This repository provides a PyTorch implementation of NaturalSpeech 2, a zero-shot speech and singing synthesizer. It targets ML/AI engineers and researchers in the TTS field, offering a novel approach to text-to-speech synthesis using a neural audio codec and a latent diffusion model for non-autoregressive generation, enabling natural and expressive speech.

How It Works

The system leverages a latent diffusion model operating on continuous latent vectors from a neural audio codec (Encodec). This approach allows for non-autoregressive generation of speech, contributing to naturalness and efficiency. The implementation focuses on denoising diffusion and incorporates improvements to transformer components, aiming for state-of-the-art performance.

Quick Start & Requirements

• Install: pip install naturalspeech2-pytorch
• Requirements: PyTorch, CUDA (implied by .cuda() calls), naturalspeech2-pytorch library.
• Usage examples and a Trainer class are provided in the README.
• Official Docs: Not explicitly linked, but the README serves as primary documentation.

Highlighted Details

• Zero-shot speech and singing synthesis capabilities.
• Utilizes latent diffusion models with continuous latent vectors.
• Non-autoregressive generation for natural speech.
• Supports conditioning on text and speech prompts.
• Includes a Trainer class for simplified training and sampling loops.

Maintenance & Community

• Developed by lucidrains, with contributions acknowledged from Manmay.
• Mentions Huggingface for sponsorships and the accelerate library.
• The project is marked as "wip" (work in progress).

Licensing & Compatibility

• The README does not explicitly state a license. Given the nature of the project and its dependencies, users should verify licensing for commercial or closed-source use.

Limitations & Caveats

• The project is marked as "wip," indicating ongoing development and potential for breaking changes.
• Some features, like automatic slicing of audio for prompts and specific conditioning methods, are still under development or require further consultation.
• The usage examples imply a need for significant computational resources (GPU) for training and inference.