Instructions to use bgstud/whisper-tiny-commonAccentAug-vs-libirAugm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bgstud/whisper-tiny-commonAccentAug-vs-libirAugm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bgstud/whisper-tiny-commonAccentAug-vs-libirAugm")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bgstud/whisper-tiny-commonAccentAug-vs-libirAugm") model = AutoModelForSpeechSeq2Seq.from_pretrained("bgstud/whisper-tiny-commonAccentAug-vs-libirAugm") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 79db90e87bfd83fc7a51d6a0a585ee4604a92644473942384862e4bb0c405bbb
- Size of remote file:
- 151 MB
- SHA256:
- f77cbbe7eca95b5c0d6d4d5e34983bb63af8213162ea3eb758ec0c3bf63ea2ed
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