Instructions to use sakardev/whisper-tiny-ku with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sakardev/whisper-tiny-ku with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sakardev/whisper-tiny-ku")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("sakardev/whisper-tiny-ku") model = AutoModelForSpeechSeq2Seq.from_pretrained("sakardev/whisper-tiny-ku", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sakardev/whisper-tiny-ku: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/sakardev/whisper-tiny-ku/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sakardev/whisper-tiny-ku/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sakardev/whisper-tiny-ku/resolve/main/pytorch_model.bin
151 MB
- Xet hash:
- 432731bcfea098a5d69abf385d6a87c1ee98063cab7e0e1c0b4b204437cafe56
- Size of remote file:
- 151 MB
- SHA256:
- b06e3854512e1681b9278eb75c476e8b1652365dfe1b5d756418f487b37376c5
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