Instructions to use thak123/whisper-small-gom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thak123/whisper-small-gom with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="thak123/whisper-small-gom")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("thak123/whisper-small-gom") model = AutoModelForSpeechSeq2Seq.from_pretrained("thak123/whisper-small-gom", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from thak123/whisper-small-gom: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/thak123/whisper-small-gom/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://thak123/whisper-small-gom/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/thak123/whisper-small-gom/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- e457394b233542a870f7c8d2f61c2cee30d85181e3f69b7fcf0c745261953ba8
- Size of remote file:
- 967 MB
- SHA256:
- d6f52bea90b7125d2e3d67a0cc80195f887ad26bb285afbd9dc256ac43aece13
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