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[FastPitch] Why do you hierarchically predict the variance features (pitch and energy)? #1357

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changjinhan opened this issue Oct 5, 2023 · 2 comments
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enhancement New feature or request

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@changjinhan
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Thank you always for sharing your thoughtful code.

As we can see in FastPitch code, you added the pitch embedding to encoder output before passing the energy predictor.

enc_out = enc_out + pitch_emb.transpose(1, 2)

Why did you chose the hierarchical variance feature prediction instead of parallel prediction like the FastSpeech2(paper version)?
Are there any performance advantages?

@changjinhan changjinhan added the enhancement New feature or request label Oct 5, 2023
@hervenzoghe
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Hello 😌. I hope you're well and that you are having a good day.

Sorry 😅 I don't know how it happened and sorry for that. I was trying to build my own model for my data for my local language and I faced issues. I don't know how I did what you said.

Can you please 🥺 tell me how I can use FastPitch to build my own model in Colab or another notebook?

I have issues with the base configuration: docker, NGC Container in Colab. How can I solve this?

@hervenzoghe
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hervenzoghe commented Oct 6, 2023 via email

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