musicgen-medium

MusicGen - Medium - 1.5B 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** (this checkpoint) large 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: 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! 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. 3. Listen to the audio samples either in an ipynb notebook: Or save them as a `.wav` file using a third-party library, e.g. `scipy`: 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: 1. First install the `audiocraft` library 2. Make sure to have `ffmpeg` installed: 3. Run the following Python code: Model details Organization developing the model:** The FAIR team of Meta AI. Model date:** MusicGen was tra