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roshansh-cmu authored Mar 3, 2022
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138 changes: 69 additions & 69 deletions README.md

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34 changes: 29 additions & 5 deletions egs2/dns_ins20/enh1/README.md
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- config: ./conf/tuning/train_enh_blstm_tf.yaml
- Pretrained model: https://zenodo.org/record/4923697

|dataset|STOI|SAR|SDR|SIR|
|---|---|---|---|---|
|enhanced_cv_synthetic|0.95|18.63|18.63|0.00|
|enhanced_tt_synthetic_no_reverb|0.92|10.92|10.92|0.00|
|enhanced_tt_synthetic_with_reverb|0.85|9.31|9.31|0.00|
| dataset | STOI | SAR | SDR | SIR |
| --------------------------------- | ---- | ----- | ----- | ---- |
| enhanced_cv_synthetic | 0.95 | 18.63 | 18.63 | 0.00 |
| enhanced_tt_synthetic_no_reverb | 0.92 | 10.92 | 10.92 | 0.00 |
| enhanced_tt_synthetic_with_reverb | 0.85 | 9.31 | 9.31 | 0.00 |

<!-- Generated by ./scripts/utils/show_enh_score.sh -->
# RESULTS
## Environments
- date: `Thu Feb 10 23:11:40 CST 2022`
- python version: `3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]`
- espnet version: `espnet 0.10.5a1`
- pytorch version: `pytorch 1.9.1`
- Git hash: `6f66283b9eed7b0d5e5643feb18d8f60118a4afc`
- Commit date: `Mon Dec 13 15:30:29 2021 +0800`


## enh_train_enh_dccrn_raw

- config: ./conf/tuning/train_enh_dccrn.yaml
- download_model: https://huggingface.co/Johnson-Lsx/Shaoxiong_Lin_dns_ins20_enh_enh_train_enh_dccrn_raw

| dataset | PESQ | STOI | SAR | SDR | SIR | SI_SNR |
| --------------------------------- | ---- | ---- | ----- | ----- | ---- | ------ |
| enhanced_cv_synthetic | 3.72 | 0.98 | 24.69 | 24.69 | 0.00 | 24.22 |
| enhanced_tt_synthetic_no_reverb | 3.29 | 0.96 | 17.69 | 17.69 | 0.00 | 17.50 |
| enhanced_tt_synthetic_with_reverb | 2.54 | 0.81 | 10.45 | 10.45 | 0.00 | 9.72 |

Note: Here, the model is only trained on data without reverberation.
Note: Here, the PESQ score is calculated based on https://github.com/vBaiCai/python-pesq.
53 changes: 53 additions & 0 deletions egs2/dns_ins20/enh1/conf/tuning/train_enh_dccrn.yaml
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optim: adam
init: null # do not set init method here because DCCRN has its own initialization
max_epoch: 100
batch_type: folded
batch_size: 32
iterator_type: chunk
chunk_length: 64000
num_workers: 4
optim_conf:
lr: 1.0e-03
eps: 1.0e-08
weight_decay: 1.0e-7
patience: 10
val_scheduler_criterion:
- valid
- loss
best_model_criterion:
- - valid
- si_snr
- max
- - valid
- loss
- min
keep_nbest_models: 1
scheduler: reducelronplateau
scheduler_conf:
mode: min
factor: 0.7
patience: 1
model_conf:
loss_type: si_snr
encoder: stft
encoder_conf:
n_fft: 512
win_length: 400
hop_length: 100
decoder: stft
decoder_conf:
n_fft: 512
win_length: 400
hop_length: 100
separator: dccrn

criterions:
# The first criterion
- name: si_snr
conf:
eps: 1.0e-7
# the wrapper for the current criterion
# for single-talker case, we simplely use fixed_order wrapper
wrapper: fixed_order
wrapper_conf:
weight: 1.0
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