Instructions to use waxal-benchmarking/whisper-small-orm-victor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use waxal-benchmarking/whisper-small-orm-victor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="waxal-benchmarking/whisper-small-orm-victor")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("waxal-benchmarking/whisper-small-orm-victor") model = AutoModelForSpeechSeq2Seq.from_pretrained("waxal-benchmarking/whisper-small-orm-victor") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42f52c4e19eb03c713f976e62c523dad3d4e240ea485602e03ccd0eb36ddb744
- Size of remote file:
- 5.39 kB
- SHA256:
- bd9db1bef61ca043328b72faee4be8c30335e72363929bbedf6fcc67bc1d2864
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