Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use openai/whisper-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large") - Notebooks
- Google Colab
- Kaggle
add link for whisper large v3 to the readme
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by iitsg - opened
README.md
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| medium | 769 M | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) |
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| large | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large) |
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| large-v2 | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large-v2) |
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# Usage
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| medium | 769 M | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) |
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| large | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large) |
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| large-v2 | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large-v2) |
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| large-v3 | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large-v3) |
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# Usage
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