musicgen-stereo-medium

We further release a set of stereophonic capable models. Those were fine tuned for 200k updates starting from the mono models. The training data is otherwise identical and capabilities and limitations are shared with the base modes. The stereo models work by getting 2 streams of tokens from the EnCodec model, and interleaving those using the delay pattern.

Stereophonic sound, also known as stereo, is a technique used to reproduce sound with depth and direction. It uses two separate audio channels played through speakers (or headphones), which creates the impression of sound coming from multiple directions.

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.

We provide a simple API and 10 pre-trained models. The pre trained models are:

• facebook/musicgen-small: 300M model, text to music only - 🤗 Hub
• facebook/musicgen-medium: 1.5B model, text to music only - 🤗 Hub
• facebook/musicgen-melody: 1.5B model, text to music and text+melody to music - 🤗 Hub
• facebook/musicgen-large: 3.3B model, text to music only - 🤗 Hub
• facebook/musicgen-melody-large: 3.3B model, text to music and text+melody to music - 🤗 Hub
• facebook/musicgen-stereo-*: All the previous models fine-tuned for stereo generation - small, medium, large, melody, melody large

Example

Try out MusicGen yourself!

• Audiocraft Colab:

• Hugging Face Colab:

• Hugging Face Demo:

🤗 Transformers Usage

You can run MusicGen Stereo models locally with the 🤗 Transformers library from main onward.

1. First install the 🤗 Transformers library and scipy:

pip install --upgrade pip
pip install --upgrade git+https://github.com/huggingface/transformers.git 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!

import torch
import soundfile as sf
from transformers import pipeline

synthesiser = pipeline("text-to-audio", "facebook/musicgen-stereo-medium", device="cuda:0", torch_dtype=torch.float16)

music = synthesiser("lo-fi music with a soothing melody", forward_params={"max_new_tokens": 256})

sf.write("musicgen_out.wav", music["audio"][0].T, music["sampling_rate"])

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-stereo-medium")
model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-stereo-medium").to("cuda")

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",
).to("cuda")

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].cpu().numpy(), rate=sampling_rate)

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

import soundfile as sf

sampling_rate = model.config.audio_encoder.sampling_rate
audio_values = audio_values.cpu().numpy()
sf.write("musicgen_out.wav", audio_values[0].T, sampling_rate)

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("medium")
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