Automatic Speech Recognition
Transformers
Safetensors
lite-whisper
feature-extraction
audio
whisper
hf-asr-leaderboard
custom_code
Instructions to use efficient-speech/lite-whisper-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efficient-speech/lite-whisper-small 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", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("efficient-speech/lite-whisper-small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Improve model card with abstract and sample usage
Verified signatureThis PR enhances the model card by: - Adding the full abstract of the paper, providing a more comprehensive overview of the LiteASR model and its methodology. - Including a "Sample Usage" section with a Python code snippet, making it easier for users to quickly get started with the `efficient-speech/lite-whisper-small` model using the `transformers` library. The sample code was directly adapted from the provided GitHub repository's "Quick Start" guide. These improvements aim to make the model card more informative and accessible for the community.8cb607c
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Update README.md
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Upload LiteWhisperForConditionalGeneration
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initial commit
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