espnet_codec_encodec_large_v1.1

ESPnet2 Codec model

ftshijt/espnet_codec_encodec_large_v1.1

This model was trained by ftshijt using amuse 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 734f1235b3dd3c444822b6337fbb2e417e75e321
pip install -e .
cd egs2/amuse/codec_speechlm
./run.sh --skip_data_prep false --skip_train true --download_model ftshijt/espnet_codec_encodec_large_v1.1

Codec config

<details><summary>expand</summary>

config: conf/train_encodec_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_train_encodec_large_v1.1_raw_fs16000
ngpu: 1
seed: 777
num_workers: 1
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 36201
dist_launcher: null
multiprocessing_distributed: true
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: 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_raw/train/audio_shape
valid_shape_file:
- exp/codec_stats_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
truncate_audio: false
chunk_length: 32000
chunk_shift_ratio: 0.5
num_cache_chunks: 128
chunk_excluded_key_prefixes: []
chunk_default_fs: null
train_data_path_and_name_and_type:
-   - dump/raw/train/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: 0.3
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: 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
    use_loss_balancer: false
required:
- output_dir
version: '202402'
distributed: true

</details>

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}
}