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NATURALSPEECH2 Pytorch

NATURALSPEECH2 Pytorch is an in-progress Python implementation of NaturalSpeech 2 for zero-shot text-to-speech and singing synthesis, using a neural audio codec and latent diffusion.

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Independent OpenViST project profile, based on linked sources.

NATURALSPEECH2 Pytorch — OpenViST project profile in Audio & Voice AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want to study or adapt a PyTorch research implementation for speech or singing synthesis, rather than access a hosted generation service.

Who it is for

  • ML engineers exploring speech-generation models in PyTorch.
  • Speech researchers comparing diffusion-based synthesis approaches.

Documented uses

  • Train a diffusion model on audio using the documented model and codec components.
  • Sample generated audio from an unconditioned model.
  • Explore conditioned sampling using text, text lengths, and an audio prompt.
  • Use the Trainer class to organize training and sampling over a speech folder.
Before you choose

Requirements & limits

The public repository lists an MIT license and documents installation with pip.

  • The repository describes itself as a work in progress.
  • The implementation uses denoising diffusion rather than score-based SDE.
  • The README includes unconditioned and text-and-prompt-conditioned code examples.
  • Automatic prompt-audio slicing appears in the TODO list.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Train and sample with the Trainer example

Use the documented Trainer workflow to train a diffusion model and then sample from it.

Before you start

  • Install naturalspeech2-pytorch.
  • Create a NaturalSpeech2 diffusion model with a model and codec as shown in the README.
  • Provide a speech-data folder at the path used for folder.

Steps

  1. Import Trainer from naturalspeech2_pytorch.

  2. Create Trainer with diffusion_model=diffusion, folder='/path/to/speech', train_batch_size=16, and gradient_accumulate_every=2.

  3. Call trainer.train() to run the training and sampling loop managed by the Trainer.

Expected outcome

The README presents `Trainer` as handling the training and sampling loop for the supplied diffusion model and speech folder.

Guide limitations

  • The repository labels the implementation as a work in progress.
  • The example does not specify speech-data preparation or describe the Trainer's sampling output or how to retrieve it.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
15 October 2026
Original website title
lucidrains/naturalspeech2-pytorch · Official homepage title · retained in its source language
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