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
Commit ·
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Parent(s): e5aba7b
Update README.md
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README.md
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@@ -376,7 +376,7 @@ predict utterance level timestamps by passing `return_timestamps=True`:
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>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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>>> sample = ds[0]["audio"]
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>>> prediction = pipe(sample)["text"]
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" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel."
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>>> # we can also return timestamps for the predictions
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>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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>>> sample = ds[0]["audio"]
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>>> prediction = pipe(sample.copy())["text"]
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" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel."
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>>> # we can also return timestamps for the predictions
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