Instructions to use sharpcoder/wav2vec2_bjorn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sharpcoder/wav2vec2_bjorn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sharpcoder/wav2vec2_bjorn")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("sharpcoder/wav2vec2_bjorn") model = AutoModelForCTC.from_pretrained("sharpcoder/wav2vec2_bjorn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download vocab.json from sharpcoder/wav2vec2_bjorn: direct link, hf CLI and curl.
- Browser
- Download file 241 Bytes
-
https://huggingface.co/sharpcoder/wav2vec2_bjorn/resolve/main/vocab.json
- Command line
-
hf download hf://sharpcoder/wav2vec2_bjorn/vocab.json
-
curl -L -o vocab.json https://huggingface.co/sharpcoder/wav2vec2_bjorn/resolve/main/vocab.json
241 Bytes
| {"i": 1, "e": 2, "p": 3, "h": 4, "c": 5, "r": 6, "x": 7, "m": 8, "v": 9, "w": 10, "|": 0, "j": 12, ".": 13, "d": 14, "y": 15, "a": 16, "f": 17, "s": 18, "l": 19, "u": 20, "o": 21, "n": 22, "b": 23, "t": 24, "g": 25, "[UNK]": 25, "[PAD]": 26} |