Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use rossevine/Model_G_Wav2Vec2_Version3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rossevine/Model_G_Wav2Vec2_Version3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rossevine/Model_G_Wav2Vec2_Version3")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rossevine/Model_G_Wav2Vec2_Version3") model = AutoModelForCTC.from_pretrained("rossevine/Model_G_Wav2Vec2_Version3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
pytorch_model.bin
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runs/Aug23_17-02-40_hpc-Aquarium2/events.out.tfevents.1692784988.hpc-Aquarium2.40417.0
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