MusicGen Large 3.3B 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: 2. Run inference via the Text to Audio (TTA) pipeline. You can infer the MusicGen model via th…
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