A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis

Related tags

Deep Learning text-to-speech waveglow neural-vocoder
Overview

WaveGlow

A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis

Quick Start:

  1. Install requirements:
pip install -r requirements.txt
  1. Download dataset:
wget http://festvox.org/cmu_arctic/cmu_arctic/packed/cmu_us_slt_arctic-0.95-release.tar.bz2
tar xf cmu_us_slt_arctic-0.95-release.tar.bz2

  1. Extract features: feature extracting pipeline is the same as tacotron

  2. Training with default hyperparams:

python train.py
  1. Synthesize from model:
python generate.py --checkpoint=/path/to/model --local_condition_file=/path/to/local_conditon

Notes:

  • This is not official implementation, some details are not necessarily correct.
  • Work in progress.
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Comments
  • How to choose upsampling rte for conditioning?

    How to choose upsampling rte for conditioning?

    @npuichigo

    I have local conditioning of following shape 1 X 1000 X 50 (for 1 sec) with input speech of 16k sampling rate. Can you recommend, which up sampling factor I should choose ? and How to estimate up sampling factor ? Thanks

    opened by ajinkyakulkarni14 5
  • Undefined name: Where is _GetFileAndLine() defined?

    Undefined name: Where is _GetFileAndLine() defined?

    https://github.com/npuichigo/waveglow/search?q=_GetFileAndLine

    flake8 testing of https://github.com/npuichigo/waveglow on Python 3.7.1

    $ flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics

    ./waveglow/logging.py:144:38: F821 undefined name '_GetFileAndLine'
    count = _GetNextLogCountPerToken(_GetFileAndLine())
                                     ^
./waveglow/logging.py:157:38: F821 undefined name '_GetFileAndLine'
    count = _GetNextLogCountPerToken(_GetFileAndLine())
                                     ^
2     F821 undefined name '_GetFileAndLine'
2
    opened by cclauss 1
  • Early Output implementation is same as the paper?

    Early Output implementation is same as the paper?

    Hi,

    It's a nice work! I found that you output half of current channels for early output. But in the paper, they output constant channels for early output. In their case, output 2 channels. Am I wrong? Or there are some tricks?

    opened by dhgrs 1
  • negative loss value

    negative loss value

    i'm using your code to train my dataset,the codes ran ok while loss value is negative ,is this normal?

    INFO:pytorch:Let's use 1 GPUs!
C:\Python35\lib\site-packages\torch\nn\modules\upsampling.py:122: UserWarning: nn.Upsampling is deprecated. Use nn.functional.interpolate instead.
  warnings.warn("nn.Upsampling is deprecated. Use nn.functional.interpolate instead.")
INFO:pytorch:[1,   1] loss: 8774.103
INFO:pytorch:[1,   2] loss: 247.321
INFO:pytorch:[1,   3] loss: 2267.624
INFO:pytorch:[1,   4] loss: 2223.914
INFO:pytorch:[1,   5] loss: 21230.785
INFO:pytorch:[1,   6] loss: -4516.562
INFO:pytorch:[1,   7] loss: -1636.356
INFO:pytorch:[1,   8] loss: -1802.424
INFO:pytorch:[1,   9] loss: -973.106
INFO:pytorch:[1,  10] loss: -2390.099
INFO:pytorch:[1,  11] loss: -3500.059
INFO:pytorch:[1,  12] loss: -2850.755
INFO:pytorch:[1,  13] loss: -4785.271
INFO:pytorch:[1,  14] loss: -5666.863
INFO:pytorch:[1,  15] loss: -6398.563
INFO:pytorch:[1,  16] loss: 6809.037
INFO:pytorch:[1,  17] loss: 6204.096
INFO:pytorch:[1,  18] loss: -9287.712
INFO:pytorch:[1,  19] loss: -3614.344
    opened by shartoo 3
Owner
Yuchao Zhang
speech synthesis/machine learning
Yuchao Zhang
GitHub
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