Transformers
TensorBoard
Safetensors
Indonesian
pyannet
speaker-diarization
speaker-segmentation
Generated from Trainer
Instructions to use whitneyten/pydiarize-Dataset-Manual-dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whitneyten/pydiarize-Dataset-Manual-dataset with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("whitneyten/pydiarize-Dataset-Manual-dataset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from whitneyten/pydiarize-Dataset-Manual-dataset: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/whitneyten/pydiarize-Dataset-Manual-dataset/resolve/main/training_args.bin
- Command line
-
hf download hf://whitneyten/pydiarize-Dataset-Manual-dataset/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/whitneyten/pydiarize-Dataset-Manual-dataset/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 14148db1c5228088cacb9e9358969b75680937f8e9de5bb720749adaba5f9ccb
- Size of remote file:
- 5.37 kB
- SHA256:
- 859201540f888620ca69f634f09345240846a4402c0b4f6be35247702ed1cdaa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.