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--- |
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license: cc-by-nc-4.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: wav2vec2-base-fi-voxpopuli-v2-finetuned |
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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-base-fi-voxpopuli-v2-finetuned |
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This model is a fine-tuned version of [facebook/wav2vec2-base-fi-voxpopuli-v2](https://huggingface.co./facebook/wav2vec2-base-fi-voxpopuli-v2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1316 |
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- Wer: 0.1498 |
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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: 0.0001 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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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: 500 |
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- num_epochs: 10 |
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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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| 1.575 | 0.33 | 500 | 0.7454 | 0.7048 | |
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| 0.5838 | 0.66 | 1000 | 0.2377 | 0.2608 | |
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| 0.5692 | 1.0 | 1500 | 0.2014 | 0.2244 | |
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| 0.5112 | 1.33 | 2000 | 0.1885 | 0.2013 | |
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| 0.4857 | 1.66 | 2500 | 0.1881 | 0.2120 | |
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| 0.4821 | 1.99 | 3000 | 0.1603 | 0.1894 | |
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| 0.4531 | 2.32 | 3500 | 0.1594 | 0.1865 | |
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| 0.4411 | 2.65 | 4000 | 0.1641 | 0.1874 | |
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| 0.4437 | 2.99 | 4500 | 0.1545 | 0.1874 | |
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| 0.4191 | 3.32 | 5000 | 0.1565 | 0.1770 | |
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| 0.4158 | 3.65 | 5500 | 0.1696 | 0.1867 | |
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| 0.4032 | 3.98 | 6000 | 0.1561 | 0.1746 | |
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| 0.4003 | 4.31 | 6500 | 0.1432 | 0.1749 | |
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| 0.4059 | 4.64 | 7000 | 0.1390 | 0.1690 | |
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| 0.4019 | 4.98 | 7500 | 0.1291 | 0.1646 | |
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| 0.3811 | 5.31 | 8000 | 0.1485 | 0.1755 | |
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| 0.3955 | 5.64 | 8500 | 0.1351 | 0.1659 | |
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| 0.3562 | 5.97 | 9000 | 0.1328 | 0.1614 | |
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| 0.3646 | 6.3 | 9500 | 0.1329 | 0.1584 | |
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| 0.351 | 6.64 | 10000 | 0.1342 | 0.1554 | |
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| 0.3408 | 6.97 | 10500 | 0.1422 | 0.1509 | |
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| 0.3562 | 7.3 | 11000 | 0.1309 | 0.1528 | |
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| 0.3335 | 7.63 | 11500 | 0.1305 | 0.1506 | |
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| 0.3491 | 7.96 | 12000 | 0.1365 | 0.1560 | |
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| 0.3538 | 8.29 | 12500 | 0.1293 | 0.1512 | |
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| 0.3338 | 8.63 | 13000 | 0.1328 | 0.1511 | |
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| 0.3509 | 8.96 | 13500 | 0.1304 | 0.1520 | |
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| 0.3431 | 9.29 | 14000 | 0.1360 | 0.1517 | |
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| 0.3309 | 9.62 | 14500 | 0.1328 | 0.1514 | |
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| 0.3252 | 9.95 | 15000 | 0.1316 | 0.1498 | |
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### Framework versions |
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- Transformers 4.19.1 |
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- Pytorch 1.11.0+cu102 |
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- Datasets 2.2.1 |
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- Tokenizers 0.11.0 |
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