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# MAGNeT - Medium - 1.5B - 30secs
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MAGNeT is a text-to-music model capable of generating high-quality
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It is a masked generative non-autoregressive Transformer trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz.
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Unlike prior work, MAGNeT doesn't require neither semantic token conditioning nor model cascading, and it generates all 4 codebooks using a single non-autoregressive Transformer.
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# MAGNeT - Medium - 1.5B - 30secs
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MAGNeT is a text-to-music and text-to-sound model capable of generating high-quality audio samples conditioned on text descriptions.
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It is a masked generative non-autoregressive Transformer trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz.
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Unlike prior work, MAGNeT doesn't require neither semantic token conditioning nor model cascading, and it generates all 4 codebooks using a single non-autoregressive Transformer.
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