Instructions to use mohitsha/whisper-tiny-dynamic-quantized-onnxruntime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohitsha/whisper-tiny-dynamic-quantized-onnxruntime with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mohitsha/whisper-tiny-dynamic-quantized-onnxruntime")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mohitsha/whisper-tiny-dynamic-quantized-onnxruntime") model = AutoModelForSpeechSeq2Seq.from_pretrained("mohitsha/whisper-tiny-dynamic-quantized-onnxruntime", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download encoder_model_quantized.onnx from mohitsha/whisper-tiny-dynamic-quantized-onnxruntime: direct link, hf CLI and curl.
- Browser
- Download file 10.1 MB
-
https://huggingface.co/mohitsha/whisper-tiny-dynamic-quantized-onnxruntime/resolve/main/encoder_model_quantized.onnx
- Command line
-
hf download hf://mohitsha/whisper-tiny-dynamic-quantized-onnxruntime/encoder_model_quantized.onnx
-
curl -L -o encoder_model_quantized.onnx https://huggingface.co/mohitsha/whisper-tiny-dynamic-quantized-onnxruntime/resolve/main/encoder_model_quantized.onnx
10.1 MB
- Xet hash:
- f1fd90750e3bde2438cb8bb59b53414a894ff55bffcc765968eba7b0dbe58cea
- Size of remote file:
- 10.1 MB
- SHA256:
- ace0a495eec4095a84292cb581da36da202def25eede0c5b4937e60b6c4f603a
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