Instructions to use bgstud/whisper-tiny-libirClean-vs-commonNative with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bgstud/whisper-tiny-libirClean-vs-commonNative with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bgstud/whisper-tiny-libirClean-vs-commonNative")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bgstud/whisper-tiny-libirClean-vs-commonNative") model = AutoModelForSpeechSeq2Seq.from_pretrained("bgstud/whisper-tiny-libirClean-vs-commonNative") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 39086acd91dc0395bc17062d2eb54fefadbc635d1d5f95172b6f17f41a71778f
- Size of remote file:
- 151 MB
- SHA256:
- 58475aa3b1b4f59f89356a308173a5d3b5f3f2f5281a8058593ad033603ad923
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