Automatic Speech Recognition
Transformers
Safetensors
lite-whisper
feature-extraction
audio
whisper
hf-asr-leaderboard
custom_code
Eval Results
Instructions to use efficient-speech/lite-whisper-large-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efficient-speech/lite-whisper-large-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="efficient-speech/lite-whisper-large-v3", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("efficient-speech/lite-whisper-large-v3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add Open ASR Leaderboard evaluation results (#3)
- Add Open ASR Leaderboard evaluation results (62024e1e43167e7ed7efea4a77fdc91e38e199b6) Co-authored-by: Nathan Habib <SaylorTwift@users.noreply.huggingface.co>0803b28 -
Add Open ASR Leaderboard evaluation results (#4)
- Add Open ASR Leaderboard evaluation results (7a17c3d6e0c2595de8efc8a8dd55021e1f8d81a9) Co-authored-by: Nathan Habib <SaylorTwift@users.noreply.huggingface.co>3ed2b44
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Improve model card: add link to code and example usage (#1)
Verified signature- Improve model card: add link to code and example usage (e642600c900164946ec1791311955e78ae44d2a3) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>6444716 -
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