Spaces:
Sleeping
Sleeping
# AudioLCM: Text-to-Audio Generation with Latent Consistency Models | |
#### Huadai Liu, Rongjie Huang, Yang Liu, Hengyuan Cao, Jialei Wang, Xize Cheng, Siqi Zheng, Zhou Zhao | |
PyTorch Implementation of [AudioLCM]: a efficient and high-quality text-to-audio generation with latent consistency model. | |
<!-- [![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2301.12661) | |
[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-blue)](https://huggingface.co/spaces/AIGC-Audio/Make_An_Audio) | |
[![GitHub Stars](https://img.shields.io/github/stars/Text-to-Audio/Make-An-Audio?style=social)](https://github.com/Text-to-Audio/Make-An-Audio) --> | |
We provide our implementation and pretrained models as open source in this repository. | |
Visit our [demo page](https://audiolcm.github.io/) for audio samples. | |
<!-- [Text-to-Audio HuggingFace Space](https://huggingface.co/spaces/AIGC-Audio/Make_An_Audio) | [Audio Inpainting HuggingFace Space](https://huggingface.co/spaces/AIGC-Audio/Make_An_Audio_inpaint) --> | |
## News | |
<!-- - Jan, 2023: **[Make-An-Audio](https://arxiv.org/abs/2207.06389)** submitted to arxiv. --> | |
- June, 2024: **[AudioLCM]** released in Github. | |
## Quick Started | |
We provide an example of how you can generate high-fidelity samples quickly using AudioLCM. | |
To try on your own dataset, simply clone this repo in your local machine provided with NVIDIA GPU + CUDA cuDNN and follow the below instructions. | |
### Support Datasets and Pretrained Models | |
Simply run following command to download the weights from [Google drive](https://drive.google.com/drive/folders/1zZTI3-nHrUIywKFqwxlFO6PjB66JA8jI?usp=drive_link). | |
Download bert-base-uncased weights from [Hugging Face](https://huggingface.co/google-bert/bert-base-uncased). Down load t5-v1_1-large weights from [Hugging Face](https://huggingface.co/google/t5-v1_1-large). Download CLAP weights from [Hugging Face](https://huggingface.co/microsoft/msclap/blob/main/CLAP_weights_2022.pth). | |
``` | |
Download: | |
audiolcm.ckpt and put it into ./ckpts | |
BigVGAN vocoder and put it into ./vocoder/logs/bigvnat16k93.5w | |
t5-v1_1-large and put it into ./ldm/modules/encoders/CLAP | |
bert-base-uncased and put it into ./ldm/modules/encoders/CLAP | |
CLAP_weights_2022.pth and put it into ./wav_evaluation/useful_ckpts/CLAP | |
``` | |
<!-- The directory structure should be: | |
``` | |
useful_ckpts/ | |
βββ bigvgan | |
β βββ args.yml | |
β βββ best_netG.pt | |
βββ CLAP | |
β βββ config.yml | |
β βββ CLAP_weights_2022.pth | |
βββ maa1_full.ckpt | |
``` --> | |
### Dependencies | |
See requirements in `requirement.txt`: | |
## Inference with pretrained model | |
```bash | |
python scripts/txt2audio_for_lcm.py --ddim_steps 2 -b configs/audiolcm.yaml --sample_rate 16000 --vocoder-ckpt vocoder/logs/bigvnat16k93.5w --outdir results --test-dataset audiocaps -r ckpt/audiolcm.ckpt | |
``` | |
# Train | |
## dataset preparation | |
We can't provide the dataset download link for copyright issues. We provide the process code to generate melspec. | |
Before training, we need to construct the dataset information into a tsv file, which includes name (id for each audio), dataset (which dataset the audio belongs to), audio_path (the path of .wav file),caption (the caption of the audio) ,mel_path (the processed melspec file path of each audio). We provide a tsv file of audiocaps test set: ./audiocaps_test_16000_struct.tsv as a sample. | |
### generate the melspec file of audio | |
Assume you have already got a tsv file to link each caption to its audio_path, which mean the tsv_file have "name","audio_path","dataset" and "caption" columns in it. | |
To get the melspec of audio, run the following command, which will save mels in ./processed | |
```bash | |
python ldm/data/preprocess/mel_spec.py --tsv_path tmp.tsv | |
``` | |
Add the duration into the tsv file | |
```bash | |
python ldm/data/preprocess/add_duration.py | |
``` | |
## Train variational autoencoder | |
Assume we have processed several datasets, and save the .tsv files in data/*.tsv . Replace **data.params.spec_dir_path** with the **data**(the directory that contain tsvs) in the config file. Then we can train VAE with the following command. If you don't have 8 gpus in your machine, you can replace --gpus 0,1,...,gpu_nums | |
```bash | |
python main.py --base configs/train/vae.yaml -t --gpus 0,1,2,3,4,5,6,7 | |
``` | |
The training result will be save in ./logs/ | |
## train latent diffsuion | |
After Trainning VAE, replace model.params.first_stage_config.params.ckpt_path with your trained VAE checkpoint path in the config file. | |
Run the following command to train Diffusion model | |
```bash | |
python main.py --base configs/autoencoder1d.yaml -t --gpus 0,1,2,3,4,5,6,7 | |
``` | |
The training result will be save in ./logs/ | |
# Evaluation | |
## generate audiocaps samples | |
```bash | |
python scripts/txt2audio_for_lcm.py --ddim_steps 2 -b configs/audiolcm.yaml --sample_rate 16000 --vocoder-ckpt vocoder/logs/bigvnat16k93.5w --outdir results --test-dataset audiocaps -r ckpt/audiolcm.ckpt | |
``` | |
## calculate FD,FAD,IS,KL | |
install [audioldm_eval](https://github.com/haoheliu/audioldm_eval) by | |
```bash | |
git clone [email protected]:haoheliu/audioldm_eval.git | |
``` | |
Then test with: | |
```bash | |
python scripts/test.py --pred_wavsdir {the directory that saves the audios you generated} --gt_wavsdir {the directory that saves audiocaps test set waves} | |
``` | |
## calculate Clap_score | |
```bash | |
python wav_evaluation/cal_clap_score.py --tsv_path {the directory that saves the audios you generated}/result.tsv | |
``` | |
## Acknowledgements | |
This implementation uses parts of the code from the following Github repos: | |
[Make-An-Audio](https://github.com/Text-to-Audio/Make-An-Audio) | |
[CLAP](https://github.com/LAION-AI/CLAP), | |
[Stable Diffusion](https://github.com/CompVis/stable-diffusion), | |
as described in our code. | |
<!-- ## Citations ## | |
If you find this code useful in your research, please consider citing: | |
```bibtex | |
@article{huang2023make, | |
title={Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models}, | |
author={Huang, Rongjie and Huang, Jiawei and Yang, Dongchao and Ren, Yi and Liu, Luping and Li, Mingze and Ye, Zhenhui and Liu, Jinglin and Yin, Xiang and Zhao, Zhou}, | |
journal={arXiv preprint arXiv:2301.12661}, | |
year={2023} | |
} | |
``` --> | |
# Disclaimer ## | |
Any organization or individual is prohibited from using any technology mentioned in this paper to generate someone's speech without his/her consent, including but not limited to government leaders, political figures, and celebrities. If you do not comply with this item, you could be in violation of copyright laws. |