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change grinffin-lim algorithm to wavenet-based vocoder
See original GitHub issueDoes anyone plan to use wavenet-based vocoder, insteading of griffin-lim algoritm, which greatly increase the audio quality in the single-speaker experiment of Deep Voice 2 and Deep Voice 3. And in Deep Voice 3,both deep voice 3 + wavenet and tacotron + wavenet achieves the highest MOS of 3.78,Furthmore it is claimed that deep voice 3 + wavenet is faster to trian and faster to convege than tacotron+wavenet
Issue Analytics
- State:
- Created 6 years ago
- Reactions:6
- Comments:7 (1 by maintainers)
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arXiv:2007.05764v1 [eess.AS] 11 Jul 2020
The proposed approach applies Fast Griffin Lim. Algorithm (FGLA) instead Griffin Lim algorithm (GLA) as vocoder in the speech synthesis phase.
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Tacotron, instead of using Griffin-Lim, we train a WaveNet-based neural vocoder to convert from linear spectrograms to audio waveforms.
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I’ve been trying to use wavenet insteading of griffin-lim recently .
I wonder why Griffin-Lim samples from here https://nv-adlr.github.io/WaveGlow have as good quality as other methods? Is it large number of iterations or it’s just good output of tacotron?