Add wavernn example pipeline #749
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| bits = 16 if self.mode == 'MOL' else self.n_bits | ||
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| x = (x + 1.) * (2 ** bits - 1) / 2 | ||
| x = torch.clamp(x, min=0, max=2 ** bits - 1) | ||
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| return mel.squeeze(0), x.int().squeeze(0) |
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This converts representation of a waveform from [-1, 1] to 16-bit integer representation. For instance, this is done in load_wav already. Since this is an important step and can be generalized, let's make this into a function within torchaudio. One point of discussion is whether we add that directly in WaveRNN.
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This function has been added as normalized_waveform_to_bits function in processing.py.
Codecov Report
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## master #749 +/- ##
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+ Coverage 89.87% 89.88% +0.01%
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Lines 2666 2660 -6
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- Hits 2396 2391 -5
+ Misses 270 269 -1
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btw, can you add a README.md to discuss the pipeline? |
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It'd be nice to get a baseline by comparing the error you get here to the output obtained by Griffin-Lim, say, and in other norms too L^1, L^2 for instance. |
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Since this is not the original WaveRNN model, I'd recommend renaming it as "FatchordWaveRNN" or something similar. |
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LGTM. Minor things to address:
- fix jit in wavernn in separate pull request Fix output type of upsampling #801
- change two command line parameters
- fix default format in docstring of wavernn model Update form of default value in docstring #802
…ials Fix formatting and clean up tutorial on quantized transfer learning
Co-authored-by: Shen Li <shenli@devfair017.maas>
This is a reference example using WaveRNN model to train on LJSpeech. The structure will be inspired by #632 and WaveRNN.
There are at least a few more things to do:
Add torchaudio transforms on mel-spectrogram.Related to #446
Stack:
Add MelResNet Block #705, #751Add Upsampling Block #724Add WaveRNN Model #735Add example pipeline with WaveRNN #749
Remove underscore of wavernn model #810
cc @cpuhrsch @zhangguanheng66
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