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Alpha-VLLM/
Lumina-T2Audio

Name: Lumina-T2Audio
Availability: InStock

apache-2.0

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Lumina Text-to-Audio

Lumina Text-to-Audio is a music generation model developed based on FlagDiT. It uses T5-v1.1-XXL as the text encoder and Vocoder as the decoder.

[!Warning] The current version of Lumina Text-to-Audio requires the use of structure caption for audio generation. We will soon release a version that does not require structure caption.

Generation Model: Flag-DiT
Text Encoder: T5-v1.1-XXL
VAE: Make an Audio 2, finetuned from Make an Audio
Decoder: Vocoder
Lumina-T2Audio Checkpoints: huggingface

📰 News

[2024-06-19] 🚀🚀🚀 We release the initial version of Lumina-T2Audio for text-to-audio generation.

Installation

Before installation, ensure that you have a working nvcc

bash

# The command should work and show the same version number as in our case. (12.1 in our case).
nvcc --version

On some outdated distros (e.g., CentOS 7), you may also want to check that a late enough version of gcc is available

bash

# The command should work and show a version of at least 6.0.
# If not, consult distro-specific tutorials to obtain a newer version or build manually.
gcc --version

Downloading Lumina-T2X repo from github:

git clone https://github.com/Alpha-VLLM/Lumina-T2X

1. Create a conda environment and install PyTorch

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

bash

conda create -n Lumina_T2X -y
conda activate Lumina_T2X
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

2. Install dependencies

[!Warning] The environment dependencies for Lumina-T2Audio are different from those for Lumina-T2I. Please install the appropriate environment.

Installing Lumina-T2Audio dependencies:

bash

cd .. # If you are in the `lumina_audio` directory, execute this line.
pip install -e ".[audio]"

or you can use requirements.txt to install the environment.

bash

cd lumina_audio # If you are not in the `lumina_audio` folder, run this line.
pip install -r requirements.txt

3. Install `flash-attn`

pip install flash-attn --no-build-isolation

4. Install nvidia apex (optional)

[!Warning] While Apex can improve efficiency, it is not a must to make Lumina-T2X work.

Note that Lumina-T2X works smoothly with either:

Apex not installed at all; OR

Apex successfully installed with CUDA and C++ extensions.

However, it will fail when:

A Python-only build of Apex is installed.

If the error No module named 'fused_layer_norm_cuda' appears, it typically means you are using a Python-only build of Apex. To resolve this, please run pip uninstall apex, and Lumina-T2X should then function correctly.

You can clone the repo and install following the official guidelines (note that we expect a full build, i.e., with CUDA and C++ extensions)

bash

pip install ninja
git clone https://github.com/NVIDIA/apex
cd apex
# if pip >= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with the same key...
pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./
# otherwise
pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Inference

Preparation

Prepare the pretrained checkpoints.

⭐⭐ (Recommended) you can use huggingface-cli downloading our model:

huggingface-cli download --resume-download Alpha-VLLM/Lumina-T2Audio --local-dir /path/to/ckpt

or using git for cloning the model you want to use:

git clone https://huggingface.co/Alpha-VLLM/Lumina-T2Audio

Web Demo

To host a local gradio demo for interactive inference, run the following command:

updated AutoencoderKL ckpt path

you should update configs/lumina-text2audio.yaml to set AutoencoderKL checkpoint path. Please replace /path/to/ckpt with the path where your checkpoints are located (<real_ckpt_path>).

diff

  ...
        depth: 16
        max_len: 1000

    first_stage_config:
      target: models.autoencoder1d.AutoencoderKL
      params:
        embed_dim: 20
        monitor: val/rec_loss
        - ckpt_path: /path/to/ckpt/maa2/maa2.ckpt
        + ckpt_path: <real_ckpt_path>/maa2/maa2.ckpt
        ddconfig:
          double_z: true
          in_channels: 80
          out_ch: 80
  ...
    cond_stage_config:
      target: models.encoders.modules.FrozenCLAPFLANEmbedder
      params:
        - weights_path: /path/to/ckpt/CLAP/CLAP_weights_2022.pth
        + weights_path: <real_ckpt_path>/CLAP/CLAP_weights_2022.pth

setting Lumina-T2Audio and Vocoder checkpoint path and run demo

Please replace /path/to/ckpt with the actual downloaded path.

bash

# `/path/to/ckpt` should be a directory containing `audio_generation`, `maa2`, and `bigvnat`.

# default
python -u demo_audio.py \
    --ckpt "/path/to/ckpt/audio_generation" \
    --vocoder_ckpt "/path/to/ckpt/bigvnat" \
    --config_path "configs/lumina-text2audio.yaml" \
    --sample_rate 16000

or you can run run_audio.sh script for web demo after updating AutoencoderKL ckpt path on configs/lumina-text2audio.yaml, and updating --ckpt, and --vocoder_ckpt on run_audio.sh.

setting openai api key for generating structure caption.

Please replace the line in n2s_openai.py:

diff

- openai_key = 'your openai api key here'
+ openai_key = '<your real openai api key>'

If you have other relay station APIs, please modify the base_url accordingly. The default setting uses OpenAI's base_url.

diff

- base_url = ""
+ base_url = "<your base url>"

running the demo

bash run_audio.sh

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.

Alpha-VLLM/Lumina-T2Audio

Lumina Text-to-Audio

📰 News

Installation

1. Create a conda environment and install PyTorch

2. Install dependencies

3. Install flash-attn

4. Install nvidia apex (optional)

Inference

Preparation

Web Demo

Disclaimer

Alpha-VLLM/
Lumina-T2Audio

3. Install `flash-attn`