AudioLDM 2

This repo currently support Text-to-Audio (including Music) and Text-to-Speech Generation.

Change Log

• 2023-08-27: Add two new checkpoints!
  • 🌟 48kHz AudioLDM model: Now we support high-fidelity audio generation! Use this checkpoint simply by setting "--model_name audioldm_48k"
  • 16kHz improved AudioLDM model: Trained with more data and optimized model architecture.

TODO

• Add the text-to-speech checkpoint
• Open-source the AudioLDM training code.
• Support the generation of longer audio (> 10s)
• Optimizing the inference speed of the model.
• Integration with the Diffusers library

Web APP

1. Prepare running environment

conda create -n audioldm python=3.8; conda activate audioldm
pip3 install git+https://github.com/haoheliu/AudioLDM2.git
git clone https://github.com/haoheliu/AudioLDM2; cd AudioLDM2

2. Start the web application (powered by Gradio)

python3 app.py

3. A link will be printed out. Click the link to open the browser and play.

Commandline Usage

Installation

Prepare running environment

# Optional
conda create -n audioldm python=3.8; conda activate audioldm
# Install AudioLDM
pip3 install git+https://github.com/haoheliu/AudioLDM2.git

If you plan to play around with text-to-speech generation. Please also make sure you have installed espeak. On linux you can do it by

sudo apt-get install espeak

Run the model in commandline

• Generate sound effect or Music based on a text prompt

audioldm2 -t "Musical constellations twinkling in the night sky, forming a cosmic melody."

• Generate sound effect or music based on a list of text

audioldm2 -tl batch.lst

• Generate speech based on (1) the transcription and (2) the description of the speaker

audioldm2 -t "A female reporter is speaking full of emotion" --transciption "Wish you have a good day"

audioldm2 -t "A female reporter is speaking" --transciption "Wish you have a good day"

Text-to-Speech use the audioldm2-speech-gigaspeech checkpoint by default. If you like to run TTS with LJSpeech pretrained checkpoint, simply set --model_name audioldm2-speech-ljspeech.

Random Seed Matters

Sometimes model may not perform well (sounds wired or low quality) when changing into a different hardware. In this case, please adjust the random seed and find the optimal one for your hardware.

audioldm2 --seed 1234 -t "Musical constellations twinkling in the night sky, forming a cosmic melody."

Pretrained Models

You can choose model checkpoint by setting up "model_name":

# CUDA
audioldm2 --model_name "audioldm_48k" --device cuda -t "Musical constellations twinkling in the night sky, forming a cosmic melody."

# MPS
audioldm2 --model_name "audioldm_48k" --device mps -t "Musical constellations twinkling in the night sky, forming a cosmic melody."

We have five checkpoints you can choose:

1. audioldm_48k (default): This checkpoint can generate high fidelity sound effect and music.
2. audioldm2-full: Generate both sound effect and music generation with the AudioLDM2 architecture.
3. audioldm_16k_crossattn_t5: The improved version of AudioLDM 1.0.
4. audioldm2-full-large-1150k: Larger version of audioldm2-full.
5. audioldm2-music-665k: Music generation.
6. audioldm2-speech-gigaspeech (default for TTS): Text-to-Speech, trained on GigaSpeech Dataset.
7. audioldm2-speech-ljspeech: Text-to-Speech, trained on LJSpeech Dataset.

We currently support 3 devices:

• cpu
• cuda
• mps ( Notice that the computation requires about 20GB of RAM. )

Other options

  usage: audioldm2 [-h] [-t TEXT] [-tl TEXT_LIST] [-s SAVE_PATH]
                 [--model_name {audioldm2-full,audioldm2-music-665k,audioldm2-full-large-1150k,audioldm2-speech-ljspeech,audioldm2-speech-gigaspeech}] [-d DEVICE]
                 [-b BATCHSIZE] [--ddim_steps DDIM_STEPS] [-gs GUIDANCE_SCALE] [-n N_CANDIDATE_GEN_PER_TEXT]
                 [--seed SEED]

  optional arguments:
    -h, --help            show this help message and exit
    -t TEXT, --text TEXT  Text prompt to the model for audio generation
    --transcription TRANSCRIPTION
                        Transcription used for speech synthesis
    -tl TEXT_LIST, --text_list TEXT_LIST
                          A file that contains text prompt to the model for audio generation
    -s SAVE_PATH, --save_path SAVE_PATH
                          The path to save model output
    --model_name {audioldm2-full,audioldm2-music-665k,audioldm2-full-large-1150k,audioldm2-speech-ljspeech,audioldm2-speech-gigaspeech}
                          The checkpoint you gonna use
    -d DEVICE, --device DEVICE
                          The device for computation. If not specified, the script will automatically choose the device based on your environment. [cpu, cuda, mps, auto]
    -b BATCHSIZE, --batchsize BATCHSIZE
                          Generate how many samples at the same time
    --ddim_steps DDIM_STEPS
                          The sampling step for DDIM
    -gs GUIDANCE_SCALE, --guidance_scale GUIDANCE_SCALE
                          Guidance scale (Large => better quality and relavancy to text; Small => better diversity)
    -n N_CANDIDATE_GEN_PER_TEXT, --n_candidate_gen_per_text N_CANDIDATE_GEN_PER_TEXT
                          Automatic quality control. This number control the number of candidates (e.g., generate three audios and choose the best to show you). A Larger value usually lead to better quality with
                          heavier computation
    --seed SEED           Change this value (any integer number) will lead to a different generation result.

Cite this work

If you found this tool useful, please consider citing

@article{liu2023audioldm2,
  title={{AudioLDM 2}: Learning Holistic Audio Generation with Self-supervised Pretraining},
  author={Haohe Liu and Qiao Tian and Yi Yuan and Xubo Liu and Xinhao Mei and Qiuqiang Kong and Yuping Wang and Wenwu Wang and Yuxuan Wang and Mark D. Plumbley},
  journal={arXiv preprint arXiv:2308.05734},
  year={2023}
}

@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={Proceedings of the International Conference on Machine Learning},
  year={2023}
}