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)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("efficient-speech/lite-whisper-large-v3", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add Acknowledgement and Citation sections
#2
by nielsr HF Staff - opened
This PR enhances the model card for Lite-Whisper large-v3 by incorporating the "Acknowledgement" and "Citation" sections. These sections were found in the project's GitHub README and provide essential context and attribution for the LiteASR model.
No other changes were deemed necessary as the existing metadata, paper link, GitHub link, and sample usage are already accurate and complete according to the provided information.