Instructions to use jadasdn/asr_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jadasdn/asr_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jadasdn/asr_model")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jadasdn/asr_model") model = AutoModelForCTC.from_pretrained("jadasdn/asr_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 546fa9f41ac450c6f41d08eff8be9454bbd3471fb0b5bc8fbd885a0828736547
- Size of remote file:
- 4.54 kB
- SHA256:
- c64fdee830e9d68ceff3db34faf54a38610af21cdd94366c8eb2a24cda6c9e60
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