Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Sundanese
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use OwLim/whisper-sundanese-java-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OwLim/whisper-sundanese-java-finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="OwLim/whisper-sundanese-java-finetune")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("OwLim/whisper-sundanese-java-finetune") model = AutoModelForSpeechSeq2Seq.from_pretrained("OwLim/whisper-sundanese-java-finetune", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from OwLim/whisper-sundanese-java-finetune: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/OwLim/whisper-sundanese-java-finetune/resolve/main/training_args.bin
- Command line
-
hf download hf://OwLim/whisper-sundanese-java-finetune/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/OwLim/whisper-sundanese-java-finetune/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- 292fec17a9864309d6c216349ac4735ecf7a442f5dd572069a017271149165df
- Size of remote file:
- 5.5 kB
- SHA256:
- 7dc81aad9a31d10aed38ce93f2010661242a17c0e7051950ee4ea0dae614069f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.