fish-speech/fish_speech/configs/vits_decoder_pretrain.yaml

defaults:
  - base
  - _self_

project: vits_decoder
ckpt_path: checkpoints/Bert-VITS2/ensemble.pth
resume_weights_only: true

# Lightning Trainer
trainer:
  accelerator: gpu
  devices: auto
  strategy: ddp_find_unused_parameters_true
  precision: 32
  max_steps: 1_000_000
  val_check_interval: 1000
  benchmark: false

sample_rate: 44100
hop_length: 512
num_mels: 128
n_fft: 2048
win_length: 2048

# Dataset Configuration
tokenizer:
  _target_: transformers.AutoTokenizer.from_pretrained
  pretrained_model_name_or_path: fishaudio/fish-speech-1

# Dataset Configuration
train_dataset:
  _target_: fish_speech.datasets.vits.VITSDataset
  filelist: data/source/Genshin/filelist.train.txt
  sample_rate: ${sample_rate}
  hop_length: ${hop_length}
  suffix: ".lab"
  tokenizer: ${tokenizer}
  sentence_mask_ratio: 0.2

val_dataset:
  _target_: fish_speech.datasets.vits.VITSDataset
  filelist: data/source/Genshin/filelist.test.txt
  sample_rate: ${sample_rate}
  hop_length: ${hop_length}
  suffix: ".lab"
  tokenizer: ${tokenizer}

data:
  _target_: fish_speech.datasets.vits.VITSDataModule
  train_dataset: ${train_dataset}
  val_dataset: ${val_dataset}
  num_workers: 4
  batch_size: 8
  val_batch_size: 4
  tokenizer: ${tokenizer}

# Model Configuration
model:
  _target_: fish_speech.models.vits_decoder.VITSDecoder
  sample_rate: ${sample_rate}
  hop_length: ${hop_length}
  freeze_discriminator: false

  weight_mel: 45.0
  weight_kl: 1.0

  generator:
    _target_: fish_speech.models.vits_decoder.modules.models.SynthesizerTrn
    spec_channels: 1025
    segment_size: 32
    inter_channels: 192
    hidden_channels: 192
    filter_channels: 768
    n_heads: 2
    n_layers: 6
    kernel_size: 3
    p_dropout: 0.1
    resblock: "1"
    resblock_kernel_sizes: [3, 7, 11]
    resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
    upsample_rates: [8, 8, 2, 2, 2]
    upsample_initial_channel: 512
    upsample_kernel_sizes: [16, 16, 8, 2, 2]
    gin_channels: 512
    vq_mask_ratio: 0.2
    ref_mask_ratio: 0.2

  discriminator:
    _target_: fish_speech.models.vits_decoder.modules.models.EnsembledDiscriminator
    periods: [2, 3, 5, 7, 11]

  mel_transform:
    _target_: fish_speech.utils.spectrogram.LogMelSpectrogram
    sample_rate: ${sample_rate}
    n_fft: ${n_fft}
    hop_length: ${hop_length}
    win_length: ${win_length}
    n_mels: ${num_mels}

  spec_transform:
    _target_: fish_speech.utils.spectrogram.LinearSpectrogram
    n_fft: ${n_fft}
    hop_length: ${hop_length}
    win_length: ${win_length}
    mode: pow2_sqrt
  
  optimizer:
    _target_: torch.optim.AdamW
    _partial_: true
    lr: 1e-4
    betas: [0.8, 0.99]
    eps: 1e-6

  lr_scheduler:
    _target_: torch.optim.lr_scheduler.ExponentialLR
    _partial_: true
    gamma: 0.999999

callbacks:
  grad_norm_monitor:
    sub_module: 
      - generator
      - discriminator

  model_checkpoint:
    every_n_train_steps: 1000
    save_top_k: 10