Instructions to use hts98/whisper-tiny-paper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hts98/whisper-tiny-paper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hts98/whisper-tiny-paper")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hts98/whisper-tiny-paper") model = AutoModelForSpeechSeq2Seq.from_pretrained("hts98/whisper-tiny-paper", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hts98/whisper-tiny-paper: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/hts98/whisper-tiny-paper/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hts98/whisper-tiny-paper/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hts98/whisper-tiny-paper/resolve/main/pytorch_model.bin
151 MB
- Xet hash:
- d342f8f7e1c44921a870bb4aa0cfb3bb3f863407d272d7c13803c7f36294e675
- Size of remote file:
- 151 MB
- SHA256:
- f6bc933ffbb66f99448601a5406ebf8b49ea24e9f651c9a8a53fa55f777bd7d5
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