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libritts_encodec_16k

espnet / libritts_encodec_16k

Run on huggingface.co
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Total runs: 29
24-hour runs: 0
7-day runs: 2
30-day runs: 29
Model's Last Updated: September 18 2026
audio-to-audio

Introduction of libritts_encodec_16k

Model Details of libritts_encodec_16k

ESPnet2 Codec model
espnet/libritts_encodec_16k

This model was trained by Jinchuan using libritts recipe in espnet .

Demo: How to use in ESPnet2

Follow the ESPnet installation instructions if you haven't done that already. 

cd espnet
git checkout 1974048563d7c57e11f670d24bac8fb4b5aba4ef
pip install -e .
cd egs2/libritts/codec1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/libritts_encodec_16k
Codec config
expand 
config: conf/train_encodec_16k.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_encodec_ss4_16k
ngpu: 1
seed: 777
num_workers: 1
num_att_plot: 0
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: true
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
use_tf32: false
collect_stats: false
write_collected_feats: false
max_epoch: 120
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: 5
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: 8
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/stats_16k/train/audio_shape
valid_shape_file:
- exp/stats_16k/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: 16000
chunk_shift_ratio: 0.5
num_cache_chunks: 64
chunk_excluded_key_prefixes: []
chunk_default_fs: null
train_data_path_and_name_and_type:
-   - dump_16k/raw/train-clean-460/wav.scp
    - audio
    - sound
valid_data_path_and_name_and_type:
-   - dump_16k/raw/dev-clean/wav.scp
    - audio
    - sound
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: adam
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: adam
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: 0.3
model_conf: {}
use_preprocessor: true
codec: encodec
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:
        msstft_discriminator_params:
            filters: 32
            in_channels: 1
            out_channels: 1
            norm: weight_norm
            n_ffts:
            - 1024
            - 2048
            - 512
            - 256
            - 128
            hop_lengths:
            - 256
            - 512
            - 128
            - 64
            - 32
            win_lengths:
            - 1024
            - 2048
            - 512
            - 256
            - 128
            activation: LeakyReLU
            activation_params:
                negative_slope: 0.3
    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: 0.0
    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
    use_loss_balancer: false
required:
- output_dir
version: '202402'
distributed: false
Citing ESPnet 
@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: 

@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}
}

Runs of espnet libritts_encodec_16k on huggingface.co

29
Total runs
0
24-hour runs
0
3-day runs
2
7-day runs
29
30-day runs

More Information About libritts_encodec_16k huggingface.co Model

More libritts_encodec_16k license Visit here:

https://choosealicense.com/licenses/cc-by-4.0

libritts_encodec_16k huggingface.co

libritts_encodec_16k huggingface.co is an AI model on huggingface.co that provides libritts_encodec_16k's model effect (), which can be used instantly with this espnet libritts_encodec_16k model. huggingface.co supports a free trial of the libritts_encodec_16k model, and also provides paid use of the libritts_encodec_16k. Support call libritts_encodec_16k model through api, including Node.js, Python, http.

libritts_encodec_16k huggingface.co Url

https://huggingface.co/espnet/libritts_encodec_16k

espnet libritts_encodec_16k online free

libritts_encodec_16k huggingface.co is an online trial and call api platform, which integrates libritts_encodec_16k's modeling effects, including api services, and provides a free online trial of libritts_encodec_16k, you can try libritts_encodec_16k online for free by clicking the link below.

espnet libritts_encodec_16k online free url in huggingface.co:

https://huggingface.co/espnet/libritts_encodec_16k

libritts_encodec_16k install

libritts_encodec_16k is an open source model from GitHub that offers a free installation service, and any user can find libritts_encodec_16k on GitHub to install. At the same time, huggingface.co provides the effect of libritts_encodec_16k install, users can directly use libritts_encodec_16k installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

libritts_encodec_16k install url in huggingface.co:

https://huggingface.co/espnet/libritts_encodec_16k

Url of libritts_encodec_16k

libritts_encodec_16k huggingface.co Url

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espnet
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