Automatic Speech Recognition
Transformers
PyTorch
Safetensors
whisper
feature-extraction
audio
hf-asr-leaderboard
Instructions to use versae/whisper-large-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use versae/whisper-large-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="versae/whisper-large-v3")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("versae/whisper-large-v3") model = AutoModel.from_pretrained("versae/whisper-large-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b8d69de98f33cf22a2ecba40d7429324bf29d0d333bc6feab04907de070b4d29
- Size of remote file:
- 6.17 GB
- SHA256:
- bcaf9e46381d570df3cd4052c761ccb2aa686ff9e5914dd98c2f32b3be0ee72a
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