fish-speech-1 / fish_speech /configs /text2semantic_pretrain.yaml
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defaults:
- base
- [email protected]: dual_ar_2_codebook_small
- _self_
project: text2semantic_pretrain_dual_ar_debug
max_length: 2048
# Lightning Trainer
trainer:
accumulate_grad_batches: 1
gradient_clip_val: 1.0
gradient_clip_algorithm: 'norm'
max_steps: 1_000_000
precision: bf16-true
limit_val_batches: 10
# 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.text.AutoAugTextDataset
proto_files:
- data/protos/train
tokenizer: ${tokenizer}
max_length: ${max_length}
num_codebooks: ${model.model.config.num_codebooks}
use_speaker: false
interactive_prob: 0.5
val_dataset:
_target_: fish_speech.datasets.text.AutoAugTextDataset
proto_files:
- data/protos/test
tokenizer: ${tokenizer}
max_length: ${max_length}
num_codebooks: ${model.model.config.num_codebooks}
use_speaker: false
interactive_prob: 0.5
data:
_target_: fish_speech.datasets.text.TextDataModule
train_dataset: ${train_dataset}
val_dataset: ${val_dataset}
num_workers: 4
batch_size: 8
tokenizer: ${tokenizer}
max_length: ${max_length}
# Model Configuration
model:
_target_: fish_speech.models.text2semantic.TextToSemantic
model: {}
optimizer:
_target_: torch.optim.AdamW
_partial_: true
lr: 3e-4
weight_decay: 0.01
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: 2000
num_training_steps: ${trainer.max_steps}
final_lr_ratio: 0.1