Pytorch Implementation of WaveNODE

PyTorch implementation of WaveNODE

Abstract

In recent years, various flow-based generative models have been proposed to generate highfidelity waveforms in real-time. However, these models require either a well-trained teacher network or a number of flow steps making them memory-inefficient. In this paper, we propose a novel generative model called WaveNODE which exploits a continuous normalizing flow for speech synthesis. Unlike the conventional models, WaveNODE places no constraint on the function used for flow operation, thus allowing the usage of more flexible and complex functions. Moreover, WaveNODE can be optimized to maximize the likelihood without requiring any teacher network or auxiliary loss terms. We experimentally show that WaveNODE achieves comparable performance in terms of mean opinion score (MOS) and conditional log-likelihood (CLL) with fewer parameters compared to the existing models.

Requirements

• PyTorch 1.3.1
• Python 3.7.3
• Librosa
• torchdiffeq : https://github.com/rtqichen/torchdiffeq

Examples

Dataset

LJSpeech : https://keithito.com/LJ-Speech-Dataset/

Preprocessing

python preprocessing.py --in_dir=LJSpeech-1.1 --out_dir=DATASETS/ljspeech

Training

python train.py --batch_size=20 --n_block=4 --split_period=2 --scale_init=4 --n_layer_wvn=4 --n_channel_wvn=128 --d_i=3 --log_interval=50 --synth_interval=250

Audio Sample Generation

python synthesize.py --load_step=27140 --tol_synth=1e-3 --num_synth=10 --batch_size=20 --n_block=4 --scale_init=4 --split_period=2 --n_layer_wvn=4 --n_channel_wvn=128 --d_i=3

Test Conditional Log-Likelihood

python test_cll.py --load_step=27140 --batch_size=20 --n_block=4 --scale_init=4 --split_period=2 --n_layer_wvn=4 --n_channel_wvn=128 --d_i=3

Test Sampling Speed

python test_speed.py --load_step=27140 --batch_size=20 --n_block=4 --scale_init=4 --split_period=2 --n_layer_wvn=4 --n_channel_wvn=128 --d_i=3

Audio Samples

Audio examples : https://wavenode-example.github.io/

Reference

• FFJORD : https://github.com/rtqichen/ffjord
• WaveGlow : https://github.com/NVIDIA/waveglow
• FloWaveNet : https://github.com/ksw0306/FloWaveNet
• PointFlow : https://github.com/stevenygd/PointFlow