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wav2vec2-base-fi-voxpopuli-v2-finetuned

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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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+
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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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+
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+ # wav2vec2-base-fi-voxpopuli-v2-finetuned
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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