musicgen-large

MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods, like MusicLM, 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.

MusicGen was published in Simple and Controllable Music Generation by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez.

Four checkpoints are released:

• small
• medium
• large (this checkpoint)
• melody

Example

Try out MusicGen yourself!

• Audiocraft Colab:

• Hugging Face Colab:

• Hugging Face Demo:

🤗 Transformers Usage

You can run MusicGen locally with the 🤗 Transformers library from version 4.31.0 onwards.

1. First install the 🤗 Transformers library and scipy:

pip install --upgrade pip
pip install --upgrade transformers scipy

2. Run inference via the Text-to-Audio (TTA) pipeline. You can infer the MusicGen model via the TTA pipeline in just a few lines of code!

from transformers import pipeline
import scipy

synthesiser = pipeline("text-to-audio", "facebook/musicgen-large")

music = synthesiser("lo-fi music with a soothing melody", forward_params={"do_sample": True})

scipy.io.wavfile.write("musicgen_out.wav", rate=music["sampling_rate"], data=music["audio"])

3. Run inference via the Transformers modelling code. You can use the processor + generate code to convert text into a mono 32 kHz audio waveform for more fine-grained control.

from transformers import AutoProcessor, MusicgenForConditionalGeneration

processor = AutoProcessor.from_pretrained("facebook/musicgen-large")
model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-large")

inputs = processor(
    text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"],
    padding=True,
    return_tensors="pt",
)

audio_values = model.generate(**inputs, max_new_tokens=256)

4. Listen to the audio samples either in an ipynb notebook:

from IPython.display import Audio

sampling_rate = model.config.audio_encoder.sampling_rate
Audio(audio_values[0].numpy(), rate=sampling_rate)

Or save them as a .wav file using a third-party library, e.g. scipy:

import scipy

sampling_rate = model.config.audio_encoder.sampling_rate
scipy.io.wavfile.write("musicgen_out.wav", rate=sampling_rate, data=audio_values[0, 0].numpy())

For more details on using the MusicGen model for inference using the 🤗 Transformers library, refer to the MusicGen docs.

Audiocraft Usage

You can also run MusicGen locally through the original [Audiocraft library]((https://github.com/facebookresearch/audiocraft):

1. First install the audiocraft library

pip install git+https://github.com/facebookresearch/audiocraft.git

2. Make sure to have ffmpeg installed:

apt get install ffmpeg

3. Run the following Python code:

from audiocraft.models import MusicGen
from audiocraft.data.audio import audio_write

model = MusicGen.get_pretrained("large")
model.set_generation_params(duration=8)  # generate 8 seconds.

descriptions = ["happy rock", "energetic EDM"]

wav = model.generate(descriptions)  # generates 2 samples.

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")

Model details

Organization developing the model: The FAIR team of Meta AI.

Model date: MusicGen was trained between April 2023 and May 2023.

Model version: This is the version 1 of the model.

Model type: MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation.

Paper or resources for more information: More information can be found in the paper Simple and Controllable Music Generation.

Citation details:

@misc{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},
      eprint={2306.05284},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}

License: Code is released under MIT, model weights are released under CC-BY-NC 4.0.

Where to send questions or comments about the model: Questions and comments about MusicGen can be sent via the Github repository of the project, or by opening an issue.

Intended use

Primary intended use: The primary use of MusicGen is research on AI-based music generation, including:

• Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science
• Generation of music guided by text or melody to understand current abilities of generative AI models by mac