Automatic Speech Recognition
Transformers
Safetensors
lite-whisper
feature-extraction
audio
whisper
hf-asr-leaderboard
custom_code
Instructions to use efficient-speech/lite-whisper-tiny-acc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efficient-speech/lite-whisper-tiny-acc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="efficient-speech/lite-whisper-tiny-acc", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("efficient-speech/lite-whisper-tiny-acc", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card with detailed abstract and sample usage
#1
by nielsr HF Staff - opened
This PR enhances the model card by:
- Replacing the brief introductory summary with the more detailed abstract from the paper/GitHub repository, providing users with a comprehensive overview of LiteASR's methodology and benefits.
- Adding a "Sample Usage" section, including a direct code snippet from the official GitHub repository. This will allow users to quickly understand how to run the model using the
transformerslibrary, improving accessibility and usability on the Hugging Face Hub.
All existing metadata and links (including the arXiv paper link) are retained as per the community guidelines.