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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("openai/whisper-small.en") model = AutoModelForMultimodalLM.from_pretrained("openai/whisper-small.en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9234cba9c104310e009fcb76b1785063db9801bf8b6b161d70ce3378b880c7ae
- Size of remote file:
- 967 MB
- SHA256:
- 6014ac49b506df900f66f4aca6b0801eed7245594ace97bcaf73e0ae5b863066
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