Instructions to use CWKSC/whisper-small-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CWKSC/whisper-small-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="CWKSC/whisper-small-onnx")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("CWKSC/whisper-small-onnx") model = AutoModelForSpeechSeq2Seq.from_pretrained("CWKSC/whisper-small-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download decoder_model.onnx from CWKSC/whisper-small-onnx: direct link, hf CLI and curl.
- Browser
- Download file 774 MB
-
https://huggingface.co/CWKSC/whisper-small-onnx/resolve/main/decoder_model.onnx
- Command line
-
hf download hf://CWKSC/whisper-small-onnx/decoder_model.onnx
-
curl -L -o decoder_model.onnx https://huggingface.co/CWKSC/whisper-small-onnx/resolve/main/decoder_model.onnx
774 MB
- Xet hash:
- b4b45e1386bf008736b3f6571f8c70e5e2bfaf4b1eb8e81c2e25d911efd45011
- Size of remote file:
- 774 MB
- SHA256:
- 2cb0b4392373471d5bbfa056d48336b74907e1542583d069dd8fb8fa8868df46
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