Instructions to use cportoca/whisper-tiny-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cportoca/whisper-tiny-finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cportoca/whisper-tiny-finetune")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("cportoca/whisper-tiny-finetune") model = AutoModelForSpeechSeq2Seq.from_pretrained("cportoca/whisper-tiny-finetune", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cportoca/whisper-tiny-finetune: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/cportoca/whisper-tiny-finetune/resolve/main/training_args.bin
- Command line
-
hf download hf://cportoca/whisper-tiny-finetune/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cportoca/whisper-tiny-finetune/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- 5d25dd2d45e7e65370f2468b0dd430ae138619af7aaa30268bd15ed9483f812e
- Size of remote file:
- 5.18 kB
- SHA256:
- aa939a1950180b5ba94c74e973b96d0acfb110fa6fcfb1b8b8669b25ba37f52e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.