Instructions to use Soupis/opus-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Soupis/opus-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Soupis/opus-base")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Soupis/opus-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("Soupis/opus-base") - Notebooks
- Google Colab
- Kaggle
sant commited on
End of training
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README.md
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Wer:
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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### Framework versions
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0163
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- Wer: 11.1424
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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| 0.0228 | 0.3668 | 1000 | 0.0179 | 13.6776 |
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| 0.0218 | 0.7337 | 2000 | 0.0163 | 11.1424 |
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### Framework versions
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