--- tags: - audio - audio-to-audio language: en datasets: - urgent24 license: cc-by-4.0 library_name: espnet --- ## ESPnet2 ENH model ### `wyz/tfgridnet_for_urgent24` This model was trained by Wangyou Zhang using the [urgent24](https://github.com/espnet/espnet/blob/master/egs2/urgent24/enh1) recipe in [espnet](https://github.com/espnet/espnet/). This model is provided as a pre-trained baseline model for the [URGENT 2024 Challenge](https://urgent-challenge.github.io/urgent2024). ### 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 pip install -e . cd egs2/urgent24/enh1 ./run.sh --skip_data_prep false --skip_train true --is_tse_task true --download_model wyz/tfgridnet_for_urgent24 ``` To use the model in the Python interface, you could use the following code: > Please make sure you are using the latest ESPnet by installing from the source: > ``` > python -m pip install git+https://github.com/espnet/espnet > ``` ```python import soundfile as sf from espnet2.bin.enh_inference import SeparateSpeech # For model downloading + loading model = SeparateSpeech.from_pretrained( model_tag="wyz/tfgridnet_for_urgent24", normalize_output_wav=True, device="cuda", ) # For loading a downloaded model # model = SeparateSpeech( # train_config="exp/xxx/config.yaml", # model_file="exp/xx/valid.loss.best.pth", # normalize_output_wav=True, # device="cuda", # ) audio, fs = sf.read("/path/to/noisy/utt1.flac") enhanced = model(audio[None, :], fs=fs)[0] ``` ## ENH config
expand ``` config: conf/tuning/train_enh_tfgridnet.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_tfgridnet_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: 54825 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: 40 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min keep_nbest_models: 1 nbest_averaging_interval: 0 grad_clip: 1.0 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: 8000 batch_size: 4 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 shuffle_within_batch: false sort_batch: descending multiple_iterator: false chunk_length: 200 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 chunk_excluded_key_prefixes: [] chunk_default_fs: 50 chunk_max_abs_length: 100000 chunk_discard_short_samples: true 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/utt2category - category - text - - dump/raw/train/utt2fs - fs - text_int valid_data_path_and_name_and_type: - - dump/raw/validation/wav.scp - speech_mix - sound - - dump/raw/validation/spk1.scp - speech_ref1 - sound - - dump/raw/validation/utt2category - category - text - - dump/raw/validation/utt2fs - fs - text_int allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 allow_multi_rates: true 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: 1.0e-05 scheduler: steplr scheduler_conf: step_size: 2 gamma: 0.99 init: null model_conf: normalize_variance_per_ch: true categories: - 1ch_8000Hz - 1ch_16000Hz - 1ch_22050Hz - 1ch_24000Hz - 1ch_32000Hz - 1ch_44100Hz - 1ch_48000Hz criterions: - name: mr_l1_tfd conf: window_sz: - 256 - 512 - 768 - 1024 hop_sz: null eps: 1.0e-08 time_domain_weight: 0.5 normalize_variance: true wrapper: fixed_order wrapper_conf: weight: 1.0 - name: si_snr conf: eps: 1.0e-07 wrapper: fixed_order wrapper_conf: weight: 0.0 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: true channel_reordering: true categories: - 1ch_8000Hz - 1ch_16000Hz - 1ch_22050Hz - 1ch_24000Hz - 1ch_32000Hz - 1ch_44100Hz - 1ch_48000Hz speech_segment: null avoid_allzero_segment: true flexible_numspk: false dynamic_mixing: false utt2spk: null dynamic_mixing_gain_db: 0.0 encoder: stft encoder_conf: n_fft: 256 hop_length: 128 use_builtin_complex: true default_fs: 8000 separator: tfgridnetv3 separator_conf: n_srcs: 1 n_imics: 1 n_layers: 6 lstm_hidden_units: 200 attn_n_head: 4 attn_qk_output_channel: 2 emb_dim: 48 emb_ks: 4 emb_hs: 1 activation: prelu eps: 1.0e-05 decoder: stft decoder_conf: n_fft: 256 hop_length: 128 default_fs: 8000 mask_module: multi_mask mask_module_conf: {} preprocessor: enh 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} } ```