Instructions to use fkHug/modelFromWav2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fkHug/modelFromWav2vec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="fkHug/modelFromWav2vec")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("fkHug/modelFromWav2vec") model = AutoModelForCTC.from_pretrained("fkHug/modelFromWav2vec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download vocab.json from fkHug/modelFromWav2vec: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/fkHug/modelFromWav2vec/resolve/main/vocab.json
- Command line
-
hf download hf://fkHug/modelFromWav2vec/vocab.json
-
curl -L -o vocab.json https://huggingface.co/fkHug/modelFromWav2vec/resolve/main/vocab.json
349 Bytes
| {"d": 0, "'": 1, "ô": 2, "m": 3, "s": 4, "r": 5, "q": 6, "g": 7, "p": 8, "î": 9, "x": 10, "o": 11, "k": 12, "e": 13, "a": 14, "u": 15, "i": 16, "ë": 17, "œ": 18, "é": 19, "h": 21, "b": 22, "z": 23, "â": 24, "’": 25, "c": 26, "t": 27, "v": 28, "j": 29, "f": 30, "y": 31, "l": 32, "n": 33, "w": 34, "è": 35, "|": 20, "[UNK]": 36, "[PAD]": 37} |