000917 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000917.flac
+cv_kir_000918 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000918.flac
+cv_kir_000919 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000919.flac
+cv_kir_000920 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000920.flac
+cv_kir_000921 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000921.flac
+cv_kir_000922 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000922.flac
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+cv_kir_000924 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000924.flac
+cv_kir_000925 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000925.flac
+cv_kir_000926 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000926.flac
+cv_kir_000927 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000927.flac
+cv_kir_000928 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000928.flac
+cv_kir_000929 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000929.flac
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+cv_kir_000932 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000932.flac
+cv_kir_000933 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000933.flac
+cv_kir_000934 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000934.flac
+cv_kir_000935 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000935.flac
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+cv_kir_000938 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000938.flac
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+cv_kir_000951 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000951.flac
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+cv_kir_000958 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000958.flac
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+cv_kir_000962 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000962.flac
+cv_kir_000963 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000963.flac
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+cv_kir_000968 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000968.flac
+cv_kir_000969 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000969.flac
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+cv_kir_000978 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000978.flac
+cv_kir_000979 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000979.flac
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+cv_kir_000981 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000981.flac
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+cv_kir_000985 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kir_000985.flac
+cv_kmr_000723 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kmr_000723.flac
+cv_kmr_000724 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kmr_000724.flac
+cv_kmr_000725 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kmr_000725.flac
+cv_kmr_000726 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kmr_000726.flac
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+cv_kmr_000728 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kmr_000728.flac
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+cv_kmr_000731 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kmr_000731.flac
+cv_kmr_000732 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kmr_000732.flac
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+cv_kmr_000735 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_kmr_000735.flac
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+cv_mal_000131 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_mal_000131.flac
+cv_mal_000132 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_mal_000132.flac
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+cv_mal_000140 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_mal_000140.flac
+cv_mal_000141 dump/raw/org/dev_1h_lid/data/format.11/data_wav/cv_mal_000141.flac
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.12.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.12.scp
new file mode 100644
index 0000000000000000000000000000000000000000..5afe56193405a243cf1d149a3ca0972a96ce8f05
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.12.scp
@@ -0,0 +1,760 @@
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+cv_myv_000717 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_myv_000717.flac
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+cv_myv_000719 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_myv_000719.flac
+cv_myv_000720 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_myv_000720.flac
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+cv_myv_000722 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_myv_000722.flac
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+cv_myv_000724 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_myv_000724.flac
+cv_myv_000725 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_myv_000725.flac
+cv_myv_000726 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_myv_000726.flac
+cv_myv_000727 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_myv_000727.flac
+cv_myv_000728 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_myv_000728.flac
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+cv_nan_000092 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_nan_000092.flac
+cv_nan_000093 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_nan_000093.flac
+cv_nan_000094 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_nan_000094.flac
+cv_nan_000095 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_nan_000095.flac
+cv_nan_000096 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_nan_000096.flac
+cv_nan_000097 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_nan_000097.flac
+cv_nan_000098 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_nan_000098.flac
+cv_nan_000099 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_nan_000099.flac
+cv_nan_000100 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_nan_000100.flac
+cv_nan_000101 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_nan_000101.flac
+cv_nan_000102 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_nan_000102.flac
+cv_nan_000103 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_nan_000103.flac
+cv_nan_000104 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_nan_000104.flac
+cv_nan_000105 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_nan_000105.flac
+cv_nan_000106 dump/raw/org/dev_1h_lid/data/format.12/data_wav/cv_nan_000106.flac
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.13.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.13.scp
new file mode 100644
index 0000000000000000000000000000000000000000..9b23e476a7808c58d0be372df91fb623d4c15bf3
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.13.scp
@@ -0,0 +1,760 @@
+cv_nan_000107 dump/raw/org/dev_1h_lid/data/format.13/data_wav/cv_nan_000107.flac
+cv_nan_000108 dump/raw/org/dev_1h_lid/data/format.13/data_wav/cv_nan_000108.flac
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+cv_nan_000110 dump/raw/org/dev_1h_lid/data/format.13/data_wav/cv_nan_000110.flac
+cv_nan_000111 dump/raw/org/dev_1h_lid/data/format.13/data_wav/cv_nan_000111.flac
+cv_nan_000112 dump/raw/org/dev_1h_lid/data/format.13/data_wav/cv_nan_000112.flac
+cv_nan_000113 dump/raw/org/dev_1h_lid/data/format.13/data_wav/cv_nan_000113.flac
+cv_nan_000114 dump/raw/org/dev_1h_lid/data/format.13/data_wav/cv_nan_000114.flac
+cv_nan_000115 dump/raw/org/dev_1h_lid/data/format.13/data_wav/cv_nan_000115.flac
+cv_nan_000116 dump/raw/org/dev_1h_lid/data/format.13/data_wav/cv_nan_000116.flac
+cv_nan_000117 dump/raw/org/dev_1h_lid/data/format.13/data_wav/cv_nan_000117.flac
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+cv_nan_000120 dump/raw/org/dev_1h_lid/data/format.13/data_wav/cv_nan_000120.flac
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+cv_ron_000046 dump/raw/org/dev_1h_lid/data/format.13/data_wav/cv_ron_000046.flac
+cv_ron_000047 dump/raw/org/dev_1h_lid/data/format.13/data_wav/cv_ron_000047.flac
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.14.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.14.scp
new file mode 100644
index 0000000000000000000000000000000000000000..93248ca3a25b3d41a2998f59618bc3b66cb865bf
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.14.scp
@@ -0,0 +1,760 @@
+cv_ron_000048 dump/raw/org/dev_1h_lid/data/format.14/data_wav/cv_ron_000048.flac
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.15.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.15.scp
new file mode 100644
index 0000000000000000000000000000000000000000..5b845d263dcbcf96597779315c52998540a4306e
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.15.scp
@@ -0,0 +1,759 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.16.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.16.scp
new file mode 100644
index 0000000000000000000000000000000000000000..8c51ed90626d45eaf8707906d16b3352e8866db2
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.16.scp
@@ -0,0 +1,759 @@
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+cv_uzb_000675 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000675.flac
+cv_uzb_000676 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000676.flac
+cv_uzb_000677 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000677.flac
+cv_uzb_000678 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000678.flac
+cv_uzb_000679 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000679.flac
+cv_uzb_000680 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000680.flac
+cv_uzb_000681 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000681.flac
+cv_uzb_000682 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000682.flac
+cv_uzb_000683 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000683.flac
+cv_uzb_000684 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000684.flac
+cv_uzb_000685 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000685.flac
+cv_uzb_000686 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000686.flac
+cv_uzb_000687 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000687.flac
+cv_uzb_000688 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000688.flac
+cv_uzb_000689 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000689.flac
+cv_uzb_000690 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000690.flac
+cv_uzb_000691 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000691.flac
+cv_uzb_000692 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000692.flac
+cv_uzb_000693 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000693.flac
+cv_uzb_000694 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000694.flac
+cv_uzb_000695 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000695.flac
+cv_uzb_000696 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000696.flac
+cv_uzb_000697 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000697.flac
+cv_uzb_000698 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000698.flac
+cv_uzb_000699 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000699.flac
+cv_uzb_000700 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000700.flac
+cv_uzb_000701 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000701.flac
+cv_uzb_000702 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000702.flac
+cv_uzb_000703 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000703.flac
+cv_uzb_000704 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000704.flac
+cv_uzb_000705 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000705.flac
+cv_uzb_000706 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000706.flac
+cv_uzb_000707 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000707.flac
+cv_uzb_000708 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000708.flac
+cv_uzb_000709 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000709.flac
+cv_uzb_000710 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000710.flac
+cv_uzb_000711 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000711.flac
+cv_uzb_000712 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000712.flac
+cv_uzb_000713 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000713.flac
+cv_uzb_000714 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000714.flac
+cv_uzb_000715 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000715.flac
+cv_uzb_000716 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000716.flac
+cv_uzb_000717 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000717.flac
+cv_uzb_000718 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000718.flac
+cv_uzb_000719 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000719.flac
+cv_uzb_000720 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000720.flac
+cv_uzb_000721 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000721.flac
+cv_uzb_000722 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000722.flac
+cv_uzb_000723 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000723.flac
+cv_uzb_000724 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000724.flac
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.17.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.17.scp
new file mode 100644
index 0000000000000000000000000000000000000000..7772449f8d44c3b2c204441c1a6a278fed8143b9
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.17.scp
@@ -0,0 +1,759 @@
+cv_uzb_000725 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000725.flac
+cv_uzb_000726 dump/raw/org/dev_1h_lid/data/format.16/data_wav/cv_uzb_000726.flac
+cv_uzb_000727 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000727.flac
+cv_uzb_000728 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000728.flac
+cv_uzb_000729 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000729.flac
+cv_uzb_000730 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000730.flac
+cv_uzb_000731 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000731.flac
+cv_uzb_000732 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000732.flac
+cv_uzb_000733 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000733.flac
+cv_uzb_000734 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000734.flac
+cv_uzb_000735 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000735.flac
+cv_uzb_000736 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000736.flac
+cv_uzb_000737 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000737.flac
+cv_uzb_000738 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000738.flac
+cv_uzb_000739 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000739.flac
+cv_uzb_000740 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000740.flac
+cv_uzb_000741 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000741.flac
+cv_uzb_000742 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000742.flac
+cv_uzb_000743 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000743.flac
+cv_uzb_000744 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000744.flac
+cv_uzb_000745 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000745.flac
+cv_uzb_000746 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000746.flac
+cv_uzb_000747 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000747.flac
+cv_uzb_000748 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000748.flac
+cv_uzb_000749 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000749.flac
+cv_uzb_000750 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000750.flac
+cv_uzb_000751 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000751.flac
+cv_uzb_000752 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000752.flac
+cv_uzb_000753 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000753.flac
+cv_uzb_000754 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000754.flac
+cv_uzb_000755 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000755.flac
+cv_uzb_000756 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000756.flac
+cv_uzb_000757 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000757.flac
+cv_uzb_000758 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000758.flac
+cv_uzb_000759 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_uzb_000759.flac
+cv_vie_000933 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000933.flac
+cv_vie_000934 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000934.flac
+cv_vie_000935 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000935.flac
+cv_vie_000936 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000936.flac
+cv_vie_000937 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000937.flac
+cv_vie_000938 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000938.flac
+cv_vie_000939 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000939.flac
+cv_vie_000940 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000940.flac
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+cv_vie_000944 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000944.flac
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+cv_vie_000948 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000948.flac
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+cv_vie_000950 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000950.flac
+cv_vie_000951 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000951.flac
+cv_vie_000952 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000952.flac
+cv_vie_000953 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000953.flac
+cv_vie_000954 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000954.flac
+cv_vie_000955 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000955.flac
+cv_vie_000956 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000956.flac
+cv_vie_000957 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000957.flac
+cv_vie_000958 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000958.flac
+cv_vie_000959 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000959.flac
+cv_vie_000960 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000960.flac
+cv_vie_000961 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000961.flac
+cv_vie_000962 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000962.flac
+cv_vie_000963 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000963.flac
+cv_vie_000964 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000964.flac
+cv_vie_000965 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000965.flac
+cv_vie_000966 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000966.flac
+cv_vie_000967 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000967.flac
+cv_vie_000968 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000968.flac
+cv_vie_000969 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000969.flac
+cv_vie_000970 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000970.flac
+cv_vie_000971 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000971.flac
+cv_vie_000972 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000972.flac
+cv_vie_000973 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000973.flac
+cv_vie_000974 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000974.flac
+cv_vie_000975 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000975.flac
+cv_vie_000976 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000976.flac
+cv_vie_000977 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000977.flac
+cv_vie_000978 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000978.flac
+cv_vie_000979 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000979.flac
+cv_vie_000980 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000980.flac
+cv_vie_000981 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000981.flac
+cv_vie_000982 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000982.flac
+cv_vie_000983 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000983.flac
+cv_vie_000984 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000984.flac
+cv_vie_000985 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000985.flac
+cv_vie_000986 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000986.flac
+cv_vie_000987 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000987.flac
+cv_vie_000988 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000988.flac
+cv_vie_000989 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000989.flac
+cv_vie_000990 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000990.flac
+cv_vie_000991 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000991.flac
+cv_vie_000992 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000992.flac
+cv_vie_000993 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000993.flac
+cv_vie_000994 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000994.flac
+cv_vie_000995 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000995.flac
+cv_vie_000996 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000996.flac
+cv_vie_000997 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000997.flac
+cv_vie_000998 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000998.flac
+cv_vie_000999 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_000999.flac
+cv_vie_001000 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001000.flac
+cv_vie_001001 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001001.flac
+cv_vie_001002 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001002.flac
+cv_vie_001003 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001003.flac
+cv_vie_001004 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001004.flac
+cv_vie_001005 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001005.flac
+cv_vie_001006 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001006.flac
+cv_vie_001007 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001007.flac
+cv_vie_001008 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001008.flac
+cv_vie_001009 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001009.flac
+cv_vie_001010 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001010.flac
+cv_vie_001011 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001011.flac
+cv_vie_001012 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001012.flac
+cv_vie_001013 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001013.flac
+cv_vie_001014 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001014.flac
+cv_vie_001015 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001015.flac
+cv_vie_001016 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001016.flac
+cv_vie_001017 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001017.flac
+cv_vie_001018 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001018.flac
+cv_vie_001019 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001019.flac
+cv_vie_001020 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001020.flac
+cv_vie_001021 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001021.flac
+cv_vie_001022 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001022.flac
+cv_vie_001023 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001023.flac
+cv_vie_001024 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001024.flac
+cv_vie_001025 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001025.flac
+cv_vie_001026 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001026.flac
+cv_vie_001027 dump/raw/org/dev_1h_lid/data/format.17/data_wav/cv_vie_001027.flac
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.18.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.18.scp
new file mode 100644
index 0000000000000000000000000000000000000000..46a16e698c4a428cbe7e6fd87134406c64d9f35e
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.18.scp
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.19.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.19.scp
new file mode 100644
index 0000000000000000000000000000000000000000..fe908e1d7edf05ae33296bfd05f9926070928b82
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.19.scp
@@ -0,0 +1,759 @@
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+fleurs_ita_000022 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000022.flac
+fleurs_ita_000023 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000023.flac
+fleurs_ita_000024 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000024.flac
+fleurs_ita_000025 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000025.flac
+fleurs_ita_000026 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000026.flac
+fleurs_ita_000027 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000027.flac
+fleurs_ita_000028 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000028.flac
+fleurs_ita_000029 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000029.flac
+fleurs_ita_000030 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000030.flac
+fleurs_ita_000031 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000031.flac
+fleurs_ita_000032 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000032.flac
+fleurs_ita_000033 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000033.flac
+fleurs_ita_000034 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000034.flac
+fleurs_ita_000035 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000035.flac
+fleurs_ita_000036 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000036.flac
+fleurs_ita_000037 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000037.flac
+fleurs_ita_000038 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000038.flac
+fleurs_ita_000039 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000039.flac
+fleurs_ita_000040 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000040.flac
+fleurs_ita_000041 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000041.flac
+fleurs_ita_000042 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000042.flac
+fleurs_ita_000043 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_ita_000043.flac
+fleurs_jav_000000 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_jav_000000.flac
+fleurs_jav_000001 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_jav_000001.flac
+fleurs_jav_000002 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_jav_000002.flac
+fleurs_jav_000003 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_jav_000003.flac
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.2.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.2.scp
new file mode 100644
index 0000000000000000000000000000000000000000..ba7f9448e9b5d2cb0160a7945baae0716204b69d
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.2.scp
@@ -0,0 +1,760 @@
+LAD_nld_000182 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_nld_000182.flac
+LAD_nld_000183 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_nld_000183.flac
+LAD_nld_000184 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_nld_000184.flac
+LAD_nld_000185 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_nld_000185.flac
+LAD_nld_000186 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_nld_000186.flac
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+LAD_nld_000188 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_nld_000188.flac
+LAD_nld_000189 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_nld_000189.flac
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+LAD_rus_000242 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000242.flac
+LAD_rus_000243 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000243.flac
+LAD_rus_000244 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000244.flac
+LAD_rus_000245 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000245.flac
+LAD_rus_000246 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000246.flac
+LAD_rus_000247 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000247.flac
+LAD_rus_000248 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000248.flac
+LAD_rus_000249 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000249.flac
+LAD_rus_000250 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000250.flac
+LAD_rus_000251 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000251.flac
+LAD_rus_000252 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000252.flac
+LAD_rus_000253 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000253.flac
+LAD_rus_000254 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000254.flac
+LAD_rus_000255 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000255.flac
+LAD_rus_000256 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000256.flac
+LAD_rus_000257 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000257.flac
+LAD_rus_000258 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000258.flac
+LAD_rus_000259 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000259.flac
+LAD_rus_000260 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000260.flac
+LAD_rus_000261 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000261.flac
+LAD_rus_000262 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000262.flac
+LAD_rus_000263 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000263.flac
+LAD_rus_000264 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000264.flac
+LAD_rus_000265 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000265.flac
+LAD_rus_000266 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000266.flac
+LAD_rus_000267 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000267.flac
+LAD_rus_000268 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000268.flac
+LAD_rus_000269 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000269.flac
+LAD_rus_000270 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000270.flac
+LAD_rus_000271 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000271.flac
+LAD_rus_000272 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000272.flac
+LAD_rus_000273 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000273.flac
+LAD_rus_000274 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000274.flac
+LAD_rus_000275 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000275.flac
+LAD_rus_000276 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000276.flac
+LAD_rus_000277 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000277.flac
+LAD_rus_000278 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000278.flac
+LAD_rus_000279 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000279.flac
+LAD_rus_000280 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000280.flac
+LAD_rus_000281 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000281.flac
+LAD_rus_000282 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000282.flac
+LAD_rus_000283 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000283.flac
+LAD_rus_000284 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000284.flac
+LAD_rus_000285 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000285.flac
+LAD_rus_000286 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000286.flac
+LAD_rus_000287 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000287.flac
+LAD_rus_000288 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000288.flac
+LAD_rus_000289 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000289.flac
+LAD_rus_000290 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000290.flac
+LAD_rus_000291 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000291.flac
+LAD_rus_000292 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000292.flac
+LAD_rus_000293 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000293.flac
+LAD_rus_000294 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000294.flac
+LAD_rus_000295 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000295.flac
+LAD_rus_000296 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000296.flac
+LAD_rus_000297 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000297.flac
+LAD_rus_000298 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000298.flac
+LAD_rus_000299 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000299.flac
+LAD_rus_000300 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000300.flac
+LAD_rus_000301 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000301.flac
+LAD_rus_000302 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000302.flac
+LAD_rus_000303 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000303.flac
+LAD_rus_000304 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000304.flac
+LAD_rus_000305 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000305.flac
+LAD_rus_000306 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000306.flac
+LAD_rus_000307 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000307.flac
+LAD_rus_000308 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000308.flac
+LAD_rus_000309 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000309.flac
+LAD_rus_000310 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000310.flac
+LAD_rus_000311 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000311.flac
+LAD_rus_000312 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000312.flac
+LAD_rus_000313 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000313.flac
+LAD_rus_000314 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000314.flac
+LAD_rus_000315 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000315.flac
+LAD_rus_000316 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000316.flac
+LAD_rus_000317 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000317.flac
+LAD_rus_000318 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000318.flac
+LAD_rus_000319 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000319.flac
+LAD_rus_000320 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000320.flac
+LAD_rus_000321 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000321.flac
+LAD_rus_000322 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000322.flac
+LAD_rus_000323 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000323.flac
+LAD_rus_000324 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000324.flac
+LAD_rus_000325 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000325.flac
+LAD_rus_000326 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000326.flac
+LAD_rus_000327 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000327.flac
+LAD_rus_000328 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000328.flac
+LAD_rus_000329 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000329.flac
+LAD_rus_000330 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000330.flac
+LAD_rus_000331 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000331.flac
+LAD_rus_000332 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000332.flac
+LAD_rus_000333 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000333.flac
+LAD_rus_000334 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000334.flac
+LAD_rus_000335 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000335.flac
+LAD_rus_000336 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000336.flac
+LAD_rus_000337 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000337.flac
+LAD_rus_000338 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000338.flac
+LAD_rus_000339 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000339.flac
+LAD_rus_000340 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000340.flac
+LAD_rus_000341 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000341.flac
+LAD_rus_000342 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000342.flac
+LAD_rus_000343 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000343.flac
+LAD_rus_000344 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000344.flac
+LAD_rus_000345 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000345.flac
+LAD_rus_000346 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000346.flac
+LAD_rus_000347 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000347.flac
+LAD_rus_000348 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000348.flac
+LAD_rus_000349 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000349.flac
+LAD_rus_000350 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000350.flac
+LAD_rus_000351 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000351.flac
+LAD_rus_000352 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000352.flac
+LAD_rus_000353 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000353.flac
+LAD_rus_000354 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000354.flac
+LAD_rus_000355 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000355.flac
+LAD_rus_000356 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000356.flac
+LAD_rus_000357 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000357.flac
+LAD_rus_000358 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000358.flac
+LAD_rus_000359 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000359.flac
+LAD_rus_000360 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000360.flac
+LAD_rus_000361 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000361.flac
+LAD_rus_000362 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000362.flac
+LAD_rus_000363 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000363.flac
+LAD_rus_000364 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000364.flac
+LAD_rus_000365 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000365.flac
+LAD_rus_000366 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000366.flac
+LAD_rus_000367 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000367.flac
+LAD_rus_000368 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000368.flac
+LAD_rus_000369 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000369.flac
+LAD_rus_000370 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000370.flac
+LAD_rus_000371 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000371.flac
+LAD_rus_000372 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000372.flac
+LAD_rus_000373 dump/raw/org/dev_1h_lid/data/format.2/data_wav/LAD_rus_000373.flac
+M-AILABS_deu_000087 dump/raw/org/dev_1h_lid/data/format.2/data_wav/M-AILABS_deu_000087.flac
+M-AILABS_deu_000088 dump/raw/org/dev_1h_lid/data/format.2/data_wav/M-AILABS_deu_000088.flac
+M-AILABS_deu_000089 dump/raw/org/dev_1h_lid/data/format.2/data_wav/M-AILABS_deu_000089.flac
+M-AILABS_deu_000090 dump/raw/org/dev_1h_lid/data/format.2/data_wav/M-AILABS_deu_000090.flac
+M-AILABS_deu_000091 dump/raw/org/dev_1h_lid/data/format.2/data_wav/M-AILABS_deu_000091.flac
+M-AILABS_deu_000092 dump/raw/org/dev_1h_lid/data/format.2/data_wav/M-AILABS_deu_000092.flac
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+M-AILABS_rus_000118 dump/raw/org/dev_1h_lid/data/format.2/data_wav/M-AILABS_rus_000118.flac
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+M-AILABS_rus_000120 dump/raw/org/dev_1h_lid/data/format.2/data_wav/M-AILABS_rus_000120.flac
+M-AILABS_rus_000121 dump/raw/org/dev_1h_lid/data/format.2/data_wav/M-AILABS_rus_000121.flac
+M-AILABS_rus_000122 dump/raw/org/dev_1h_lid/data/format.2/data_wav/M-AILABS_rus_000122.flac
+M-AILABS_rus_000123 dump/raw/org/dev_1h_lid/data/format.2/data_wav/M-AILABS_rus_000123.flac
+M-AILABS_rus_000124 dump/raw/org/dev_1h_lid/data/format.2/data_wav/M-AILABS_rus_000124.flac
+M-AILABS_rus_000125 dump/raw/org/dev_1h_lid/data/format.2/data_wav/M-AILABS_rus_000125.flac
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.20.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.20.scp
new file mode 100644
index 0000000000000000000000000000000000000000..6fc085bc94667b38025f48a69aa5b9580e350c42
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.20.scp
@@ -0,0 +1,759 @@
+fleurs_jav_000004 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_jav_000004.flac
+fleurs_jav_000005 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_jav_000005.flac
+fleurs_jav_000007 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_jav_000007.flac
+fleurs_jav_000008 dump/raw/org/dev_1h_lid/data/format.19/data_wav/fleurs_jav_000008.flac
+fleurs_jav_000009 dump/raw/org/dev_1h_lid/data/format.20/data_wav/fleurs_jav_000009.flac
+fleurs_jav_000010 dump/raw/org/dev_1h_lid/data/format.20/data_wav/fleurs_jav_000010.flac
+fleurs_jav_000011 dump/raw/org/dev_1h_lid/data/format.20/data_wav/fleurs_jav_000011.flac
+fleurs_jav_000012 dump/raw/org/dev_1h_lid/data/format.20/data_wav/fleurs_jav_000012.flac
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.21.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.21.scp
new file mode 100644
index 0000000000000000000000000000000000000000..70830c1430ff9d9e84433a5d36de65300dc7ef2d
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.21.scp
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.22.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.22.scp
new file mode 100644
index 0000000000000000000000000000000000000000..38490bc659fcc28c773e0970019752ea12d6f92e
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.22.scp
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.23.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.23.scp
new file mode 100644
index 0000000000000000000000000000000000000000..485ce861b175d6f51643f92e2149594ed5e30d80
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.23.scp
@@ -0,0 +1,759 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.24.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.24.scp
new file mode 100644
index 0000000000000000000000000000000000000000..805544e80787f6a975cefaf32ad587f87ae1d051
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.25.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.25.scp
new file mode 100644
index 0000000000000000000000000000000000000000..4693dbca41ba48609a080d2ba08057705f75f29a
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.26.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.26.scp
new file mode 100644
index 0000000000000000000000000000000000000000..081a828052e8d49eea2aa35a8d1bafb21a88aa30
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.27.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.27.scp
new file mode 100644
index 0000000000000000000000000000000000000000..cbc7a6750588a79ddbed394d23839d6569092392
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.27.scp
@@ -0,0 +1,759 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.28.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.28.scp
new file mode 100644
index 0000000000000000000000000000000000000000..ee33f0fa034659cb3d0847ee52241c3a28ac5c7d
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.28.scp
@@ -0,0 +1,759 @@
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+nchlt_nso_001070 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_nso_001070.flac
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+nchlt_tsn_001082 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001082.flac
+nchlt_tsn_001083 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001083.flac
+nchlt_tsn_001084 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001084.flac
+nchlt_tsn_001085 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001085.flac
+nchlt_tsn_001086 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001086.flac
+nchlt_tsn_001087 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001087.flac
+nchlt_tsn_001088 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001088.flac
+nchlt_tsn_001089 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001089.flac
+nchlt_tsn_001090 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001090.flac
+nchlt_tsn_001091 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001091.flac
+nchlt_tsn_001092 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001092.flac
+nchlt_tsn_001093 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001093.flac
+nchlt_tsn_001094 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001094.flac
+nchlt_tsn_001095 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001095.flac
+nchlt_tsn_001096 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001096.flac
+nchlt_tsn_001097 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001097.flac
+nchlt_tsn_001098 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001098.flac
+nchlt_tsn_001099 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001099.flac
+nchlt_tsn_001100 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001100.flac
+nchlt_tsn_001101 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001101.flac
+nchlt_tsn_001102 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001102.flac
+nchlt_tsn_001103 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001103.flac
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.29.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.29.scp
new file mode 100644
index 0000000000000000000000000000000000000000..8a56bc9f3cc385acb855ce1ec7d1d7e5b6e284a9
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.29.scp
@@ -0,0 +1,759 @@
+nchlt_tsn_001104 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001104.flac
+nchlt_tsn_001105 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001105.flac
+nchlt_tsn_001106 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001106.flac
+nchlt_tsn_001107 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001107.flac
+nchlt_tsn_001108 dump/raw/org/dev_1h_lid/data/format.28/data_wav/nchlt_tsn_001108.flac
+nchlt_tsn_001109 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001109.flac
+nchlt_tsn_001110 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001110.flac
+nchlt_tsn_001111 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001111.flac
+nchlt_tsn_001112 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001112.flac
+nchlt_tsn_001113 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001113.flac
+nchlt_tsn_001114 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001114.flac
+nchlt_tsn_001115 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001115.flac
+nchlt_tsn_001116 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001116.flac
+nchlt_tsn_001117 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001117.flac
+nchlt_tsn_001118 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001118.flac
+nchlt_tsn_001119 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001119.flac
+nchlt_tsn_001120 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001120.flac
+nchlt_tsn_001121 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001121.flac
+nchlt_tsn_001122 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001122.flac
+nchlt_tsn_001123 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001123.flac
+nchlt_tsn_001124 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001124.flac
+nchlt_tsn_001125 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001125.flac
+nchlt_tsn_001126 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001126.flac
+nchlt_tsn_001127 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001127.flac
+nchlt_tsn_001128 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001128.flac
+nchlt_tsn_001129 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001129.flac
+nchlt_tsn_001130 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001130.flac
+nchlt_tsn_001131 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001131.flac
+nchlt_tsn_001132 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001132.flac
+nchlt_tsn_001133 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001133.flac
+nchlt_tsn_001134 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001134.flac
+nchlt_tsn_001135 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001135.flac
+nchlt_tsn_001136 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001136.flac
+nchlt_tsn_001137 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001137.flac
+nchlt_tsn_001138 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001138.flac
+nchlt_tsn_001139 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001139.flac
+nchlt_tsn_001140 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001140.flac
+nchlt_tsn_001141 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001141.flac
+nchlt_tsn_001142 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001142.flac
+nchlt_tsn_001143 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001143.flac
+nchlt_tsn_001144 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001144.flac
+nchlt_tsn_001145 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001145.flac
+nchlt_tsn_001146 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001146.flac
+nchlt_tsn_001147 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001147.flac
+nchlt_tsn_001148 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001148.flac
+nchlt_tsn_001149 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001149.flac
+nchlt_tsn_001150 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001150.flac
+nchlt_tsn_001151 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001151.flac
+nchlt_tsn_001152 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001152.flac
+nchlt_tsn_001153 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001153.flac
+nchlt_tsn_001154 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001154.flac
+nchlt_tsn_001155 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001155.flac
+nchlt_tsn_001156 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001156.flac
+nchlt_tsn_001157 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001157.flac
+nchlt_tsn_001158 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001158.flac
+nchlt_tsn_001159 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001159.flac
+nchlt_tsn_001160 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001160.flac
+nchlt_tsn_001161 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001161.flac
+nchlt_tsn_001162 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001162.flac
+nchlt_tsn_001163 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001163.flac
+nchlt_tsn_001164 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001164.flac
+nchlt_tsn_001165 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001165.flac
+nchlt_tsn_001166 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001166.flac
+nchlt_tsn_001167 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001167.flac
+nchlt_tsn_001168 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001168.flac
+nchlt_tsn_001169 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001169.flac
+nchlt_tsn_001170 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001170.flac
+nchlt_tsn_001171 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001171.flac
+nchlt_tsn_001172 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001172.flac
+nchlt_tsn_001173 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001173.flac
+nchlt_tsn_001174 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001174.flac
+nchlt_tsn_001175 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001175.flac
+nchlt_tsn_001176 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001176.flac
+nchlt_tsn_001177 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001177.flac
+nchlt_tsn_001178 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001178.flac
+nchlt_tsn_001179 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001179.flac
+nchlt_tsn_001180 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001180.flac
+nchlt_tsn_001181 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001181.flac
+nchlt_tsn_001182 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001182.flac
+nchlt_tsn_001183 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001183.flac
+nchlt_tsn_001184 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001184.flac
+nchlt_tsn_001185 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001185.flac
+nchlt_tsn_001186 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001186.flac
+nchlt_tsn_001187 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001187.flac
+nchlt_tsn_001188 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001188.flac
+nchlt_tsn_001189 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001189.flac
+nchlt_tsn_001190 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001190.flac
+nchlt_tsn_001191 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001191.flac
+nchlt_tsn_001192 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001192.flac
+nchlt_tsn_001193 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001193.flac
+nchlt_tsn_001194 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001194.flac
+nchlt_tsn_001195 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001195.flac
+nchlt_tsn_001196 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001196.flac
+nchlt_tsn_001197 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001197.flac
+nchlt_tsn_001198 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001198.flac
+nchlt_tsn_001199 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001199.flac
+nchlt_tsn_001200 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001200.flac
+nchlt_tsn_001201 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001201.flac
+nchlt_tsn_001202 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001202.flac
+nchlt_tsn_001203 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001203.flac
+nchlt_tsn_001204 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001204.flac
+nchlt_tsn_001205 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001205.flac
+nchlt_tsn_001206 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001206.flac
+nchlt_tsn_001207 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001207.flac
+nchlt_tsn_001208 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tsn_001208.flac
+nchlt_tso_000831 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000831.flac
+nchlt_tso_000832 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000832.flac
+nchlt_tso_000833 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000833.flac
+nchlt_tso_000834 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000834.flac
+nchlt_tso_000835 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000835.flac
+nchlt_tso_000836 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000836.flac
+nchlt_tso_000837 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000837.flac
+nchlt_tso_000838 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000838.flac
+nchlt_tso_000839 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000839.flac
+nchlt_tso_000840 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000840.flac
+nchlt_tso_000841 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000841.flac
+nchlt_tso_000842 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000842.flac
+nchlt_tso_000843 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000843.flac
+nchlt_tso_000844 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000844.flac
+nchlt_tso_000845 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000845.flac
+nchlt_tso_000846 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000846.flac
+nchlt_tso_000847 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000847.flac
+nchlt_tso_000848 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000848.flac
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+nchlt_tso_000850 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000850.flac
+nchlt_tso_000851 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000851.flac
+nchlt_tso_000852 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000852.flac
+nchlt_tso_000853 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000853.flac
+nchlt_tso_000854 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000854.flac
+nchlt_tso_000855 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000855.flac
+nchlt_tso_000856 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000856.flac
+nchlt_tso_000857 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000857.flac
+nchlt_tso_000858 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000858.flac
+nchlt_tso_000859 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000859.flac
+nchlt_tso_000860 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000860.flac
+nchlt_tso_000861 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000861.flac
+nchlt_tso_000862 dump/raw/org/dev_1h_lid/data/format.29/data_wav/nchlt_tso_000862.flac
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.3.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.3.scp
new file mode 100644
index 0000000000000000000000000000000000000000..329c577bdf0bd9e5a0174b96eac6a3bfca920283
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.3.scp
@@ -0,0 +1,760 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.30.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.30.scp
new file mode 100644
index 0000000000000000000000000000000000000000..52dcb409d39f7715cd1fd90835709e042a0626a1
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.30.scp
@@ -0,0 +1,759 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.31.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.31.scp
new file mode 100644
index 0000000000000000000000000000000000000000..47b0f718ab3d1d7bd5bd199ab0c749ef3680d5d8
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.31.scp
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.32.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.32.scp
new file mode 100644
index 0000000000000000000000000000000000000000..868439efd69c64e957cd9a8c3f09655704848709
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.32.scp
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.4.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.4.scp
new file mode 100644
index 0000000000000000000000000000000000000000..ec2bb67f3d2d12e05d7b6ccc597c067cc65ff0f9
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.4.scp
@@ -0,0 +1,760 @@
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+cv_asm_000656 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000656.flac
+cv_asm_000657 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000657.flac
+cv_asm_000658 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000658.flac
+cv_asm_000659 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000659.flac
+cv_asm_000660 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000660.flac
+cv_asm_000661 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000661.flac
+cv_asm_000662 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000662.flac
+cv_asm_000663 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000663.flac
+cv_asm_000664 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000664.flac
+cv_asm_000665 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000665.flac
+cv_asm_000666 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000666.flac
+cv_asm_000667 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000667.flac
+cv_asm_000668 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000668.flac
+cv_asm_000669 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000669.flac
+cv_asm_000670 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000670.flac
+cv_asm_000671 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000671.flac
+cv_asm_000672 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000672.flac
+cv_asm_000673 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000673.flac
+cv_asm_000674 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000674.flac
+cv_asm_000675 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000675.flac
+cv_asm_000676 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000676.flac
+cv_asm_000677 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000677.flac
+cv_asm_000678 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000678.flac
+cv_asm_000679 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000679.flac
+cv_asm_000680 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000680.flac
+cv_asm_000681 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000681.flac
+cv_asm_000682 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000682.flac
+cv_asm_000683 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000683.flac
+cv_asm_000684 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000684.flac
+cv_asm_000685 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000685.flac
+cv_asm_000686 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000686.flac
+cv_asm_000687 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000687.flac
+cv_asm_000688 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000688.flac
+cv_asm_000689 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000689.flac
+cv_asm_000690 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000690.flac
+cv_asm_000691 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000691.flac
+cv_asm_000692 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000692.flac
+cv_asm_000693 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000693.flac
+cv_asm_000694 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000694.flac
+cv_asm_000695 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000695.flac
+cv_asm_000696 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000696.flac
+cv_asm_000697 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000697.flac
+cv_asm_000698 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000698.flac
+cv_asm_000699 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000699.flac
+cv_asm_000700 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000700.flac
+cv_asm_000701 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000701.flac
+cv_asm_000702 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000702.flac
+cv_asm_000703 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000703.flac
+cv_asm_000704 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000704.flac
+cv_asm_000705 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000705.flac
+cv_asm_000706 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000706.flac
+cv_asm_000707 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000707.flac
+cv_asm_000708 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000708.flac
+cv_asm_000709 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000709.flac
+cv_asm_000710 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000710.flac
+cv_asm_000711 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000711.flac
+cv_asm_000712 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000712.flac
+cv_asm_000713 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000713.flac
+cv_asm_000714 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000714.flac
+cv_asm_000715 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000715.flac
+cv_asm_000716 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000716.flac
+cv_asm_000717 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000717.flac
+cv_asm_000718 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000718.flac
+cv_asm_000719 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000719.flac
+cv_asm_000720 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000720.flac
+cv_asm_000721 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000721.flac
+cv_asm_000722 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000722.flac
+cv_asm_000723 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000723.flac
+cv_asm_000724 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000724.flac
+cv_asm_000725 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000725.flac
+cv_asm_000726 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000726.flac
+cv_asm_000727 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000727.flac
+cv_asm_000728 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000728.flac
+cv_asm_000729 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000729.flac
+cv_asm_000730 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000730.flac
+cv_asm_000731 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000731.flac
+cv_asm_000732 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000732.flac
+cv_asm_000733 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000733.flac
+cv_asm_000734 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000734.flac
+cv_asm_000735 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000735.flac
+cv_asm_000736 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000736.flac
+cv_asm_000737 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000737.flac
+cv_asm_000738 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000738.flac
+cv_asm_000739 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000739.flac
+cv_asm_000740 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000740.flac
+cv_asm_000741 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000741.flac
+cv_asm_000742 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000742.flac
+cv_asm_000743 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000743.flac
+cv_asm_000744 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000744.flac
+cv_asm_000745 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000745.flac
+cv_asm_000746 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000746.flac
+cv_asm_000747 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000747.flac
+cv_asm_000748 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000748.flac
+cv_asm_000749 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000749.flac
+cv_asm_000750 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000750.flac
+cv_asm_000751 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000751.flac
+cv_asm_000752 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000752.flac
+cv_asm_000753 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000753.flac
+cv_asm_000754 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000754.flac
+cv_asm_000755 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000755.flac
+cv_asm_000756 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000756.flac
+cv_asm_000757 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000757.flac
+cv_asm_000758 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000758.flac
+cv_asm_000759 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000759.flac
+cv_asm_000760 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000760.flac
+cv_asm_000761 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000761.flac
+cv_asm_000762 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000762.flac
+cv_asm_000763 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000763.flac
+cv_asm_000764 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000764.flac
+cv_asm_000765 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000765.flac
+cv_asm_000766 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000766.flac
+cv_asm_000767 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000767.flac
+cv_asm_000768 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000768.flac
+cv_asm_000769 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000769.flac
+cv_asm_000770 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000770.flac
+cv_asm_000771 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000771.flac
+cv_asm_000772 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000772.flac
+cv_asm_000773 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000773.flac
+cv_asm_000774 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000774.flac
+cv_asm_000775 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000775.flac
+cv_asm_000776 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000776.flac
+cv_asm_000777 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000777.flac
+cv_asm_000778 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000778.flac
+cv_asm_000779 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000779.flac
+cv_asm_000780 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_asm_000780.flac
+cv_bak_000667 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000667.flac
+cv_bak_000668 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000668.flac
+cv_bak_000669 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000669.flac
+cv_bak_000670 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000670.flac
+cv_bak_000671 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000671.flac
+cv_bak_000672 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000672.flac
+cv_bak_000673 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000673.flac
+cv_bak_000674 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000674.flac
+cv_bak_000675 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000675.flac
+cv_bak_000676 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000676.flac
+cv_bak_000677 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000677.flac
+cv_bak_000678 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000678.flac
+cv_bak_000679 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000679.flac
+cv_bak_000680 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000680.flac
+cv_bak_000681 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000681.flac
+cv_bak_000682 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000682.flac
+cv_bak_000683 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000683.flac
+cv_bak_000684 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000684.flac
+cv_bak_000685 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000685.flac
+cv_bak_000686 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000686.flac
+cv_bak_000687 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000687.flac
+cv_bak_000688 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000688.flac
+cv_bak_000689 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000689.flac
+cv_bak_000690 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000690.flac
+cv_bak_000691 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000691.flac
+cv_bak_000692 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000692.flac
+cv_bak_000693 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000693.flac
+cv_bak_000694 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000694.flac
+cv_bak_000695 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000695.flac
+cv_bak_000696 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000696.flac
+cv_bak_000697 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000697.flac
+cv_bak_000698 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000698.flac
+cv_bak_000699 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000699.flac
+cv_bak_000700 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000700.flac
+cv_bak_000701 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000701.flac
+cv_bak_000702 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000702.flac
+cv_bak_000703 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000703.flac
+cv_bak_000704 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000704.flac
+cv_bak_000705 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000705.flac
+cv_bak_000706 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000706.flac
+cv_bak_000707 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000707.flac
+cv_bak_000708 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000708.flac
+cv_bak_000709 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000709.flac
+cv_bak_000710 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000710.flac
+cv_bak_000711 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000711.flac
+cv_bak_000712 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000712.flac
+cv_bak_000713 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000713.flac
+cv_bak_000714 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000714.flac
+cv_bak_000715 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000715.flac
+cv_bak_000716 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000716.flac
+cv_bak_000717 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000717.flac
+cv_bak_000718 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000718.flac
+cv_bak_000719 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000719.flac
+cv_bak_000720 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000720.flac
+cv_bak_000721 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000721.flac
+cv_bak_000722 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000722.flac
+cv_bak_000723 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000723.flac
+cv_bak_000724 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bak_000724.flac
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+cv_bre_001148 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001148.flac
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+cv_bre_001157 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001157.flac
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+cv_bre_001213 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001213.flac
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+cv_bre_001217 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001217.flac
+cv_bre_001218 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001218.flac
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+cv_bre_001220 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001220.flac
+cv_bre_001221 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001221.flac
+cv_bre_001222 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001222.flac
+cv_bre_001223 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001223.flac
+cv_bre_001224 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001224.flac
+cv_bre_001225 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001225.flac
+cv_bre_001226 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001226.flac
+cv_bre_001227 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001227.flac
+cv_bre_001228 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001228.flac
+cv_bre_001229 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001229.flac
+cv_bre_001230 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001230.flac
+cv_bre_001231 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001231.flac
+cv_bre_001232 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001232.flac
+cv_bre_001233 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001233.flac
+cv_bre_001234 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001234.flac
+cv_bre_001235 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001235.flac
+cv_bre_001236 dump/raw/org/dev_1h_lid/data/format.4/data_wav/cv_bre_001236.flac
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.5.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.5.scp
new file mode 100644
index 0000000000000000000000000000000000000000..0a48a1923545448559f33b58553441a7453a44ca
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.5.scp
@@ -0,0 +1,760 @@
+cv_bre_001237 dump/raw/org/dev_1h_lid/data/format.5/data_wav/cv_bre_001237.flac
+cv_bre_001238 dump/raw/org/dev_1h_lid/data/format.5/data_wav/cv_bre_001238.flac
+cv_bre_001239 dump/raw/org/dev_1h_lid/data/format.5/data_wav/cv_bre_001239.flac
+cv_bre_001240 dump/raw/org/dev_1h_lid/data/format.5/data_wav/cv_bre_001240.flac
+cv_bre_001241 dump/raw/org/dev_1h_lid/data/format.5/data_wav/cv_bre_001241.flac
+cv_bre_001242 dump/raw/org/dev_1h_lid/data/format.5/data_wav/cv_bre_001242.flac
+cv_bre_001243 dump/raw/org/dev_1h_lid/data/format.5/data_wav/cv_bre_001243.flac
+cv_bre_001244 dump/raw/org/dev_1h_lid/data/format.5/data_wav/cv_bre_001244.flac
+cv_bre_001245 dump/raw/org/dev_1h_lid/data/format.5/data_wav/cv_bre_001245.flac
+cv_bre_001246 dump/raw/org/dev_1h_lid/data/format.5/data_wav/cv_bre_001246.flac
+cv_bre_001247 dump/raw/org/dev_1h_lid/data/format.5/data_wav/cv_bre_001247.flac
+cv_bre_001248 dump/raw/org/dev_1h_lid/data/format.5/data_wav/cv_bre_001248.flac
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+cv_bre_001250 dump/raw/org/dev_1h_lid/data/format.5/data_wav/cv_bre_001250.flac
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+cv_cmn_000694 dump/raw/org/dev_1h_lid/data/format.5/data_wav/cv_cmn_000694.flac
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.6.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.6.scp
new file mode 100644
index 0000000000000000000000000000000000000000..a91311500d90455d0c1c88ca9fee8c169408c660
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.6.scp
@@ -0,0 +1,760 @@
+cv_cmn_000695 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000695.flac
+cv_cmn_000696 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000696.flac
+cv_cmn_000697 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000697.flac
+cv_cmn_000698 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000698.flac
+cv_cmn_000699 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000699.flac
+cv_cmn_000700 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000700.flac
+cv_cmn_000701 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000701.flac
+cv_cmn_000702 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000702.flac
+cv_cmn_000703 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000703.flac
+cv_cmn_000704 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000704.flac
+cv_cmn_000705 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000705.flac
+cv_cmn_000706 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000706.flac
+cv_cmn_000707 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000707.flac
+cv_cmn_000708 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000708.flac
+cv_cmn_000709 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000709.flac
+cv_cmn_000710 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000710.flac
+cv_cmn_000711 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000711.flac
+cv_cmn_000712 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000712.flac
+cv_cmn_000713 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000713.flac
+cv_cmn_000714 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000714.flac
+cv_cmn_000715 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000715.flac
+cv_cmn_000716 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000716.flac
+cv_cmn_000717 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000717.flac
+cv_cmn_000718 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000718.flac
+cv_cmn_000719 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000719.flac
+cv_cmn_000720 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000720.flac
+cv_cmn_000721 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000721.flac
+cv_cmn_000722 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000722.flac
+cv_cmn_000723 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000723.flac
+cv_cmn_000724 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000724.flac
+cv_cmn_000725 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000725.flac
+cv_cmn_000726 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000726.flac
+cv_cmn_000727 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000727.flac
+cv_cmn_000728 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000728.flac
+cv_cmn_000729 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000729.flac
+cv_cmn_000730 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000730.flac
+cv_cmn_000731 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cmn_000731.flac
+cv_cnh_000886 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000886.flac
+cv_cnh_000887 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000887.flac
+cv_cnh_000888 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000888.flac
+cv_cnh_000889 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000889.flac
+cv_cnh_000890 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000890.flac
+cv_cnh_000891 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000891.flac
+cv_cnh_000892 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000892.flac
+cv_cnh_000893 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000893.flac
+cv_cnh_000894 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000894.flac
+cv_cnh_000895 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000895.flac
+cv_cnh_000896 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000896.flac
+cv_cnh_000897 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000897.flac
+cv_cnh_000898 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000898.flac
+cv_cnh_000899 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000899.flac
+cv_cnh_000900 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000900.flac
+cv_cnh_000901 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000901.flac
+cv_cnh_000902 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000902.flac
+cv_cnh_000903 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000903.flac
+cv_cnh_000904 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000904.flac
+cv_cnh_000905 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000905.flac
+cv_cnh_000906 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000906.flac
+cv_cnh_000907 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000907.flac
+cv_cnh_000908 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000908.flac
+cv_cnh_000909 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000909.flac
+cv_cnh_000910 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000910.flac
+cv_cnh_000911 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000911.flac
+cv_cnh_000912 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000912.flac
+cv_cnh_000913 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000913.flac
+cv_cnh_000914 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000914.flac
+cv_cnh_000915 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000915.flac
+cv_cnh_000916 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000916.flac
+cv_cnh_000917 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000917.flac
+cv_cnh_000918 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000918.flac
+cv_cnh_000919 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000919.flac
+cv_cnh_000920 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000920.flac
+cv_cnh_000921 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000921.flac
+cv_cnh_000922 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000922.flac
+cv_cnh_000923 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000923.flac
+cv_cnh_000924 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000924.flac
+cv_cnh_000925 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000925.flac
+cv_cnh_000926 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000926.flac
+cv_cnh_000927 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000927.flac
+cv_cnh_000928 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000928.flac
+cv_cnh_000929 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000929.flac
+cv_cnh_000930 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000930.flac
+cv_cnh_000931 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000931.flac
+cv_cnh_000932 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000932.flac
+cv_cnh_000933 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000933.flac
+cv_cnh_000934 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000934.flac
+cv_cnh_000935 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000935.flac
+cv_cnh_000936 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000936.flac
+cv_cnh_000937 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000937.flac
+cv_cnh_000938 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000938.flac
+cv_cnh_000939 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000939.flac
+cv_cnh_000940 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000940.flac
+cv_cnh_000941 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000941.flac
+cv_cnh_000942 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000942.flac
+cv_cnh_000943 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000943.flac
+cv_cnh_000944 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000944.flac
+cv_cnh_000945 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000945.flac
+cv_cnh_000946 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000946.flac
+cv_cnh_000947 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000947.flac
+cv_cnh_000948 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000948.flac
+cv_cnh_000949 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000949.flac
+cv_cnh_000950 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000950.flac
+cv_cnh_000951 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000951.flac
+cv_cnh_000952 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000952.flac
+cv_cnh_000953 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000953.flac
+cv_cnh_000954 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000954.flac
+cv_cnh_000955 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000955.flac
+cv_cnh_000956 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000956.flac
+cv_cnh_000957 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000957.flac
+cv_cnh_000958 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000958.flac
+cv_cnh_000959 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000959.flac
+cv_cnh_000960 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000960.flac
+cv_cnh_000961 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000961.flac
+cv_cnh_000962 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000962.flac
+cv_cnh_000963 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000963.flac
+cv_cnh_000964 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000964.flac
+cv_cnh_000965 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000965.flac
+cv_cnh_000966 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000966.flac
+cv_cnh_000967 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000967.flac
+cv_cnh_000968 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000968.flac
+cv_cnh_000969 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000969.flac
+cv_cnh_000970 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000970.flac
+cv_cnh_000971 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000971.flac
+cv_cnh_000972 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000972.flac
+cv_cnh_000973 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000973.flac
+cv_cnh_000974 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000974.flac
+cv_cnh_000975 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000975.flac
+cv_cnh_000976 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000976.flac
+cv_cnh_000977 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000977.flac
+cv_cnh_000978 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000978.flac
+cv_cnh_000979 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000979.flac
+cv_cnh_000980 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000980.flac
+cv_cnh_000981 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000981.flac
+cv_cnh_000982 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000982.flac
+cv_cnh_000983 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000983.flac
+cv_cnh_000984 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000984.flac
+cv_cnh_000985 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000985.flac
+cv_cnh_000986 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000986.flac
+cv_cnh_000987 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000987.flac
+cv_cnh_000988 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000988.flac
+cv_cnh_000989 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000989.flac
+cv_cnh_000990 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000990.flac
+cv_cnh_000991 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000991.flac
+cv_cnh_000992 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000992.flac
+cv_cnh_000993 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000993.flac
+cv_cnh_000994 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000994.flac
+cv_cnh_000995 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000995.flac
+cv_cnh_000996 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000996.flac
+cv_cnh_000997 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000997.flac
+cv_cnh_000998 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000998.flac
+cv_cnh_000999 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_000999.flac
+cv_cnh_001000 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001000.flac
+cv_cnh_001001 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001001.flac
+cv_cnh_001002 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001002.flac
+cv_cnh_001003 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001003.flac
+cv_cnh_001004 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001004.flac
+cv_cnh_001005 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001005.flac
+cv_cnh_001006 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001006.flac
+cv_cnh_001007 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001007.flac
+cv_cnh_001008 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001008.flac
+cv_cnh_001009 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001009.flac
+cv_cnh_001010 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001010.flac
+cv_cnh_001011 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001011.flac
+cv_cnh_001012 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001012.flac
+cv_cnh_001013 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001013.flac
+cv_cnh_001014 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001014.flac
+cv_cnh_001015 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001015.flac
+cv_cnh_001016 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001016.flac
+cv_cnh_001017 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001017.flac
+cv_cnh_001018 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001018.flac
+cv_cnh_001019 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001019.flac
+cv_cnh_001020 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001020.flac
+cv_cnh_001021 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001021.flac
+cv_cnh_001022 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001022.flac
+cv_cnh_001023 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001023.flac
+cv_cnh_001024 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001024.flac
+cv_cnh_001025 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001025.flac
+cv_cnh_001026 dump/raw/org/dev_1h_lid/data/format.6/data_wav/cv_cnh_001026.flac
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.7.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.7.scp
new file mode 100644
index 0000000000000000000000000000000000000000..2c228d3ac83171836effba956dcdbfcb789290e6
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.7.scp
@@ -0,0 +1,760 @@
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+cv_fra_000698 dump/raw/org/dev_1h_lid/data/format.7/data_wav/cv_fra_000698.flac
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.8.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.8.scp
new file mode 100644
index 0000000000000000000000000000000000000000..760ab84d2a4778453fd4a28ec17e087434dcef66
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.8.scp
@@ -0,0 +1,760 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.9.scp b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.9.scp
new file mode 100644
index 0000000000000000000000000000000000000000..759102984b540c30d8f68dab80905ef952ffc5de
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/valid.9.scp
@@ -0,0 +1,760 @@
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/run.sh b/exp/asr_stats_multilingual_1h_lid_lid_1h/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f6f52462b30653305c63b7a2c051ed5b0d79199c
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/run.sh
@@ -0,0 +1 @@
+./asr.sh --ngpu 1 --gpu_inference true --stage 10 --stop_stage 10 --lang multilingual_1h --nj 32 --inference_nj 32 --inference_asr_model valid.loss.ave.pth --local_data_opts '--duration 1h --lid true --multilingual true --single_lang xty --nlsyms_txt data/local/nlsyms.txt' --nlsyms_txt data/local/nlsyms.txt --use_lm false --lm_config conf/train_lm.yaml --token_type char --feats_type raw --feats_normalize utterance_mvn --max_wav_duration 60 --asr_config conf/tuning/train_asr_fbank_single.yaml --inference_config conf/decode_asr.yaml --train_set train_1h_lid --valid_set dev_1h_lid --test_sets test_1h_lid --bpe_train_text data/train_1h_lid/text --asr_tag train_asr_fbank_single_multilingual_1h_lid_1h --local_score_opts 'false true' --asr_stats_dir exp/asr_stats_multilingual_1h_lid_1h --lm_train_text data/train_1h_lid/text --stage 10 "$@"; exit $?
diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/train/feats_lengths_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/train/feats_lengths_stats.npz
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index 0000000000000000000000000000000000000000..e14189962bfc3e45752fd84b40e48753d0b4ef13
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/train/feats_lengths_stats.npz
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+version https://git-lfs.github.com/spec/v1
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/train/feats_stats.npz b/exp/asr_stats_multilingual_1h_lid_lid_1h/train/feats_stats.npz
new file mode 100644
index 0000000000000000000000000000000000000000..26734fb1c08fbda12484e67d3cda39f5a14d5a13
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/train/feats_stats.npz
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/train/speech_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/train/speech_shape
new file mode 100644
index 0000000000000000000000000000000000000000..dc4aff5251469e7762cdd5eb5cdbbdc7922f2a4b
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/train/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/train/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..0b19aa2228bda159e5dd071ca5d909cc8a2c27cb
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/train/text_shape
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/train/text_shape.char b/exp/asr_stats_multilingual_1h_lid_lid_1h/train/text_shape.char
new file mode 100644
index 0000000000000000000000000000000000000000..316055232581c1a78425a16bfb806b1336c3efa2
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/train/text_shape.char
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+LAD_nld_000606 75,6454
+LAD_nld_000607 126,6454
+LAD_nld_000608 117,6454
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+LAD_nld_000610 99,6454
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+LAD_nld_000641 131,6454
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+LAD_nld_000661 63,6454
+LAD_nld_000662 78,6454
+LAD_nld_000663 86,6454
+LAD_nld_000664 103,6454
+LAD_nld_000665 129,6454
+LAD_nld_000666 103,6454
+LAD_nld_000667 117,6454
+LAD_nld_000668 68,6454
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+LAD_nld_000670 53,6454
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+LAD_nld_000676 91,6454
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+LAD_nld_000683 44,6454
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+LAD_nld_000685 90,6454
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+LAD_nld_000687 90,6454
+LAD_nld_000688 100,6454
+LAD_nld_000689 71,6454
+LAD_nld_000690 77,6454
+LAD_nld_000691 93,6454
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+LAD_nld_000693 45,6454
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+LAD_nld_000696 74,6454
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+LAD_nld_000702 70,6454
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+LAD_nld_000704 57,6454
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+LAD_nld_000706 84,6454
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+LAD_nld_000708 68,6454
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+LAD_nld_000710 122,6454
+LAD_nld_000711 60,6454
+LAD_nld_000712 95,6454
+LAD_nld_000713 92,6454
+LAD_nld_000714 76,6454
+LAD_nld_000715 118,6454
+LAD_nld_000716 86,6454
+LAD_nld_000717 82,6454
+LAD_nld_000718 79,6454
+LAD_nld_000719 100,6454
+LAD_nld_000720 104,6454
+LAD_nld_000721 76,6454
+LAD_nld_000722 93,6454
+LAD_nld_000723 81,6454
+LAD_nld_000724 66,6454
+LAD_nld_000725 65,6454
+LAD_nld_000726 102,6454
+LAD_nld_000727 58,6454
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+LAD_nld_000729 69,6454
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+LAD_nld_000732 82,6454
+LAD_nld_000733 95,6454
+LAD_nld_000734 64,6454
+LAD_nld_000735 100,6454
+LAD_nld_000736 122,6454
+LAD_nld_000737 83,6454
+LAD_nld_000738 112,6454
+LAD_nld_000739 96,6454
+LAD_nld_000740 58,6454
+LAD_nld_000741 99,6454
+LAD_nld_000742 84,6454
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+LAD_nld_000744 105,6454
+LAD_nld_000745 56,6454
+LAD_nld_000746 98,6454
+LAD_nld_000747 64,6454
+LAD_nld_000748 104,6454
+LAD_nld_000749 97,6454
+LAD_nld_000750 105,6454
+LAD_nld_000751 71,6454
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+LAD_nld_000755 96,6454
+LAD_nld_000756 90,6454
+LAD_nld_000757 133,6454
+LAD_nld_000758 74,6454
+LAD_nld_000759 94,6454
+LAD_nld_000760 81,6454
+LAD_nld_000761 77,6454
+LAD_nld_000762 52,6454
+LAD_nld_000763 53,6454
+LAD_nld_000764 56,6454
+LAD_nld_000765 120,6454
+LAD_nld_000766 91,6454
+LAD_nld_000767 68,6454
+LAD_nld_000768 41,6454
+LAD_nld_000769 95,6454
+LAD_nld_000770 46,6454
+LAD_nld_000771 79,6454
+LAD_nld_000772 89,6454
+LAD_nld_000773 96,6454
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+LAD_nld_000775 98,6454
+LAD_nld_000776 107,6454
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+LAD_nld_000780 72,6454
+LAD_nld_000781 106,6454
+LAD_nld_000782 95,6454
+LAD_nld_000783 74,6454
+LAD_nld_000784 100,6454
+LAD_nld_000785 82,6454
+LAD_nld_000786 93,6454
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+LAD_nld_000788 94,6454
+LAD_nld_000789 60,6454
+LAD_nld_000790 103,6454
+LAD_nld_000791 90,6454
+LAD_nld_000792 50,6454
+LAD_nld_000793 87,6454
+LAD_nld_000794 73,6454
+LAD_nld_000795 92,6454
+LAD_nld_000796 79,6454
+LAD_nld_000797 97,6454
+LAD_nld_000798 92,6454
+LAD_nld_000799 43,6454
+LAD_nld_000800 93,6454
+LAD_nld_000801 122,6454
+LAD_nld_000802 84,6454
+LAD_nld_000803 88,6454
+LAD_nld_000804 59,6454
+LAD_nld_000805 62,6454
+LAD_nld_000806 103,6454
+LAD_nld_000807 93,6454
+LAD_nld_000808 130,6454
+LAD_nld_000809 101,6454
+LAD_nld_000810 77,6454
+LAD_nld_000811 81,6454
+LAD_nld_000812 34,6454
+LAD_nld_000813 54,6454
+LAD_nld_000814 116,6454
+LAD_nld_000815 92,6454
+LAD_nld_000816 69,6454
+LAD_nld_000817 98,6454
+LAD_nld_000818 106,6454
+LAD_nld_000819 99,6454
+LAD_nld_000820 109,6454
+LAD_nld_000821 108,6454
+LAD_nld_000822 94,6454
+LAD_nld_000823 86,6454
+LAD_nld_000824 109,6454
+LAD_nld_000825 104,6454
+LAD_nld_000826 115,6454
+LAD_nld_000827 85,6454
+LAD_nld_000828 58,6454
+LAD_nld_000829 72,6454
+LAD_nld_000830 77,6454
+LAD_nld_000831 107,6454
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/valid/text_shape b/exp/asr_stats_multilingual_1h_lid_lid_1h/valid/text_shape
new file mode 100644
index 0000000000000000000000000000000000000000..abbf25775056a895ceeacef1d6f86d6cfbaf7eb8
--- /dev/null
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diff --git a/exp/asr_stats_multilingual_1h_lid_lid_1h/valid/text_shape.char b/exp/asr_stats_multilingual_1h_lid_lid_1h/valid/text_shape.char
new file mode 100644
index 0000000000000000000000000000000000000000..28513728133c9d4e29f7d9e08621034437bdb164
--- /dev/null
+++ b/exp/asr_stats_multilingual_1h_lid_lid_1h/valid/text_shape.char
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