Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use openai/whisper-medium.en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-medium.en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-medium.en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-medium.en") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-medium.en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6763bf812bbb9ae1e107316b5ad7502bb8c8701f20f6c2db9bba179d3ecaf8d
- Size of remote file:
- 3.06 GB
- SHA256:
- 9817baf8ac8822dd38d7ba3feff91047898c4dae9afb7f7370afe05c5d6a0a34
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