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
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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: 1.8440
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- Wer: 0.8471
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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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| 5.2882 | 0.7962 | 500 | 3.0818 | 1.0 |
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| 2.1156 | 1.5924 | 1000 | 1.8269 | 0.9243 |
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| 1.5045 | 2.3885 | 1500 | 1.6462 | 0.883 |
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| 1.3346 | 3.1847 | 2000 | 1.5850 | 0.8725 |
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| 1.2459 | 3.9809 | 2500 | 1.5096 | 0.8451 |
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| 1.1498 | 4.7771 | 3000 | 1.5949 | 0.8427 |
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| 1.0867 | 5.5732 | 3500 | 1.5995 | 0.8213 |
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| 1.0427 | 6.3694 | 4000 | 1.5592 | 0.8244 |
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| 0.9884 | 7.1656 | 4500 | 1.5782 | 0.8113 |
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| 0.9380 | 7.9618 | 5000 | 1.7195 | 0.8268 |
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| 0.8799 | 8.7580 | 5500 | 1.7174 | 0.8207 |
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| 0.8320 | 9.5541 | 6000 | 1.7601 | 0.8261 |
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| 0.7904 | 10.3503 | 6500 | 1.7749 | 0.8493 |
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| 0.7460 | 11.1465 | 7000 | 1.8440 | 0.8471 |
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### Framework versions
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