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_model_quantized.onnx from mohitsha/whisper-tiny-smooth-quant: direct link, hf CLI and curl.
- Browser
- Download file 90.4 MB
-
https://huggingface.co/mohitsha/whisper-tiny-smooth-quant/resolve/main/decoder_model_quantized.onnx
- Command line
-
hf download hf://mohitsha/whisper-tiny-smooth-quant/decoder_model_quantized.onnx
-
curl -L -o decoder_model_quantized.onnx https://huggingface.co/mohitsha/whisper-tiny-smooth-quant/resolve/main/decoder_model_quantized.onnx
90.4 MB
- Xet hash:
- 1d2c2ea99eb3f53d422eb452ba22abffd657496b8cd365645c42ab3af5e711dc
- Size of remote file:
- 90.4 MB
- SHA256:
- 8ac3d251b9734724eb7ed1274a2d57b0bf53b7a51a905f18d7cfe6ad04dc94b7
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