Vector-Quantized Autoregressive Predictive Coding
For an improved version of VQ-APC, please refer to this repository.
This repository contains the official implementation of Vector-Quantized Autoregressive Predictive Coding (VQ-APC).
VQ-APC is an extension of APC, which defines a self-supervised task for learning high-level speech representation from unannotated speech. For dependencies and data preprocessing, please refer to the implementation of APC. After the data are ready, here's an example command to train your own VQ-APC model:
python train_vqapc.py --rnn_num_layers 3 \
--rnn_hidden_size 512 \
--rnn_dropout 0.1 \
--rnn_residual \
--codebook_size 128 \
--code_dim 512 \
--gumbel_temperature 0.5 \
--apply_VQ 0 0 1 \
--optimizer adam \
--batch_size 32 \
--learning_rate 0.0001 \
--epochs 10 \
--n_future 5 \
--librispeech_home ./librispeech_data/preprocessed \
--train_partition train-clean-360 \
--train_sampling 1. \
--val_partition dev-clean \
--val_sampling 1. \
--exp_name my_exp \
--store_path ./logs
Argument descriptions are available in train_vqapc.py.
TODOs
- Add scripts that get the learned codebook(s) (essentially the parameters of the
nn.Linearlayer used to implement the VQ layers) - Add scripts that visualize the code-phone co-occurrence (Figure 3 in the paper)
Reference
Please kindly cite our work if you find this repository useful:
@inproceedings{chung2020vqapc,
title = {Vector-quantized autoregressive predictive coding},
autohor = {Chung, Yu-An and Tang, Hao and Glass, James},
booktitle = {Interspeech},
year = {2020}
}
Contact
You can reach me out via email. Questions and feedback are welcome.