Automatic Speech Recognition
Transformers
Safetensors
lite-whisper
feature-extraction
audio
whisper
hf-asr-leaderboard
custom_code
Instructions to use efficient-speech/lite-whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efficient-speech/lite-whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="efficient-speech/lite-whisper-base", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("efficient-speech/lite-whisper-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add abstract and Transformers sample usage
#1
by nielsr HF Staff - opened
This PR improves the model card by:
- Updating the main heading to the official paper title for better discoverability and context.
- Adding the full paper abstract to provide a comprehensive overview of the model's methodology and contributions.
- Including a "Sample Usage" section with a
transformers-compatible code snippet, directly sourced from the GitHub README. This enables users to easily get started with the model.
Existing metadata, benchmark results, and citation information remain unchanged as they are already accurate and well-formatted.