File size: 6,355 Bytes
4c6424c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5238467
 
 
 
 
 
 
 
 
 
b40a60c
5238467
 
 
 
32b0714
5238467
 
 
 
 
 
 
e61b298
b15aea0
5238467
 
 
 
 
 
 
 
 
 
 
 
 
 
0c75a46
c81b8e6
0c75a46
bffb181
32b0714
 
5238467
 
 
 
9138f15
 
 
 
5238467
 
 
 
 
9138f15
 
 
c81b8e6
9138f15
 
5238467
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
23fe483
5238467
 
 
 
 
 
 
 
 
 
 
076e107
5238467
 
5fff830
 
 
 
 
5238467
 
504d7b7
 
 
 
 
 
5238467
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
---
title: "MusicGen"
python_version: "3.9"
tags:
  - "music generation"
  - "language models"
  - "LLMs"
app_file: "app_batched.py"
emoji: 🎵
colorFrom: white
colorTo: blue
sdk: gradio
sdk_version: 3.34.0
pinned: true
license: "cc-by-nc-4.0"
---
# Audiocraft
![docs badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_docs/badge.svg)
![linter badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_linter/badge.svg)
![tests badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_tests/badge.svg)

Audiocraft is a PyTorch library for deep learning research on audio generation. At the moment, it contains the code for MusicGen, a state-of-the-art controllable text-to-music model.

## MusicGen

Audiocraft provides the code and models for MusicGen, [a simple and controllable model for music generation][arxiv]. MusicGen is a single stage auto-regressive
Transformer model trained over a 32kHz <a href="https://github.com/facebookresearch/encodec">EnCodec tokenizer</a> with 4 codebooks sampled at 50 Hz. Unlike existing methods like [MusicLM](https://arxiv.org/abs/2301.11325), MusicGen doesn't require a self-supervised semantic representation, and it generates
all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict
them in parallel, thus having only 50 auto-regressive steps per second of audio.
Check out our [sample page][musicgen_samples] or test the available demo!

<a target="_blank" href="https://colab.research.google.com/drive/1-Xe9NCdIs2sCUbiSmwHXozK6AAhMm7_i?usp=sharing">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
<a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen">
  <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HugginFace"/>
</a>
<br>

We use 20K hours of licensed music to train MusicGen. Specifically, we rely on an internal dataset of 10K high-quality music tracks, and on the ShutterStock and Pond5 music data.

## Installation
Audiocraft requires Python 3.9, PyTorch 2.0.0, and a GPU with at least 16 GB of memory (for the medium-sized model). To install Audiocraft, you can run the following:

```shell
# Best to make sure you have torch installed first, in particular before installing xformers.
# Don't run this if you already have PyTorch installed.
pip install 'torch>=2.0'
# Then proceed to one of the following
pip install -U audiocraft  # stable release
pip install -U git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft  # bleeding edge
pip install -e .  # or if you cloned the repo locally
```

## Usage
We offer a number of way to interact with MusicGen:
1. You can play with MusicGen by running the jupyter notebook at [`demo.ipynb`](./demo.ipynb) locally, or use the provided [colab notebook](https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing).
2. You can use the gradio demo locally by running `python app.py`.
3. A demo is also available on the [`facebook/MusicGen`  HuggingFace Space](https://huggingface.co/spaces/facebook/MusicGen) (huge thanks to all the HF team for their support).
4. Finally, you can run the [Gradio demo with a Colab GPU](https://colab.research.google.com/drive/1-Xe9NCdIs2sCUbiSmwHXozK6AAhMm7_i?usp=sharing),
as adapted from [@camenduru Colab](https://github.com/camenduru/MusicGen-colab).

## API

We provide a simple API and 4 pre-trained models. The pre trained models are:
- `small`: 300M model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-small)
- `medium`: 1.5B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-medium)
- `melody`: 1.5B model, text to music and text+melody to music - [🤗 Hub](https://huggingface.co/facebook/musicgen-melody)
- `large`: 3.3B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-large)

We observe the best trade-off between quality and compute with the `medium` or `melody` model.
In order to use MusicGen locally **you must have a GPU**. We recommend 16GB of memory, but smaller
GPUs will be able to generate short sequences, or longer sequences with the `small` model.

**Note**: Please make sure to have [ffmpeg](https://ffmpeg.org/download.html) installed when using newer version of `torchaudio`.
You can install it with:
```
apt-get install ffmpeg
```

See after a quick example for using the API.

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

model = MusicGen.get_pretrained('melody')
model.set_generation_params(duration=8)  # generate 8 seconds.
wav = model.generate_unconditional(4)    # generates 4 unconditional audio samples
descriptions = ['happy rock', 'energetic EDM', 'sad jazz']
wav = model.generate(descriptions)  # generates 3 samples.

melody, sr = torchaudio.load('./assets/bach.mp3')
# generates using the melody from the given audio and the provided descriptions.
wav = model.generate_with_chroma(descriptions, melody[None].expand(3, -1, -1), sr)

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


## Model Card

See [the model card page](./MODEL_CARD.md).

## FAQ

#### Will the training code be released?

Yes. We will soon release the training code for MusicGen and EnCodec.


#### I need help on Windows

@FurkanGozukara made a complete tutorial for [Audiocraft/MusicGen on Windows](https://youtu.be/v-YpvPkhdO4)


## Citation
```
@article{copet2023simple,
      title={Simple and Controllable Music Generation},
      author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},
      year={2023},
      journal={arXiv preprint arXiv:2306.05284},
}
```

## License
* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE).
* The weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights).

[arxiv]: https://arxiv.org/abs/2306.05284
[musicgen_samples]: https://ai.honu.io/papers/musicgen/