Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use rossevine/Check_Model_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rossevine/Check_Model_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rossevine/Check_Model_1")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rossevine/Check_Model_1") model = AutoModelForCTC.from_pretrained("rossevine/Check_Model_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4abe89a64cfd7c070b040db8bf5005897c846907bc1c5a4c67c35bab5414c365
- Size of remote file:
- 1.26 GB
- SHA256:
- 058041815a8619afd2281387da8cfad419ded04214d3506f4c5560347ceab2ea
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