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AI モデル 85003
- qwen3-tts-0.6b-base-GGUF↗
cstr
- Qwen3.8-27B-AWQ-FP8↗
cyankiwi
- Qwen3.8-Flash-Next-OrcaUncensored-IQ2-Light↗
dongnhdev
- CherryPit-1.0-2B-GGUF↗
DRUPEs
- Hermes-2-Pro-Llama-3-8B-imatrix-GGUF↗
duyntnet
- Impish_LLAMA_4B-imatrix-GGUF↗
duyntnet
- Qwen1.5-MoE-A2.7B-Chat-imatrix-GGUF↗
duyntnet
- gliner2-multi-sentiment-mixed-c42↗
erickdp
- ScaleCUA-qwen3.5-osworld-sft↗
extreme1228
- mms-tts-fra↗
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.