688 lines
29 KiB
Python
688 lines
29 KiB
Python
#!/usr/bin/env python3
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import sys
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from pathlib import Path
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project_root = Path(__file__).parent.parent
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sys.path.insert(0, str(project_root / "src"))
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import contextlib
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from typing import Dict, Optional
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import argbind
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import torch
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from tensorboardX import SummaryWriter
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from torch.optim import AdamW
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from transformers import get_cosine_schedule_with_warmup
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import signal
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import os
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os.environ['TOKENIZERS_PARALLELISM'] = 'false'
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try:
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from safetensors.torch import save_file
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SAFETENSORS_AVAILABLE = True
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except ImportError:
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SAFETENSORS_AVAILABLE = False
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print("Warning: safetensors not available, will use pytorch format")
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from voxcpm.model import VoxCPMModel
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from voxcpm.model.voxcpm import LoRAConfig
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from voxcpm.training import (
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Accelerator,
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BatchProcessor,
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TrainingTracker,
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build_dataloader,
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load_audio_text_datasets,
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)
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@argbind.bind(without_prefix=True)
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def train(
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pretrained_path: str,
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train_manifest: str,
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val_manifest: str = "",
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sample_rate: int = 16_000,
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batch_size: int = 1,
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grad_accum_steps: int = 1,
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num_workers: int = 2,
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num_iters: int = 100_000,
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log_interval: int = 100,
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valid_interval: int = 1_000,
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save_interval: int = 10_000,
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learning_rate: float = 1e-4,
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weight_decay: float = 1e-2,
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warmup_steps: int = 1_000,
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max_steps: int = 100_000,
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max_batch_tokens: int = 0,
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save_path: str = "checkpoints",
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tensorboard: str = "",
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lambdas: Dict[str, float] = {"loss/diff": 1.0, "loss/stop": 1.0},
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lora: dict = None,
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config_path: str = "",
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# Distribution options (for LoRA checkpoints)
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hf_model_id: str = "", # HuggingFace model ID (e.g., "openbmb/VoxCPM1.5")
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distribute: bool = False, # If True, save hf_model_id as base_model; otherwise save pretrained_path
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):
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_ = config_path
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# Validate distribution options
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if lora is not None and distribute and not hf_model_id:
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raise ValueError("hf_model_id is required when distribute=True")
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accelerator = Accelerator(amp=True)
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save_dir = Path(save_path)
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tb_dir = Path(tensorboard) if tensorboard else save_dir / "logs"
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# Only create directories on rank 0 to avoid race conditions
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if accelerator.rank == 0:
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save_dir.mkdir(parents=True, exist_ok=True)
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tb_dir.mkdir(parents=True, exist_ok=True)
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accelerator.barrier() # Wait for directory creation
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writer = SummaryWriter(log_dir=str(tb_dir)) if accelerator.rank == 0 else None
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tracker = TrainingTracker(writer=writer, log_file=str(save_dir / "train.log"), rank=accelerator.rank)
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base_model = VoxCPMModel.from_local(pretrained_path, optimize=False, training=True, lora_config=LoRAConfig(**lora) if lora else None)
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tokenizer = base_model.text_tokenizer
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train_ds, val_ds = load_audio_text_datasets(
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train_manifest=train_manifest,
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val_manifest=val_manifest,
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sample_rate=sample_rate,
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)
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def tokenize(batch):
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text_list = batch["text"]
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text_ids = [tokenizer(text) for text in text_list]
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return {"text_ids": text_ids}
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train_ds = train_ds.map(tokenize, batched=True, remove_columns=["text"])
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# Save original validation texts for audio generation display
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val_texts = None
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if val_ds is not None:
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val_texts = list(val_ds["text"]) # Save original texts
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val_ds = val_ds.map(tokenize, batched=True, remove_columns=["text"])
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dataset_cnt = int(max(train_ds["dataset_id"])) + 1 if "dataset_id" in train_ds.column_names else 1
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num_train_samples = len(train_ds)
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# ------------------------------------------------------------------ #
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# Optional: filter samples by estimated token count to avoid OOM
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# Enabled when max_batch_tokens > 0:
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# max_sample_len = max_batch_tokens // batch_size
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# Samples exceeding this length will be dropped
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# ------------------------------------------------------------------ #
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if max_batch_tokens and max_batch_tokens > 0:
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from voxcpm.training.data import compute_sample_lengths
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audio_vae_fps = base_model.audio_vae.sample_rate / base_model.audio_vae.hop_length
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est_lengths = compute_sample_lengths(
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train_ds,
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audio_vae_fps=audio_vae_fps,
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patch_size=base_model.config.patch_size,
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)
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max_sample_len = max_batch_tokens // batch_size if batch_size > 0 else max(est_lengths)
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keep_indices = [i for i, L in enumerate(est_lengths) if L <= max_sample_len]
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if len(keep_indices) < len(train_ds) and accelerator.rank == 0:
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tracker.print(
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f"Filtering {len(train_ds) - len(keep_indices)} / {len(train_ds)} "
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f"training samples longer than {max_sample_len} tokens "
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f"(max_batch_tokens={max_batch_tokens})."
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)
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train_ds = train_ds.select(keep_indices)
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train_loader = build_dataloader(
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train_ds,
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accelerator=accelerator,
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batch_size=batch_size,
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num_workers=num_workers,
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drop_last=True,
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)
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val_loader = (
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build_dataloader(
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val_ds,
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accelerator=accelerator,
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batch_size=batch_size,
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num_workers=num_workers,
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drop_last=False,
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)
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if val_ds is not None
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else None
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)
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batch_processor = BatchProcessor(
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config=base_model.config,
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audio_vae=base_model.audio_vae,
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dataset_cnt=dataset_cnt,
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device=accelerator.device,
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)
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# Save audio_vae for audio generation
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audio_vae_for_gen = base_model.audio_vae
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del base_model.audio_vae
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model = accelerator.prepare_model(base_model)
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unwrapped_model = accelerator.unwrap(model)
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unwrapped_model.train()
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# Only print param info on rank 0 to avoid cluttered output
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if accelerator.rank == 0:
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for name, param in model.named_parameters():
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print(name, param.requires_grad)
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optimizer = AdamW(
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(p for p in model.parameters() if p.requires_grad),
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lr=learning_rate,
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weight_decay=weight_decay,
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)
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# Cosine + warmup scheduler from transformers:
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# - num_warmup_steps: warmup steps
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# - num_training_steps: total training steps (outer step count)
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total_training_steps = max_steps if max_steps > 0 else num_iters
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scheduler = get_cosine_schedule_with_warmup(
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optimizer,
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num_warmup_steps=warmup_steps,
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num_training_steps=total_training_steps,
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)
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# Try to load checkpoint and resume training
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start_step = 0
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if accelerator.rank == 0:
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start_step = load_checkpoint(model, optimizer, scheduler, save_dir)
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# Broadcast start_step to all processes
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if hasattr(accelerator, 'all_reduce'):
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start_step_tensor = torch.tensor(start_step, device=accelerator.device)
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accelerator.all_reduce(start_step_tensor)
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start_step = int(start_step_tensor.item())
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if start_step > 0 and accelerator.rank == 0:
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tracker.print(f"Resuming training from step {start_step}")
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# Resume tracker for signal handler to read current step
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resume = {"step": start_step}
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# Register signal handler to save checkpoint on termination (SIGTERM/SIGINT)
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def _signal_handler(signum, frame, _model=model, _optim=optimizer, _sched=scheduler, _save_dir=save_dir, _pretrained=pretrained_path, _hf_id=hf_model_id, _dist=distribute, _resume=resume):
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try:
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cur_step = int(_resume.get("step", start_step))
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except Exception:
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cur_step = start_step
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print(f"Signal {signum} received. Saving checkpoint at step {cur_step} ...")
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try:
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save_checkpoint(_model, _optim, _sched, _save_dir, cur_step, _pretrained, _hf_id, _dist)
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print("Checkpoint saved. Exiting.")
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except Exception as e:
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print(f"Error saving checkpoint on signal: {e}")
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os._exit(0)
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signal.signal(signal.SIGTERM, _signal_handler)
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signal.signal(signal.SIGINT, _signal_handler)
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# Manual epoch management instead of itertools.cycle to support DistributedSampler.set_epoch()
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grad_accum_steps = max(int(grad_accum_steps), 1)
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data_epoch = 0
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train_iter = iter(train_loader)
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def get_next_batch():
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"""Get next batch, handles epoch boundary and DistributedSampler."""
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nonlocal train_iter, data_epoch
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try:
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return next(train_iter)
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except StopIteration:
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data_epoch += 1
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# Key: set DistributedSampler epoch to ensure different data order each epoch
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sampler = getattr(train_loader, 'sampler', None)
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if hasattr(sampler, 'set_epoch'):
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sampler.set_epoch(data_epoch)
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train_iter = iter(train_loader)
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return next(train_iter)
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with tracker.live():
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for step in range(start_step, num_iters):
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# update resume step so signal handler can save current progress
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resume["step"] = step
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tracker.step = step
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optimizer.zero_grad(set_to_none=True)
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# Gradient accumulation: accumulate gradients over micro-batches before optimizer step
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loss_dict = {}
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for micro_step in range(grad_accum_steps):
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batch = get_next_batch()
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processed = batch_processor(batch)
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# Only sync gradients on the last micro-batch
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# Use no_sync() for intermediate steps to reduce communication overhead
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is_last_micro_step = (micro_step == grad_accum_steps - 1)
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sync_context = contextlib.nullcontext() if is_last_micro_step else accelerator.no_sync()
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with sync_context:
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with accelerator.autocast(dtype=torch.bfloat16):
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outputs = model(
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processed["text_tokens"],
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processed["text_mask"],
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processed["audio_feats"],
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processed["audio_mask"],
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processed["loss_mask"],
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processed["position_ids"],
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processed["labels"],
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progress=step / max(1, num_iters),
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)
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total_loss = 0.0
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for key, value in outputs.items():
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if key.startswith("loss/"):
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weight = lambdas.get(key, 1.0)
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loss_value = value * weight / grad_accum_steps
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total_loss = total_loss + loss_value
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# Record raw loss from last micro-batch for logging
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loss_dict[key] = value.detach()
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# Accumulate gradients (normalized by grad_accum_steps)
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accelerator.backward(total_loss)
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# After all micro-batches, do unscale / grad_norm / step
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scaler = getattr(accelerator, "scaler", None)
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if scaler is not None:
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scaler.unscale_(optimizer)
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# Use large max_norm to only compute grad_norm without actual clipping
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grad_norm = torch.nn.utils.clip_grad_norm_(unwrapped_model.parameters(), max_norm=1e9)
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accelerator.step(optimizer)
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accelerator.update()
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scheduler.step()
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if step % log_interval == 0 or step == num_iters - 1:
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loss_values = {k: v.item() if isinstance(v, torch.Tensor) else float(v) for k, v in loss_dict.items()}
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loss_values["lr"] = float(optimizer.param_groups[0]["lr"])
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# Approximate epoch: seen samples / total samples (considering grad_accum and batch_size)
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epoch = (step * grad_accum_steps * batch_size) / max(1, num_train_samples)
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loss_values["epoch"] = float(epoch)
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loss_values["grad_norm"] = float(grad_norm)
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tracker.log_metrics(loss_values, split="train")
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if val_loader is not None and (step % valid_interval == 0 or step == num_iters - 1):
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validate(model, val_loader, batch_processor, accelerator, tracker, lambdas,
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writer=writer, step=step, val_ds=val_ds, audio_vae=audio_vae_for_gen,
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sample_rate=sample_rate, val_texts=val_texts, tokenizer=tokenizer,
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valid_interval=valid_interval)
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if (step % save_interval == 0 or step == num_iters - 1) and accelerator.rank == 0:
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save_checkpoint(model, optimizer, scheduler, save_dir, step, pretrained_path, hf_model_id, distribute)
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if accelerator.rank == 0:
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save_checkpoint(model, optimizer, scheduler, save_dir, num_iters, pretrained_path, hf_model_id, distribute)
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if writer:
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writer.close()
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def validate(model, val_loader, batch_processor, accelerator, tracker, lambdas,
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writer=None, step=0, val_ds=None, audio_vae=None, sample_rate=22050,
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val_texts=None, tokenizer=None, valid_interval=1000):
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"""Validate and generate sample audio"""
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import numpy as np
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from collections import defaultdict
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model.eval()
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total_losses = []
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sub_losses = defaultdict(list) # Track individual sub-losses
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num_batches = 0
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max_val_batches = 10
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with torch.no_grad():
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for batch in val_loader:
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if num_batches >= max_val_batches:
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break
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processed = batch_processor(batch)
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with accelerator.autocast(dtype=torch.bfloat16):
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outputs = model(
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processed["text_tokens"],
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processed["text_mask"],
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processed["audio_feats"],
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processed["audio_mask"],
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processed["loss_mask"],
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processed["position_ids"],
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processed["labels"],
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progress=0.0,
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sample_generate=False,
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)
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total = 0.0
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for key, value in outputs.items():
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if key.startswith("loss/"):
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weighted_loss = lambdas.get(key, 1.0) * value
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total += weighted_loss
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sub_losses[key].append(value.detach())
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total_losses.append(total.detach())
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num_batches += 1
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if total_losses:
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# Compute mean total loss
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mean_total_loss = torch.stack(total_losses).mean()
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accelerator.all_reduce(mean_total_loss)
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# Compute mean of each sub-loss
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val_metrics = {"loss/total": mean_total_loss.item()}
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for key, values in sub_losses.items():
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mean_sub_loss = torch.stack(values).mean()
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accelerator.all_reduce(mean_sub_loss)
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val_metrics[key] = mean_sub_loss.item()
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tracker.log_metrics(val_metrics, split="val")
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# Generate sample audio for TensorBoard display
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if writer is not None and val_ds is not None and audio_vae is not None and accelerator.rank == 0:
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try:
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generate_sample_audio(model, val_ds, audio_vae, writer, step, accelerator, sample_rate,
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val_texts=val_texts, tokenizer=tokenizer, valid_interval=valid_interval,
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tracker=tracker)
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except Exception as e:
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tracker.print(f"[Warning] Failed to generate sample audio: {e}")
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import traceback
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import io
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buf = io.StringIO()
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traceback.print_exc(file=buf)
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tracker.print(buf.getvalue())
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else:
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# Log why audio generation was skipped
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missing = []
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if writer is None:
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missing.append("writer")
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if val_ds is None:
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missing.append("val_ds")
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if audio_vae is None:
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missing.append("audio_vae")
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if missing and accelerator.rank == 0:
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tracker.print(f"[Warning] Skip audio generation: missing {', '.join(missing)}")
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model.train()
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def compute_mel_spectrogram(audio_np, sample_rate, n_mels=128):
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"""Compute Mel Spectrogram (dB) using librosa"""
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import numpy as np
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import librosa
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audio_np = audio_np.flatten().astype(np.float32)
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mel = librosa.feature.melspectrogram(y=audio_np, sr=sample_rate, n_mels=n_mels, fmax=sample_rate // 2)
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return librosa.power_to_db(mel, ref=np.max)
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def create_mel_figure(gen_audio_np, gen_mel, sample_rate, step=None, ref_audio_np=None, ref_mel=None):
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"""
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Create mel spectrogram figure: show comparison if reference audio exists, otherwise show generated only
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"""
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import librosa.display
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fmax = sample_rate // 2
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step_str = f" @ Step {step}" if step is not None else ""
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if ref_audio_np is not None and ref_mel is not None:
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# Comparison mode: reference vs generated
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fig, (ax_ref, ax_gen) = plt.subplots(2, 1, figsize=(12, 8))
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img_ref = librosa.display.specshow(ref_mel, sr=sample_rate, x_axis='time', y_axis='mel', fmax=fmax, cmap='viridis', ax=ax_ref)
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ax_ref.set_title(f'Reference (GT) - {len(ref_audio_np)/sample_rate:.2f}s{step_str}', fontsize=10, fontweight='bold', color='#28A745')
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plt.colorbar(img_ref, ax=ax_ref, format='%+2.0f dB', pad=0.02)
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img_gen = librosa.display.specshow(gen_mel, sr=sample_rate, x_axis='time', y_axis='mel', fmax=fmax, cmap='viridis', ax=ax_gen)
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ax_gen.set_title(f'Generated - {len(gen_audio_np)/sample_rate:.2f}s', fontsize=10, fontweight='bold', color='#DC3545')
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plt.colorbar(img_gen, ax=ax_gen, format='%+2.0f dB', pad=0.02)
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else:
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# Single figure mode: show generated only
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fig, ax = plt.subplots(figsize=(12, 4))
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img = librosa.display.specshow(gen_mel, sr=sample_rate, x_axis='time', y_axis='mel', fmax=fmax, cmap='viridis', ax=ax)
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ax.set_title(f'Generated - {len(gen_audio_np)/sample_rate:.2f}s{step_str}', fontsize=11, fontweight='bold')
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plt.colorbar(img, ax=ax, format='%+2.0f dB', pad=0.02)
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plt.tight_layout()
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return fig
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def normalize_audio(audio_np):
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"""Normalize audio to [-0.9, 0.9]"""
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import numpy as np
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max_val = np.abs(audio_np).max()
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return audio_np / max_val * 0.9 if max_val > 0 else audio_np
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def generate_sample_audio(model, val_ds, audio_vae, writer, step, accelerator, sample_rate=22050,
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val_texts=None, tokenizer=None, pretrained_path=None, valid_interval=1000,
|
|
tracker=None):
|
|
"""Select 2 fixed validation samples, generate audio and log to TensorBoard"""
|
|
import numpy as np
|
|
|
|
log = tracker.print if tracker else print
|
|
num_samples = min(2, len(val_ds))
|
|
log(f"[Audio] Starting audio generation for {num_samples} samples at step {step}")
|
|
|
|
unwrapped_model = accelerator.unwrap(model)
|
|
|
|
for i in range(num_samples):
|
|
sample = val_ds[i]
|
|
text = val_texts[i] if val_texts and i < len(val_texts) else "Hello, this is a test."
|
|
|
|
# Load reference audio
|
|
ref_audio_np = None
|
|
try:
|
|
if "audio" in sample and isinstance(sample["audio"], dict) and "array" in sample["audio"]:
|
|
ref_audio_np = np.array(sample["audio"]["array"], dtype=np.float32)
|
|
ref_sr = sample["audio"].get("sampling_rate", sample_rate)
|
|
if ref_sr != sample_rate:
|
|
import torchaudio.functional as F
|
|
ref_audio_np = F.resample(torch.from_numpy(ref_audio_np).unsqueeze(0), ref_sr, sample_rate).squeeze(0).numpy()
|
|
log(f"[Audio] Loaded reference audio for sample {i}: duration={len(ref_audio_np)/sample_rate:.2f}s")
|
|
except Exception as e:
|
|
log(f"[Warning] Failed to load reference audio: {e}")
|
|
|
|
try:
|
|
# Inference setup
|
|
unwrapped_model.eval()
|
|
unwrapped_model.to(torch.bfloat16)
|
|
unwrapped_model.audio_vae = audio_vae.to(torch.float32)
|
|
|
|
log(f"[Audio] Generating sample {i} with text: '{text[:50]}...'")
|
|
with torch.no_grad():
|
|
generated = unwrapped_model.generate(target_text=text, inference_timesteps=10, cfg_value=2.0)
|
|
|
|
# Restore training setup
|
|
unwrapped_model.to(torch.float32)
|
|
unwrapped_model.audio_vae = None
|
|
|
|
if generated is None or len(generated) == 0:
|
|
log(f"[Warning] Generated audio is empty for sample {i}")
|
|
continue
|
|
|
|
# Process generated audio
|
|
gen_audio_np = generated.cpu().float().numpy().flatten() if isinstance(generated, torch.Tensor) else np.array(generated, dtype=np.float32).flatten()
|
|
gen_audio_np = normalize_audio(gen_audio_np)
|
|
|
|
tag = f"val_sample_{i}"
|
|
writer.add_audio(f"{tag}/generated_audio", gen_audio_np, global_step=step, sample_rate=sample_rate)
|
|
log(f"[Audio] Generated audio for sample {i}: duration={len(gen_audio_np)/sample_rate:.2f}s")
|
|
|
|
# Log reference audio
|
|
if ref_audio_np is not None:
|
|
writer.add_audio(f"{tag}/reference_audio", normalize_audio(ref_audio_np), global_step=step, sample_rate=sample_rate)
|
|
|
|
# Generate mel spectrogram figure
|
|
try:
|
|
mel_gen = compute_mel_spectrogram(gen_audio_np, sample_rate)
|
|
mel_ref = compute_mel_spectrogram(ref_audio_np, sample_rate) if ref_audio_np is not None else None
|
|
fig = create_mel_figure(gen_audio_np, mel_gen, sample_rate, step, ref_audio_np, mel_ref)
|
|
writer.add_figure(f"{tag}/mel_spectrogram", fig, global_step=step)
|
|
log(f"[Audio] Created mel spectrogram figure for sample {i}")
|
|
except Exception as e:
|
|
log(f"[Warning] Failed to create mel spectrogram: {e}")
|
|
|
|
except Exception as e:
|
|
log(f"[Warning] Failed to generate audio for sample {i}: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
|
|
|
|
def load_checkpoint(model, optimizer, scheduler, save_dir: Path):
|
|
"""
|
|
Load the latest checkpoint if it exists.
|
|
Returns the step number to resume from, or 0 if no checkpoint found.
|
|
"""
|
|
latest_folder = save_dir / "latest"
|
|
if not latest_folder.exists():
|
|
return 0
|
|
|
|
unwrapped = model.module if hasattr(model, "module") else model
|
|
lora_cfg = unwrapped.lora_config
|
|
|
|
# Load model weights
|
|
if lora_cfg is not None:
|
|
# LoRA: load lora_weights
|
|
lora_weights_path = latest_folder / "lora_weights.safetensors"
|
|
if not lora_weights_path.exists():
|
|
lora_weights_path = latest_folder / "lora_weights.ckpt"
|
|
|
|
if lora_weights_path.exists():
|
|
if lora_weights_path.suffix == ".safetensors":
|
|
from safetensors.torch import load_file
|
|
state_dict = load_file(str(lora_weights_path))
|
|
else:
|
|
ckpt = torch.load(lora_weights_path, map_location="cpu")
|
|
state_dict = ckpt.get("state_dict", ckpt)
|
|
|
|
# Load only lora weights
|
|
unwrapped.load_state_dict(state_dict, strict=False)
|
|
print(f"Loaded LoRA weights from {lora_weights_path}")
|
|
else:
|
|
# Full finetune: load model.safetensors or pytorch_model.bin
|
|
model_path = latest_folder / "model.safetensors"
|
|
if not model_path.exists():
|
|
model_path = latest_folder / "pytorch_model.bin"
|
|
|
|
if model_path.exists():
|
|
if model_path.suffix == ".safetensors":
|
|
from safetensors.torch import load_file
|
|
state_dict = load_file(str(model_path))
|
|
else:
|
|
ckpt = torch.load(model_path, map_location="cpu")
|
|
state_dict = ckpt.get("state_dict", ckpt)
|
|
|
|
unwrapped.load_state_dict(state_dict, strict=False)
|
|
print(f"Loaded model weights from {model_path}")
|
|
|
|
# Load optimizer state
|
|
optimizer_path = latest_folder / "optimizer.pth"
|
|
if optimizer_path.exists():
|
|
optimizer.load_state_dict(torch.load(optimizer_path, map_location="cpu"))
|
|
print(f"Loaded optimizer state from {optimizer_path}")
|
|
|
|
# Load scheduler state
|
|
scheduler_path = latest_folder / "scheduler.pth"
|
|
if scheduler_path.exists():
|
|
scheduler.load_state_dict(torch.load(scheduler_path, map_location="cpu"))
|
|
print(f"Loaded scheduler state from {scheduler_path}")
|
|
|
|
# Try to infer step from checkpoint folders
|
|
step_folders = [d for d in save_dir.iterdir() if d.is_dir() and d.name.startswith("step_")]
|
|
if step_folders:
|
|
steps = [int(d.name.split("_")[1]) for d in step_folders]
|
|
resume_step = max(steps)
|
|
print(f"Resuming from step {resume_step}")
|
|
return resume_step
|
|
|
|
return 0
|
|
|
|
|
|
def save_checkpoint(model, optimizer, scheduler, save_dir: Path, step: int, pretrained_path: str = None, hf_model_id: str = "", distribute: bool = False):
|
|
"""
|
|
Save checkpoint with different strategies for full finetune vs LoRA:
|
|
- Full finetune: save non-vae weights to model.safetensors (or pytorch_model.bin if safetensors unavailable)
|
|
- LoRA: save only lora weights to lora_weights.safetensors (or lora_weights.ckpt if safetensors unavailable)
|
|
"""
|
|
import shutil
|
|
|
|
save_dir.mkdir(parents=True, exist_ok=True)
|
|
tag = "latest" if step == 0 else f"step_{step:07d}"
|
|
folder = save_dir / tag
|
|
folder.mkdir(parents=True, exist_ok=True)
|
|
|
|
unwrapped = model.module if hasattr(model, "module") else model
|
|
full_state = unwrapped.state_dict()
|
|
lora_cfg = unwrapped.lora_config
|
|
|
|
if lora_cfg is not None:
|
|
# LoRA finetune: save only lora_A/lora_B weights
|
|
state_dict = {k: v for k, v in full_state.items() if "lora_" in k}
|
|
if SAFETENSORS_AVAILABLE:
|
|
save_file(state_dict, folder / "lora_weights.safetensors")
|
|
else:
|
|
torch.save({"state_dict": state_dict}, folder / "lora_weights.ckpt")
|
|
|
|
# Save LoRA config and base model path to a separate JSON file
|
|
# If distribute=True, save hf_model_id; otherwise save local pretrained_path
|
|
import json
|
|
base_model_to_save = hf_model_id if distribute else (str(pretrained_path) if pretrained_path else None)
|
|
lora_info = {
|
|
"base_model": base_model_to_save,
|
|
"lora_config": lora_cfg.model_dump() if hasattr(lora_cfg, "model_dump") else vars(lora_cfg),
|
|
}
|
|
with open(folder / "lora_config.json", "w", encoding="utf-8") as f:
|
|
json.dump(lora_info, f, indent=2, ensure_ascii=False)
|
|
else:
|
|
# Full finetune: save non-vae weights to model.safetensors
|
|
state_dict = {k: v for k, v in full_state.items() if not k.startswith("audio_vae.")}
|
|
if SAFETENSORS_AVAILABLE:
|
|
save_file(state_dict, folder / "model.safetensors")
|
|
else:
|
|
torch.save({"state_dict": state_dict}, folder / "pytorch_model.bin")
|
|
|
|
# Copy config files from pretrained path
|
|
if pretrained_path:
|
|
pretrained_dir = Path(pretrained_path)
|
|
files_to_copy = ["config.json", "audiovae.pth", "tokenizer.json", "special_tokens_map.json", "tokenizer_config.json"]
|
|
for fname in files_to_copy:
|
|
src = pretrained_dir / fname
|
|
if src.exists():
|
|
shutil.copy2(src, folder / fname)
|
|
|
|
torch.save(optimizer.state_dict(), folder / "optimizer.pth")
|
|
torch.save(scheduler.state_dict(), folder / "scheduler.pth")
|
|
|
|
# Update (or create) a `latest` symlink pointing to the most recent checkpoint folder
|
|
latest_link = save_dir / "latest"
|
|
try:
|
|
if latest_link.exists() or latest_link.is_symlink():
|
|
# remove existing link or directory
|
|
if latest_link.is_dir() and not latest_link.is_symlink():
|
|
shutil.rmtree(latest_link)
|
|
else:
|
|
latest_link.unlink()
|
|
# Create a symlink pointing to the new folder
|
|
os.symlink(str(folder), str(latest_link))
|
|
except Exception:
|
|
# If symlink creation fails (e.g., on Windows or permission issues), fall back to copying
|
|
try:
|
|
if latest_link.exists():
|
|
if latest_link.is_dir():
|
|
shutil.rmtree(latest_link)
|
|
else:
|
|
latest_link.unlink()
|
|
shutil.copytree(folder, latest_link)
|
|
except Exception:
|
|
print(f"Warning: failed to update latest checkpoint link at {latest_link}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from voxcpm.training.config import load_yaml_config
|
|
|
|
args = argbind.parse_args()
|
|
config_file = args.get("config_path")
|
|
# If YAML config provided, use YAML args to call train
|
|
if config_file:
|
|
yaml_args = load_yaml_config(config_file)
|
|
train(**yaml_args)
|
|
else:
|
|
# Otherwise use command line args (parsed by argbind)
|
|
with argbind.scope(args):
|
|
train() |