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AI Models 535

  • rubaistt_v2_medium↗

    islomov

    Classic Whisper medium model fine-tuned for Uzbek language. The dataset included of diverse audio: publicly available podcasts, Tashkent dialect podcasts, news, google fleurs, USC and Common Voice 17. Data quality was mixed with 50% human transcribed and 50% pseudo-transcribed using Gemini 2.5 Pro. Difference between v1 is that v2 is fully open-sourced. Due to some conflicts with data partners, v1 was removed, and the 500-hour dataset was excluded. Instead, new and different datasets were included—all of which will be open-sourced. Training scripts will also be open-sourced. The entire process will be fully repeatable. Special attention was given to Tashkent dialect audio materials, resulting in strong performance on this dialect. Future versions will include other regional dialects to improve overall coverage.

  • whisper-small-uyghur-common-voice↗

    ixxan

  • distil-medium.en↗

    distil-whisper

    Distil-Whisper was proposed in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling. It is a distilled version of the Whisper model that is **6 times faster**, 49% smaller, and performs **within 1% WER** on out-of-distribution evaluation sets. This is the repository for distil-medium.en, a distilled variant of Whisper medium.en. **Note:** Distil-Whisper is currently only available for English speech recognition. We are working with the community to distill Whisper on other languages. If you are interested in distilling Whisper in your language, check out the provided training code. We will update the Distil-Whisper repository with multilingual checkpoints when ready!

  • whisper-large-v2-bg↗

    anuragshas

  • whisper-finetuned-somali-stt↗

    hamaada

  • whisper-large-v3-turbo-cantonese-yue-english↗

    JackyHoCL

  • whisper-base.en↗

    onnx-community

    https://huggingface.co/openai/whisper-base.en with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

  • whisper-large-v3-turbo-int4-ov↗

    OpenVINO

  • faster-whisper-base-ar-quran↗

    OdyAsh

  • whisper-tunisian-dialect↗

    TuniSpeech-AI