Instructions to use controngo/whisper-baset2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use controngo/whisper-baset2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="controngo/whisper-baset2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("controngo/whisper-baset2") model = AutoModelForSpeechSeq2Seq.from_pretrained("controngo/whisper-baset2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75a3c73db890d5b3381e9fb7862451862275dbfd1c8ab17487173ec7ef707aba
- Size of remote file:
- 5.11 kB
- SHA256:
- 4a910569a2bae159191448cf2b696601d27038d55397b597e1df0c1e4cd5747a
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