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 pytorch_model.fp32-00002-of-00002.bin from openai/whisper-large-v3: direct link, hf CLI and curl.
- Browser
- Download file 1.18 GB
-
https://huggingface.co/openai/whisper-large-v3/resolve/main/pytorch_model.fp32-00002-of-00002.bin
- Command line
-
hf download hf://openai/whisper-large-v3/pytorch_model.fp32-00002-of-00002.bin
-
curl -L -o pytorch_model.fp32-00002-of-00002.bin https://huggingface.co/openai/whisper-large-v3/resolve/main/pytorch_model.fp32-00002-of-00002.bin
1.18 GB
- Xet hash:
- e6698c125a7a8c0f524291056a6f8a7f97d04a14f6a680af296ecb994c464e90
- Size of remote file:
- 1.18 GB
- SHA256:
- aadd3bdfecf9bc81e6ef6acf3c1ccaf3124c82eb06a94190064a28fdee096d64
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