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--- |
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tags: |
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- espnet |
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- audio |
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- audio-to-audio |
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language: en |
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datasets: |
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- VCTK_DEMAND |
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license: cc-by-4.0 |
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--- |
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## ESPnet2 ENH model |
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### `wyz/vctk_bsrnn_medium_noncausal` |
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This model was trained by wyz based on the universal_se_v1 recipe in [espnet](https://github.com/espnet/espnet/). More information can be found at https://github.com/Emrys365/se-scaling. |
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### Demo: How to use in ESPnet2 |
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Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) |
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if you haven't done that already. |
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To use the model in the Python interface, you could use the following code: |
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```python |
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import soundfile as sf |
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from espnet2.bin.enh_inference import SeparateSpeech |
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# For model downloading + loading |
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model = SeparateSpeech.from_pretrained( |
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model_tag="wyz/vctk_bsrnn_medium_noncausal", |
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normalize_output_wav=True, |
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device="cuda", |
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) |
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# For loading a downloaded model |
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# model = SeparateSpeech( |
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# train_config="exp_vctk/enh_train_enh_bsrnn_medium_noncausal_raw/config.yaml", |
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# model_file="exp_vctk/enh_train_enh_bsrnn_medium_noncausal_raw/xxxx.pth", |
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# normalize_output_wav=True, |
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# device="cuda", |
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# ) |
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audio, fs = sf.read("/path/to/noisy/utt1.flac") |
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enhanced = model(audio[None, :], fs=fs)[0] |
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``` |
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<!-- Generated by ./scripts/utils/show_enh_score.sh --> |
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# RESULTS |
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## Environments |
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- date: `Wed Feb 28 01:40:25 EST 2024` |
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- python version: `3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]` |
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- espnet version: `espnet 202304` |
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- pytorch version: `pytorch 2.0.1+cu118` |
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- Git hash: `443028662106472c60fe8bd892cb277e5b488651` |
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- Commit date: `Thu May 11 03:32:59 2023 +0000` |
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## enhanced_test_16k |
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|dataset|PESQ_WB|STOI|SAR|SDR|SIR|SI_SNR|OVRL|SIG|BAK|P808_MOS| |
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|---|---|---|---|---|---|---|---|---|---|---| |
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|chime4_et05_real_isolated_6ch_track|1.11|48.88|-3.44|-3.44|0.00|-31.29|2.78|3.09|3.79|3.47| |
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|chime4_et05_simu_isolated_6ch_track|1.22|69.37|5.61|5.61|0.00|-0.58|2.60|2.89|3.80|3.13| |
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|dns20_tt_synthetic_no_reverb|2.34|93.88|14.32|14.32|0.00|13.42|3.27|3.56|4.02|3.91| |
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|reverb_et_real_8ch_multich|1.06|37.83|0.59|0.59|0.00|-4.19|2.19|2.51|3.50|2.88| |
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|reverb_et_simu_8ch_multich|1.55|77.68|7.89|7.89|0.00|-11.68|2.91|3.24|3.84|3.58| |
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|whamr_tt_mix_single_reverb_max_16k|1.28|73.13|4.95|4.95|0.00|0.52|2.66|2.95|3.83|3.38| |
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## enhanced_test_48k |
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|dataset|STOI|SAR|SDR|SIR|SI_SNR|OVRL|SIG|BAK|P808_MOS| |
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|---|---|---|---|---|---|---|---|---|---| |
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|vctk_noisy_tt_2spk|95.08|20.39|20.39|0.00|19.28|3.17|3.46|3.99|3.53| |
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## ENH config |
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<details><summary>expand</summary> |
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``` |
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config: conf/tuning/train_enh_bsrnn_medium_noncausal.yaml |
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print_config: false |
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log_level: INFO |
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dry_run: false |
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iterator_type: chunk |
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output_dir: exp_vctk/enh_train_enh_bsrnn_medium_noncausal_raw |
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ngpu: 1 |
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seed: 0 |
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num_workers: 4 |
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num_att_plot: 3 |
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dist_backend: nccl |
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dist_init_method: env:// |
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dist_world_size: null |
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dist_rank: null |
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local_rank: 0 |
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dist_master_addr: null |
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dist_master_port: null |
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dist_launcher: null |
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multiprocessing_distributed: false |
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unused_parameters: true |
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sharded_ddp: false |
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cudnn_enabled: true |
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cudnn_benchmark: false |
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cudnn_deterministic: true |
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collect_stats: false |
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write_collected_feats: false |
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max_epoch: 100 |
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patience: 40 |
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val_scheduler_criterion: |
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- valid |
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- loss |
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early_stopping_criterion: |
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- valid |
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- loss |
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- min |
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best_model_criterion: |
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- - valid |
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- loss |
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- min |
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keep_nbest_models: 1 |
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nbest_averaging_interval: 0 |
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grad_clip: 5.0 |
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grad_clip_type: 2.0 |
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grad_noise: false |
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accum_grad: 1 |
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no_forward_run: false |
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resume: true |
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save_interval: 1000 |
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train_dtype: float32 |
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use_amp: false |
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log_interval: null |
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use_matplotlib: true |
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use_tensorboard: true |
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create_graph_in_tensorboard: false |
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use_wandb: false |
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wandb_project: null |
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wandb_id: null |
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wandb_entity: null |
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wandb_name: null |
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wandb_model_log_interval: -1 |
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detect_anomaly: false |
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pretrain_path: null |
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init_param: [] |
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ignore_init_mismatch: false |
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freeze_param: [] |
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num_iters_per_epoch: 8000 |
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num_iters_valid: null |
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batch_size: 4 |
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valid_batch_size: null |
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batch_bins: 1000000 |
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valid_batch_bins: null |
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train_shape_file: |
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- exp_vctk/enh_stats_16k/train/speech_mix_shape |
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- exp_vctk/enh_stats_16k/train/speech_ref1_shape |
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- exp_vctk/enh_stats_16k/train/dereverb_ref1_shape |
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valid_shape_file: |
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- exp_vctk/enh_stats_16k/valid/speech_mix_shape |
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- exp_vctk/enh_stats_16k/valid/speech_ref1_shape |
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- exp_vctk/enh_stats_16k/valid/dereverb_ref1_shape |
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batch_type: folded |
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valid_batch_type: null |
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fold_length: |
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- 80000 |
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- 80000 |
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- 80000 |
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sort_in_batch: descending |
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sort_batch: descending |
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multiple_iterator: false |
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chunk_length: 32000 |
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chunk_shift_ratio: 0.5 |
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num_cache_chunks: 1024 |
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chunk_excluded_key_prefixes: [] |
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chunk_discard_short_samples: false |
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train_data_path_and_name_and_type: |
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- - dump/raw/vctk_noisy_tr_26spk/wav.scp |
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- speech_mix |
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- sound |
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- - dump/raw/vctk_noisy_tr_26spk/spk1.scp |
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- speech_ref1 |
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- sound |
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- - dump/raw/vctk_noisy_tr_26spk/dereverb1.scp |
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- dereverb_ref1 |
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- sound |
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- - dump/raw/vctk_noisy_tr_26spk/utt2category |
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- category |
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- text |
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- - dump/raw/vctk_noisy_tr_26spk/utt2fs |
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- fs |
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- text_int |
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valid_data_path_and_name_and_type: |
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- - dump/raw/vctk_noisy_cv_2spk/wav.scp |
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- speech_mix |
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- sound |
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- - dump/raw/vctk_noisy_cv_2spk/spk1.scp |
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- speech_ref1 |
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- sound |
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- - dump/raw/vctk_noisy_cv_2spk/dereverb1.scp |
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- dereverb_ref1 |
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- sound |
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- - dump/raw/vctk_noisy_cv_2spk/utt2category |
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- category |
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- text |
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- - dump/raw/vctk_noisy_cv_2spk/utt2fs |
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- fs |
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- text_int |
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allow_variable_data_keys: false |
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max_cache_size: 0.0 |
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max_cache_fd: 32 |
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allow_multi_rates: true |
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valid_max_cache_size: null |
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exclude_weight_decay: false |
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exclude_weight_decay_conf: {} |
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optim: adam |
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optim_conf: |
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lr: 0.001 |
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eps: 1.0e-08 |
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weight_decay: 1.0e-05 |
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scheduler: steplr |
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scheduler_conf: |
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step_size: 2 |
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gamma: 0.99 |
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init: null |
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model_conf: |
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normalize_variance_per_ch: true |
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categories: |
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- 1ch_8k |
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- 1ch_8k_r |
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- 1ch_16k_r |
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- 1ch_48k |
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- 1ch_24k |
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- 1ch_16k |
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- 2ch_8k |
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- 2ch_8k_r |
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- 2ch_16k |
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- 2ch_16k_r |
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- 5ch_8k |
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- 5ch_16k |
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- 8ch_8k_r |
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- 8ch_16k_r |
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criterions: |
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- name: mr_l1_tfd |
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conf: |
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window_sz: |
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- 256 |
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- 512 |
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- 768 |
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- 1024 |
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hop_sz: null |
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eps: 1.0e-08 |
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time_domain_weight: 0.5 |
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normalize_variance: true |
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wrapper: fixed_order |
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wrapper_conf: |
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weight: 1.0 |
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- name: si_snr |
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conf: |
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eps: 1.0e-07 |
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wrapper: fixed_order |
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wrapper_conf: |
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weight: 0.0 |
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speech_volume_normalize: null |
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rir_scp: null |
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rir_apply_prob: 1.0 |
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noise_scp: null |
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noise_apply_prob: 1.0 |
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noise_db_range: '13_15' |
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short_noise_thres: 0.5 |
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use_reverberant_ref: false |
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num_spk: 1 |
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num_noise_type: 1 |
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sample_rate: 8000 |
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force_single_channel: true |
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channel_reordering: true |
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categories: |
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- 1ch_8k |
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- 1ch_8k_r |
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- 1ch_16k_r |
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- 1ch_48k |
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- 1ch_24k |
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- 1ch_16k |
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- 2ch_8k |
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- 2ch_8k_r |
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- 2ch_16k |
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- 2ch_16k_r |
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- 5ch_8k |
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- 5ch_16k |
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- 8ch_8k_r |
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- 8ch_16k_r |
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speech_segment: null |
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avoid_allzero_segment: true |
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flexible_numspk: false |
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dynamic_mixing: false |
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utt2spk: null |
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dynamic_mixing_gain_db: 0.0 |
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encoder: stft |
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encoder_conf: |
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n_fft: 960 |
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hop_length: 480 |
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use_builtin_complex: true |
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default_fs: 48000 |
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separator: bsrnn |
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separator_conf: |
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num_spk: 1 |
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num_channels: 128 |
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num_layers: 6 |
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target_fs: 48000 |
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ref_channel: 0 |
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causal: false |
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decoder: stft |
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decoder_conf: |
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n_fft: 960 |
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hop_length: 480 |
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default_fs: 48000 |
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mask_module: multi_mask |
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mask_module_conf: {} |
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preprocessor: enh |
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preprocessor_conf: {} |
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required: |
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- output_dir |
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version: '202304' |
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distributed: false |
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``` |
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</details> |
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### Citing ESPnet |
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```BibTex |
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@inproceedings{watanabe2018espnet, |
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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}, |
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title={{ESPnet}: End-to-End Speech Processing Toolkit}, |
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year={2018}, |
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booktitle={Proceedings of Interspeech}, |
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pages={2207--2211}, |
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doi={10.21437/Interspeech.2018-1456}, |
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url={http://dx.doi.org/10.21437/Interspeech.2018-1456} |
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} |
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@inproceedings{ESPnet-SE, |
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author = {Chenda Li and Jing Shi and Wangyou Zhang and Aswin Shanmugam Subramanian and Xuankai Chang and |
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Naoyuki Kamo and Moto Hira and Tomoki Hayashi and Christoph B{"{o}}ddeker and Zhuo Chen and Shinji Watanabe}, |
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title = {ESPnet-SE: End-To-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration}, |
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booktitle = {{IEEE} Spoken Language Technology Workshop, {SLT} 2021, Shenzhen, China, January 19-22, 2021}, |
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pages = {785--792}, |
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publisher = {{IEEE}}, |
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year = {2021}, |
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url = {https://doi.org/10.1109/SLT48900.2021.9383615}, |
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doi = {10.1109/SLT48900.2021.9383615}, |
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timestamp = {Mon, 12 Apr 2021 17:08:59 +0200}, |
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biburl = {https://dblp.org/rec/conf/slt/Li0ZSCKHHBC021.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
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or arXiv: |
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```bibtex |
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@misc{watanabe2018espnet, |
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title={ESPnet: End-to-End Speech Processing Toolkit}, |
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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}, |
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year={2018}, |
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eprint={1804.00015}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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