| # Overflow TTS |
|
|
| 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: |
| ``` |
|
|
| ## Overflow Model |
| ```{eval-rst} |
| .. autoclass:: TTS.tts.models.overflow.Overflow |
| :members: |
| ``` |
