Instructions to use espnet/mls-multi_soundstream_16k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ESPnet
How to use espnet/mls-multi_soundstream_16k with ESPnet:
unknown model type (must be text-to-speech or automatic-speech-recognition)
- Notebooks
- Google Colab
- Kaggle
| tags: | |
| - espnet | |
| - audio | |
| - codec | |
| language: multilingual | |
| datasets: | |
| - amuse | |
| license: cc-by-4.0 | |
| ## ESPnet2 Codec model | |
| ### `espnet/mls-multi_soundstream_16k` | |
| This model was trained by ftshijt using amuse 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 9baec3a7b10b784cb721849e19caed19e8ac45bc | |
| pip install -e . | |
| cd egs2/amuse/codec1 | |
| ./run.sh --skip_data_prep false --skip_train true --download_model espnet/mls-multi_soundstream_16k | |
| ``` | |
| ## Codec config | |
| <details><summary>expand</summary> | |
| ``` | |
| config: conf/train_soundstream4_large_v1.1.yaml | |
| print_config: false | |
| log_level: INFO | |
| drop_last_iter: false | |
| dry_run: false | |
| iterator_type: chunk | |
| valid_iterator_type: null | |
| output_dir: exp/codec_mls_multi_soundstream4_large_v1.1 | |
| ngpu: 1 | |
| seed: 777 | |
| num_workers: 1 | |
| num_att_plot: 0 | |
| dist_backend: nccl | |
| dist_init_method: env:// | |
| dist_world_size: 2 | |
| dist_rank: 0 | |
| local_rank: 0 | |
| dist_master_addr: localhost | |
| dist_master_port: 58685 | |
| dist_launcher: null | |
| multiprocessing_distributed: true | |
| unused_parameters: true | |
| sharded_ddp: false | |
| use_deepspeed: false | |
| deepspeed_config: null | |
| cudnn_enabled: true | |
| cudnn_benchmark: false | |
| cudnn_deterministic: false | |
| use_tf32: true | |
| collect_stats: false | |
| write_collected_feats: false | |
| max_epoch: 360 | |
| patience: null | |
| val_scheduler_criterion: | |
| - valid | |
| - loss | |
| early_stopping_criterion: | |
| - valid | |
| - loss | |
| - min | |
| best_model_criterion: | |
| - - valid | |
| - mel_loss | |
| - min | |
| - - train | |
| - mel_loss | |
| - min | |
| - - train | |
| - total_count | |
| - max | |
| keep_nbest_models: 5 | |
| nbest_averaging_interval: 0 | |
| grad_clip: -1 | |
| 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: 50 | |
| 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: 5000 | |
| batch_size: 128 | |
| valid_batch_size: null | |
| batch_bins: 1000000 | |
| valid_batch_bins: null | |
| train_shape_file: | |
| - exp/codec_stats_mls_multi_raw/train/audio_shape | |
| valid_shape_file: | |
| - exp/codec_stats_mls_multi_raw/valid/audio_shape | |
| batch_type: unsorted | |
| valid_batch_type: null | |
| fold_length: | |
| - 256000 | |
| 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: 128 | |
| chunk_excluded_key_prefixes: [] | |
| chunk_default_fs: null | |
| chunk_max_abs_length: null | |
| chunk_discard_short_samples: true | |
| train_data_path_and_name_and_type: | |
| - - dump/raw/mls_multi/wav.scp | |
| - audio | |
| - kaldi_ark | |
| valid_data_path_and_name_and_type: | |
| - - dump/raw/dev-small/wav.scp | |
| - audio | |
| - kaldi_ark | |
| multi_task_dataset: false | |
| 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: adamw | |
| optim_conf: | |
| lr: 0.0002 | |
| betas: | |
| - 0.5 | |
| - 0.9 | |
| eps: 1.0e-09 | |
| weight_decay: 0.0 | |
| scheduler: exponentiallr | |
| scheduler_conf: | |
| gamma: 0.999875 | |
| optim2: adamw | |
| optim2_conf: | |
| lr: 0.0002 | |
| betas: | |
| - 0.5 | |
| - 0.9 | |
| eps: 1.0e-09 | |
| weight_decay: 0.0 | |
| scheduler2: exponentiallr | |
| scheduler2_conf: | |
| gamma: 0.999875 | |
| generator_first: true | |
| skip_discriminator_prob: 0.0 | |
| model_conf: {} | |
| use_preprocessor: true | |
| codec: soundstream | |
| codec_conf: | |
| sampling_rate: 16000 | |
| generator_params: | |
| hidden_dim: 512 | |
| encdec_channels: 1 | |
| encdec_n_filters: 32 | |
| encdec_n_residual_layers: 3 | |
| encdec_ratios: | |
| - 8 | |
| - 5 | |
| - 4 | |
| - 2 | |
| encdec_activation: ELU | |
| encdec_activation_params: | |
| alpha: 1.0 | |
| encdec_norm: weight_norm | |
| encdec_kernel_size: 7 | |
| encdec_residual_kernel_size: 7 | |
| encdec_last_kernel_size: 7 | |
| encdec_dilation_base: 2 | |
| encdec_causal: false | |
| encdec_pad_mode: reflect | |
| encdec_true_skip: false | |
| encdec_compress: 2 | |
| encdec_lstm: 2 | |
| decoder_trim_right_ratio: 1.0 | |
| decoder_final_activation: null | |
| decoder_final_activation_params: null | |
| quantizer_n_q: 32 | |
| quantizer_bins: 1024 | |
| quantizer_decay: 0.99 | |
| quantizer_kmeans_init: true | |
| quantizer_kmeans_iters: 50 | |
| quantizer_threshold_ema_dead_code: 2 | |
| quantizer_target_bandwidth: | |
| - 2 | |
| - 4 | |
| - 8 | |
| - 16 | |
| - 32 | |
| sample_rate: 16000 | |
| discriminator_params: | |
| scales: 3 | |
| scale_downsample_pooling: AvgPool1d | |
| scale_downsample_pooling_params: | |
| kernel_size: 4 | |
| stride: 2 | |
| padding: 2 | |
| scale_discriminator_params: | |
| in_channels: 1 | |
| out_channels: 1 | |
| kernel_sizes: | |
| - 15 | |
| - 41 | |
| - 5 | |
| - 3 | |
| channels: 128 | |
| max_downsample_channels: 1024 | |
| max_groups: 16 | |
| bias: true | |
| downsample_scales: | |
| - 2 | |
| - 2 | |
| - 4 | |
| - 4 | |
| - 1 | |
| nonlinear_activation: LeakyReLU | |
| nonlinear_activation_params: | |
| negative_slope: 0.1 | |
| scale_follow_official_norm: false | |
| complexstft_discriminator_params: | |
| in_channels: 1 | |
| channels: 32 | |
| strides: | |
| - - 1 | |
| - 2 | |
| - - 2 | |
| - 2 | |
| - - 1 | |
| - 2 | |
| - - 2 | |
| - 2 | |
| - - 1 | |
| - 2 | |
| - - 2 | |
| - 2 | |
| chan_mults: | |
| - 1 | |
| - 2 | |
| - 4 | |
| - 4 | |
| - 8 | |
| - 8 | |
| n_fft: 1024 | |
| hop_length: 256 | |
| win_length: 1024 | |
| stft_normalized: false | |
| generator_adv_loss_params: | |
| average_by_discriminators: false | |
| loss_type: mse | |
| discriminator_adv_loss_params: | |
| average_by_discriminators: false | |
| loss_type: mse | |
| use_feat_match_loss: true | |
| feat_match_loss_params: | |
| average_by_discriminators: false | |
| average_by_layers: false | |
| include_final_outputs: true | |
| use_mel_loss: true | |
| mel_loss_params: | |
| range_start: 6 | |
| range_end: 11 | |
| window: hann | |
| n_mels: 80 | |
| fmin: 0 | |
| fmax: null | |
| log_base: null | |
| fs: 16000 | |
| lambda_quantization: 1.0 | |
| lambda_commit: 1.0 | |
| lambda_reconstruct: 1.0 | |
| lambda_adv: 1.0 | |
| lambda_mel: 45.0 | |
| lambda_feat_match: 2.0 | |
| cache_generator_outputs: true | |
| required: | |
| - output_dir | |
| version: '202402' | |
| distributed: true | |
| ``` | |
| </details> | |
| ### 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} | |
| } | |
| ``` | |
| 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} | |
| } | |
| ``` | |