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:
- 54a02297d90d5c6e8cb6cb782dcdfc68359e2037ed6770750115eb9506b5689d
- Size of remote file:
- 3.57 kB
- SHA256:
- 29a7a509ab0cd584ce4b6fb5e473a639e364dcbbb0ce56bbe28ffab136e93f14
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