libri23mix_eend_ss

ESPnet2 DIAR model

soumi-maiti/libri23mix_eend_ss

This model was trained by soumimaiti using librimix recipe in espnet.

Demo: How to use in ESPnet2

Follow the ESPnet installation instructions if you haven't done that already.

cd espnet
git checkout d837c97c88f13ffe655a30bcff93d814f212b225
pip install -e .
cd egs2/librimix/enh_diar23
./run.sh --skip_data_prep false --skip_train true --download_model soumi-maiti/libri23mix_eend_ss

DIAR config

<details><summary>expand</summary>

config: conf/tuning/train_diar_enh_convtasnet_concat_feats_adapt.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/diar_enh_train_diar_enh_convtasnet_concat_feats_adapt
ngpu: 1
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: 0
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: false
write_collected_feats: false
max_epoch: 50
patience: 4
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - loss_enh
    - min
keep_nbest_models: 1
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 16
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
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:
- ../enh_diar1/exp/diar_enh_train_diar_enh_convtasnet_concat_feats_raw/valid.loss_enh.best.pth
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 1
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/diar_enh_stats_8k/train/speech_shape
- exp/diar_enh_stats_8k/train/text_shape
- exp/diar_enh_stats_8k/train/speech_ref1_shape
- exp/diar_enh_stats_8k/train/speech_ref2_shape
- exp/diar_enh_stats_8k/train/speech_ref3_shape
- exp/diar_enh_stats_8k/train/noise_ref1_shape
valid_shape_file:
- exp/diar_enh_stats_8k/valid/speech_shape
- exp/diar_enh_stats_8k/valid/text_shape
- exp/diar_enh_stats_8k/valid/speech_ref1_shape
- exp/diar_enh_stats_8k/valid/speech_ref2_shape
- exp/diar_enh_stats_8k/valid/speech_ref3_shape
- exp/diar_enh_stats_8k/valid/noise_ref1_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 800
- 80000
- 80000
- 80000
- 80000
- 80000
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 24000
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
-   - dump/raw/train/wav.scp
    - speech
    - sound
-   - dump/raw/train/espnet_rttm
    - text
    - rttm
-   - dump/raw/train/spk1.scp
    - speech_ref1
    - sound
-   - dump/raw/train/spk2.scp
    - speech_ref2
    - sound
-   - dump/raw/train/spk3.scp
    - speech_ref3
    - sound
-   - dump/raw/train/noise1.scp
    - noise_ref1
    - sound
valid_data_path_and_name_and_type:
-   - dump/raw/dev/wav.scp
    - speech
    - sound
-   - dump/raw/dev/espnet_rttm
    - text
    - rttm
-   - dump/raw/dev/spk1.scp
    - speech_ref1
    - sound
-   - dump/raw/dev/spk2.scp
    - speech_ref2
    - sound
-   - dump/raw/dev/spk3.scp
    - speech_ref3
    - sound
-   - dump/raw/dev/noise1.scp
    - noise_ref1
    - sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
    lr: 0.001
    eps: 1.0e-07
    weight_decay: 0
scheduler: reducelronplateau
scheduler_conf:
    mode: min
    factor: 0.5
    patience: 1
token_list: null
src_token_list: null
init: xavier_uniform
input_size: null
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: null
    zero_infinity: true
enh_criterions:
-   name: si_snr
    conf:
        eps: 1.0e-07
    wrapper: pit
    wrapper_conf:
        weight: 1.0
        independent_perm: true
        flexible_numspk: true
diar_num_spk: 3
diar_input_size: 128
enh_model_conf:
    loss_type: si_snr
asr_model_conf:
    ctc_weight: 0.5
    interctc_weight: 0.0
    ignore_id: -1
    lsm_weight: 0.0
    length_normalized_loss: false
    report_cer: true
    report_wer: true
    sym_space: <space>
    sym_blank: <blank>
    extract_feats_in_collect_stats: true
st_model_conf:
    stft_consistency: false
    loss_type: mask_mse
    mask_type: null
diar_model_conf:
    diar_weight: 0.2
    attractor_weight: 0.2
subtask_series:
- enh
- diar
model_conf:
    calc_enh_loss: true
    bypass_enh_prob: 0
use_preprocessor: true
token_type: bpe
bpemodel: null
src_token_type: bpe
src_bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
enh_encoder: conv
enh_encoder_conf:
    channel: 512
    kernel_size: 16
    stride: 8
enh_separator: tcn_nomask
enh_separator_conf:
    layer: 8
    stack: 3
    bottleneck_dim: 128
    hidden_dim: 512
    kernel: 3
    causal: false
    norm_type: gLN
enh_decoder: conv
enh_decoder_conf:
    channel: 512
    kernel_size: 16
    stride: 8
enh_mask_module: multi_mask
enh_mask_module_conf:
    max_num_spk: 3
    mask_nonlinear: relu
    bottleneck_dim: 128
frontend: default
frontend_conf: {}
specaug: null
specaug_conf: {}
normalize: utterance_mvn
normalize_conf: {}
asr_preencoder: null
asr_preencoder_conf: {}
asr_encoder: rnn
asr_encoder_conf: {}
asr_postencoder: null
asr_postencoder_conf: {}
asr_decoder: rnn
asr_decoder_conf: {}
st_preencoder: null
st_preencoder_conf: {}
st_encoder: rnn
st_encoder_conf: {}
st_postencoder: null
st_postencoder_conf: {}
st_decoder: rnn
st_decoder_conf: {}
st_extra_asr_decoder: rnn
st_extra_asr_decoder_conf: {}
st_extra_mt_decoder: rnn
st_extra_mt_decoder_conf: {}
diar_frontend: default
diar_frontend_conf:
    hop_length: 64
    fs: 8000
diar_specaug: null
diar_specaug_conf: {}
diar_normalize: utterance_mvn
diar_normalize_conf: {}
diar_encoder: transformer
diar_encoder_conf:
    input_layer: conv2d8
    num_blocks: 4
    linear_units: 512
    dropout_rate: 0.1
    output_size: 256
    attention_heads: 4
    attention_dropout_rate: 0.1
diar_decoder: linear
diar_decoder_conf: {}
label_aggregator: label_aggregator
label_aggregator_conf:
    win_length: 256
    hop_length: 64
diar_attractor: rnn
diar_attractor_conf:
    unit: 256
    layer: 1
    dropout: 0.0
    attractor_grad: true
required:
- output_dir
version: '202205'
distributed: false

</details>

Citing ESPnet

@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}

or arXiv:

@misc{watanabe2018espnet,
  title={ESPnet: End-to-End Speech Processing Toolkit}, 
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  year={2018},
  eprint={1804.00015},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}