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)

Initialization

The SoundStream paper proposes that the codebook should be initialized by the kmeans centroids of the first batch. You can easily turn on this feature with one flag kmeans_init = True, for either VectorQuantize or ResidualVQ class

import torch
from vector_quantize_pytorch import ResidualVQ

residual_vq = ResidualVQ(
    dim = 256,
    codebook_size = 256,
    num_quantizers = 4,
    kmeans_init = True,   # set to True
    kmeans_iters = 10     # number of kmeans iterations to calculate the centroids for the codebook on init
)

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

Increasing codebook usage

This repository will contain a few techniques from various papers to combat "dead" codebook entries, which is a common problem when using vector quantizers.

Lower codebook dimension

The Improved VQGAN paper proposes to have the codebook kept in a lower dimension. The encoder values are projected down before being projected back to high dimensional after quantization. You can set this with the codebook_dim hyperparameter.

import torch
from vector_quantize_pytorch import VectorQuantize

vq = VectorQuantize(
    dim = 256,
    codebook_size = 256,
    codebook_dim = 16      # paper proposes setting this to 32 or as low as 8 to increase codebook usage
)

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

Cosine similarity

The Improved VQGAN paper also proposes to l2 normalize the codes and the encoded vectors, which boils down to using cosine similarity for the distance. They claim enforcing the vectors on a sphere leads to improvements in code usage and downstream reconstruction. You can turn this on by setting use_cosine_sim = True

import torch
from vector_quantize_pytorch import VectorQuantize

vq = VectorQuantize(
    dim = 256,
    codebook_size = 256,
    use_cosine_sim = True   # set this to True
)

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

Expiring stale codes

Finally, the SoundStream paper has a scheme where they replace codes that have hits below a certain threshold with randomly selected vector from the current batch. You can set this threshold with threshold_ema_dead_code keyword.

import torch
from vector_quantize_pytorch import VectorQuantize

vq = VectorQuantize(
    dim = 256,
    codebook_size = 512,
    threshold_ema_dead_code = 2  # should actively replace any codes that have an exponential moving average cluster size less than 2
)

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

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}
}

@inproceedings{anonymous2022vectorquantized,
    title   = {Vector-quantized Image Modeling with Improved {VQGAN}},
    author  = {Anonymous},
    booktitle = {Submitted to The Tenth International Conference on Learning Representations },
    year    = {2022},
    url     = {https://openreview.net/forum?id=pfNyExj7z2},
    note    = {under review}
}