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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0cc1d2611f626b66d536651f3f328ce53c4deb1b1189b2569cbafac3b47352b5
- Size of remote file:
- 967 MB
- SHA256:
- 9ccee6487eef47d570fac7b39517b61915130628d5ebf6eae3df6f60734e82e6
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