Automatic Speech Recognition
Transformers
PyTorch
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results
Instructions to use openai/whisper-large-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-large-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v3", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download model.fp32-00001-of-00002.safetensors from openai/whisper-large-v3: direct link, hf CLI and curl.
- Browser
- Download file 4.99 GB
-
https://huggingface.co/openai/whisper-large-v3/resolve/main/model.fp32-00001-of-00002.safetensors
- Command line
-
hf download hf://openai/whisper-large-v3/model.fp32-00001-of-00002.safetensors
-
curl -L -o model.fp32-00001-of-00002.safetensors https://huggingface.co/openai/whisper-large-v3/resolve/main/model.fp32-00001-of-00002.safetensors
4.99 GB
- Xet hash:
- f8d14fae36c473920522ae494bede8dac823cd4b199b091ee48c7ea060b6c482
- Size of remote file:
- 4.99 GB
- SHA256:
- 08e0005225b3dbaf55dd13ac62926cc7e02c1025d66fa375e6fb305ff79cd4f9
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