Instructions to use Dandan0K/xls_1b_decoding_fr_decoding_test_iter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dandan0K/xls_1b_decoding_fr_decoding_test_iter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Dandan0K/xls_1b_decoding_fr_decoding_test_iter")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Dandan0K/xls_1b_decoding_fr_decoding_test_iter") model = AutoModelForCTC.from_pretrained("Dandan0K/xls_1b_decoding_fr_decoding_test_iter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download vocab.json from Dandan0K/xls_1b_decoding_fr_decoding_test_iter: direct link, hf CLI and curl.
- Browser
- Download file 537 Bytes
-
https://huggingface.co/Dandan0K/xls_1b_decoding_fr_decoding_test_iter/resolve/main/vocab.json
- Command line
-
hf download hf://Dandan0K/xls_1b_decoding_fr_decoding_test_iter/vocab.json
-
curl -L -o vocab.json https://huggingface.co/Dandan0K/xls_1b_decoding_fr_decoding_test_iter/resolve/main/vocab.json
537 Bytes
| { | |
| "'": 5, | |
| "-": 6, | |
| "</s>": 2, | |
| "<pad>": 0, | |
| "<s>": 1, | |
| "<unk>": 3, | |
| "a": 7, | |
| "b": 8, | |
| "c": 9, | |
| "d": 10, | |
| "e": 11, | |
| "f": 12, | |
| "g": 13, | |
| "h": 14, | |
| "i": 15, | |
| "j": 16, | |
| "k": 17, | |
| "l": 18, | |
| "m": 19, | |
| "n": 20, | |
| "o": 21, | |
| "p": 22, | |
| "q": 23, | |
| "r": 24, | |
| "s": 25, | |
| "t": 26, | |
| "u": 27, | |
| "v": 28, | |
| "w": 29, | |
| "x": 30, | |
| "y": 31, | |
| "z": 32, | |
| "|": 4, | |
| "à": 33, | |
| "â": 34, | |
| "ã": 35, | |
| "ç": 36, | |
| "è": 37, | |
| "é": 38, | |
| "ê": 39, | |
| "ë": 40, | |
| "î": 41, | |
| "ï": 42, | |
| "ô": 43, | |
| "ù": 44, | |
| "û": 45, | |
| "œ": 46 | |
| } | |