FastDiff provides a PyTorch implementation of a fast, high-fidelity conditional diffusion model for speech synthesis. It is designed for researchers and developers working on advanced text-to-speech (TTS) systems, offering efficient generation and integration with existing TTS pipelines.

How It Works

FastDiff leverages conditional diffusion probabilistic models to achieve high-quality speech synthesis with improved efficiency. The core approach involves a diffusion process that iteratively refines a noisy signal into coherent speech, conditioned on input text. This method aims to balance generation speed with audio fidelity, a common challenge in diffusion-based models.

Quick Start & Requirements

• Install: Clone the repository.
• Prerequisites: NVIDIA GPU with CUDA and cuDNN, PyTorch, librosa, NATSpeech.
• Pretrained Models: Checkpoints for LJSpeech, LibriTTS, VCTK, and Tacotron are available.
• Demo: An example notebook egs/demo_tacotron.ipynb is provided.
• Docs: Configuration files for supported datasets are in modules/FastDiff/config/.

Highlighted Details

• Implements FastDiff (IJCAI'22) for efficient, high-quality speech synthesis.
• Supports multiple datasets (LJSpeech, LibriTTS, VCTK) with provided checkpoints.
• Offers integration with other TTS models like Tacotron, Portaspeech, and DiffSpeech.
• Includes options for fine-tuning and inference from text, mel-spectrograms, or WAV files.

Maintenance & Community

The project was accepted by IJCAI 2022. Follow-up work, ProDiff, is also available on GitHub. The repository is not officially supported by Tencent.

Licensing & Compatibility

The repository uses code from NATSpeech, Tacotron2, and DiffWave-Vocoder. The specific license is not explicitly stated in the README, but the disclaimer prohibits using the technology to generate speech without consent, implying potential legal restrictions on commercial use or distribution of generated audio.

Limitations & Caveats

The README notes that mel-preprocessing mismatches can lead to noisy output. Fine-tuning is recommended for better quality. The disclaimer highlights legal and ethical considerations regarding the generation of speech without consent.