TSNetVocoder

This software is distributed under BSD 3-Clause license. Please see LICENSE for more details.
Paper : http://arxiv.org/abs/1810.11945
Speech samples : https://nii-yamagishilab.github.io/TSNetVocoder/index.html

Reference

Shinji Takaki, Toru Nakashika, Xin Wang, Junichi Yamagishi, "STFT spectral loss for training a neural speech waveform model," arXiv preprint arXiv:1810.11945, 2018.

Wav files need to be put in 'data/wav_trn' (training), 'data/wav_val' (validation) and 'data/wav_test' (analysis-by-synthesis) directories.
- Following file format is supported.
  - Sampling rate : 16000
  - Quantization bit : 16bit (signed-integer)
  - Number of channels : 1
- Each utterance should be stored in one wav file.
By running 00_run.py, you can find a trained model and analysis-by-synthesis wav files in 'model' and 'gen' directories, respectively.

python3 00_run.py

alphadir = {'trn' : datadir + '/alpha_trn',
            'val' : datadir + '/alpha_val',
            'test' : None}).

alpha files (format: float, extention: .alpha) need to be put in 'data/alpha_trn' and 'data/alpha_val'.
- For example, you can use voiced/unvoiced flags as alpha and extract them from speech waveform using SPTK (http://sp-tk.sourceforge.net/) as follows.

wav2raw -d ./ hoge.wav
x2x +sf hoge.raw | pitch -p 80 -o 1 | sopr -c 1.0 | interpolate -l 1 -p 257 -d > hoge.alpha