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:
- c6e049a8de051cc622d9a8a2f2cfd92bf0323589c5da000662e3d52d9aee56bb
- Size of remote file:
- 1.26 GB
- SHA256:
- 189f58d5bffdb08b83fbb3a0566c8d50e26891d3d5bd3ed604fa4cdad1b3a1ae
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