Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use rossevine/Check_Model_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rossevine/Check_Model_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rossevine/Check_Model_2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rossevine/Check_Model_2") model = AutoModelForCTC.from_pretrained("rossevine/Check_Model_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 41b5cb452e13c1c0f612cfd069e22276ea7574f8ab67b38a1413db4acd20207b
- Size of remote file:
- 1.26 GB
- SHA256:
- f27289461f34cb50d68cec137b9afa78ecf1840ac53a5376fb5f27c5674fed91
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