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Mustango: Toward Controllable Text-to-Music Generation

Demo | Model | Website and Examples | Paper | Dataset

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Meet Mustango, an exciting addition to the vibrant landscape of Multimodal Large Language Models designed for controlled music generation. Mustango leverages Latent Diffusion Model (LDM), Flan-T5, and musical features to do the magic!

πŸ”₯ Live demo available on Replicate and HuggingFace.

Quickstart Guide

Generate music from a text prompt:

import IPython
import soundfile as sf
from mustango import Mustango

model = Mustango("declare-lab/mustango")

prompt = "This is a new age piece. There is a flute playing the main melody with a lot of staccato notes. The rhythmic background consists of a medium tempo electronic drum beat with percussive elements all over the spectrum. There is a playful atmosphere to the piece. This piece can be used in the soundtrack of a children's TV show or an advertisement jingle."

music = model.generate(prompt)
sf.write(f"{prompt}.wav", audio, samplerate=16000)
IPython.display.Audio(data=audio, rate=16000)

Installation

git clone https://github.com/AMAAI-Lab/mustango
cd mustango
pip install -r requirements.txt
cd diffusers
pip install -e .

Datasets

The MusicBench dataset contains 52k music fragments with a rich music-specific text caption.

Subjective Evaluation by Expert Listeners

Model Dataset Pre-trained Overall Match ↑ Chord Match ↑ Tempo Match ↑ Audio Quality ↑ Musicality ↑ Rhythmic Presence and Stability ↑ Harmony and Consonance ↑
Tango MusicCaps βœ“ 4.35 2.75 3.88 3.35 2.83 3.95 3.84
Tango MusicBench βœ“ 4.91 3.61 3.86 3.88 3.54 4.01 4.34
Mustango MusicBench βœ“ 5.49 5.76 4.98 4.30 4.28 4.65 5.18
Mustango MusicBench βœ— 5.75 6.06 5.11 4.80 4.80 4.75 5.59

Training

We use the accelerate package from Hugging Face for multi-gpu training. Run accelerate config from terminal and set up your run configuration by the answering the questions asked.

You can now train Mustango on the MusicBench dataset using:

accelerate launch train.py \
--text_encoder_name="google/flan-t5-large" \
--scheduler_name="stabilityai/stable-diffusion-2-1" \
--unet_model_config="configs/diffusion_model_config_munet.json" \
--model_type Mustango --freeze_text_encoder --uncondition_all --uncondition_single \
--drop_sentences --random_pick_text_column --snr_gamma 5 \

The --model_type flag allows to choose either Mustango, or Tango to be trained with the same code. However, do note that you also need to change --unet_model_config to the relevant config: diffusion_model_config_munet for Mustango; diffusion_model_config for Tango.

The arguments --uncondition_all, --uncondition_single, --drop_sentences control the dropout functions as per Section 5.2 in our paper. The argument of --random_pick_text_column allows to randomly pick between two input text prompts - in the case of MusicBench, we pick between ChatGPT rephrased captions and original enhanced MusicCaps prompts, as depicted in Figure 1 in our paper.

Recommended training time from scratch on MusicBench is at least 40 epochs.

Model Zoo

We have released the following models:

Mustango Pretrained: https://huggingface.co/declare-lab/mustango-pretrained

Mustango: https://huggingface.co/declare-lab/mustango

Citation

Please consider citing the following article if you found our work useful:

@misc{melechovsky2023mustango,
      title={Mustango: Toward Controllable Text-to-Music Generation}, 
      author={Jan Melechovsky and Zixun Guo and Deepanway Ghosal and Navonil Majumder and Dorien Herremans and Soujanya Poria},
      year={2023},
      eprint={2311.08355},
      archivePrefix={arXiv},
}
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Dataset used to train declare-lab/mustango-pretrained