Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Indonesian
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use cahya/whisper-medium-id with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cahya/whisper-medium-id with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cahya/whisper-medium-id")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("cahya/whisper-medium-id") model = AutoModelForSpeechSeq2Seq.from_pretrained("cahya/whisper-medium-id", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cahya/whisper-medium-id: direct link, hf CLI and curl.
- Browser
- Download file 3.06 GB
-
https://huggingface.co/cahya/whisper-medium-id/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cahya/whisper-medium-id/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cahya/whisper-medium-id/resolve/main/pytorch_model.bin
3.06 GB
- Xet hash:
- 067ee7e46be3cdb818e3314b3becab8a575991c1ca361c77d92c62e404c3befb
- Size of remote file:
- 3.06 GB
- SHA256:
- 25d2fc2484b45df52fda018cdf2378f2e530e2f35da154f9d61b46465a7c0337
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.