pretrained_path: /path/to/VoxCPM-0.5B/
train_manifest: /path/to/train.jsonl
val_manifest: null
sample_rate: 16000
batch_size: 16
grad_accum_steps: 1  # Gradient accumulation steps, >1 can increase effective batch size without increasing memory
num_workers: 2
num_iters: 2000
log_interval: 10
valid_interval: 1000
save_interval: 1000
learning_rate: 0.0001  
weight_decay: 0.01
warmup_steps: 100
max_steps: 2000
max_batch_tokens: 8192  # Example: single batch can have at most 16k tokens, with batch_size=4, each sample can have at most 4096 tokens
save_path: /path/to/checkpoints/finetune_lora
tensorboard: /path/to/logs/finetune_lora
lambdas:
  loss/diff: 1.0
  loss/stop: 1.0

# LoRA configuration
lora:
  enable_lm: true
  enable_dit: true
  enable_proj: false
  r: 8             
  alpha: 16           
  dropout: 0.0

# Distribution options (optional)
# - If distribute=false (default): save pretrained_path as base_model in lora_config.json
# - If distribute=true: save hf_model_id as base_model (hf_model_id is required)
# hf_model_id: "openbmb/VoxCPM-0.5B"
# distribute: true       