Instructions to use mlubos/whisper-small-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlubos/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mlubos/whisper-small-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mlubos/whisper-small-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("mlubos/whisper-small-dv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14b51f9993aa3511417763c0b970fb9ee92aa753c9a686573c0740ef8a451505
- Size of remote file:
- 4.73 kB
- SHA256:
- 03428cef2b3f96bb512ea86564981e8cc3970e8fe8166fffaf1689bdccfbfd06
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