Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Korean
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Roooy/whisper-tiny-ko-common with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Roooy/whisper-tiny-ko-common with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Roooy/whisper-tiny-ko-common")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Roooy/whisper-tiny-ko-common") model = AutoModelForSpeechSeq2Seq.from_pretrained("Roooy/whisper-tiny-ko-common") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 773a80f3f46373e8ff0e1757b71b8c3210ffe38af07969551a1dbaf8b8ad2636
- Size of remote file:
- 5.3 kB
- SHA256:
- 1e0da14b902b8fb3e78e15ad09b082ed96218deda3bbbde818a8b9e537ae6b84
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.