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
- Xet hash:
- 7f4473744dd34a0f925099455cc64d91ce9304c025cd5b02f59a9874ac1305d9
- Size of remote file:
- 967 MB
- SHA256:
- a512226e7f16843bf8d8800c85dd1267080d860947530da2d68590bb657894f2
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