Coder Social home page Coder Social logo

waveglow's Issues

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?

synthesis time

How long does it take to synthesise 1 second of audio? Is is like the paper, faster than realtime?

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

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

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

数据集问题

你好，按照该仓库REDEME中的介绍，索引到tactron中数据处理的方法，但是好像数据命名问题是不一致的，也许是tactron中数据处理的部分更新了。
请问有没有其他数据预处理的模块。或者提供下数据集样式，谢谢！

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.