Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use rossevine/Model_G_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rossevine/Model_G_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rossevine/Model_G_2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rossevine/Model_G_2") model = AutoModelForCTC.from_pretrained("rossevine/Model_G_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4be14b981ff2eeb20273dffd9845df69de3f4c1ccd33dac75a6465299072f59
- Size of remote file:
- 3.96 kB
- SHA256:
- 5b10c065823190c36a077991b69552d542a14121c8af036b3af2c9a2456d9a31
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