Lightrun Answers was designed to reduce the constant googling that comes with debugging 3rd party libraries. It collects links to all the places you might be looking at while hunting down a tough bug.
And, if you’re still stuck at the end, we’re happy to hop on a call to see how we can help out.
Start / stop symbols
See original GitHub issueI’m trying to debug echoing and overly long output audio, and in the spirit of #31 and #133, seeing if I can fix things by playing around with the start and stop symbols. The Tensorflow seq2seq documentation is sparse, and I can’t tell from https://github.com/keithito/tacotron/blob/master/models/helpers.py what exactly is going on with start and stop symbols in testing and training. If I’m using my own dataset separate from blizzard/ljspeech, does the model expect:
- that my input text has <start> and <stop> symbols?
- that my target audio has an all-zero frame at the beginning as a “start” symbol, and an all-zero frame at the end?
In other words, is the Helper adding start and stop symbols automatically (the
initializefunction seems like it might be doing something like this?), or am I supposed to be providing certain things in the data? Is this different for training and testing?
Thanks in advance!
Issue Analytics
- State:
- Created 5 years ago
- Comments:8 (1 by maintainers)
Top Results From Across the Web
Top Related Medium Post
Top Related StackOverflow Question
Troubleshoot Live Code
Top Related Reddit Thread
Top Related Hackernoon Post
Top Related Tweet
Top Related Dev.to Post
Top Related Hashnode Post
Well I have referred Rayhane’s version https://github.com/Rayhane-mamah/Tacotron-2/issues/46 in which it will not stop decoding until the stop token instead of the end of full length is touched. It trains stop token targets to learn when to stop decoding and it works for synthesized audio. And I want find out whether we need to train the extra stop token targets or not.
5 hour only and 66K steps. eval-66000-linear.zip