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--- |
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license: apache-2.0 |
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datasets: |
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- amaai-lab/MusicBench |
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tags: |
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- music |
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--- |
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<div align="center"> |
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# Mustango: Toward Controllable Text-to-Music Generation |
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[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) |
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[](https://huggingface.co/spaces/declare-lab/mustango) |
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</div> |
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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! |
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🔥 Live demo available on [Replicate](https://replicate.com/declare-lab/mustango) and [HuggingFace](https://huggingface.co/spaces/declare-lab/mustango). |
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<div align="center"> |
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<img src="mustango.jpg" width="500"/> |
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</div> |
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## Quickstart Guide |
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Generate music from a text prompt: |
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```python |
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import IPython |
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import soundfile as sf |
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from mustango import Mustango |
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model = Mustango("declare-lab/mustango") |
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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." |
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music = model.generate(prompt) |
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sf.write(f"{prompt}.wav", audio, samplerate=16000) |
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IPython.display.Audio(data=audio, rate=16000) |
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``` |
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## Installation |
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```bash |
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git clone https://github.com/AMAAI-Lab/mustango |
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cd mustango |
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pip install -r requirements.txt |
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cd diffusers |
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pip install -e . |
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``` |
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## Datasets |
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The [MusicBench](https://huggingface.co/datasets/amaai-lab/MusicBench) dataset contains 52k music fragments with a rich music-specific text caption. |
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## Subjective Evaluation by Expert Listeners |
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| **Model** | **Dataset** | **Pre-trained** | **Overall Match** ↑ | **Chord Match** ↑ | **Tempo Match** ↑ | **Audio Quality** ↑ | **Musicality** ↑ | **Rhythmic Presence and Stability** ↑ | **Harmony and Consonance** ↑ | |
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|-----------|-------------|:-----------------:|:-----------:|:-----------:|:-----------:|:----------:|:----------:|:----------:|:----------:| |
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| Tango | MusicCaps | ✓ | 4.35 | 2.75 | 3.88 | 3.35 | 2.83 | 3.95 | 3.84 | |
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| Tango | MusicBench | ✓ | 4.91 | 3.61 | 3.86 | 3.88 | 3.54 | 4.01 | 4.34 | |
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| Mustango | MusicBench | ✓ | 5.49 | 5.76 | 4.98 | 4.30 | 4.28 | 4.65 | 5.18 | |
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| Mustango | MusicBench | ✗ | 5.75 | 6.06 | 5.11 | 4.80 | 4.80 | 4.75 | 5.59 | |
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## Training |
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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. |
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You can now train **Mustango** on the MusicBench dataset using: |
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```bash |
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accelerate launch train.py \ |
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--text_encoder_name="google/flan-t5-large" \ |
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--scheduler_name="stabilityai/stable-diffusion-2-1" \ |
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--unet_model_config="configs/diffusion_model_config_munet.json" \ |
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--model_type Mustango --freeze_text_encoder --uncondition_all --uncondition_single \ |
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--drop_sentences --random_pick_text_column --snr_gamma 5 \ |
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``` |
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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. |
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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. |
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Recommended training time from scratch on MusicBench is at least 40 epochs. |
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## Model Zoo |
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We have released the following models: |
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Mustango Pretrained: https://huggingface.co/declare-lab/mustango-pretrained |
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Mustango: https://huggingface.co/declare-lab/mustango |
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## Citation |
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Please consider citing the following article if you found our work useful: |
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``` |
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@misc{melechovsky2023mustango, |
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title={Mustango: Toward Controllable Text-to-Music Generation}, |
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author={Jan Melechovsky and Zixun Guo and Deepanway Ghosal and Navonil Majumder and Dorien Herremans and Soujanya Poria}, |
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year={2023}, |
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eprint={2311.08355}, |
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archivePrefix={arXiv}, |
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} |
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``` |
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