Instructions to use Dev372/output_model_dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dev372/output_model_dir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Dev372/output_model_dir")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Dev372/output_model_dir") model = AutoModelForSpeechSeq2Seq.from_pretrained("Dev372/output_model_dir", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b54f9387f00d8efae370d83ee3f43d4e10af25a6b65294d4c3e665d5e4eac4e5
- Size of remote file:
- 5.24 kB
- SHA256:
- e1cbcedc23c17dbf21f2eb06deba49bc9c996dcee8a04d2df543839e7f327759
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