Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Indonesian
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use Scrya/whisper-medium-id-augmented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Scrya/whisper-medium-id-augmented with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Scrya/whisper-medium-id-augmented")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Scrya/whisper-medium-id-augmented") model = AutoModelForSpeechSeq2Seq.from_pretrained("Scrya/whisper-medium-id-augmented", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90e82dc8e2bfb958224c4277c8c542debf20b614c386e455b30627d9055bfb63
- Size of remote file:
- 3.58 kB
- SHA256:
- 2829434cc7d249bdf683d4cbeeeb1e66ef6c22af6120ea5bf13d5d128b26a19a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.