Instructions to use CWKSC/whisper-small-onnx-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CWKSC/whisper-small-onnx-quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="CWKSC/whisper-small-onnx-quantized")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("CWKSC/whisper-small-onnx-quantized") model = AutoModelForSpeechSeq2Seq.from_pretrained("CWKSC/whisper-small-onnx-quantized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download encoder_model_quantized.onnx from CWKSC/whisper-small-onnx-quantized: direct link, hf CLI and curl.
- Browser
- Download file 98.2 MB
-
https://huggingface.co/CWKSC/whisper-small-onnx-quantized/resolve/main/encoder_model_quantized.onnx
- Command line
-
hf download hf://CWKSC/whisper-small-onnx-quantized/encoder_model_quantized.onnx
-
curl -L -o encoder_model_quantized.onnx https://huggingface.co/CWKSC/whisper-small-onnx-quantized/resolve/main/encoder_model_quantized.onnx
98.2 MB
- Xet hash:
- 36682d2d8121470a755a53496a80156d0eab270e2c0e721e65c7e8400d33fbc0
- Size of remote file:
- 98.2 MB
- SHA256:
- 0901ac19061151e523975bfec4045206ccc302d5a65fbbfc633303e783b61cef
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