Upload 539 files

exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/stats.log

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+Submitted batch job 2133206

exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/q/stats.sh

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+#!/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

exp/asr_stats_multilingual_1h_lid_lid_1h/logdir/stats.1.log

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+# 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', '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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'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', 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'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', 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'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

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]'
+- ி
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+- ম
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+- پ
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+- Ә
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+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
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+- Մ
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+- େ
+- ട
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+- گ
+- ನ
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+- Ę
+- ತ
+- च
+- দ
+- Ო
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+- '[fra]'
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+- Π
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+- ज
+- Ż
+- Κ
+- ત
+- ల
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+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
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+- خ
+- ን
+- ก
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+- െ
+- ں
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+- 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
+- ุ
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+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
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+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
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+- ফ
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+- «
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+- ራ
+- »
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+- ច
+- ચ
+- ረ
+- こ
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+- Ӱ
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+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
+- ံ
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+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
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+- 大
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+- 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]'
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+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
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+- ញ
+- ሁ
+- ޖ
+- ”
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+- ޔ
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+- ጣ
+- ઇ
+- 日
+- ޯ
+- よ
+- 고
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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
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+- 个
+- ഫ
+- Ō
+- ዘ
+- 方
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+- Ḍ
+- 学
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+- ڊ
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+- థ
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+- Җ
+- ษ
+- ಉ
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+- ਫ
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+- ル
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+- ጵ
+- 사
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+- ဘ
+- ఈ
+- 있
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+- Ҵ
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+- 所
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+- わ
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+- Ֆ
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+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
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+- ど
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+- ば
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+- ሌ
+- ጊ
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+- 자
+- „
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+- “
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+- Ӳ
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+- 们
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+- 나
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+- 外
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+- 전
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+- 全
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+- テ
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+- ڌ
+- ሄ
+- レ
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+- ៍
+- 种
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+- 此
+- 与
+- 相
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+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
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+- ·
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+- 何
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+- 哋
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+- 과
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+- ィ
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+- 미
+- 유
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+- 면
+- 원
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+- ャ
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+- ኪ
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+- ょ
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+- 発
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+- ঝ
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+- 速
+- 件
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+- ٺ
+- Ҿ
+- Ҽ
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+- 「
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+- 交
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+- 睇
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+- 모
+- 소
+- २
+- Ѓ
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+- 该
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+- ュ
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+- 구
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+- 林
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+- 經
+- 邊
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+- ಘ
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+- 報
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+- ০
+- °
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+- ጐ
+- ጆ
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+- Ӷ
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+- 員
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+- ಏ
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+- ஹ
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+- ፉ
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+- 图
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+- 放
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+- 产
+- 变
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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
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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
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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
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+model: espnet
+model_conf:
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+preencoder_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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+postencoder_conf: {}
+decoder: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
+- output_dir
+- token_list
+version: '202211'
+distributed: false

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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', 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'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', '��', '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', '��', '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', 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'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

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]'
+- ி
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+- ম
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+- پ
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+- Ә
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+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
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+- େ
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+- ನ
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+- Ę
+- ತ
+- च
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+- Ო
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+- '[fra]'
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+- Π
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+- ज
+- Ż
+- Κ
+- ત
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+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
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+- خ
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+- 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
+- ุ
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+- ঁ
+- ិ
+- Շ
+- ס
+- Ռ
+- ද
+- '[jpn]'
+- ذ
+- ശ
+- Ů
+- ከ
+- ଶ
+- េ
+- ช
+- Ώ
+- ோ
+- ំ
+- ଯ
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+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
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+- ഗ
+- ព
+- ੈ
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+- «
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+- ራ
+- »
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+- ચ
+- ረ
+- こ
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+- ূ
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+- Ɣ
+- ಣ
+- ට
+- ື
+- ޭ
+- Ū
+- ભ
+- 는
+- ຕ
+- ቸ
+- '[heb]'
+- ਇ
+- ഞ
+- ঠ
+- 我
+- ਟ
+- ూ
+- ജ
+- ڑ
+- ଉ
+- ઓ
+- झ
+- ળ
+- ೋ
+- '[mkd]'
+- ー
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+- ء
+- '[ast]'
+- ಜ
+- ਣ
+- ై
+- ੂ
+- ً
+- ഒ
+- ؟
+- ග
+- ဖ
+- り
+- โ
+- ါ
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+- 大
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+- 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]'
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+- 은
+- ធ
+- ੍
+- Đ
+- 嘅
+- 出
+- ټ
+- Ĕ
+- ス
+- 지
+- 下
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+- ញ
+- ሁ
+- ޖ
+- ”
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+- ޔ
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+- ઇ
+- 日
+- ޯ
+- よ
+- 고
+- ኛ
+- ሊ
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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
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+- ڊ
+- 部
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+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
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+- 서
+- ਫ
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+- ル
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+- ጵ
+- 사
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+- ဘ
+- ఈ
+- 있
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+- ఒ
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+- Ҵ
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+- ラ
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+- 수
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+- ਥ
+- 所
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+- わ
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+- ഈ
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+- Ֆ
+- 리
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+- 合
+- ञ
+- 間
+- ଞ
+- Ќ
+- ږ
+- 知
+- ሬ
+- 手
+- ኤ
+- ¿
+- や
+- ך
+- ៊
+- 인
+- ዚ
+- ؤ
+- 들
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+- ど
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+- ば
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+- ሌ
+- ጊ
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+- 자
+- „
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+- ጀ
+- “
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+- リ
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+- Ӳ
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+- ੜ
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+- 们
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+- 나
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+- ఏ
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+- 外
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+- 전
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+- 全
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+- 字
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+- テ
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+- ڌ
+- ሄ
+- レ
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+- ៍
+- 种
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+- 点
+- 此
+- 与
+- 相
+- 受
+- 言
+- ՝
+- ઠ
+- ဉ
+- ృ
+- 内
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+- 제
+- ਝ
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+- ·
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+- 何
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+- 哋
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+- 과
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+- Ḽ
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+- ィ
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+- 미
+- 유
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+- 면
+- 원
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+- 考
+- ャ
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+- 물
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+- ኪ
+- ঃ
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+- へ
+- マ
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+- ょ
+- 記
+- 発
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+- 咗
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+- ٠
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+- ጃ
+- ঝ
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+- ̇
+- 우
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+- 量
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+- 件
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+- ٺ
+- Ҿ
+- Ҽ
+- 太
+- 「
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+- 交
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+- 題
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+- 睇
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+- 모
+- 소
+- २
+- Ѓ
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+- 该
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+- ュ
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+- 세
+- 구
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+- 林
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+- 着
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+- 經
+- 邊
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+- Œ
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+- ၍
+- 军
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+- 向
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+- 味
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+- ಘ
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+- Ǹ
+- 回
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+- 像
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+- 報
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+- ০
+- °
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+- ጦ
+- ጐ
+- ጆ
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+- 않
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+- Ӷ
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+- キ
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+- 員
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+- ಏ
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+- ஹ
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+- ፉ
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+- 图
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+- 放
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+- 产
+- 变
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+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: null
+    zero_infinity: true
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+aux_ctc_tasks: []
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+frontend: default
+frontend_conf:
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+    fs: 16k
+specaug: specaug
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+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
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+    time_mask_width_ratio_range:
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+    attention_dropout_rate: 0.1
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+decoder_conf: {}
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+preprocessor_conf: {}
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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', 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'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', '��', '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', '��', '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', 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'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

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]'
+- ி
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+- ম
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+- پ
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+- ર
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+- Ә
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+- Ւ
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+- Ọ
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+- י
+- Რ
+- ز
+- Ñ
+- ް
+- ర
+- द
+- କ
+- ו
+- ಾ
+- ু
+- ع
+- ப
+- ف
+- Å
+- ়
+- ग
+- ่
+- Ā
+- న
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+- Մ
+- Ρ
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+- େ
+- ട
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+- گ
+- ನ
+- ம
+- Ę
+- ತ
+- च
+- দ
+- Ო
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+- '[fra]'
+- È
+- Π
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+- ज
+- Ż
+- Κ
+- ત
+- ల
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+- ា
+- '[rus]'
+- '[ben]'
+- ક
+- '[spa]'
+- મ
+- އ
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+- خ
+- ን
+- ก
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+- ब
+- െ
+- ں
+- Ý
+- ง
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+- 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
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+- ീ
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+- ડ
+- ົ
+- ބ
+- ើ
+- Ţ
+- ප
+- ែ
+- ግ
+- ഗ
+- ព
+- ੈ
+- ሚ
+- ठ
+- ຍ
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+- ೊ
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+- ਼
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+- ଳ
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+- ফ
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+- ์
+- ધ
+- «
+- ේ
+- ራ
+- »
+- れ
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+- ច
+- ચ
+- ረ
+- こ
+- Զ
+- ڭ
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+- ധ
+- Ľ
+- ൂ
+- ़
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+- Ғ
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+- Ӱ
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+- ূ
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+- Қ
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+- Չ
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+- 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
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+- 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
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+- 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
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+- థ
+- ओ
+- Җ
+- ษ
+- ಉ
+- ឹ
+- 서
+- ਫ
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+- ル
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+- ਧ
+- ቱ
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+- ණ
+- ጵ
+- 사
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+- ဘ
+- ఈ
+- 있
+- 時
+- ॅ
+- 한
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+- ऊ
+- ఒ
+- ሎ
+- ቃ
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+- ຢ
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+- Ḥ
+- ሲ
+- 家
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+- څ
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+- ち
+- 子
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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
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+- ఫ
+- ቢ
+- ৎ
+- わ
+- 過
+- ഈ
+- 으
+- 来
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+- 定
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+- 아
+- ຶ
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+- ጉ
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+- 度
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+- ឡ
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+- 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
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+- 水
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+- ଢ
+- ශ
+- ኒ
+- ሌ
+- ጊ
+- 三
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+- 그
+- 第
+- 山
+- 대
+- 자
+- „
+- 明
+- 位
+- せ
+- ښ
+- ጀ
+- “
+- 二
+- 路
+- リ
+- 加
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+- 道
+- 市
+- 表
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+- ¡
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+- 里
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+- Ӡ
+- Ӳ
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+- 부
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+- ੜ
+- ‌
+- 们
+- 更
+- 正
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+- ዶ
+- 美
+- 也
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+- タ
+- ஜ
+- ජ
+- ሙ
+- ፕ
+- 現
+- ૃ
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+- ఖ
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+- ೃ
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+- Ẓ
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+- ቤ
+- 十
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+- み
+- Ķ
+- 나
+- ۍ
+- ఏ
+- ං
+- 外
+- 海
+- 重
+- 天
+- 語
+- ۰
+- ៀ
+- 전
+- ඇ
+- 全
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+- 面
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+- 것
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+- ፋ
+- 字
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+- テ
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+- 身
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+- ෝ
+- ኝ
+- ٍ
+- 今
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+- 場
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+- 啲
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+- 상
+- ጽ
+- ቆ
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+- シ
+- 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
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+- 英
+- 代
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+- 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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+- 這
+- 트
+- ሸ
+- Џ
+- 城
+- 影
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+- Ჰ
+- ൗ
+- 哋
+- ૈ
+- Ṅ
+- 과
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+- ౌ
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+- 車
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+- 任
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+- 持
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+- ૌ
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+- 문
+- ဗ
+- ਘ
+- ඳ
+- Ḽ
+- 已
+- 造
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+- ィ
+- パ
+- Ǧ
+- 미
+- 유
+- 위
+- 면
+- 원
+- ච
+- ጸ
+- 对
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+- 客
+- 考
+- ャ
+- オ
+- ഓ
+- ዓ
+- ሃ
+- 동
+- 물
+- ආ
+- ቷ
+- ኪ
+- ঃ
+- 称
+- 活
+- 落
+- へ
+- マ
+- ��
+- ょ
+- 記
+- 発
+- ഠ
+- 發
+- 咗
+- ឋ
+- 국
+- 现
+- 兰
+- 基
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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
+- 結
+- ฮ
+- 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
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+- ޗ
+- ュ
+- ଵ
+- ১
+- 세
+- 구
+- 경
+- 비
+- ။
+- ዴ
+- 士
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+- 业
+- 林
+- 存
+- 着
+- 集
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+- 它
+- じ
+- ム
+- 観
+- ഘ
+- 經
+- 邊
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+- 、
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+- ဇ
+- ਛ
+- ጂ
+- ጭ
+- 场
+- 故
+- 型
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+- 星
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+- 며
+- 간
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+- ኘ
+- Œ
+- 镇
+- 罗
+- 长
+- 指
+- 程
+- 反
+- 接
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+- ウ
+- ブ
+- 體
+- 幾
+- 樣
+- 러
+- 요
+- 르
+- ၍
+- 军
+- 印
+- 向
+- 改
+- 治
+- 只
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+- メ
+- 味
+- プ
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+- ২
+- ಘ
+- 년
+- 오
+- ਠ
+- Ǹ
+- 回
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+- 周
+- 像
+- ޙ
+- 強
+- 報
+- 試
+- ฝ
+- ০
+- °
+- ៗ
+- 된
+- 식
+- 방
+- 개
+- 성
+- ڇ
+- ෑ
+- ጦ
+- ጐ
+- ጆ
+- ঢ
+- 间
+- 包
+- 供
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+- 神
+- Ỹ
+- グ
+- 중
+- ਐ
+- භ
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+- 马
+- 近
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+- 院
+- 取
+- 又
+- 限
+- デ
+- ェ
+- ネ
+- 達
+- 矛
+- 対
+- 業
+- 並
+- ઊ
+- 않
+- 화
+- 발
+- 조
+- ጓ
+- ጻ
+- Ӷ
+- 进
+- 达
+- 战
+- 再
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+- 半
+- 省
+- 街
+- ޚ
+- 望
+- キ
+- 盾
+- 認
+- 員
+- •
+- 當
+- ฤ
+- ಏ
+- 합
+- 선
+- 프
+- 분
+- ຟ
+- ፒ
+- 列
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+- 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
+- 演
+- 图
+- 夫
+- 放
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+- 即
+- 校
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+- 風
+- 選
+- 類
+- ៈ
+- 용
+- 무
+- 차
+- ဏ
+- 产
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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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+speech_volume_normalize: 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
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+model: espnet
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+preencoder_conf: {}
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+    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: rnn
+decoder_conf: {}
+preprocessor: default
+preprocessor_conf: {}
+required:
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