Comments (4)
Well, does that make any difference?
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As we have found, the output spectrogram should be raised by a power of 1.2. Then the voice sounds more clear
from tacotron.
Okay, let me give you an example. Correct me if I'm wrong.
Assume a value of an element of the original magnitude is 2. The best model should output 2, of course. Then it is raised by power of 1.2, that is, 2.29. before being converted into the wav form. In contrast,I raised the target value, so our model tries to learn 2.29.
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Actually, I've changed the relevant codes.
See https://github.com/Kyubyong/tacotron/blob/master/utils.py#L42 and
https://github.com/Kyubyong/tacotron/blob/master/eval.py#L65
I think this revision is closer to the paper.
Thanks, guys!
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Related Issues (20)
- Can anyone guide me how to get Audio out from eval.py file for testing ?? HOT 1
- Bus error: 10 at training HOT 6
- How to synthesize long sentences?
- About the performance of synthesis
- speaker adaption : not to update the encoder parameters
- ref_db=20, max_db=100, Where did these values come from? statistics?
- Generated wave were empty HOT 8
- I've uploaded Donald Trump speeches and transcripts HOT 1
- error in utils,py
- How can we exploit forced alignments?
- Error in data_load.py---TypeError: a bytes-like object is required, not 'str'
- Segmentation fault on training HOT 1
- Get MelSpectogram for wavenet
- IOError: [Errno 2] No such file or directory: '/data/private/voice/LJSpeech-1.0/transcript.csv'
- IOError: [Errno 2] No such file or directory: '/data/private/voice/LJSpeech-1.0/transcript.csv' HOT 1
- Transcript.csv file? HOT 1
- Different result in train and eval and synthesize mode
- how much it takes to train and on what hardware?
- Tensorflow error
- [CONTRIBUTION] Speech Dataset Generator
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