Automatic Speech Recognition
Transformers
Safetensors
lite-whisper
feature-extraction
audio
whisper
hf-asr-leaderboard
custom_code
Instructions to use efficient-speech/lite-whisper-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efficient-speech/lite-whisper-tiny 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", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("efficient-speech/lite-whisper-tiny", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card with abstract and sample usage
#1
by nielsr HF Staff - opened
This PR enhances the model card for lite-whisper-tiny by adding a detailed abstract from the paper "LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation." This provides a more comprehensive overview of the model's methodology and benefits.
Furthermore, it incorporates a "Quick Start" section with a Python code snippet, directly sourced from the official GitHub repository. This sample usage demonstrates how to load and use the model with the transformers library, making it significantly easier for users to get started. The model name in the sample code has been adjusted to efficient-speech/lite-whisper-tiny to match the specific model this card describes.