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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from openai/whisper-large: direct link, hf CLI and curl.
- Browser
- Download file 6.17 GB
-
https://huggingface.co/openai/whisper-large/resolve/refs%2Fpr%2F47/pytorch_model.bin
- Command line
-
hf download hf://openai/whisper-large@refs/pr/47/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/openai/whisper-large/resolve/refs%2Fpr%2F47/pytorch_model.bin
6.17 GB
- Xet hash:
- 92b7021f9a4afde762e786307798a5d499213187023c6a4f3e88f629a41f1aee
- Size of remote file:
- 6.17 GB
- SHA256:
- 604afaab9242337bb1fe7ede5e65db4a2b8cffd6fe87cc7ba15cff3f5b06cca9
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