mirror of https://github.com/coqui-ai/TTS.git
Update docs (#2389)
* Update docs index * Add MarryTTS docs * Update docs index * Add Overflow docspull/2390/head
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formatting_your_dataset
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what_makes_a_good_dataset
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tts_datasets
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marytts
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.. toctree::
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:maxdepth: 2
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models/vits.md
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models/forward_tts.md
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models/tacotron1-2.md
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models/overflow.md
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.. toctree::
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:maxdepth: 2
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:caption: `vocoder` Models
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```
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# Overflow TTS
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Neural HMMs are a type of neural transducer recently proposed for
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sequence-to-sequence modelling in text-to-speech. They combine the best features
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of classic statistical speech synthesis and modern neural TTS, requiring less
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data and fewer training updates, and are less prone to gibberish output caused
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by neural attention failures. In this paper, we combine neural HMM TTS with
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normalising flows for describing the highly non-Gaussian distribution of speech
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acoustics. The result is a powerful, fully probabilistic model of durations and
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acoustics that can be trained using exact maximum likelihood. Compared to
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dominant flow-based acoustic models, our approach integrates autoregression for
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improved modelling of long-range dependences such as utterance-level prosody.
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Experiments show that a system based on our proposal gives more accurate
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pronunciations and better subjective speech quality than comparable methods,
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whilst retaining the original advantages of neural HMMs. Audio examples and code
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are available at https://shivammehta25.github.io/OverFlow/.
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## Important resources & papers
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- HMM: https://de.wikipedia.org/wiki/Hidden_Markov_Model
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- OverflowTTS paper: https://arxiv.org/abs/2211.06892
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- Neural HMM: https://arxiv.org/abs/2108.13320
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- Audio Samples: https://shivammehta25.github.io/OverFlow/
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## OverflowConfig
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```{eval-rst}
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.. autoclass:: TTS.tts.configs.overflow_config.OverflowConfig
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:members:
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```
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## Overflow Model
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```{eval-rst}
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.. autoclass:: TTS.tts.models.overflow.Overflow
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:members:
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```
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