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-fast 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-fast 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-fast", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("efficient-speech/lite-whisper-large-v3-fast", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card with detailed description, sample usage, citation, and acknowledgements
#2
by nielsr HF Staff - opened
This PR enhances the model card by:
- Expanding the model description with details from the paper abstract to provide a more comprehensive overview of LiteASR.
- Adding a "Quick Start" code snippet for easy inference, directly sourced from the project's GitHub README, to guide users on how to use the model with the Hugging Face Transformers library.
- Including the "Acknowledgement" and "Citation" sections from the GitHub repository for proper attribution.
These changes make the model card more informative and user-friendly, providing immediate guidance on how to use the model and giving appropriate credit.