PyTorch implementation of Jan Chorowski, Jan 2019 paper"
This is a PyTorch implementation of https://arxiv.org/abs/1901.08810.
[Under Construction]
Update April 14, 2019
Began training on Librispeech dev (http://www.openslr.org/resources/12/dev-clean.tar.gz), see dat/example_train.log
Update May 12, 2019
First runs using vqvae mode. After ~200 iterations, only one quantized vector is used as a representative. Currently troubleshooting.
TODO
- VAE and VQVAE versions of the bottleneck / training objectives [DONE]
- Inference mode
Example training setup
code_dir=/data/ac1zy/exp/ae-wavenet
run_dir=/fastdata/ac1zy/exp/ae-wavenet/my_runs
# Get the data
cd $run_dir
wget http://www.openslr.org/resources/12/dev-clean.tar.gz
tar zxvf dev-clean.tar.gz
$code_dir/scripts/librispeech_to_rdb.sh LibriSpeech/dev-clean > librispeech.dev-clean.rdb
# Train
cd $code_dir
python train.py new -af par/arch.basic.json -tf par/train.basic.json -nb 4 -si 10 \
-rws 100000 -fpu 1.0 $run_dir/model%.ckpt $run_dir/librispeech.dev-clean.10.r1.rdb