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:
- 8f8d577d35f35c0667c8ed3ecee52df83caea718c0cec0e604e534e2f3cc7e7c
- Size of remote file:
- 151 MB
- SHA256:
- 5073624d79270cf667d82c898221ac93b0d1d0048cbe73ee53103bfb05de2585
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