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---
license: apache-2.0
datasets:
- amaai-lab/MusicBench
tags:
- music
- text-to-audio
- text-to-music
pipeline_tag: text-to-audio
---
<div align="center">
# Mustango: Toward Controllable Text-to-Music Generation
[Demo](https://replicate.com/declare-lab/mustango) | [Model](https://huggingface.co/declare-lab/mustango) | [Website and Examples](https://amaai-lab.github.io/mustango/) | [Paper](https://arxiv.org/abs/2311.08355) | [Dataset](https://huggingface.co/datasets/amaai-lab/MusicBench)
[](https://huggingface.co/spaces/declare-lab/mustango)
</div>
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!
🔥 Live demo available on [Replicate](https://replicate.com/declare-lab/mustango) and [HuggingFace](https://huggingface.co/spaces/declare-lab/mustango).
<div align="center">
<img src="mustango.jpg" width="500"/>
</div>
## Quickstart Guide
Generate music from a text prompt:
```python
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
```bash
git clone https://github.com/AMAAI-Lab/mustango
cd mustango
pip install -r requirements.txt
cd diffusers
pip install -e .
```
## Datasets
The [MusicBench](https://huggingface.co/datasets/amaai-lab/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 | ✓ | 4.35 | 2.75 | 3.88 | 3.35 | 2.83 | 3.95 | 3.84 |
| Tango | MusicBench | ✓ | 4.91 | 3.61 | 3.86 | 3.88 | 3.54 | 4.01 | 4.34 |
| Mustango | MusicBench | ✓ | 5.49 | 5.76 | 4.98 | 4.30 | 4.28 | 4.65 | 5.18 |
| Mustango | MusicBench | ✗ | 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:
```bash
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},
}
``` |