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:
- 641459e7ec0d10b6ac5d13e04a00c9086ffb467e3b9c60637ac464145ae97c65
- Size of remote file:
- 1.26 GB
- SHA256:
- e0f246c25724894cd4702f2d3fd6a610bcd6b07ca8366ebbfb6f960e0e0cd555
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