Instructions to use jadasdn/wav2vec2-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jadasdn/wav2vec2-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jadasdn/wav2vec2-2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jadasdn/wav2vec2-2") model = AutoModelForCTC.from_pretrained("jadasdn/wav2vec2-2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e90c94b51ab0981e6dabe97a167b1f71baf740f19e444b2702d6f7f104684b1a
- Size of remote file:
- 4.54 kB
- SHA256:
- 3213c35d02f1b513a74984563cbadaa12281927468f1cac2e86e6e02392559da
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