Instructions to use kavyamanohar/whisper-tiny-ml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kavyamanohar/whisper-tiny-ml with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kavyamanohar/whisper-tiny-ml")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("kavyamanohar/whisper-tiny-ml") model = AutoModelForSpeechSeq2Seq.from_pretrained("kavyamanohar/whisper-tiny-ml", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kavyamanohar/whisper-tiny-ml: direct link, hf CLI and curl.
- Browser
- Download file 5.56 kB
-
https://huggingface.co/kavyamanohar/whisper-tiny-ml/resolve/main/training_args.bin
- Command line
-
hf download hf://kavyamanohar/whisper-tiny-ml/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kavyamanohar/whisper-tiny-ml/resolve/main/training_args.bin
5.56 kB
- Xet hash:
- 440a363f83ea5c91dab02e8e8380f40e746f61e9d4b748b35714e71a2d90e3fc
- Size of remote file:
- 5.56 kB
- SHA256:
- a4fb81045fff0f45ae8063202d93512ecd160cccfa26631580f895259533a076
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