Instructions to use devkya/custom-peft-whiper-small-ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devkya/custom-peft-whiper-small-ko with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devkya/custom-peft-whiper-small-ko")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devkya/custom-peft-whiper-small-ko") model = AutoModelForSpeechSeq2Seq.from_pretrained("devkya/custom-peft-whiper-small-ko", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from devkya/custom-peft-whiper-small-ko: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/devkya/custom-peft-whiper-small-ko/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://devkya/custom-peft-whiper-small-ko/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/devkya/custom-peft-whiper-small-ko/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- ab8c666fd2982027c5584676385fcb0d8fdaeb5cd6d938b02cbc703e5f82d4a4
- Size of remote file:
- 967 MB
- SHA256:
- 7c0bb4c5f6f00be792a5fd247aa573cacee8318302733ae65ea5ef9c216683fa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.