Instructions to use eengel7/train_first_try with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eengel7/train_first_try with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="eengel7/train_first_try")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("eengel7/train_first_try") model = AutoModelForSpeechSeq2Seq.from_pretrained("eengel7/train_first_try", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 311845a8d85f813114624b8ccb7d51c4c6ee4db360818ee2d8207aa512f05c07
- Size of remote file:
- 3.57 kB
- SHA256:
- 5b72d040a32ece42fd22566cfe72f64eb46549eec478e7cb743857ca1b99c97f
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