Instructions to use scasutt/Prototype_training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scasutt/Prototype_training with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="scasutt/Prototype_training")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("scasutt/Prototype_training") model = AutoModelForCTC.from_pretrained("scasutt/Prototype_training", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update model card README.md
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README.md
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@@ -47,11 +47,11 @@ The following hyperparameters were used during training:
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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### Framework versions
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:-----:|:----:|:---------------:|:------:|
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| 0.5865 | 0.74 | 100 | 0.6499 | 0.6750 |
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| 0.5801 | 1.47 | 200 | 0.6499 | 0.6750 |
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| 0.5808 | 2.21 | 300 | 0.6499 | 0.6750 |
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| 0.5734 | 2.94 | 400 | 0.6499 | 0.6750 |
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| 0.5706 | 3.68 | 500 | 0.6499 | 0.6750 |
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### Framework versions
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