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* Update docs index * Add MarryTTS docs * Update docs index * Add Overflow docs
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# Overflow TTS | ||
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Neural HMMs are a type of neural transducer recently proposed for | ||
sequence-to-sequence modelling in text-to-speech. They combine the best features | ||
of classic statistical speech synthesis and modern neural TTS, requiring less | ||
data and fewer training updates, and are less prone to gibberish output caused | ||
by neural attention failures. In this paper, we combine neural HMM TTS with | ||
normalising flows for describing the highly non-Gaussian distribution of speech | ||
acoustics. The result is a powerful, fully probabilistic model of durations and | ||
acoustics that can be trained using exact maximum likelihood. Compared to | ||
dominant flow-based acoustic models, our approach integrates autoregression for | ||
improved modelling of long-range dependences such as utterance-level prosody. | ||
Experiments show that a system based on our proposal gives more accurate | ||
pronunciations and better subjective speech quality than comparable methods, | ||
whilst retaining the original advantages of neural HMMs. Audio examples and code | ||
are available at https://shivammehta25.github.io/OverFlow/. | ||
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## Important resources & papers | ||
- HMM: https://de.wikipedia.org/wiki/Hidden_Markov_Model | ||
- OverflowTTS paper: https://arxiv.org/abs/2211.06892 | ||
- Neural HMM: https://arxiv.org/abs/2108.13320 | ||
- Audio Samples: https://shivammehta25.github.io/OverFlow/ | ||
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## OverflowConfig | ||
```{eval-rst} | ||
.. autoclass:: TTS.tts.configs.overflow_config.OverflowConfig | ||
:members: | ||
``` | ||
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## Overflow Model | ||
```{eval-rst} | ||
.. autoclass:: TTS.tts.models.overflow.Overflow | ||
:members: | ||
``` |