Audiocraft Audio Generation

# AudioCraft Advanced Usage Guide

## Fine-tuning MusicGen

### Custom dataset preparation

```python
import os
import json
from pathlib import Path
import torchaudio

def prepare_dataset(audio_dir, output_dir, metadata_file):
    """
    Prepare dataset for MusicGen fine-tuning.

    Directory structure:
    output_dir/
    ├── audio/
    │   ├── 0001.wav
    │   ├── 0002.wav
    │   └── ...
    └── metadata.json
    """
    output_dir = Path(output_dir)
    audio_output = output_dir / "audio"
    audio_output.mkdir(parents=True, exist_ok=True)

    # Load metadata (format: {"path": "...", "description": "..."})
    with open(metadata_file) as f:
        metadata = json.load(f)

    processed = []

    for idx, item in enumerate(metadata):
        audio_path = Path(audio_dir) / item["path"]

        # Load and resample to 32kHz
        wav, sr = torchaudio.load(str(audio_path))
        if sr != 32000:
            resampler = torchaudio.transforms.Resample(sr, 32000)
            wav = resampler(wav)

        # Convert to mono if stereo
        if wav.shape[0] > 1:
            wav = wav.mean(dim=0, keepdim=True)

        # Save processed audio
        output_path = audio_output / f"{idx:04d}.wav"
        torchaudio.save(str(output_path), wav, sample_rate=32000)

        processed.append({
            "path": str(output_path.relative_to(output_dir)),
            "description": item["description"],
            "duration": wav.shape[1] / 32000
        })

    # Save processed metadata
    with open(output_dir / "metadata.json", "w") as f:
        json.dump(processed, f, indent=2)

    print(f"Processed {len(processed)} samples")
    return processed
```

### Fine-tuning with dora

```bash
# AudioCraft uses dora for experiment management
# Install dora
pip install dora-search

# Clone AudioCraft
git clone https://github.com/facebookresearch/audiocraft.git
cd audiocraft

# Create config for fine-tuning
cat > config/solver/musicgen/finetune.yaml << 'EOF'
defaults:
  - musicgen/musicgen_base
  - /model: lm/musicgen_lm
  - /conditioner: cond_base

solver: musicgen
autocast: true
autocast_dtype: float16

optim:
  epochs: 100
  batch_size: 4
  lr: 1e-4
  ema: 0.999
  optimizer: adamw

dataset:
  batch_size: 4
  num_workers: 4
  train:
    - dset: your_dataset
      root: /path/to/dataset
  valid:
    - dset: your_dataset
      root: /path/to/dataset

checkpoint:
  save_every: 10
  keep_every_states: null
EOF

# Run fine-tuning
dora run solver=musicgen/finetune
```

### LoRA fine-tuning

```python
from peft import LoraConfig, get_peft_model
from audiocraft.models import MusicGen
import torch

# Load base model
model = MusicGen.get_pretrained('facebook/musicgen-small')

# Get the language model component
lm = model.lm

# Configure LoRA
lora_config = LoraConfig(
    r=8,
    lora_alpha=16,
    target_modules=["q_proj", "v_proj", "k_proj", "out_proj"],
    lora_dropout=0.05,
    bias="none"
)

# Apply LoRA
lm = get_peft_model(lm, lora_config)
lm.print_trainable_parameters()
```

## Multi-GPU Training

### DataParallel

```python
import torch
import torch.nn as nn
from audiocraft.models import MusicGen

model = MusicGen.get_pretrained('facebook/musicgen-small')

# Wrap LM with DataParallel
if torch.cuda.device_count() > 1:
    model.lm = nn.DataParallel(model.lm)

model.to("cuda")
```

### DistributedDataParallel

```python
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

def setup(rank, world_size):
    dist.init_process_group("nccl", rank=rank, world_size=world_size)
    torch.cuda.set_device(rank)

def train(rank, world_size):
    setup(rank, world_size)

    model = MusicGen.get_pretrained('facebook/musicgen-small')
    model.lm = model.lm.to(rank)
    model.lm = DDP(model.lm, device_ids=[rank])

    # Training loop
    # ...

    dist.destroy_process_group()
```

## Custom Conditioning

### Adding new conditioners

```python
from audiocraft.modules.conditioners import BaseConditioner
import t