Automatic Speech Recognition
Transformers
Safetensors
lite-whisper
feature-extraction
audio
whisper
hf-asr-leaderboard
custom_code
Instructions to use efficient-speech/lite-whisper-base-fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efficient-speech/lite-whisper-base-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-base-fast", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("efficient-speech/lite-whisper-base-fast", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add abstract and sample usage
#1
by nielsr HF Staff - opened
This PR enhances the model card by:
- Updating the main heading to the paper title: "LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation".
- Including the full paper abstract for better context and understanding of the model.
- Adding a "Quick Start" section with both a Python code snippet and command-line examples, directly from the GitHub repository, to make it easier for users to get started with the model using the
transformerslibrary.
These additions will make the model card more informative and user-friendly.