Skip to content
FFTNet vocoder implementation
Branch: master
Clone or download
Latest commit e2a1737 Jul 24, 2018
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.ipynb_checkpoints
__pycache__
.compute
.gitignore
.install
LICENSE.txt
README.md Update README.md Jul 24, 2018
TODO.txt update TODO.txt Jul 5, 2018
audio.py
check-dataloader.ipynb Notebook update Jun 27, 2018
conf.json
conf_test_train.json
dataset.py
extract_mel.py
generic_utils.py Comment progbar since it has no use on snakepit Jul 9, 2018
model.py
mulaw-encode.ipynb
requirements.txt
run_time_test.py Optimize FFTNet inference Jul 3, 2018
setup.py
test.py EMA model averaging Jun 28, 2018
test_conf.json
test_train.py
train.py
visual.py

README.md

Unofficial Implementation of FFTNet vocode paper.

  • implement the model.
  • implement tests.
  • overfit on a single batch (sanity check).
  • linearize weights for eval time.
  • measure the run-time on GPU and CPU. (1 sec audio takes ~47 secs) If anyone knows additional tricks from the paper, let me know. So far I asked the authors but nobody returned.
  • train on LJSpeech spectrograms.
  • distill model as in Parallel WaveNet paper.
You can’t perform that action at this time.