GenerSpeech is a PyTorch implementation of a text-to-speech (TTS) model designed for zero-shot style transfer of out-of-domain custom voices. It targets researchers and developers working on expressive and customizable speech synthesis, enabling high-fidelity audio generation with novel voice characteristics.

How It Works

GenerSpeech employs a multi-level style transfer approach, enhancing its generalization capabilities to unseen voice styles. It leverages an acoustic model, a neural vocoder (HIFI-GAN), and an emotion encoder to achieve expressive and high-fidelity speech synthesis. This architecture allows for zero-shot style transfer by conditioning the speech generation on a reference audio sample.

Quick Start & Requirements

• Install: Create and activate a conda environment using conda env create -f environment.yaml and conda activate generspeech.
• Prerequisites: NVIDIA GPU with CUDA and cuDNN.
• Pretrained Models: Available for acoustic model, HIFI-GAN vocoder, and emotion encoder.
• Demo: Available at https://github.com/Rongjiehuang/GenerSpeech (link to demo page not directly provided in README, but implied).

Highlighted Details

• Zero-shot style transfer for out-of-domain custom voices.
• Multi-level style transfer for expressive TTS.
• Enhanced model generalization to OOD style references.
• PyTorch implementation of NeurIPS'22 paper.

Maintenance & Community

• Project released in December 2022.
• Codebase incorporates elements from FastDiff and NATSpeech.
• Citation details provided for academic use.

Licensing & Compatibility

• License type not explicitly stated in the README.
• Disclaimer prohibits generating speech without consent, potentially impacting commercial use or integration with closed-source applications if licensing is restrictive.

Limitations & Caveats

The README includes a disclaimer against unauthorized speech generation, which may impose ethical or legal constraints on usage. Specific licensing details for commercial use are not provided.