mms-tts-suz
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mms-tts-swe
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View details ↗mms-tts-swh
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View details ↗mms-tts-sxb
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View details ↗mms-tts-sxn
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View details ↗mms-tts-sya
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View details ↗mms-tts-syl
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View details ↗mms-tts-sza
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View details ↗mms-tts-tac
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View details ↗mms-tts-taj
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View details ↗mms-tts-tam
facebookThis model is part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the MMS Language Coverage Overview, and see all MMS-TTS checkpoints on the Hugging Face Hub: facebook/mms-tts. VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior. A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers, much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to synthesise speech with different rhythms from the same input text.
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View details ↗mms-tts-tao
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View details ↗mms-tts-tap
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View details ↗mms-tts-taq
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View details ↗mms-tts-tat
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View details ↗mms-tts-tav
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View details ↗mms-tts-tbc
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View details ↗mms-tts-tbg
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View details ↗mms-tts-tbk
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View details ↗mms-tts-tbl
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View details ↗mms-tts-tby
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View details ↗mms-tts-tbz
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View details ↗mms-tts-tca
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View details ↗mms-tts-tcc
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