Search FoxAI

AI Models 85001

• RWKV6-3B-Chn-UnlimitedRP-mini-chat-GGUF↗

  btaskel

• Anima-1.0-Turbo-Diffusers↗

  CalamitousFelicitousness

• smoothquant-qwen25-15b-g52xlarge↗

  chris320211

• Qwen2.5-0.5B-Instruct-AB-Map↗

  CleytonNobre

• ldm-text2im-large-256↗

  CompVis

• rdd2022-yolov8m↗

  dronefreak

• mms-tts-bul↗

  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-xer↗

  facebook

• sapiens-depth-1b↗

  facebook

• rus-cyrl-100mb-after-ppt-Dp-100mb-packed-bfdiso-ckpt500_seed455↗

  francesca9805