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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bdc3ef46485f49cbec96079c47e4d5ba6a59116271de0d146898f9b46691c90a
- Size of remote file:
- 967 MB
- SHA256:
- cab9604fc650d4f69fe7ec2b74cf2bb23d0b1adf0aeb3b2de777f6d2c43ace4f
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