Instructions to use swdq/Visual-novel-whisper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use swdq/Visual-novel-whisper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="swdq/Visual-novel-whisper")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("swdq/Visual-novel-whisper") model = AutoModelForSpeechSeq2Seq.from_pretrained("swdq/Visual-novel-whisper") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a194cda49e4a95a74197667ac2f2f781472e10c5975bb2a728dbabddb35e5fa
- Size of remote file:
- 5.37 kB
- SHA256:
- 05ca37fa73584e8dfa00575994ea6180b1d27797fe601085f294261185d5b5ee
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