Automatic Speech Recognition
Transformers
Safetensors
lite-whisper
feature-extraction
audio
whisper
hf-asr-leaderboard
custom_code
Instructions to use efficient-speech/lite-whisper-small-fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efficient-speech/lite-whisper-small-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-small-fast", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("efficient-speech/lite-whisper-small-fast", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add abstract and sample usage for LiteASR
#1
by nielsr HF Staff - opened
This pull request improves the model card for efficient-speech/lite-whisper-small-fast by:
- Adding the paper abstract: The full abstract from the paper has been included for better context and understanding of the LiteASR method.
- Adding a "Quick Start" section with sample usage: A code snippet demonstrating how to use the model with the Hugging Face Transformers library has been added, as found in the LiteASR GitHub repository. The code snippet has been adjusted to specifically use the
efficient-speech/lite-whisper-small-fastmodel and its correspondingopenai/whisper-smallprocessor for accuracy.
These changes provide more comprehensive information and practical guidance for users interacting with the model.