Yen-Ju_Lu_l3das22_enh_train_enh_ineube_valid.loss.ave

Usage

pip install espnet
espnet enhance noisy.wav -o enhanced.wav --model espnet/Yen-Ju_Lu_l3das22_enh_train_enh_ineube_valid.loss.ave

import soundfile as sf
from espnet2.bin.enh_inference import SeparateSpeech

enh = SeparateSpeech.from_pretrained(model_tag="espnet/Yen-Ju_Lu_l3das22_enh_train_enh_ineube_valid.loss.ave")
# channels are kept on the way in: a multi-channel model needs them
speech, rate = sf.read("noisy.wav", dtype="float32")
waves = enh(speech[None, ...], fs=rate)
# one wave per output stream, which is what tells the two tasks apart: an
# enhancement model returns one, a separation model one per speaker. This is
# what `espnet enhance` writes as enhanced.wav or enhanced.spk1.wav, ...
if len(waves) == 1:
    sf.write("enhanced.wav", waves[0][0], rate)
else:
    for i, wave in enumerate(waves, start=1):
        sf.write(f"enhanced.spk{i}.wav", wave[0], rate)

ESPnet2 ENH model

espnet/Yen-Ju_Lu_l3das22_enh_train_enh_ineube_valid.loss.ave

This model was trained by neillu23 using l3das22 recipe in espnet.

Demo: How to use in ESPnet2

cd espnet
git checkout 11d687844a544fcce6f6d0ce7a0a302e0e47d442
pip install -e .
cd egs2/l3das22/enh1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/Yen-Ju_Lu_l3das22_enh_train_enh_ineube_valid.loss.ave

<!-- Generated by ./scripts/utils/show_enh_score.sh -->

RESULTS

Environments

• date: Wed Jul 6 20:46:10 UTC 2022
• python version: 3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0]
• espnet version: espnet 202205
• pytorch version: pytorch 1.8.1
• Git hash: 77e36afdd3f069567dd33d4b5b997a26b634772b
  • Commit date: Fri Jun 17 18:32:56 2022 -0400

enh_train_enh_ineube_raw

config: conf/tuning/train_enh_ineube.yaml

ENH config

<details><summary>expand</summary>

config: conf/tuning/train_enh_ineube.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/enh_train_enh_ineube_raw
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 3
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 50409
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
max_epoch: 100
patience: 20
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - si_snr
    - max
-   - valid
    - loss
    - min
keep_nbest_models: 1
nbest_averaging_interval: 0
grad_clip: 5
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
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: 15
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/enh_stats_16k/train/speech_mix_shape
- exp/enh_stats_16k/train/speech_ref1_shape
valid_shape_file:
- exp/enh_stats_16k/valid/speech_mix_shape
- exp/enh_stats_16k/valid/speech_ref1_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 80000
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 32000
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
-   - dump/raw/train_multich/wav.scp
    - speech_mix
    - sound
-   - dump/raw/train_multich/spk1.scp
    - speech_ref1
    - sound
valid_data_path_and_name_and_type:
-   - dump/raw/dev_multich/wav.scp
    - speech_mix
    - sound
-   - dump/raw/dev_multich/spk1.scp
    - speech_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-08
    weight_decay: 1.0e-07
scheduler: reducelronplateau
scheduler_conf:
    mode: min
    factor: 0.5
    patience: 20
init: xavier_uniform
model_conf:
    stft_consistency: false
    loss_type: mask_mse
    mask_type: null
criterions:
-   name: snr
    conf: {}
    wrapper: fixed_order
    wrapper_conf:
        weight: 1.0
use_preprocessor: false
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
use_reverberant_ref: false
num_spk: 1
num_noise_type: 1
sample_rate: 8000
force_single_channel: false
encoder: same
encoder_conf: {}
separator: ineube
separator_conf:
    n_fft: 512
    stride: 128
    window: hann
    mic_channels: 8
decoder: same
decoder_conf: {}
mask_module: multi_mask
mask_module_conf: {}
required:
- output_dir
version: '202205'
distributed: true

</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}
}


@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}
}