Instructions to use mohitsha/whisper-tiny-smooth-quant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohitsha/whisper-tiny-smooth-quant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mohitsha/whisper-tiny-smooth-quant")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mohitsha/whisper-tiny-smooth-quant") model = AutoModelForSpeechSeq2Seq.from_pretrained("mohitsha/whisper-tiny-smooth-quant", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download decoder_with_past_model_quantized.onnx from mohitsha/whisper-tiny-smooth-quant: direct link, hf CLI and curl.
- Browser
- Download file 89.1 MB
-
https://huggingface.co/mohitsha/whisper-tiny-smooth-quant/resolve/main/decoder_with_past_model_quantized.onnx
- Command line
-
hf download hf://mohitsha/whisper-tiny-smooth-quant/decoder_with_past_model_quantized.onnx
-
curl -L -o decoder_with_past_model_quantized.onnx https://huggingface.co/mohitsha/whisper-tiny-smooth-quant/resolve/main/decoder_with_past_model_quantized.onnx
89.1 MB
- Xet hash:
- f71e012c6e54e681013e160f09d7460e268e31c39e4887a4e8af8752055facb9
- Size of remote file:
- 89.1 MB
- SHA256:
- 6bf6fafef624f3bc890424124e7629a898a654cd927bb57f26211d7b2a2613a8
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