Add model files

README.md

@@ -1,4 +1,58 @@
 ---
 language:
 - en
----
+---
+
+End-to-end Neural Diarization with Encoder-Decoder Based Attractors trained on AMI-headset. 
+This example could be found at `egs2/ami/diar1`. 
+
+## Configurations:
+
+- Use ESPNet's default frontend to extract features. The sampling rate is 8000 Hz, with a frame length of 25 ms and a frame shift of 10 ms. The frontend extracts 23 log-scaled Mel-filterbanks.
+- Use 4 layer stacked Transformer encoder, each outputs 256-dimensional frame-wise embeddings.
+- Use the ESPNet' standard rnn attractor (LSTM) with hidden size of 256.
+- Initial training uses data with 4 speakers for 500 epochs, following `spk4/diar_train_diar_eda_raw_spk4/config.yaml`.
+- Adaptation involves fine-tuning the model using data with 3 and 5 speakers respectively for 20 epochs respectively, using `spk3/diar_train_diar_eda_raw_spk3/config.yaml` and `spk5/diar_train_diar_eda_raw_spk5/config.yaml` respectively.
+
+## RESULTS
+
+The following results were obtained using the checkpoint `spk5/diar_train_diar_eda_raw_spk5/20epoch.pth`, tested on the test and development sets with the 4-speakers.
+
+### Environments
+- date: `Thu Dec 19 22:43:37 EST 2024`
+- python version: `3.11.10 (main, Oct  3 2024, 07:29:13) [GCC 11.2.0]`
+- espnet version: `espnet 202409`
+- pytorch version: `pytorch 2.4.0`
+- Git hash: `c12b3d59ca4fd8847edf274e56a1716474d2a30e`
+  - Commit date: `Thu Dec 19 21:58:26 2024 -0500`
+
+### spk4
+#### DER
+diarized_test
+|threshold_median_collar|DER|
+|---|---|
+|result_th0.3_med11_collar0.0|72.44|
+|result_th0.3_med1_collar0.0|74.64|
+|result_th0.4_med11_collar0.0|70.60|
+|result_th0.4_med1_collar0.0|72.30|
+|result_th0.5_med11_collar0.0|70.45|
+|result_th0.5_med1_collar0.0|72.02|
+|result_th0.6_med11_collar0.0|71.85|
+|result_th0.6_med1_collar0.0|73.41|
+|result_th0.7_med11_collar0.0|75.56|
+|result_th0.7_med1_collar0.0|77.02|
+### spk4
+#### DER
+diarized_dev
+|threshold_median_collar|DER|
+|---|---|
+|result_th0.3_med11_collar0.0|74.37|
+|result_th0.3_med1_collar0.0|75.96|
+|result_th0.4_med11_collar0.0|71.69|
+|result_th0.4_med1_collar0.0|72.94|
+|result_th0.5_med11_collar0.0|70.83|
+|result_th0.5_med1_collar0.0|72.12|
+|result_th0.6_med11_collar0.0|71.96|
+|result_th0.6_med1_collar0.0|73.34|
+|result_th0.7_med11_collar0.0|75.81|
+|result_th0.7_med1_collar0.0|76.99|

exp_eda/spk3/diar_stats_8k/train/feats_lengths_stats.npz

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exp_eda/spk3/diar_stats_8k/train/feats_stats.npz

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exp_eda/spk3/diar_train_diar_eda_adapt_raw_spk3/20epoch.pth

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exp_eda/spk3/diar_train_diar_eda_adapt_raw_spk3/config.yaml

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+config: conf/train_diar_eda_adapt.yaml
+print_config: false
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+output_dir: exp_eda/spk3/diar_train_diar_eda_adapt_raw_spk3
+ngpu: 1
+seed: 0
+num_workers: 1
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
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+multiprocessing_distributed: false
+unused_parameters: false
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+use_deepspeed: false
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+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+use_tf32: false
+collect_stats: false
+write_collected_feats: false
+max_epoch: 20
+patience: null
+val_scheduler_criterion:
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+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - valid
+    - acc
+    - max
+keep_nbest_models: 10
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+detect_anomaly: false
+use_adapter: false
+adapter: lora
+save_strategy: all
+adapter_conf: {}
+pretrain_path: null
+init_param:
+- exp_eda/spk4/diar_train_diar_eda_raw_spk4/valid.acc.best.pth
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: null
+batch_size: 64
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+category_sample_size: 10
+train_shape_file:
+- exp_eda/spk3/diar_stats_8k/train/speech_shape
+- exp_eda/spk3/diar_stats_8k/train/spk_labels_shape
+valid_shape_file:
+- exp_eda/spk3/diar_stats_8k/valid/speech_shape
+- exp_eda/spk3/diar_stats_8k/valid/spk_labels_shape
+batch_type: unsorted
+valid_batch_type: unsorted
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+- 80000
+- 800
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+sort_batch: descending
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+chunk_default_fs: null
+chunk_max_abs_length: null
+chunk_discard_short_samples: true
+train_data_path_and_name_and_type:
+-   - dump_eda/spk3/raw/train/wav.scp
+    - speech
+    - sound
+-   - dump_eda/spk3/raw/train/espnet_rttm
+    - spk_labels
+    - rttm
+valid_data_path_and_name_and_type:
+-   - dump_eda/spk3/raw/dev/wav.scp
+    - speech
+    - sound
+-   - dump_eda/spk3/raw/dev/espnet_rttm
+    - spk_labels
+    - rttm
+multi_task_dataset: false
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+allow_multi_rates: false
+valid_max_cache_size: null
+exclude_weight_decay: false
+exclude_weight_decay_conf: {}
+optim: adam
+optim_conf:
+    lr: 1.0e-05
+scheduler: null
+scheduler_conf: {}
+num_spk: 3
+init: xavier_uniform
+input_size: 322
+model_conf:
+    diar_weight: 1.0
+    attractor_weight: 1.0
+    context_size: 7
+    subsampling: 10
+use_preprocessor: true
+frontend: default
+frontend_conf:
+    fs: 8k
+    n_fft: 200
+    win_length: 200
+    hop_length: 128
+    n_mels: 23
+specaug: specaug
+specaug_conf:
+    apply_time_warp: false
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 30
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_range:
+    - 0
+    - 40
+    num_time_mask: 2
+normalize: global_mvn
+normalize_conf:
+    stats_file: exp_eda/spk3/diar_stats_8k/train/feats_stats.npz
+encoder: transformer
+encoder_conf:
+    input_layer: linear
+    num_blocks: 4
+    linear_units: 512
+    dropout_rate: 0.1
+    output_size: 256
+    attention_heads: 4
+    attention_dropout_rate: 0.1
+decoder: linear
+decoder_conf: {}
+label_aggregator: label_aggregator
+label_aggregator_conf: {}
+attractor: rnn
+attractor_conf:
+    unit: 256
+    layer: 1
+    dropout: 0.1
+    attractor_grad: true
+required:
+- output_dir
+version: '202409'
+distributed: false

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exp_eda/spk5/diar_train_diar_eda_adapt_raw_spk5/config.yaml

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+    - speech
+    - sound
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+    - spk_labels
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+    - speech
+    - sound
+-   - dump_eda/spk5/raw/dev/espnet_rttm
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