Make-An-Audio-3

Make-An-Audio 3: Transforming Text into Audio via Flow-based Large Diffusion Transformers

PyTorch Implementation of Lumina-t2x

We will provide our implementation and pretrained models as open source in this repository recently.

Use pretrained model

We provide our implementation and pretrained models as open source in this repository.

Visit our demo page for audio samples.

News

• June, 2024: Make-An-Audio-3 (Lumina-Next) released in Github.

Install dependencies

Note: You may want to adjust the CUDA version according to your driver version.

conda create -n Make_An_Audio_3 -y
conda activate Make_An_Audio_3
conda install python=3.11 pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=12.1 -c pytorch -c nvidia -y
pip install -r requirements.txt
pip install flash-attn --no-build-isolation
Install [nvidia apex](https://github.com/nvidia/apex) (optional)

Quick Started

Pretrained Models

Simply download the 500M weights from

Generate audio/music from text

python3 scripts/txt2audio_for_2cap_flow.py 
--outdir output_dir -r  checkpoints_last.ckpt  -b configs/txt2audio-cfm1-cfg-LargeDiT3.yaml --scale 3.0 
--vocoder-ckpt useful_ckpts/bigvnat --test-dataset audiocaps 

Generate audio/music from audiocaps or musiccaps test dataset

• remember to relatively change config["test_dataset]

python3 scripts/txt2audio_for_2cap_flow.py 
--outdir output_dir -r  checkpoints_last.ckpt  -b configs/txt2audio-cfm1-cfg-LargeDiT3.yaml --scale 3.0 
--vocoder-ckpt useful_ckpts/bigvnat --test-dataset testset

Generate audio/music from video

python3 scripts/video2audio_flow.py 
--outdir output_dir -r  checkpoints_last.ckpt  -b configs/video2audio-cfm1-cfg-LargeDiT1-moe.yaml --scale 3.0 
--vocoder-ckpt useful_ckpts/bigvnat --test-dataset vggsound 

Train

Data preparation

• We can't provide the dataset download link for copyright issues. We provide the process code to generate melspec, count audio duration and generate structured caption.
• 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), duration (the duration of the audio). We provide a tsv file of audiocaps test set: audiocaps_test_struct.tsv as a sample.
• We provide a tsv file of the 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

python preprocess/mel_spec.py --tsv_path tmp.tsv --num_gpus 1 --max_duration 10

Count audio duration

To count the duration of the audio and save duration information in tsv file, run the following command:

python preprocess/add_duration.py --tsv_path tmp.tsv

Generated structure caption from the original natural language caption

Firstly you need to get an authorization token in openai(https://openai.com/blog/openai-api), here is a tutorial(https://www.maisieai.com/help/how-to-get-an-openai-api-key-for-chatgpt). Then replace your key of variable openai_key in preprocess/n2s_by_openai.py. Run the following command to add structed caption, the tsv file with structured caption will be saved into {tsv_file_name}_struct.tsv:

python preprocess/n2s_by_openai.py --tsv_path tmp.tsv

Place Tsv files

After generated structure caption, put the tsv with structed caption to ./data/main_spec_dir . And put tsv files without structured caption to ./data/no_struct_dir

Modify the config data.params.main_spec_dir and data.params.main_spec_dir.other_spec_dir_path respectively in config file configs/text2audio-ConcatDiT-ae1dnat_Skl20d2_struct2MLPanylen.yaml .

Train variational autoencoder

Assume we have processed several datasets, and save the .tsv files in tsv_dir/*.tsv . Replace data.params.spec_dir_path with tsv_dir 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

python main.py --base configs/research/autoencoder/autoencoder1d_kl20_natbig_r1_down2_disc2.yaml -t --gpus 0,1,2,3,4,5,6,7

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

python main.py --base configs/research/text2audio/text2audio-ConcatDiT-ae1dnat_Skl20d2_freezeFlananylen_drop.yaml -t  --gpus 0,1,2,3,4,5,6,7

Evaluation

Please refer to Make-An-Audio.

Acknowledgements

This implementation uses parts of the code from the following Github repos: Make-An-Audio, AudioLCM, CLAP, as described in our code.

Citations

If you find this code useful in your research, please consider citing:

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.