--- license: cc-by-4.0 --- ## ESPnet2 ENH model ### `kohei0209/tfgridnet_urgent25` This model was trained by Kohei Saijo using the [urgent25](https://github.com/kohei0209/espnet/tree/urgent2025/egs2/urgent25/enh1) recipe based on [espnet](https://github.com/espnet/espnet/). Note that **the recipe has not merged to the ESPnet main branch yet and the code is in the [fork repository](https://github.com/kohei0209/espnet/tree/urgent2025/egs2/urgent25/enh1)**. This model is provided as a pre-trained baseline model for the [URGENT 2025 Challenge](https://urgent-challenge.github.io/urgent2025). ### 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. ```python import soundfile as sf from espnet2.bin.enh_inference import SeparateSpeech # For model downloading + loading model = SeparateSpeech.from_pretrained( model_tag="kohei0209/tfgridnet_urgent25", 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_dm.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_dm_raw ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false use_deepspeed: false deepspeed_config: null cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true use_tf32: false collect_stats: false write_collected_feats: false max_epoch: 30 patience: 5 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min keep_nbest_models: 5 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: - exp/enh_train_enh_tfgridnet_raw_1stchallenge/21epoch.pth ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: 4000 batch_size: 2 valid_batch_size: 4 batch_bins: 1000000 valid_batch_bins: null category_sample_size: 10 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: 128 chunk_excluded_key_prefixes: [] chunk_default_fs: 50 chunk_max_abs_length: 144000 chunk_discard_short_samples: true train_data_path_and_name_and_type: - - dump/raw/speech_train_track1/wav.scp - speech_mix - sound - - dump/raw/speech_train_track1/spk1.scp - speech_ref1 - sound - - dump/raw/speech_train_track1/utt2category - category - text - - dump/raw/speech_train_track1/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 multi_task_dataset: false 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: 0.0001 eps: 1.0e-08 weight_decay: 1.0e-05 scheduler: warmupsteplr scheduler_conf: step_size: 1 gamma: 0.98 warmup_steps: 4000 init: null model_conf: normalize_variance_per_ch: true categories: - 1ch_8000Hz - 1ch_16000Hz - 1ch_22050Hz - 1ch_24000Hz - 1ch_32000Hz - 1ch_44100Hz - 1ch_48000Hz - 1ch_8000Hz_reverb - 1ch_16000Hz_reverb - 1ch_22050Hz_reverb - 1ch_24000Hz_reverb - 1ch_32000Hz_reverb - 1ch_44100Hz_reverb - 1ch_48000Hz_reverb 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: 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: 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: speech_volume_normalize: 0.5_1.0 rir_scp: dump/raw/rir_train.scp rir_apply_prob: 0.5 noise_scp: dump/raw/noise_train.scp noise_apply_prob: 1.0 noise_db_range: '-5_15' force_single_channel: true channel_reordering: true categories: - 1ch_8000Hz - 1ch_16000Hz - 1ch_22050Hz - 1ch_24000Hz - 1ch_32000Hz - 1ch_44100Hz - 1ch_48000Hz - 1ch_8000Hz_reverb - 1ch_16000Hz_reverb - 1ch_22050Hz_reverb - 1ch_24000Hz_reverb - 1ch_32000Hz_reverb - 1ch_44100Hz_reverb - 1ch_48000Hz_reverb data_aug_effects: - - 1.0 - bandwidth_limitation - res_type: random - - 1.0 - clipping - min_quantile: 0.1 max_quantile: 0.9 - - 1.0 - - - 0.5 - codec - format: mp3 encoder: null qscale: - 1 - 10 - - 0.5 - codec - format: ogg encoder: - vorbis - opus qscale: - -1 - 10 - - 1.0 - packet_loss - packet_duration_ms: 20 packet_loss_rate: - 0.05 - 0.25 max_continuous_packet_loss: 10 data_aug_num: - 1 - 3 data_aug_prob: 0.75 diffusion_model: null diffusion_model_conf: {} required: - output_dir version: '202409' distributed: false ```
### 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} } ```