Automatic Speech Recognition
Transformers
Safetensors
lite-whisper
feature-extraction
audio
whisper
hf-asr-leaderboard
custom_code
Instructions to use efficient-speech/lite-whisper-tiny-fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efficient-speech/lite-whisper-tiny-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-tiny-fast", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("efficient-speech/lite-whisper-tiny-fast", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Improve model card with abstract, detailed usage, and comprehensive benchmarks
Verified signatureThis PR enhances the model card for `efficient-speech/lite-whisper-tiny-fast` by: 1. **Adding a top-level title**: Using the paper's title `# LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation` for better structure and clarity. 2. **Including the paper abstract**: Providing essential context about the model's design and findings directly from the paper. 3. **Adding a "Quick Start" section with sample usage**: Incorporating a Python code snippet from the GitHub README to demonstrate how to use the model with the `transformers` library. The `processor` in the example has been updated to `openai/whisper-tiny` to match the `base_model` of this specific repository, ensuring consistency and correctness for users. 4. **Updating the "Benchmark Results" table**: Replacing the existing table with the more comprehensive version found in the LiteASR GitHub repository, which covers a wider range of compressed Whisper models and provides a fuller picture of the project'…601b040
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Update README.md
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Update README.md
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Upload LiteWhisperForConditionalGeneration
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initial commit
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