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 encoder_model_quantized.onnx from mohitsha/whisper-tiny-smooth-quant: direct link, hf CLI and curl.
- Browser
- Download file 11.8 MB
-
https://huggingface.co/mohitsha/whisper-tiny-smooth-quant/resolve/main/encoder_model_quantized.onnx
- Command line
-
hf download hf://mohitsha/whisper-tiny-smooth-quant/encoder_model_quantized.onnx
-
curl -L -o encoder_model_quantized.onnx https://huggingface.co/mohitsha/whisper-tiny-smooth-quant/resolve/main/encoder_model_quantized.onnx
11.8 MB
- Xet hash:
- daecb17994cd498e02bbadf93ed11cf9a26aee783f79e54cdb63b5066ccc70b1
- Size of remote file:
- 11.8 MB
- SHA256:
- 52a9c1d1cd191307509b8b5c7cd50fbc7bf994c0fe495d6fca101c5d7b3e19ee
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