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")# pip install -U transformers accelerate # 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
Download tokenizer.json from versae/whisper-large-v3: direct link, hf CLI and curl.
- Browser
- Download file 2.48 MB
-
https://huggingface.co/versae/whisper-large-v3/resolve/main/tokenizer.json
- Command line
-
hf download hf://versae/whisper-large-v3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/versae/whisper-large-v3/resolve/main/tokenizer.json
2.48 MB
File too large to display, you can check the raw version instead.