🎙️ VITS: State-of-the-Art Text-to-Speech Implementation

A PyTorch implementation of VITS: Conditional Variational Autoencoder with Adversarial Learning

Features • Installation • Quick Start • Training

🌟 Overview

This project implements VITS (Conditional Variational Autoencoder with Adversarial Learning), a state-of-the-art end-to-end Text-to-Speech model that directly generates waveforms from text. Key features include:

• End-to-end text-to-speech synthesis
• Parallel sampling for ultra-fast inference
• High-quality audio generation
• Multi-speaker support
• Emotion and style control

📊 Requirements

• Python 3.8+
• CUDA-compatible GPU (8GB+ VRAM)
• 16GB+ RAM
• 50GB+ disk space

🚀 Installation

1. Create and activate virtual environment:

   python -m venv venv
   
   # Linux/Mac
   source venv/bin/activate
   
   # Windows
   .\venv\Scripts\activate

2. Install PyTorch:

   # Windows/Linux with CUDA 11.8
   pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118
   
   # CPU only
   pip install torch torchaudio

3. Install dependencies:

   pip install -r requirements.txt

4. Verify installation:

   python -c "import torch; print(f'PyTorch: {torch.__version__}, CUDA: {torch.cuda.is_available()}')"

📊 Dataset Preparation

Linux/macOS

mkdir -p data/raw/LJSpeech-1.1
wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2 -P data/raw
tar -xvf data/raw/LJSpeech-1.1.tar.bz2 -C data/raw
rm data/raw/LJSpeech-1.1.tar.bz2

Windows (PowerShell)

New-Item -ItemType Directory -Force -Path "data\raw\LJSpeech-1.1"
Invoke-WebRequest -Uri "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2" -OutFile "data\raw\LJSpeech-1.1.tar.bz2"
& 'C:\Program Files\7-Zip\7z.exe' x "data\raw\LJSpeech-1.1.tar.bz2" -o"data\raw"
& 'C:\Program Files\7-Zip\7z.exe' x "data\raw\LJSpeech-1.1.tar" -o"data\raw"
Remove-Item "data\raw\LJSpeech-1.1.tar*"

Python (Cross-platform)

import os, requests, tarfile
from pathlib import Path

data_dir = Path("data/raw/LJSpeech-1.1")
data_dir.mkdir(parents=True, exist_ok=True)

url = "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2"
archive_path = data_dir.parent / "LJSpeech-1.1.tar.bz2"

print("Downloading LJSpeech dataset...")
response = requests.get(url, stream=True)
with open(archive_path, 'wb') as f:
    for chunk in response.iter_content(chunk_size=8192):
        f.write(chunk)

print("Extracting dataset...")
with tarfile.open(archive_path, 'r:bz2') as tar:
    tar.extractall(path=data_dir.parent)
archive_path.unlink()

🎯 Training

1. Prepare dataset:

   python scripts/prepare_dataset.py --config configs/vits_config.yaml

2. Start training:

   # Single GPU
   python scripts/train.py --config configs/vits_config.yaml
   
   # Multi-GPU (e.g., 4 GPUs)
   python scripts/train.py --config configs/vits_config.yaml --world_size 4

3. Monitor training:

   # TensorBoard
   tensorboard --logdir data/logs
   
   # Weights & Biases monitoring is automatic if enabled in config

🎵 Inference

from src.inference import VITS

# Initialize model
vits = VITS(checkpoint="path/to/checkpoint")

# Basic synthesis
audio = vits.synthesize(
    text="Hello, world!",
    speaker_id=0,
    speed_factor=1.0
)

# Save audio
vits.save_audio(audio, "output.wav")

# Batch processing
texts = [
    "First sentence.",
    "Second sentence.",
    "Third sentence."
]
audios = vits.synthesize_batch(texts, speaker_id=0)

🧠 Model Architecture

Text → [Text Encoder] → Hidden States
                              ↓
                    [Posterior Encoder]
                              ↓
                    [Flow Decoder] → Audio
                              ↓
              [Multi-Period Discriminator]
              [Multi-Scale Discriminator]

Key components:

1. Text Encoder: Transformer-based with multi-head attention
2. Flow Decoder: Normalizing flows with residual coupling
3. Posterior Encoder: WaveNet-style architecture
4. Discriminators: Multi-period and multi-scale for quality
5. Voice Conversion: Optional cross-speaker style transfer

🔧 Troubleshooting

Common Issues

1. Out of Memory (OOM):

   # Reduce batch size in config
   # Enable gradient accumulation
   # Use mixed precision (fp16)

2. Poor Audio Quality:

   • Check preprocessing parameters
   • Verify loss convergence
   • Ensure proper normalization

3. Slow Training:

   • Enable mixed precision
   • Use DDP for multi-GPU
   • Optimize dataloader workers

📚 Citation

@inproceedings{kim2021vits,
  title={Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech},
  author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee},
  booktitle={International Conference on Machine Learning},
  year={2021}
}

📜 License

MIT License - see LICENSE file

🙏 Acknowledgments

• Official VITS Implementation
• LJSpeech Dataset
• PyTorch

Made with ❤️ by the TTS Team

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