Instructions to use nrshoudi/wav2vec_new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nrshoudi/wav2vec_new with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nrshoudi/wav2vec_new")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("nrshoudi/wav2vec_new") model = AutoModelForCTC.from_pretrained("nrshoudi/wav2vec_new", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1dfa69d9a2193977bca2dd6472b1c22ae012c1b1b4440a96181eb20684f4572c
- Size of remote file:
- 5.11 kB
- SHA256:
- f8b31963a5334321441ab99b1749b3369c485f5c3fabef2e530125d91e77ff82
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