Instructions to use jadasdn/output2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jadasdn/output2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jadasdn/output2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jadasdn/output2") model = AutoModelForCTC.from_pretrained("jadasdn/output2") - Notebooks
- Google Colab
- Kaggle
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| "'": 1, | |
| "[PAD]": 29, | |
| "[UNK]": 28, | |
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| "b": 20, | |
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| "d": 12, | |
| "e": 4, | |
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| "g": 13, | |
| "h": 9, | |
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| "k": 14, | |
| "l": 10, | |
| "m": 16, | |
| "n": 0, | |
| "o": 2, | |
| "p": 26, | |
| "q": 19, | |
| "r": 23, | |
| "s": 3, | |
| "t": 15, | |
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| "z": 24, | |
| "|": 25 | |
| } | |