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by romaniv
3571 days ago
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Maybe I'm reading this paper incorrectly, but it seems that in this system "voice" is part of the model parameters not inputs. What they did was train the same model with multiple reader voices while using one of the inputs to keep track of which voice the model was currently trained on. So the model can switch between different voices, but only between those which it was trained on. "The conditioning was applied by feeding the speaker ID to the model in the form of a one-hot vector. The dataset consisted of 44 hours of data from 109 different speakers." Am I missing something? |
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"In order to use WaveNet to turn text into speech, we have to tell it what the text is. We do this by transforming the text into a sequence of linguistic and phonetic features (which contain information about the current phoneme, syllable, word, etc.) and by feeding it into WaveNet."
The raw audio from Step 3 was (in principle) generated by that input on a properly trained WaveNet. We need to recover that so we can transfer it to the target WaveNet.
How a specific WaveNet instance is configured (as you point out, it's part of the model parameters) is an implementation detail that is irrelevant for the steps I proposed.