Instructions to use HarshitaDiddee/AmericasNLP_Guarani with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HarshitaDiddee/AmericasNLP_Guarani with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="HarshitaDiddee/AmericasNLP_Guarani")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("HarshitaDiddee/AmericasNLP_Guarani") model = AutoModelForCTC.from_pretrained("HarshitaDiddee/AmericasNLP_Guarani", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from HarshitaDiddee/AmericasNLP_Guarani: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/HarshitaDiddee/AmericasNLP_Guarani/resolve/main/training_args.bin
- Command line
-
hf download hf://HarshitaDiddee/AmericasNLP_Guarani/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/HarshitaDiddee/AmericasNLP_Guarani/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- bc71547442076a275f2ee740da06dcc9d160673bdf290b36fe70a47ef1a75cdb
- Size of remote file:
- 3.38 kB
- SHA256:
- b0ad4c188735422c12de408c2ce61841eeaff87c290d3be59bdffc9232fa4997
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.