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Audio Diffusion Pytorch

Audio Diffusion Pytorch is a Python library built on PyTorch that provides customizable diffusion-model components for waveform audio generation. Its documented model types include unconditional and text-conditioned generation, diffusion autoencoding, upsampling and vocoding.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Audio Diffusion 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 source-level PyTorch components to configure and train an audio diffusion workflow, rather than a hosted generator or a package that includes ready-made pretrained models.

Who it is for

  • Python and PyTorch developers building or studying audio-generation models.
  • Researchers prototyping text-conditioned waveform generation, audio upsampling or diffusion vocoding.

Documented uses

  • Train an unconditional waveform diffusion model and generate samples from noise.
  • Condition audio generation on text embeddings using the documented T5-base setup.
  • Develop diffusion workflows for audio upsampling or mel-spectrogram-to-waveform conversion.
  • Customize the library’s generic diffusion components for other data formats.
Before you choose

Requirements & limits

The repository is public and MIT-licensed. The README documents installation through pip; it does not provide pretrained models.

  • Text-conditioned generation requires the Transformers package in addition to the library.
  • The documented workflows train models on supplied audio waveforms; pretrained models are not included.
  • The README labels its example configurations indicative and untested.
  • Core U-Net, diffusion and sampler components are described as customizable beyond audio formats.

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

Set up the library for a text-conditioned audio workflow

Install the library and identify the documented dependencies and model configuration for text-conditioned generation.

Before you start

  • Python environment with PyTorch
  • Transformers package for the text-conditioning example

Steps

  1. Install the package with the command below.

    Documented command
    pip install audio-diffusion-pytorch

  2. Install Transformers with the command below for the documented text-conditioning workflow.

    Documented command
    pip install transformers

  3. Review the README example for a DiffusionModel configured with text conditioning, T5-base embeddings and cross-attention.

Expected outcome

The documented package and additional dependency are installed, and the example identifies the components to configure for text-conditioned audio generation.

Guide limitations

  • No pretrained models are provided.
  • The README says its shown configurations are indicative and untested; this guide does not establish that the example runs successfully.
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
archinetai/audio-diffusion-pytorch · Official homepage title · retained in its source language
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