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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6b35cc0d1d116806f09888798ce36c9f5950465d52468218cfc78c71e7e3809
- Size of remote file:
- 3.06 GB
- SHA256:
- 9cab9e33c423511099b0f7b9b8f54eb37c5acfdbdd1e363765621b6cec24657c
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