SNAC 🍿 Multi S cale N eural A udio C odec (SNAC) compressess audio into discrete codes at a low bitrate. 👉 This model was primarily trained on speech data, and its recommended use case is speech synthesis. See below for other pretrained models. 🔗 GitHub repository: https://github.com/hubertsiuzdak/snac/ Overview SNAC encodes audio into hierarchical tokens similarly to SoundStream, EnCodec, and DAC. However, SNAC introduces a simple change where coarse tokens are sampled less frequently, covering a broader time span. This model compresses 24 kHz audio into discrete codes at a 0.98 kbps bitrate. It uses 3 RVQ levels with token rates of 12, 23, and 47 Hz. Pretrained models Currently, all models support only single audio channel (mono). Model Bitrate Sample Rate Params Recommended use case hubertsiuzdak/snac 24khz (this model) 0.98 kbps 24 kHz 19.8 M 🗣️ Speech hubertsiuzdak/snac 32khz 1.9 kbps 32 kHz 54.5 M 🎸 Music / Sound Effects hubertsiuzdak/snac 44khz 2.6 kbps 44 kHz 54.5 M 🎸 Music / Sound Effects Usage Install it using: To encode (and decode) audio with SNAC in Python, use the following code: You can also encode and reconstruct in a single call: ⚠️ Note that codes is a list…
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