Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use jcrkn/wav2vec2-large-xls-r-300m-bretonwelsh-colab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcrkn/wav2vec2-large-xls-r-300m-bretonwelsh-colab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jcrkn/wav2vec2-large-xls-r-300m-bretonwelsh-colab")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jcrkn/wav2vec2-large-xls-r-300m-bretonwelsh-colab") model = AutoModelForCTC.from_pretrained("jcrkn/wav2vec2-large-xls-r-300m-bretonwelsh-colab", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "'": 1, | |
| "[PAD]": 50, | |
| "[UNK]": 49, | |
| "a": 2, | |
| "b": 3, | |
| "c": 4, | |
| "d": 5, | |
| "e": 6, | |
| "f": 7, | |
| "g": 8, | |
| "h": 9, | |
| "i": 10, | |
| "j": 11, | |
| "k": 12, | |
| "l": 13, | |
| "m": 14, | |
| "n": 15, | |
| "o": 16, | |
| "p": 17, | |
| "q": 18, | |
| "r": 19, | |
| "s": 20, | |
| "t": 21, | |
| "u": 22, | |
| "v": 23, | |
| "w": 24, | |
| "x": 25, | |
| "y": 26, | |
| "z": 27, | |
| "|": 0, | |
| "¬": 28, | |
| "à": 29, | |
| "á": 30, | |
| "â": 31, | |
| "ä": 32, | |
| "é": 33, | |
| "ê": 34, | |
| "ë": 35, | |
| "í": 36, | |
| "î": 37, | |
| "ï": 38, | |
| "ñ": 39, | |
| "ò": 40, | |
| "ô": 41, | |
| "ö": 42, | |
| "û": 43, | |
| "ÿ": 44, | |
| "ŵ": 45, | |
| "ŷ": 46, | |
| "–": 47, | |
| "—": 48 | |
| } | |