fish-speech/fish_speech/configs/text2semantic_sft_medium.yaml

defaults:
  - base
  - _self_

project: text2semantic_sft_medium_delay
max_length: 4096
use_delay_pattern: false
ckpt_path: results/text2semantic_pretrain_medium_4_in_8_codebooks/checkpoints/step_000100000.ckpt
resume_weights_only: true

# Lightning Trainer
trainer:
  accumulate_grad_batches: 1
  gradient_clip_val: 1.0
  gradient_clip_algorithm: 'norm'
  max_steps: 10_000
  precision: bf16-true
  limit_val_batches: 10
  val_check_interval: 500

# Dataset Configuration
tokenizer:
  _target_: transformers.AutoTokenizer.from_pretrained
  pretrained_model_name_or_path: fishaudio/speech-lm-v1

# Dataset Configuration
train_dataset:
  _target_: fish_speech.datasets.text.AutoAugTextDataset
  use_data_server: false
  proto_files:
    - data/protos/sft/train_Genshin.protos
    - data/protos/sft/sft.protos
  tokenizer: ${tokenizer}
  max_length: ${max_length}
  num_codebooks: ${model.model.config.num_codebooks}
  use_speaker: true
  phones_prob: 0.5
  interactive_prob: 0.5
  use_delay_pattern: ${use_delay_pattern}

val_dataset:
  _target_: fish_speech.datasets.text.AutoAugTextDataset
  use_data_server: false
  proto_files:
    - data/protos/sft/val_Genshin.protos
  tokenizer: ${tokenizer}
  max_length: ${max_length}
  num_codebooks: ${model.model.config.num_codebooks}
  use_speaker: true
  phones_prob: 0.5
  interactive_prob: 0.5
  use_delay_pattern: ${use_delay_pattern}

data:
  _target_: fish_speech.datasets.text.TextDataModule
  train_dataset: ${train_dataset}
  val_dataset: ${val_dataset}
  num_workers: 4
  batch_size: 16
  tokenizer: ${tokenizer}
  max_length: ${max_length}

# Model Configuration
model:
  _target_: fish_speech.models.text2semantic.TextToSemantic

  model:
    # ~ 130M parameters, for debug purpose
    _target_: fish_speech.models.text2semantic.llama.Transformer
    config:
      _target_: fish_speech.models.text2semantic.llama.ModelArgs
      max_seq_len: 4096
      vocab_size: 36408
      n_layer: 24
      n_head: 16
      dim: 1024
      rope_base: 10000
      norm_eps: 1e-5
      num_in_codebooks: 4 # input codebook size
      num_codebooks: 8  # output codebook size
      codebook_size: 264 # codebook size 256 + 2 special tokens
      dropout: 0
      neft_alpha: 0

  optimizer:
    _target_: bitsandbytes.optim.AdamW8bit
    _partial_: true
    lr: 4e-5
    weight_decay: 0
    betas: [0.9, 0.95]
    eps: 1e-5

  lr_scheduler:
    _target_: torch.optim.lr_scheduler.LambdaLR
    _partial_: true
    lr_lambda:
      _target_: fish_speech.scheduler.get_cosine_schedule_with_warmup_lr_lambda
      _partial_: true
      num_warmup_steps: 100
      num_training_steps: ${trainer.max_steps}
      final_lr_ratio: 0

callbacks:
  model_checkpoint:
    every_n_train_steps: 1000
    save_top_k: 10