Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use openai/whisper-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-medium")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-medium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3882842b99ae4e30c55d4fbfbcd430e0a2603e53c9dce1270a3ee19a7b51d8e8
- Size of remote file:
- 3.06 GB
- SHA256:
- a0719162ff90716276ee4cceb13b8bb6fc87601bb71a6b0bfdde08708c9fe550
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