AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
Paper • 2301.12503 • Published • 1
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("haoheliu/AudioLDM-S-Full", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Model Card for Model ID
Generate any audio from text using your imagination
Model Details
Model Description
- Developed by: Haohe Liu
- License: CC-BY-NC-SA 4.0
Model Sources
- Repository: https://github.com/haoheliu/AudioLDM
- Paper: https://arxiv.org/abs/2301.12503
- Demo: https://audioldm.github.io/
Direct Use
https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation
Bias, Risks, and Limitations
TODO
Training Details
Training Data
TODO
Evaluation
TODO
Testing Data, Factors & Metrics
Testing Data
TODO
Metrics
TODO
Results
TODO
BibTeX:
@article{liu2023audioldm,
title={AudioLDM: Text-to-Audio Generation with Latent Diffusion Models},
author={Liu, Haohe and Chen, Zehua and Yuan, Yi and Mei, Xinhao and Liu, Xubo and Mandic, Danilo and Wang, Wenwu and Plumbley, Mark D},
journal={arXiv preprint arXiv:2301.12503},
year={2023}
}
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