Instructions to use khalidey/ID2223_Lab2_Whisper_SV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khalidey/ID2223_Lab2_Whisper_SV with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="khalidey/ID2223_Lab2_Whisper_SV")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("khalidey/ID2223_Lab2_Whisper_SV") model = AutoModelForSpeechSeq2Seq.from_pretrained("khalidey/ID2223_Lab2_Whisper_SV", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from khalidey/ID2223_Lab2_Whisper_SV: direct link, hf CLI and curl.
- Browser
- Download file 1.93 GB
-
https://huggingface.co/khalidey/ID2223_Lab2_Whisper_SV/resolve/main/optimizer.pt
- Command line
-
hf download hf://khalidey/ID2223_Lab2_Whisper_SV/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/khalidey/ID2223_Lab2_Whisper_SV/resolve/main/optimizer.pt
1.93 GB
- Xet hash:
- 5f7bd12a70897cccad5a57bf74ffef8c05d231e5d9313ca5c2ea9e9a3d8ef22d
- Size of remote file:
- 1.93 GB
- SHA256:
- 4471784c01a9be28fab76646476f5cfb25e90eb4207edcd525c03bd186964c7b
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