README.md

---
license: cc-by-nc-4.0
tags:
- encodec
- audio
- music
- audiocraft
---


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# MultiBand Diffusion

<!-- Provide a quick summary of what the model is/does. -->

This repository contains the weights for Meta's MultiBand Diffusion models, described in this research paper: [From Discrete Tokens to High Fidelity Audio using MultiBand Diffusion][arxiv].

MultiBand diffusion is a collection of 4 models that can decode tokens from <a href="https://github.com/facebookresearch/encodec">EnCodec tokenizer</a> into waveform audio.


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** Meta
- **Model type:** Diffusion Models
- **License:** The models weights in this repository are released under the CC-BY-NC 4.0 license.

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [AudioCraft repo](https://github.com/facebookresearch/audiocraft/tree/main)
- **Paper:** [From Discrete Tokens to High Fidelity Audio using MultiBand Diffusion](https://dl.fbaipublicfiles.com/encodec/Diffusion/paper.pdf)



## Installation

Please follow the AudioCraft installation instructions from the [README](../README.md).


## Usage

[AudioCraft library](https://github.com/facebookresearch/audiocraft/tree/main) offers a number of way to use MultiBand Diffusion:
1. A MusicGen demo includes a toggle to try diffusion decoder. You can use the demo locally by running [`python -m demos.musicgen_app --share`](https://github.com/facebookresearch/audiocraft/tree/main/demos/musicgen_app.py), or through a [MusicGen Colab](https://colab.research.google.com/drive/1JlTOjB-G0A2Hz3h8PK63vLZk4xdCI5QB?usp=sharing).
2. You can play with MusicGen by running the jupyter notebook at [`demos/musicgen_demo.ipynb`](https://github.com/facebookresearch/audiocraft/tree/main/demos/musicgen_demo.ipynb) locally (if you have a GPU).

## API

[AudioCraft library](https://github.com/facebookresearch/audiocraft/tree/main) provides a simple API and pre-trained models for MusicGen and for EnCodec at 24 khz for 3 bitrates (1.5 kbps, 3 kbps and 6 kbps).

See after a quick example for using MultiBandDiffusion with the MusicGen API:

```python
import torchaudio
from audiocraft.models import MusicGen, MultiBandDiffusion
from audiocraft.data.audio import audio_write

model = MusicGen.get_pretrained('facebook/musicgen-melody')
mbd = MultiBandDiffusion.get_mbd_musicgen()
model.set_generation_params(duration=8)  # generate 8 seconds.
wav, tokens = model.generate_unconditional(4, return_tokens=True)    # generates 4 unconditional audio samples and keep the tokens for MBD generation
descriptions = ['happy rock', 'energetic EDM', 'sad jazz']
wav_diffusion = mbd.tokens_to_wav(tokens)
wav, tokens = model.generate(descriptions, return_tokens=True)  # generates 3 samples and keep the tokens.
wav_diffusion = mbd.tokens_to_wav(tokens)
melody, sr = torchaudio.load('./assets/bach.mp3')
# Generates using the melody from the given audio and the provided descriptions, returns audio and audio tokens.
wav, tokens = model.generate_with_chroma(descriptions, melody[None].expand(3, -1, -1), sr, return_tokens=True)
wav_diffusion = mbd.tokens_to_wav(tokens)

for idx, one_wav in enumerate(wav):
    # Will save under {idx}.wav and {idx}_diffusion.wav, with loudness normalization at -14 db LUFS for comparing the methods.
    audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
    audio_write(f'{idx}_diffusion', wav_diffusion[idx].cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
```

For the compression task (and to compare with [EnCodec](https://github.com/facebookresearch/encodec)):

```python
import torch
from audiocraft.models import MultiBandDiffusion
from encodec import EncodecModel
from audiocraft.data.audio import audio_read, audio_write

bandwidth = 3.0  # 1.5, 3.0, 6.0
mbd = MultiBandDiffusion.get_mbd_24khz(bw=bandwidth)
encodec = EncodecModel.get_encodec_24khz()

somepath = ''
wav, sr = audio_read(somepath)
with torch.no_grad():
    compressed_encodec = encodec(wav)
    compressed_diffusion = mbd.regenerate(wav, sample_rate=sr)

audio_write('sample_encodec', compressed_encodec.squeeze(0).cpu(), mbd.sample_rate, strategy="loudness", loudness_compressor=True)
audio_write('sample_diffusion', compressed_diffusion.squeeze(0).cpu(), mbd.sample_rate, strategy="loudness", loudness_compressor=True)
```


## Training

A [DiffusionSolver](https://github.com/facebookresearch/audiocraft/tree/main/audiocraft/solvers/diffusion.py) implements Meta diffusion training pipeline.
It generates waveform audio conditioned on the embeddings extracted from a pre-trained EnCodec model
(see [EnCodec documentation from the AudioCraft library](https://github.com/facebookresearch/audiocraft/tree/main/ENCODEC.md) for more details on how to train such model).

Note that **the library do NOT provide any of the datasets** used for training our diffusion models.
We provide a dummy dataset containing just a few examples for illustrative purposes.

### Example configurations and grids

One can train diffusion models as described in the paper by using this [dora grid](https://github.com/facebookresearch/audiocraft/tree/main/audiocraft/grids/diffusion/4_bands_base_32khz.py).
```shell
# 4 bands MBD trainning
dora grid diffusion.4_bands_base_32khz
```

### Learn more

Learn more about AudioCraft training pipelines in the [dedicated section](https://github.com/facebookresearch/audiocraft/tree/main/TRAINING.md).



## Citation

```
@article{sanroman2023fromdi,
  title={From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion},
  author={San Roman, Robin and Adi, Yossi and Deleforge, Antoine and Serizel, Romain and Synnaeve, Gabriel and Défossez, Alexandre},
  journal={arXiv preprint arXiv:},
  year={2023}
}
```


[arxiv]: https://dl.fbaipublicfiles.com/encodec/Diffusion/paper.pdf
[mbd_samples]: https://ai.honu.io/papers/mbd/