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--- |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- NbAiLab/NPSC |
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- generated_from_trainer |
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model-index: |
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- name: wav2vec2-xlsr-300M-NPSC-OH |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# wav2vec2-xlsr-300M-NPSC-OH |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NBAILAB/NPSC - 16K_MP3 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1692 |
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- Wer: 0.1663 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 7.5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 13 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 2000 |
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- num_epochs: 15.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 3.1638 | 0.66 | 500 | 3.0686 | 1.0 | |
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| 2.9311 | 1.31 | 1000 | 2.9208 | 1.0 | |
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| 2.4175 | 1.97 | 1500 | 1.5009 | 0.9049 | |
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| 1.4442 | 2.63 | 2000 | 0.4426 | 0.3783 | |
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| 1.2624 | 3.28 | 2500 | 0.3193 | 0.2998 | |
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| 1.1889 | 3.94 | 3000 | 0.2867 | 0.2630 | |
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| 1.1315 | 4.6 | 3500 | 0.2566 | 0.2444 | |
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| 1.0864 | 5.26 | 4000 | 0.2368 | 0.2294 | |
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| 1.093 | 5.91 | 4500 | 0.2240 | 0.2151 | |
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| 1.0368 | 6.57 | 5000 | 0.2117 | 0.2056 | |
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| 1.0178 | 7.23 | 5500 | 0.2020 | 0.1954 | |
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| 1.0035 | 7.88 | 6000 | 0.2005 | 0.1924 | |
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| 0.9759 | 8.54 | 6500 | 0.1971 | 0.1863 | |
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| 0.9795 | 9.2 | 7000 | 0.1892 | 0.1812 | |
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| 0.9601 | 9.85 | 7500 | 0.1863 | 0.1795 | |
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| 0.9673 | 10.51 | 8000 | 0.1809 | 0.1761 | |
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| 0.9233 | 11.17 | 8500 | 0.1818 | 0.1755 | |
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| 0.9382 | 11.83 | 9000 | 0.1767 | 0.1741 | |
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| 0.9242 | 12.48 | 9500 | 0.1743 | 0.1703 | |
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| 0.9703 | 13.14 | 10000 | 0.1711 | 0.1711 | |
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| 0.9139 | 13.8 | 10500 | 0.1718 | 0.1672 | |
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| 0.9073 | 14.45 | 11000 | 0.1700 | 0.1665 | |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.18.2.dev0 |
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- Tokenizers 0.11.0 |
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