Instructions to use jadasdn/wav2vec2-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jadasdn/wav2vec2-4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jadasdn/wav2vec2-4")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jadasdn/wav2vec2-4") model = AutoModelForCTC.from_pretrained("jadasdn/wav2vec2-4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb545066c1677acf0983ea18cb4a46ce1a8c5b08e1093c039f7541feae9beb51
- Size of remote file:
- 4.54 kB
- SHA256:
- c6757cfcf8b213f5f891f0c70aff140600b1006ad3ebb2a7e5cfc5dfa699c340
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