Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use openai/whisper-base.en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-base.en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-base.en") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-base.en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0616a5e8a48f28cd902f054cb7f67cc69ab9cf2a78c4e0a0763c9006904e7ab5
- Size of remote file:
- 291 MB
- SHA256:
- 40ecea6544a8a7a684d822c7519d05aa2b9cadff93113b7a01a25f0135b30149
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