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:
- 65ff61bb5cbbc7067defdcefddb2a43c0e8bdbd867d51dbaf9a2205bf08eb15a
- Size of remote file:
- 151 MB
- SHA256:
- 4ef68474661966fe96ca848fd528a755f3bd56623a7271e4fa46b38b8cd1952c
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