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AI モデル 84759
- cagliostro-v3.5↗
bench-labs
- Qwen3.8-27B-OrcaRouter-Uncensored-MTP-GGUF↗
codavidgarcia
- x-asr-zh-en-GGUF↗
cstr
- Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-NEO-IMATRIX-MAX-MTP↗
DavidAU
- detr-resnet-50_finetuned_cppe5↗
devonho
- pythia-70m-seed7↗
EleutherAI
- Muse-Glimmer-30B-NVFP4-GGUF↗
esatapedico
- mms-tts-ade↗
facebook
- mms-tts-kir↗
facebook
This 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.
- mms-tts-nin↗
facebook