Instructions to use vipul22/whisper-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vipul22/whisper-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vipul22/whisper-tiny")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("vipul22/whisper-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("vipul22/whisper-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vipul22/whisper-tiny: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/vipul22/whisper-tiny/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vipul22/whisper-tiny/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vipul22/whisper-tiny/resolve/main/pytorch_model.bin
151 MB
- Xet hash:
- 90ca5754094b89c693a23315d81cc9da5349c772a139e3b6f72f2983a75b40e9
- Size of remote file:
- 151 MB
- SHA256:
- 584eb3421b231c27bf8f4be78e753aa58d7f89ccc4b63084f9da50649f8cfc39
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