MIDI-LLM/README.md

MIDI-LLM

Built on Llama 3.2 (1B) with an extended vocabulary for MIDI tokens.

Research Paper

• Shih-Lun Wu, Yoon Kim, and Cheng-Zhi Anna Huang.
  "MIDI-LLM: Adapting Large Language Models for Text-to-MIDI Music Generation."
  NeurIPS AI4Music Workshop, 2025.
  [Live Demo] [Model] [Paper (coming soon)]

Setup

• A GPU with 16GB+ VRAM and CUDA 12.x is recommended

• Install Miniconda / Anaconda

• Create and activate Python 3.11 conda environment

conda create -n midi-llm python=3.11
conda activate midi-llm

• Install packages + download soundfont for MIDI-to-audio synthesis

# Conda pkgs for audio processing & synthesis
conda install conda-forge::ffmpeg
conda install conda-forge::fluidsynth

# Soundfont (credit -- '@Frank Wen' https://member.keymusician.com/Member/FluidR3_GM/README.html)
wget https://keymusician01.s3.amazonaws.com/FluidR3_GM.zip
mkdir -p soundfonts
unzip FluidR3_GM.zip -d ./soundfonts/FluidR3_GM
rm FluidR3_GM.zip

• Install PyTorch with pip

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu126

(Note: this is an example for CUDA 12.6, check PyTorch website if you're on other CUDA versions)

• Check if PyTorch works correctly on CUDA GPU

python -c "import torch; x = torch.randn(30, 30).cuda(); y = x.clone(); z = torch.mm(x, y); print(f'GPU works correctly, output shape: {z.shape}')"

• Install other dependencies

pip install -r requirements.txt

• Verify all installation

python -c "import torch; from vllm import LLM; from anticipation.convert import events_to_midi; print('Setup successful')"

Inference (Generation) Usage

IMPORTANT: We provide two inference backends with different trade-offs:

• vLLM (generate_vllm.py): Faster token generation but more complex setup and longer initialization. Recommended for batch inference (multiple prompts) or interactive sessions.
• Transformers (generate_transformers.py): Simpler setup and faster initialization, but slower generation. Recommended for quick single-prompt testing.

Both scripts share the same arguments (except for --fp8 quantization, which only works in vLLM) and output format.

Example 1: Single prompt (use transformers)

python generate_transformers.py \
    --prompt "A cheerful rock song with bright electric guitars"

Outputs 4 MIDIs (and synthesized MP3s) conditioned on the same prompt by default.

Example 2: Batch generation from file (use vLLM)

python generate_vllm.py \
    --prompts_file some_example_prompts.txt \
    --fp8 \
    --no-synthesize

• some_example_prompts.txt should contain one prompt per line.
• --fp8 performs FP8 quantization for faster inference.
• --no-synthesize skips audio synthesis (outputs MIDI only).

Example 3: Interactive mode (use vLLM)

python generate_vllm.py \
    --interactive \
    --output_root generations_interactive/ \
    --n_outputs 1

Loads the model once, then lets you enter prompts continuously. Press Enter with an empty prompt to exit.

• Outputs will be stored under generations_interactive/
• --n_outputs 1 generates only 1 output for each prompt

More options

See full options for either script with:

python generate_transformers.py --help # or
python generate_vllm.py --help

Inference output structure

[output_root]/
└── 2025-10-30_143022/           # Session timestamp
    ├── 20251030_143022_prompt_1/
    │   ├── prompt.txt
    │   ├── gen_1.mid
    │   ├── gen_1.mp3
    │   └── ...
    └── generation_stats.json

Example Prompts

Here are some example prompts to get you started. The model can work with both detailed descriptions similar to what's seen at training, and creative free-form prompts.

In-Domain Examples (from validation set)

Example 1: Rock with pop influence

A melodic and energetic rock song with a touch of pop influence, featuring synth 
strings, piano, distortion guitar, synth voice, and drums, all contributing to a 
blend of happy and dark moods. Set in the key of A minor with a 4/4 time signature, 
this fast-paced track showcases a chord progression of Bm, Cmaj7, and Gmaj7.

Example 2: Classical soundtrack

A slow and relaxing classical piece featuring a church organ and French horn, likely 
to be used as a soundtrack in a dramatic or emotional film. Written in A minor and 4/4 
time. The chord progression of E7, Am, and E contributes to the piece's sentimental 
atmosphere.

Creative Custom Prompts

Example 3: Road trip song

An energetic and motivating pop song you love to hear on a long road trip.

Example 4: Sunday picnic jazz

Upbeat and playful jazz music with lively saxophones, like you're going out on a 
Sunday picnic.

Citation

If you find our repo and model useful, please cite our research as

@inproceedings{wu2025midillm,
  title={{MIDI-LLM}: Adapting large language models for text-to-{MIDI} music generation},
  author={Wu, Shih-Lun and Kim, Yoon and Huang, Cheng-Zhi Anna},
  booktitle={Proc. NeurIPS AI4Music Workshop},
  year={2025}
}