Vector Quantization - Pytorch

A vector quantization library originally transcribed from Deepmind's tensorflow implementation, made conveniently into a package. It uses exponential moving averages to update the dictionary.

VQ has been successfully used by Deepmind and OpenAI for high quality generation of images (VQ-VAE-2) and music (Jukebox).

Install

$ pip install vector-quantize-pytorch

Usage

import torch
from vector_quantize_pytorch import VectorQuantize

vq = VectorQuantize(
    dim = 256,
    codebook_size = 512,     # codebook size
    decay = 0.8,             # the exponential moving average decay, lower means the dictionary will change faster
    commitment = 1.          # the weight on the commitment loss
)

x = torch.randn(1, 1024, 256)
quantized, indices, commit_loss = vq(x) # (1, 1024, 256), (1, 1024), (1)

Variants

This paper proposes to use multiple vector quantizers to recursively quantize the residuals of the waveform. You can use this with the ResidualVQ class and one extra initialization parameter.

import torch
from vector_quantize_pytorch import ResidualVQ

residual_vq = ResidualVQ(
    dim = 256,
    num_quantizers = 8,      # specify number of quantizers
    codebook_size = 1024,    # codebook size
)

x = torch.randn(1, 1024, 256)
quantized, indices, commit_loss = residual_vq(x)

# (1, 1024, 256), (8, 1, 1024), (8, 1)
# (batch, seq, dim), (quantizer, batch, seq), (quantizer, batch)

Citations

@misc{oord2018neural,
    title   = {Neural Discrete Representation Learning},
    author  = {Aaron van den Oord and Oriol Vinyals and Koray Kavukcuoglu},
    year    = {2018},
    eprint  = {1711.00937},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}

@misc{zeghidour2021soundstream,
    title   = {SoundStream: An End-to-End Neural Audio Codec},
    author  = {Neil Zeghidour and Alejandro Luebs and Ahmed Omran and Jan Skoglund and Marco Tagliasacchi},
    year    = {2021},
    eprint  = {2107.03312},
    archivePrefix = {arXiv},
    primaryClass = {cs.SD}
}