--- tags: - espnet - audio - audio-to-audio language: en datasets: - wsj_kinect license: cc-by-4.0 --- ## ESPnet2 ENH model ### `atharva253/tfgridnetv2_wsj_kinect` This model was trained by Atharva Anand Joshi using wsj_kinect recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 37828ea9708cd2f541220fdfe180457c7f7d67f1 pip install -e . cd egs2/wsj_kinect/enh1 ./run.sh --skip_data_prep false --skip_train true --download_model atharva253/tfgridnetv2_wsj_kinect ``` # RESULTS ## Environments - date: `Mon Apr 22 17:21:05 EDT 2024` - python version: `3.9.18 (main, Sep 11 2023, 13:41:44) [GCC 11.2.0]` - espnet version: `espnet 202402` - pytorch version: `pytorch 2.1.0` - Git hash: `37828ea9708cd2f541220fdfe180457c7f7d67f1` - Commit date: `Thu Mar 21 22:52:57 2024 -0400` ## enh_train_enh_tfgridnetv2_tf_lr-patience3_patience5_I_1_J_1_D_128_batch_8_raw config: conf/tuning/train_enh_tfgridnetv2_tf_lr-patience3_patience5_I_1_J_1_D_128_batch_8.yaml |dataset|STOI|SAR|SDR|SIR|SI_SNR| |---|---|---|---|---|---| |enhanced_cv|85.97|10.51|10.07|21.63|9.61| |enhanced_tt|88.76|11.22|10.69|21.36|10.26| ## ENH config
expand ``` config: conf/tuning/train_enh_tfgridnetv2_tf_lr-patience3_patience5_I_1_J_1_D_128_batch_8.yaml print_config: false log_level: INFO drop_last_iter: false dry_run: false iterator_type: chunk valid_iterator_type: null output_dir: exp/enh_train_enh_tfgridnetv2_tf_lr-patience3_patience5_I_1_J_1_D_128_batch_8_raw ngpu: 1 seed: 0 num_workers: 4 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: 45443 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 150 patience: 5 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: 5.0 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 use_adapter: false adapter: lora save_strategy: all adapter_conf: {} pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 8 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 - exp/enh_stats_16k/train/speech_ref2_shape valid_shape_file: - exp/enh_stats_16k/valid/speech_mix_shape - exp/enh_stats_16k/valid/speech_ref1_shape - exp/enh_stats_16k/valid/speech_ref2_shape batch_type: folded valid_batch_type: null fold_length: - 80000 - 80000 - 80000 sort_in_batch: descending shuffle_within_batch: false sort_batch: descending multiple_iterator: false chunk_length: 32000 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 chunk_excluded_key_prefixes: [] chunk_default_fs: null train_data_path_and_name_and_type: - - dump/raw/tr/wav.scp - speech_mix - sound - - dump/raw/tr/spk1.scp - speech_ref1 - sound - - dump/raw/tr/spk2.scp - speech_ref2 - sound valid_data_path_and_name_and_type: - - dump/raw/cv/wav.scp - speech_mix - sound - - dump/raw/cv/spk1.scp - speech_ref1 - sound - - dump/raw/cv/spk2.scp - speech_ref2 - sound allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 allow_multi_rates: false 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: 0.5 patience: 3 init: xavier_uniform model_conf: stft_consistency: false loss_type: mask_mse mask_type: null flexible_numspk: false extract_feats_in_collect_stats: false normalize_variance: false normalize_variance_per_ch: false categories: [] category_weights: [] criterions: - name: si_snr conf: eps: 1.0e-07 wrapper: pit wrapper_conf: weight: 1.0 independent_perm: true 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 channel_reordering: false categories: [] speech_segment: null avoid_allzero_segment: true flexible_numspk: false dynamic_mixing: false utt2spk: null dynamic_mixing_gain_db: 0.0 encoder: same encoder_conf: {} separator: tfgridnetv2 separator_conf: n_srcs: 2 n_fft: 128 stride: 64 window: hann n_imics: 4 n_layers: 6 lstm_hidden_units: 192 attn_n_head: 4 attn_approx_qk_dim: 512 emb_dim: 128 emb_ks: 1 emb_hs: 1 activation: prelu eps: 1.0e-05 decoder: same decoder_conf: {} mask_module: multi_mask mask_module_conf: {} preprocessor: null preprocessor_conf: {} diffusion_model: null diffusion_model_conf: {} required: - output_dir version: '202402' distributed: true ```
### Citing ESPnet ```BibTex @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: ```bibtex @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} } ```