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AI 模型 535
- whisper-base.en↗
OpenAI
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need for fine-tuning. Whisper was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al. from OpenAI. The original code repository can be found here. **Disclaimer**: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card.
- Liliths-Whisper-L3.3-70b-0.1-i1-GGUF↗
mradermacher
- whisper-large-v3-russian↗
antony66
- Hadra-ASR-whisper-medium↗
algerian-nlp
- whisper-small-ckb↗
razhan
- whisper-large-v3-french↗
bofenghuang
- whisper-tiny-french-cased↗
qanastek
- whisper-tiny.en↗
eai6
This model is a fine-tuned version of openai/whisper-tiny.en on the Azure-dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.0680 - Wer: 44.3486 The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - training_steps: 1000
- kotoba-whisper-v2.2↗
kotoba-tech
_Kotoba-Whisper-v2.2_ is a Japanese ASR model based on kotoba-tech/kotoba-whisper-v2.0, with additional postprocessing stacks integrated as `pipeline`. The new features includes (i) speaker diarization with diarizers and (ii) adding punctuation with punctuators. The pipeline has been developed through the collaboration between Asahi Ushio and Kotoba Technologies Kotoba-Whisper-v2.2 is supported in the Hugging Face 🤗 Transformers library from version 4.39 onwards. To run the model, first install the latest version of Transformers. To load pre-trained diarization models from the Hub, you'll first need to accept the terms-of-use for the following two models: 1. pyannote/segmentation-3.0 2. pyannote/speaker-diarization-3.1
- whisper-small.en-gguf↗
handy-computer
Ported from upstream commit e872752, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper small.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.