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:
- 600ef19230bd2ad6cc96475e51e579452ff0004b5c70e8d86a50f14f2deaf6cf
- Size of remote file:
- 3.96 kB
- SHA256:
- 6a9092ffb32772532ab689f5d0d27e2da70c52ce04ef89477e4a687ae44021ed
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