Emilia-vits-espnet2

ESPnet2 TTS model

NewGame/Emilia-vits-espnet2

This model was trained by NewGamezzz using emilia 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 7d46ef980eb56d14c23201d26ff4eceec97d0b3a
pip install -e .
cd egs2/emilia/tts1
./run.sh --skip_data_prep false --skip_train true --download_model NewGame/Emilia-vits-espnet2

TTS config

<details><summary>expand</summary>

config: conf/tuning/train_xvector_vits_large_grad_clip_original_batch_size.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: exp/tts_train_xvector_vits_large_grad_clip_original_batch_size_raw_phn_tacotron_g2p_en
ngpu: 1
seed: 777
num_workers: 0
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 53813
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: true
sharded_ddp: false
use_deepspeed: false
deepspeed_config: null
gradient_as_bucket_view: true
ddp_comm_hook: null
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
use_tf32: false
collect_stats: false
write_collected_feats: false
max_epoch: 800
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - train
    - total_count
    - max
keep_nbest_models: 10
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: 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: 1000
batch_size: 20
valid_batch_size: null
batch_bins: 8000000
valid_batch_bins: null
category_sample_size: 10
upsampling_factor: 0.5
category_upsampling_factor: 0.5
dataset_upsampling_factor: 0.5
dataset_scaling_factor: 1.2
max_batch_size: null
min_batch_size: 1
train_shape_file:
- exp/tts_stats_raw_linear_spectrogram_phn_tacotron_g2p_en/train/text_shape.phn
- exp/tts_stats_raw_linear_spectrogram_phn_tacotron_g2p_en/train/speech_shape
valid_shape_file:
- exp/tts_stats_raw_linear_spectrogram_phn_tacotron_g2p_en/valid/text_shape.phn
- exp/tts_stats_raw_linear_spectrogram_phn_tacotron_g2p_en/valid/speech_shape
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 204800
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
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/tr_no_dev/text
    - text
    - text
-   - dump/raw/tr_no_dev/wav.scp
    - speech
    - sound
-   - dump/xvector/tr_no_dev/xvector.scp
    - spembs
    - kaldi_ark
valid_data_path_and_name_and_type:
-   - dump/raw/dev/text
    - text
    - text
-   - dump/raw/dev/wav.scp
    - speech
    - sound
-   - dump/xvector/dev/xvector.scp
    - spembs
    - 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:
    - 0.8
    - 0.99
    eps: 1.0e-09
    weight_decay: 0.0
scheduler: exponentiallr
scheduler_conf:
    gamma: 0.999
optim2: adamw
optim2_conf:
    lr: 0.0002
    betas:
    - 0.8
    - 0.99
    eps: 1.0e-09
    weight_decay: 0.0
scheduler2: exponentiallr
scheduler2_conf:
    gamma: 0.999
generator_first: false
skip_discriminator_prob: 0.0
token_list:
- <blank>
- <unk>
- ' '
- T
- AH0
- N
- S
- R
- D
- L
- K
- IH1
- DH
- M
- Z
- EH1
- AH1
- AE1
- IH0
- ','
- W
- UW1
- AY1
- IY1
- .
- P
- V
- B
- AA1
- F
- ER0
- IY0
- EY1
- AO1
- OW1
- HH
- Y
- NG
- G
- SH
- AW1
- JH
- TH
- CH
- ER1
- UH1
- '?'
- OW0
- EH2
- IH2
- EY2
- AY2
- OW2
- AA2
- AE2
- UW0
- EH0
- OY1
- AH2
- AO2
- AE0
- ZH
- AY0
- UW2
- AA0
- IY2
- AO0
- '...'
- AW2
- '!'
- EY0
- UH2
- ER2
- ''''
- AW0
- UH0
- OY2
- OY0
- '... ...'
- . ...
- . .
- ..
- . . .
- . . . .
- . . . . .
- '... .'
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: phn
bpemodel: null
non_linguistic_symbols: null
cleaner: tacotron
g2p: g2p_en
feats_extract: linear_spectrogram
feats_extract_conf:
    n_fft: 1024
    hop_length: 256
    win_length: null
normalize: null
normalize_conf: {}
tts: vits
tts_conf:
    generator_type: vits_generator
    generator_params:
        hidden_channels: 256
        spks: -1
        spk_embed_dim: 192
        global_channels: 256
        segment_size: 32
        text_encoder_attention_heads: 4
        text_encoder_ffn_expand: 4
        text_encoder_blocks: 8
        text_encoder_positionwise_layer_type: conv1d
        text_encoder_positionwise_conv_kernel_size: 3
        text_encoder_positional_encoding_layer_type: rel_pos
        text_encoder_self_attention_layer_type: rel_selfattn
        text_encoder_activation_type: swish
        text_encoder_normalize_before: true
        text_encoder_dropout_rate: 0.1
        text_encoder_positional_dropout_rate: 0.0
        text_encoder_attention_dropout_rate: 0.1
        use_macaron_style_in_text_encoder: true
        use_conformer_conv_in_text_encoder: false
        text_encoder_conformer_kernel_size: -1
        decoder_kernel_size: 7
        decoder_channels: 768
        decoder_upsample_scales:
        - 8
        - 8
        - 2
        - 2
        decoder_upsample_kernel_sizes:
        - 16
        - 16
        - 4
        - 4
        decoder_resblock_kernel_sizes:
        - 3
        - 7
        - 11
        decoder_resblock_dilations:
        -   - 1
            - 3
            - 5
        -   - 1
            - 3
            - 5
        -   - 1
            - 3
            - 5
        use_weight_norm_in_decoder: true
        posterior_encoder_kernel_size: 5
        posterior_encoder_layers: 16
        posterior_encoder_stacks: 1
        posterior_encoder_base_dilation: 1
        posterior_encoder_dropout_rate: 0.0
        use_weight_norm_in_posterior_encoder: true
        flow_flows: 4
        flow_kernel_size: 5
        flow_base_dilation: 1
        flow_layers: 4
        flow_dropout_rate: 0.0
        use_weight_norm_in_flow: true
        use_only_mean_in_flow: true
        stochastic_duration_predictor_kernel_size: 3
        stochastic_duration_predictor_dropout_rate: 0.5
        stochastic_duration_predictor_flows: 4
        stochastic_duration_predictor_dds_conv_layers: 3
        vocabs: 87
        aux_channels: 513
    discriminator_type: hifigan_multi_scale_multi_period_discriminator
    discriminator_params:
        scales: 1
        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
            use_weight_norm: true
            use_spectral_norm: false
        follow_official_norm: false
        periods:
        - 2
        - 3
        - 5
        - 7
        - 11
        period_discriminator_params:
            in_channels: 1
            out_channels: 1
            kernel_sizes:
            - 5
            - 3
            channels: 32
            downsample_scales:
            - 3
            - 3
            - 3
            - 3
            - 1
            max_downsample_channels: 1024
            bias: true
            nonlinear_activation: LeakyReLU
            nonlinear_activation_params:
                negative_slope: 0.1
            use_weight_norm: true
            use_spectral_norm: false
    generator_adv_loss_params:
        average_by_discriminators: false
        loss_type: mse
    discriminator_adv_loss_params:
        average_by_discriminators: false
        loss_type: mse
    feat_match_loss_params:
        average_by_discriminators: false
        average_by_layers: false
        include_final_outputs: true
    mel_loss_params:
        fs: 22050
        n_fft: 1024
        hop_length: 256
        win_length: null
        window: hann
        n_mels: 80
        fmin: 0
        fmax: null
        log_base: null
    lambda_adv: 1.0
    lambda_mel: 45.0
    lambda_feat_match: 2.0
    lambda_dur: 1.0
    lambda_kl: 1.0
    sampling_rate: 22050
    cache_generator_outputs: true
    plot_pred_mos: false
    mos_pred_tool: utmos
pitch_extract: null
pitch_extract_conf: {}
pitch_normalize: null
pitch_normalize_conf: {}
energy_extract: null
energy_extract_conf: {}
energy_normalize: null
energy_normalize_conf: {}
required:
- output_dir
- token_list
version: '202509'
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}
}




@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}

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}
}