Automatic Speech Recognition
Transformers
Safetensors
lite-whisper
feature-extraction
audio
whisper
hf-asr-leaderboard
custom_code
Instructions to use efficient-speech/lite-whisper-base-acc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efficient-speech/lite-whisper-base-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-base-acc", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("efficient-speech/lite-whisper-base-acc", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card with abstract, quick start, and detailed methodology
#1
by nielsr HF Staff - opened
This PR enhances the model card for efficient-speech/lite-whisper-base-acc by:
- Adding a descriptive H1 title for better clarity.
- Including the paper abstract for a more detailed introduction to LiteASR.
- Adding a "Quick Start" code snippet for immediate model usage with the
transformerslibrary, adapted for this specificbase-accmodel. - Expanding the "Benchmark Results" to cover larger LiteASR models, providing a comprehensive view of the family's performance.
- Integrating the "Motivations and Methodology" section with explanatory figures to provide deeper technical insights into the compression scheme.
These additions significantly improve the model card's informativeness and user-friendliness, highlighting the efficiency and performance benefits of LiteASR.