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Wangyou_Zhang_librimix_train_enh_tse_td_speakerbeam_raw

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metadata
tags:
  - espnet
  - audio
  - audio-to-audio
language: en
datasets:
  - librimix
license: cc-by-4.0

ESPnet2 ENH model

espnet/Wangyou_Zhang_librimix_train_enh_tse_td_speakerbeam_raw

This model was trained by Wangyou Zhang 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

pip install -e .
cd egs2/librimix/tse1
./run.sh --skip_data_prep false --skip_train true --is_tse_task true --download_model espnet/Wangyou_Zhang_librimix_train_enh_tse_td_speakerbeam_raw

RESULTS

Environments

  • date: Mon Jun 5 22:42:07 CST 2023
  • python version: 3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]
  • espnet version: espnet 202301
  • pytorch version: pytorch 2.0.1
  • Git hash: ``
    • Commit date: ``

enh_train_raw

config: ./conf/train.yaml

dataset PESQ_WB STOI SAR SDR SIR SI_SNR OVRL SIG BAK P808_MOS
dev 1.08 64.43 7.18 -1.71 0.08 -1.81 1.60 2.26 1.62 2.68
test 1.08 64.56 6.90 -1.83 0.09 -1.93 1.63 2.33 1.66 2.71
enhanced_dev 1.73 86.50 12.50 11.40 24.83 10.58 2.95 3.24 3.92 3.23
enhanced_test 1.73 87.36 12.34 11.47 24.51 10.74 2.99 3.29 3.91 3.25

ENH config

expand 
config: ./conf/train.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/enh_train_raw
ngpu: 1
seed: 0
num_workers: 2
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 43837
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: true
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
skip_stats_npz: false
max_epoch: 100
patience: 20
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - snr
    - max
-   - valid
    - loss
    - min
keep_nbest_models: 1
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: 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: null
batch_size: 16
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/enh_stats_train_dev_16k/train/speech_mix_shape
- exp/enh_stats_train_dev_16k/train/speech_ref1_shape
- exp/enh_stats_train_dev_16k/train/enroll_ref1_shape
- exp/enh_stats_train_dev_16k/train/speech_ref2_shape
- exp/enh_stats_train_dev_16k/train/enroll_ref2_shape
valid_shape_file:
- exp/enh_stats_train_dev_16k/valid/speech_mix_shape
- exp/enh_stats_train_dev_16k/valid/speech_ref1_shape
- exp/enh_stats_train_dev_16k/valid/enroll_ref1_shape
- exp/enh_stats_train_dev_16k/valid/speech_ref2_shape
- exp/enh_stats_train_dev_16k/valid/enroll_ref2_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 80000
- 80000
- 80000
- 80000
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 48000
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes:
- enroll_ref
train_data_path_and_name_and_type:
-   - dump/raw/train/wav.scp
    - speech_mix
    - sound
-   - dump/raw/train/spk1.scp
    - speech_ref1
    - sound
-   - dump/raw/train/enroll_spk1.scp
    - enroll_ref1
    - text
-   - dump/raw/train/spk2.scp
    - speech_ref2
    - sound
-   - dump/raw/train/enroll_spk2.scp
    - enroll_ref2
    - text
valid_data_path_and_name_and_type:
-   - dump/raw/dev/wav.scp
    - speech_mix
    - sound
-   - dump/raw/dev/spk1.scp
    - speech_ref1
    - sound
-   - dump/raw/dev/enroll_spk1.scp
    - enroll_ref1
    - text
-   - dump/raw/dev/spk2.scp
    - speech_ref2
    - sound
-   - dump/raw/dev/enroll_spk2.scp
    - enroll_ref2
    - text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
    lr: 0.001
    eps: 1.0e-08
    weight_decay: 0
scheduler: reducelronplateau
scheduler_conf:
    mode: min
    factor: 0.7
    patience: 3
init: null
model_conf:
    num_spk: 2
    share_encoder: true
criterions:
-   name: snr
    conf:
        eps: 1.0e-07
    wrapper: fixed_order
    wrapper_conf:
        weight: 1.0
-   name: l1_fd
    conf:
        only_for_test: true
    wrapper: fixed_order
    wrapper_conf:
        weight: 0.0
-   name: l1_td
    conf:
        only_for_test: true
    wrapper: fixed_order
    wrapper_conf:
        weight: 0.0
-   name: mse_fd
    conf:
        only_for_test: true
    wrapper: fixed_order
    wrapper_conf:
        weight: 0.0
-   name: mse_td
    conf:
        only_for_test: true
    wrapper: fixed_order
    wrapper_conf:
        weight: 0.0
train_spk2enroll: data/train-100/spk2enroll.json
enroll_segment: 48000
load_spk_embedding: false
load_all_speakers: false
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
speech_volume_normalize: null
use_reverberant_ref: false
num_spk: 1
num_noise_type: 1
sample_rate: 8000
force_single_channel: false
channel_reordering: false
categories: []
encoder: conv
encoder_conf:
    channel: 256
    kernel_size: 32
    stride: 16
extractor: td_speakerbeam
extractor_conf:
    layer: 8
    stack: 4
    bottleneck_dim: 256
    hidden_dim: 512
    skip_dim: 256
    kernel: 3
    causal: false
    norm_type: gLN
    pre_nonlinear: prelu
    nonlinear: relu
    i_adapt_layer: 7
    adapt_layer_type: mul
    adapt_enroll_dim: 256
    use_spk_emb: false
decoder: conv
decoder_conf:
    channel: 256
    kernel_size: 32
    stride: 16
preprocessor: tse
preprocessor_conf: {}
required:
- output_dir
version: '202301'
distributed: true

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}
}


@inproceedings{ESPnet-SE,
  author = {Chenda Li and Jing Shi and Wangyou Zhang and Aswin Shanmugam Subramanian and Xuankai Chang and
  Naoyuki Kamo and Moto Hira and Tomoki Hayashi and Christoph B{"{o}}ddeker and Zhuo Chen and Shinji Watanabe},
  title = {ESPnet-SE: End-To-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration},
  booktitle = {{IEEE} Spoken Language Technology Workshop, {SLT} 2021, Shenzhen, China, January 19-22, 2021},
  pages = {785--792},
  publisher = {{IEEE}},
  year = {2021},
  url = {https://doi.org/10.1109/SLT48900.2021.9383615},
  doi = {10.1109/SLT48900.2021.9383615},
  timestamp = {Mon, 12 Apr 2021 17:08:59 +0200},
  biburl = {https://dblp.org/rec/conf/slt/Li0ZSCKHHBC021.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

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}
}