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Residual Vector Quantization (RVQ) is a useful type of quantization that can compress a whole vector into a few integers, making it more efficient than other types of quantization. It is particularly effective for encoding speech and audio more efficiently than traditional codecs like MP3, as seen in models such as SoundStream and EnCodec. This video explains how RVQ iteratively represents vectors in terms of codebook vector entries to achieve incrementally higher fidelity representation as bitrate is increased.
0:00 - Introduction
1:10 - Encodec model architecture
2:05 - Quantization in machine learning
3:56 - Codebook quantization
5:04 - Residual vector quantization
7:54 - RVQ and bitrate in EnCodec
9:08 - EnCodec audio compression examples
10:18 - Learning codebook vectors
11:31 - Codebook updates
12:15 - Encoder commitment loss
References:
SoundStream paper (2021): https://arxiv.org/abs/2107.03312
EnCodec paper (2022): https://arxiv.org/abs/2210.13438
Blog post by Assembly AI: https://www.assemblyai.com/blog/what-is-residual-vector-quantization/