Instructions to use jadasdn/wav2vec2-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jadasdn/wav2vec2-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jadasdn/wav2vec2-1")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jadasdn/wav2vec2-1") model = AutoModelForCTC.from_pretrained("jadasdn/wav2vec2-1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ecb4cbc22ad04b07a3cd4c3d74c04831e735e6047efda1daaa133c9f72713dc1
- Size of remote file:
- 4.54 kB
- SHA256:
- c64a5c46c2063c6551945a069e05211bd2e8c26f274dbb6dbe94fd26051650a2
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