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-# 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/
-├── bigvgan
-│   ├── args.yml
-│   └── best_netG.pt
-├── CLAP
-│   ├── config.yml
-│   └── CLAP_weights_2022.pth
-└── 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 git@github.com: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.