Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use openai/whisper-small.en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-small.en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-small.en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-small.en") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-small.en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
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"errors": "replace",
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"model_max_length": 1024,
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"name_or_path": "/
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"pad_token": null,
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"processor_class": "WhisperProcessor",
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"special_tokens_map_file": null,
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"errors": "replace",
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"model_max_length": 1024,
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"name_or_path": "openai/whisper-small.en",
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"pad_token": null,
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"processor_class": "WhisperProcessor",
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"special_tokens_map_file": null,
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