Instructions to use yacoubagbane/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yacoubagbane/checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="yacoubagbane/checkpoints")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("yacoubagbane/checkpoints") model = AutoModelForCTC.from_pretrained("yacoubagbane/checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
yacoubagbane/wav2vec2-base-timit-dyula_koumankan_new_regex_UNIV
Browse files- README.md +18 -10
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README.md
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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## Model description
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### Training results
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| Training Loss | Epoch
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### Framework versions
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.9349
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- Wer: 0.8429
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## Model description
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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| 5.4705 | 0.7962 | 500 | 3.0918 | 1.0 |
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| 2.3153 | 1.5924 | 1000 | 1.9470 | 0.937 |
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| 1.5547 | 2.3885 | 1500 | 1.6620 | 0.8839 |
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| 1.3604 | 3.1847 | 2000 | 1.5991 | 0.8663 |
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| 1.2648 | 3.9809 | 2500 | 1.5240 | 0.8471 |
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| 1.1763 | 4.7771 | 3000 | 1.6433 | 0.8594 |
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| 1.1084 | 5.5732 | 3500 | 1.5223 | 0.8255 |
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| 1.0614 | 6.3694 | 4000 | 1.5046 | 0.8253 |
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| 1.0154 | 7.1656 | 4500 | 1.6364 | 0.8187 |
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| 0.9633 | 7.9618 | 5000 | 1.5997 | 0.8247 |
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| 0.9058 | 8.7580 | 5500 | 1.6880 | 0.8254 |
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| 0.8616 | 9.5541 | 6000 | 1.7325 | 0.8258 |
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| 0.8303 | 10.3503 | 6500 | 1.6496 | 0.8275 |
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| 0.7738 | 11.1465 | 7000 | 1.9349 | 0.8429 |
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### Framework versions
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