Instructions to use Siphh/wabLab2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Siphh/wabLab2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Siphh/wabLab2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Siphh/wabLab2") model = AutoModelForSpeechSeq2Seq.from_pretrained("Siphh/wabLab2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dcb4435e4aedbab0ee4c731b0e32e90baf2edfccde835f752b0fd74efa265c4d
- Size of remote file:
- 967 MB
- SHA256:
- bfb9067e918361ed9739a922242e35b6c4e7b95453a769cc66b9ce1d6fe6b8e2
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