🟣 EzAudio is a diffusion-based text-to-audio generation model. Designed for real-world audio applications, EzAudio brings together high-quality audio synthesis with lower computational demands.
🎛 Play with EzAudio for text-to-audio generation, editing, and inpainting: EzAudio Space
🎮 EzAudio-ControlNet Demo is available: EzAudio-ControlNet Space
- 2025.05 EzAudio has been accepted for an oral presentation at InternSpeech 2025.
Clone the repository:
git clone [email protected]:haidog-yaqub/EzAudio.git
Install the dependencies:
cd EzAudio
pip install -r requirements.txt
Download checkponts (Optional): https://huggingface.co/OpenSound/EzAudio
You can use the model with the following code:
from api.ezaudio import EzAudio
import torch
import soundfile as sf
# load model
device = 'cuda' if torch.cuda.is_available() else 'cpu'
ezaudio = EzAudio(model_name='s3_xl', device=device)
# text to audio genertation
prompt = "a dog barking in the distance"
sr, audio = ezaudio.generate_audio(prompt)
sf.write(f'{prompt}.wav', audio, sr)
# audio inpainting
prompt = "A train passes by, blowing its horns"
original_audio = 'egs/edit_example.wav'
sr, audio = ezaudio.editing_audio(prompt, boundary=2, gt_file=original_audio,
mask_start=1, mask_length=5)
sf.write(f'{prompt}_edit.wav', audio, sr)ControlNet Usage:
from api.ezaudio import EzAudio
import torch
import soundfile as sf
# load model
device = 'cuda' if torch.cuda.is_available() else 'cpu'
controlnet = EzAudio_ControlNet(model_name='energy', device=device)
prompt = 'dog barking'
# path for audio reference
audio_path = 'egs/reference.mp3'
sr, audio = controlnet.generate_audio(prompt, audio_path=audio_path)
sf.write(f"{prompt}_control.wav", audio, samplerate=sr)Refer to the VAE training section in our work SoloAudio
Prepare your data (see example in src/dataset/meta_example.csv), then run:
cd src
accelerate launch train.py- Release Gradio Demo along with checkpoints EzAudio Space
- Release ControlNet Demo along with checkpoints EzAudio ControlNet Space
- Release inference code
- Release training pipeline and dataset
- Improve API and support automatic ckpts downloading
If you find the code useful for your research, please consider citing:
@article{hai2024ezaudio,
title={EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion Transformer},
author={Hai, Jiarui and Xu, Yong and Zhang, Hao and Li, Chenxing and Wang, Helin and Elhilali, Mounya and Yu, Dong},
journal={arXiv preprint arXiv:2409.10819},
year={2024}
}Some codes are borrowed from or inspired by: U-Vit, Pixel-Art, Huyuan-DiT, and Stable Audio.