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AI モデル 535

• distil-small.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-small.en, a distilled variant of Whisper small.en. It is the **smallest Distil-Whisper checkpoint**, with just 166M parameters, making it the ideal choice for memory constrained applications (e.g. on-device). For most other applications, the distil-medium.en or distil-large-v2 checkpoints are recommended, since they are both faster and achieve better WER results:

• whisper-base.en-gguf↗

  handy-computer

  Ported from upstream commit 911407f, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper base.en — converted to GGUF for transcribe.cpp. English-only; faster than the multilingual model at the same size. Encoder-decoder transformer; 30-second windows with chunked long-form decoding. WER on the full LibriSpeech test-clean split (2,620 utterances), batch size 1, timestamps none. Figures without a commit were published before provenance was recorded.

• Liliths-Whisper-L3.3-70b-0.2b-i1-GGUF↗

  mradermacher

• Whisper-Medicalv1↗

  Crystalcareai

• whisper-tamil-large-v2↗

  vasista22

  This model is a fine-tuned version of openai/whisper-large-v2 on the Tamil data available from multiple publicly available ASR corpuses. It has been fine-tuned as a part of the Whisper fine-tuning sprint. **NOTE:** The code used to train this model is available for re-use in the whisper-finetune repository. In order to evaluate this model on an entire dataset, the evaluation codes available in the whisper-finetune repository can be used.

• whisper-large-v3-turbo-gguf↗

  voconly-org

• whisper-large-v3↗

  ivrit-ai

  Language detection capability of this model has been degraded during training - it is intended for mostly-hebrew audio transcription. Language token should be explicitly set to Hebrew. Additionally, the tanslation task was not trained and also degraded. This model would not be able to translate in any reasonable capacity. Please follow the original model card for usage details - replacing with this model name. You can also fine other weight formats ad quantizations on the ivrit ai HF page.

• whisper-small.en↗

  onnx-community

  https://huggingface.co/openai/whisper-small.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-Base-En↗

  qualcomm

• kana-whisper↗

  0x3