Comments (2)
Thank you for your reply. I will continue to report on my work
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Hi @Kerry0123,
Yeah I get about 0.225x real-time with the 16kHz model. There are a number of tricks you can try to get improved speeds. You could probably apply most of the optimizations from the WaveRNN paper. Specifically, you'd need to implement:
- a single persistent GPU operation for sampling.
- structured sparcity.
- subscale sampling.
Unfortunately, I don't have much time to work on these optimizations but I'd be happy to accept and review any pull requests if you're interested in working on it.
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Related Issues (20)
- 24kHz and 10 bit mu-law model HOT 2
- Question about preprocess.py HOT 1
- Usage of audio_slice_frames, sample_frames, pad HOT 8
- Generating samples from generated Mel-spectrograms HOT 3
- Result remains little noise, but loss does not decrease HOT 9
- Changing parameters HOT 2
- How long does it takes to train from the scratch? HOT 4
- About Speaker Voice HOT 4
- preprocessing_mel question HOT 6
- generate_audio questions
- Why the embedding layer instead of the one-hot audio vector? HOT 1
- audio_slice_frames in v0.2
- audio_slice_frames deprecation in v0.2 HOT 1
- Help needed. Trying to get vocoder working with output from a ML Tracotron HOT 5
- num_steps of training for those demo sample? HOT 5
- Result with other datasets HOT 1
- Inference speed comparison HOT 1
- mulaw encdoing HOT 1
- What's the capacity of this network? HOT 14
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