Instructions to use hadiqa123/whisper-small-ur with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hadiqa123/whisper-small-ur with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hadiqa123/whisper-small-ur")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hadiqa123/whisper-small-ur") model = AutoModelForSpeechSeq2Seq.from_pretrained("hadiqa123/whisper-small-ur", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hadiqa123/whisper-small-ur: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/hadiqa123/whisper-small-ur/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hadiqa123/whisper-small-ur/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hadiqa123/whisper-small-ur/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- d682c24d457bb99fcfaad37e42cd3657610fae88db7f21f9cc1abb130ae00d28
- Size of remote file:
- 967 MB
- SHA256:
- 3b3c757fa628f831d1bb39f703aeac4b94f0a045e4ab37f572a3716d193dcd3b
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