mms-tts-tcs
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mms-tts-tcz
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View details ↗mms-tts-tdj
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View details ↗mms-tts-ted
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View details ↗mms-tts-tee
facebook基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mms-tts-tel
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-tem
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View details ↗mms-tts-teo
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View details ↗mms-tts-ter
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View details ↗mms-tts-tes
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View details ↗mms-tts-tew
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View details ↗mms-tts-tex
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View details ↗mms-tts-tfr
facebook基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mms-tts-tgj
facebook基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mms-tts-tgk
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-tgl
facebook基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mms-tts-tgo
facebook基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mms-tts-tgp
facebook基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mms-tts-tha
facebook基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mms-tts-thk
facebook基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mms-tts-thl
facebook基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mms-tts-tih
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View details ↗mms-tts-tik
facebook基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mms-tts-tir
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.
Input Text→ Output Audio
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