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Ezaudio

EzAudio is an open-source, diffusion-based text-to-audio model with Python code for audio generation, editing, inpainting, reference-conditioned generation and model training.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Ezaudio — 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 EzAudio when a task calls for prompt-based sound generation, changing a section of an existing recording, inpainting audio, or conditioning output on a reference recording. The linked Spaces offer demos; the repository supports local Python use and model training.

Who it is for

  • Audio-AI developers exploring text-prompted sound generation and editing.
  • Researchers or practitioners who want to work with or train a text-to-audio diffusion model.

Documented uses

  • Generate an audio clip from a descriptive text prompt.
  • Edit an existing audio file by specifying a prompt and boundaries.
  • Fill masked portions of audio with generated content.
  • Generate audio using both a text prompt and a reference audio file with the ControlNet variant.
  • Prepare dataset metadata and run the documented diffusion-model training pipeline.
Before you choose

Requirements & limits

The public GitHub repository lists an MIT license. The README links to Hugging Face Spaces for the standard and ControlNet demos, and to Hugging Face for model checkpoints.

  • The repository includes Python inference examples and uses PyTorch in its device-selection example; it shows CPU or CUDA selection.
  • The README provides an example for editing audio with a prompt and boundary and mask parameters.
  • Training requires prepared dataset metadata; the README points to an example CSV and an Accelerate launch command.
  • Autoencoder training is referred to the VAE training section of the separate SoloAudio project.

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

Install EzAudio for local inference

Set up the repository and dependencies before using its documented Python inference examples.

Before you start

  • Git and Python environment capable of installing the repository's requirements.
  • Repository clone and the dependencies listed in requirements.txt.
  • Model checkpoints are optional to download from the linked Hugging Face repository.

Steps

  1. Clone the EzAudio repository.

  2. Enter the EzAudio directory.

  3. Install dependencies with pip install -r requirements.txt.

  4. Optionally download checkpoints from the linked Hugging Face model repository.

Expected outcome

The documented repository setup and dependency installation are complete; the README then provides Python examples for generation and editing.

Guide limitations

  • This guide covers installation only; it does not claim the environment or inference was tested.
  • The README calls checkpoint download optional and does not specify hardware requirements beyond showing CPU/CUDA device selection.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
17 October 2026
Original website title
haidog-yaqub/EzAudio · Official homepage title · retained in its source language
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