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AI Models 84759

• tinystories-gpt2-3M↗

  calum

• wav2vec2-xls-r-juznevesti-sr↗

  classla

  This model for Serbian ASR is based on the facebook/wav2vec2-xls-r-300m model and was fine-tuned with 58 hours of audio and transcripts from Južne vesti, programme '15 minuta'. For more info on the dataset creation see this repo. Tested with `transformers==4.18.0`, `torch==1.11.0`, and `SoundFile==0.10.3.post1`.

• CLOSP-Visual↗

  DarthReca

• KawaiiRealisticAsian_v0.7↗

  digiplay

• Qwen3.6-27B-AWQ-INT4↗

  drawais

• OLMo-2-7B-Instruct-GGUF↗

  DuoNeural

• fasttext-tr-vectors↗

  facebook

• mms-tts-tgk↗

  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-xsu↗

  facebook

• MobileLLM-ParetoQ-350M-4-bit↗

  facebook