""" LoRA Trainer - Handles LoRA training for custom models """ import torch import torchaudio from pathlib import Path import logging from typing import List, Dict, Any, Optional, Callable import json from datetime import datetime logger = logging.getLogger(__name__) class LoRATrainer: """Manages LoRA training for ACE-Step model.""" def __init__(self, config: Dict[str, Any]): """ Initialize LoRA trainer. Args: config: Configuration dictionary """ self.config = config self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.training_dir = Path(config.get("training_dir", "lora_training")) self.training_dir.mkdir(exist_ok=True) self.model = None self.lora_config = None logger.info(f"LoRA Trainer initialized on {self.device}") def prepare_dataset(self, audio_files: List[str]) -> List[str]: """ Prepare audio files for training. Args: audio_files: List of audio file paths Returns: List of prepared file paths """ try: logger.info(f"Preparing {len(audio_files)} files for training...") prepared_dir = self.training_dir / "prepared_data" / datetime.now().strftime("%Y%m%d_%H%M%S") prepared_dir.mkdir(parents=True, exist_ok=True) prepared_files = [] for i, file_path in enumerate(audio_files): try: # Load audio audio, sr = torchaudio.load(file_path) # Resample to target sample rate if needed target_sr = self.config.get("sample_rate", 44100) if sr != target_sr: resampler = torchaudio.transforms.Resample(sr, target_sr) audio = resampler(audio) # Convert to mono if needed (for some training scenarios) if audio.shape[0] > 1 and self.config.get("force_mono", False): audio = torch.mean(audio, dim=0, keepdim=True) # Normalize audio = audio / (torch.abs(audio).max() + 1e-8) # Split long files into chunks if needed chunk_duration = self.config.get("chunk_duration", 30) # seconds chunk_samples = int(chunk_duration * target_sr) if audio.shape[1] > chunk_samples: # Split into chunks num_chunks = audio.shape[1] // chunk_samples for j in range(num_chunks): start = j * chunk_samples end = start + chunk_samples chunk = audio[:, start:end] # Save chunk chunk_path = prepared_dir / f"audio_{i:04d}_chunk_{j:02d}.wav" torchaudio.save( str(chunk_path), chunk, target_sr, encoding="PCM_S", bits_per_sample=16 ) prepared_files.append(str(chunk_path)) else: # Save as-is output_path = prepared_dir / f"audio_{i:04d}.wav" torchaudio.save( str(output_path), audio, target_sr, encoding="PCM_S", bits_per_sample=16 ) prepared_files.append(str(output_path)) except Exception as e: logger.warning(f"Failed to process {file_path}: {e}") continue # Save dataset metadata metadata = { "num_files": len(prepared_files), "original_files": len(audio_files), "sample_rate": target_sr, "prepared_at": datetime.now().isoformat(), "files": prepared_files } metadata_path = prepared_dir / "metadata.json" with open(metadata_path, 'w') as f: json.dump(metadata, f, indent=2) logger.info(f"✅ Prepared {len(prepared_files)} training files") return prepared_files except Exception as e: logger.error(f"Dataset preparation failed: {e}") raise def initialize_lora(self, rank: int = 16, alpha: int = 32): """ Initialize LoRA configuration. Args: rank: LoRA rank alpha: LoRA alpha """ try: from peft import LoraConfig, get_peft_model self.lora_config = LoraConfig( r=rank, lora_alpha=alpha, target_modules=["q_proj", "v_proj", "k_proj", "o_proj"], # Attention layers lora_dropout=0.1, bias="none", task_type="CAUSAL_LM" ) logger.info(f"✅ LoRA initialized: rank={rank}, alpha={alpha}") except Exception as e: logger.error(f"LoRA initialization failed: {e}") raise def load_lora(self, lora_path: str): """ Load existing LoRA model for continued training. Args: lora_path: Path to LoRA model """ try: from peft import PeftModel from transformers import AutoModel # Load base model base_model = AutoModel.from_pretrained( self.config.get("model_path", "ACE-Step/ACE-Step-v1-3.5B"), torch_dtype=torch.float16 if self.device.type == "cuda" else torch.float32 ) # Load with LoRA self.model = PeftModel.from_pretrained(base_model, lora_path) logger.info(f"✅ Loaded LoRA from {lora_path}") except Exception as e: logger.error(f"Failed to load LoRA: {e}") raise def train( self, dataset_path: str, model_name: str, learning_rate: float = 1e-4, batch_size: int = 4, num_epochs: int = 10, progress_callback: Optional[Callable] = None ) -> str: """ Train LoRA model. Args: dataset_path: Path to prepared dataset model_name: Name for the trained model learning_rate: Learning rate batch_size: Batch size num_epochs: Number of epochs progress_callback: Optional callback for progress updates Returns: Path to trained model """ try: logger.info(f"Starting LoRA training: {model_name}") # Load dataset dataset = self._load_dataset(dataset_path) # Load base model if not already loaded if self.model is None: from transformers import AutoModel from peft import get_peft_model base_model = AutoModel.from_pretrained( self.config.get("model_path", "ACE-Step/ACE-Step-v1-3.5B"), torch_dtype=torch.float16 if self.device.type == "cuda" else torch.float32, device_map="auto" ) self.model = get_peft_model(base_model, self.lora_config) self.model.train() # Setup optimizer optimizer = torch.optim.AdamW( self.model.parameters(), lr=learning_rate, weight_decay=0.01 ) # Training loop total_steps = (len(dataset) // batch_size) * num_epochs step = 0 for epoch in range(num_epochs): epoch_loss = 0.0 for batch_idx in range(0, len(dataset), batch_size): batch = dataset[batch_idx:batch_idx + batch_size] # Forward pass (simplified - actual implementation would be more complex) loss = self._training_step(batch) # Backward pass optimizer.zero_grad() loss.backward() optimizer.step() epoch_loss += loss.item() step += 1 # Progress callback if progress_callback: progress_callback(step, total_steps, loss.item()) avg_loss = epoch_loss / (len(dataset) // batch_size) logger.info(f"Epoch {epoch+1}/{num_epochs} - Loss: {avg_loss:.4f}") # Save trained model output_dir = self.training_dir / "models" / model_name output_dir.mkdir(parents=True, exist_ok=True) self.model.save_pretrained(str(output_dir)) # Save training info info = { "model_name": model_name, "learning_rate": learning_rate, "batch_size": batch_size, "num_epochs": num_epochs, "dataset_size": len(dataset), "trained_at": datetime.now().isoformat(), "lora_config": { "rank": self.lora_config.r, "alpha": self.lora_config.lora_alpha } } info_path = output_dir / "training_info.json" with open(info_path, 'w') as f: json.dump(info, f, indent=2) logger.info(f"✅ Training complete! Model saved to {output_dir}") return str(output_dir) except Exception as e: logger.error(f"Training failed: {e}") raise def _load_dataset(self, dataset_path: str) -> List[Dict[str, Any]]: """Load prepared dataset.""" dataset_path = Path(dataset_path) # Load metadata metadata_path = dataset_path / "metadata.json" if metadata_path.exists(): with open(metadata_path, 'r') as f: metadata = json.load(f) files = metadata.get("files", []) else: # Scan directory for audio files files = list(dataset_path.glob("*.wav")) dataset = [] for file_path in files: dataset.append({ "path": str(file_path), "audio": None # Lazy loading }) return dataset def _training_step(self, batch: List[Dict[str, Any]]) -> torch.Tensor: """ Perform single training step. This is a simplified placeholder - actual implementation would: 1. Load audio from batch 2. Encode to latent space 3. Generate predictions 4. Calculate loss 5. Return loss Args: batch: Training batch Returns: Loss tensor """ # Placeholder loss calculation # Actual implementation would process audio through model loss = torch.tensor(0.5, requires_grad=True, device=self.device) return loss def export_for_inference(self, lora_path: str, output_path: str): """ Export LoRA model for inference. Args: lora_path: Path to LoRA model output_path: Output path for exported model """ try: # Load LoRA self.load_lora(lora_path) # Merge LoRA with base model merged_model = self.model.merge_and_unload() # Save merged model merged_model.save_pretrained(output_path) logger.info(f"✅ Exported model to {output_path}") except Exception as e: logger.error(f"Export failed: {e}") raise