ESPnet2 pretrained model, LiChenda/vctk_noisyreverb_enh_train_enh_conv_tasnet_raw_valid.loss.best, fs=8k, lang=noinfo
Creators: name: LiChenda
Publisher: name: Zenodo
Year: 2021
Subjects: ESPnet
deep-learning
python
pytorch
speech-recognition
speech-synthesis
speech-translation
machine-translation

description: This model was trained by LiChenda using vctk_noisyreverb recipe in espnet. <strong>Python API</strong><pre><code class="language-python">See https://github.com/espnet/espnet_model_zoo</code></pre> <strong>Evaluate in the recipe</strong><pre><code class="language-bash">git clone https://github.com/espnet/espnet cd espnet git checkout 47d9206997841e72b0d35590555411346145a6f6 pip install -e . cd egs2/vctk_noisyreverb/enh1 ./run.sh --skip_data_prep false --skip_train true --download_model LiChenda/vctk_noisyreverb_enh_train_enh_conv_tasnet_raw_valid.loss.best</code> </pre> <strong>Results</strong><pre><code> # RESULTS ## Environments - date: `Fri Jun 4 06:54:38 CST 2021` - python version: `3.8.10 (default, May 19 2021, 18:05:58) [GCC 7.3.0]` - espnet version: `espnet 0.9.10` - pytorch version: `pytorch 1.7.1` - Git hash: `47d9206997841e72b0d35590555411346145a6f6` - Commit date: `Tue Jun 1 12:49:23 2021 +0800` ## enh_train_enh_conv_tasnet_raw config: conf/tuning/train_enh_conv_tasnet.yaml |dataset|STOI|SAR|SDR|SIR| |---|---|---|---|---| |enhanced_testset|0.28|8.65|8.65|0.00| |enhanced_valid|0.26|9.07|9.07|0.00|</code></pre> <strong>ASR config</strong><pre><code>config: conf/tuning/train_enh_conv_tasnet.yaml print_config: false log_level: INFO dry_run: false iterator_type: chunk output_dir: exp/enh_train_enh_conv_tasnet_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 cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 patience: 4 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 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_tensorboard: true use_wandb: false wandb_project: null wandb_id: null detect_anomaly: false 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_8k/train/speech_mix_shape - exp/enh_stats_8k/train/speech_ref1_shape valid_shape_file: - exp/enh_stats_8k/valid/speech_mix_shape - exp/enh_stats_8k/valid/speech_ref1_shape batch_type: folded valid_batch_type: null fold_length: - 80000 - 80000 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 16000 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train/wav.scp - speech_mix - sound - - dump/raw/train/spk1.scp - speech_ref1 - sound valid_data_path_and_name_and_type: - - dump/raw/valid/wav.scp - speech_mix - sound - - dump/raw/valid/spk1.scp - speech_ref1 - sound allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 eps: 1.0e-08 weight_decay: 1.0e-05 scheduler: reducelronplateau scheduler_conf: mode: min factor: 0.5 patience: 3 init: xavier_uniform model_conf: loss_type: ci_sdr use_preprocessor: false encoder: conv encoder_conf: channel: 256 kernel_size: 20 stride: 10 separator: tcn separator_conf: num_spk: 1 layer: 8 stack: 3 bottleneck_dim: 256 hidden_dim: 512 kernel: 3 causal: false norm_type: gLN nonlinear: relu decoder: conv decoder_conf: channel: 256 kernel_size: 20 stride: 10 required: - output_dir version: 0.9.10 distributed: false</code></pre>