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:
- 01cb42870bd990b11a91c257833cd12640328ec1caa466a3184eeaeeb5f575ec
- Size of remote file:
- 3.96 kB
- SHA256:
- d57c90ecf2a2c6cf815846f7322a48e13dfb38724f7d2c3b5f9dbc48ad91ba35
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