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  # MusicGen - Medium - 1.5B
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- MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts.
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  It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz.
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  Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass.
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  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.
 
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  # MusicGen - Medium - 1.5B
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+ MusicGen is a text-to-music model capable of generating high-quality music samples conditioned on text descriptions or audio prompts.
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  It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz.
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  Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass.
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  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.