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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice |
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model-index: |
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- name: wav2vec2-base-ft-cv3-v3 |
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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-ft-cv3-v3 |
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the "mozilla-foundation/common_voice_3_0 english" dataset: "train" and "validation" splits are used for training while "test" split is used for validation. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5787 |
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- Wer: 0.2470 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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- num_epochs: 12 |
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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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| 3.5935 | 0.1 | 500 | 3.0085 | 1.0 | |
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| 1.6296 | 0.21 | 1000 | 1.0879 | 0.5895 | |
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| 0.7154 | 0.31 | 1500 | 0.8224 | 0.4839 | |
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| 0.6387 | 0.42 | 2000 | 0.7290 | 0.4302 | |
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| 0.5322 | 0.52 | 2500 | 0.6864 | 0.4044 | |
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| 0.497 | 0.63 | 3000 | 0.6294 | 0.3746 | |
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| 0.4659 | 0.73 | 3500 | 0.6388 | 0.3745 | |
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| 0.4452 | 0.84 | 4000 | 0.6122 | 0.3570 | |
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| 0.4356 | 0.94 | 4500 | 0.5770 | 0.3443 | |
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| 0.3976 | 1.05 | 5000 | 0.6145 | 0.3296 | |
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| 0.3767 | 1.15 | 5500 | 0.6099 | 0.3325 | |
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| 0.3704 | 1.25 | 6000 | 0.5998 | 0.3263 | |
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| 0.3541 | 1.36 | 6500 | 0.6070 | 0.3250 | |
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| 0.3592 | 1.46 | 7000 | 0.6076 | 0.3352 | |
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| 0.3508 | 1.57 | 7500 | 0.5712 | 0.3239 | |
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| 0.3437 | 1.67 | 8000 | 0.5729 | 0.3202 | |
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| 0.352 | 1.78 | 8500 | 0.5465 | 0.3100 | |
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| 0.34 | 1.88 | 9000 | 0.5418 | 0.3059 | |
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| 0.4086 | 1.99 | 9500 | 0.5189 | 0.3053 | |
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| 0.2968 | 2.09 | 10000 | 0.5373 | 0.3076 | |
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| 0.2968 | 2.2 | 10500 | 0.5602 | 0.3061 | |
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| 0.2956 | 2.3 | 11000 | 0.5651 | 0.3051 | |
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| 0.2863 | 2.41 | 11500 | 0.5476 | 0.2982 | |
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| 0.2852 | 2.51 | 12000 | 0.5579 | 0.2954 | |
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| 0.292 | 2.61 | 12500 | 0.5451 | 0.2953 | |
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| 0.2877 | 2.72 | 13000 | 0.5468 | 0.2905 | |
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| 0.285 | 2.82 | 13500 | 0.5283 | 0.2908 | |
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| 0.2872 | 2.93 | 14000 | 0.5240 | 0.2867 | |
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| 0.3286 | 3.03 | 14500 | 0.5078 | 0.2846 | |
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| 0.2526 | 3.14 | 15000 | 0.5373 | 0.2836 | |
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| 0.2494 | 3.24 | 15500 | 0.5566 | 0.2861 | |
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| 0.2534 | 3.35 | 16000 | 0.5378 | 0.2859 | |
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| 0.2435 | 3.45 | 16500 | 0.5225 | 0.2813 | |
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| 0.3144 | 3.56 | 17000 | 0.5203 | 0.2808 | |
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| 0.2501 | 3.66 | 17500 | 0.5176 | 0.2785 | |
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| 0.2469 | 3.76 | 18000 | 0.5022 | 0.2795 | |
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| 0.242 | 3.87 | 18500 | 0.5228 | 0.2757 | |
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| 0.242 | 3.97 | 19000 | 0.5024 | 0.2788 | |
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| 0.2205 | 4.08 | 19500 | 0.5318 | 0.2729 | |
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| 0.2149 | 4.18 | 20000 | 0.5492 | 0.2763 | |
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| 0.2186 | 4.29 | 20500 | 0.5599 | 0.2769 | |
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| 0.2191 | 4.39 | 21000 | 0.5493 | 0.2695 | |
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| 0.218 | 4.5 | 21500 | 0.5385 | 0.2709 | |
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| 0.2046 | 4.6 | 22000 | 0.5326 | 0.2718 | |
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| 0.2064 | 4.71 | 22500 | 0.5591 | 0.2725 | |
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| 0.2066 | 4.81 | 23000 | 0.5283 | 0.2700 | |
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| 0.2102 | 4.92 | 23500 | 0.5456 | 0.2713 | |
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| 0.3345 | 5.02 | 24000 | 0.5474 | 0.2698 | |
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| 0.1891 | 5.12 | 24500 | 0.5466 | 0.2672 | |
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| 0.1954 | 5.23 | 25000 | 0.5691 | 0.2731 | |
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| 0.1971 | 5.33 | 25500 | 0.5595 | 0.2741 | |
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| 0.1995 | 5.44 | 26000 | 0.5609 | 0.2716 | |
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| 0.1911 | 5.54 | 26500 | 0.5513 | 0.2684 | |
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| 0.1954 | 5.65 | 27000 | 0.5282 | 0.2683 | |
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| 0.193 | 5.75 | 27500 | 0.5460 | 0.2644 | |
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| 0.1974 | 5.86 | 28000 | 0.5415 | 0.2650 | |
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| 0.1947 | 5.96 | 28500 | 0.5227 | 0.2656 | |
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| 0.1836 | 6.07 | 29000 | 0.5361 | 0.2743 | |
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| 0.1741 | 6.17 | 29500 | 0.5637 | 0.2649 | |
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| 0.1776 | 6.27 | 30000 | 0.5705 | 0.2680 | |
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| 0.1747 | 6.38 | 30500 | 0.5587 | 0.2667 | |
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| 0.1761 | 6.48 | 31000 | 0.5480 | 0.2683 | |
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| 0.1715 | 6.59 | 31500 | 0.5547 | 0.2627 | |
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| 0.2424 | 6.69 | 32000 | 0.5254 | 0.2610 | |
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| 0.1756 | 6.8 | 32500 | 0.5301 | 0.2633 | |
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| 0.1761 | 6.9 | 33000 | 0.5267 | 0.2658 | |
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| 0.1751 | 7.01 | 33500 | 0.5611 | 0.2677 | |
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| 0.1653 | 7.11 | 34000 | 0.5617 | 0.2663 | |
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| 0.1591 | 7.22 | 34500 | 0.5435 | 0.2642 | |
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| 0.1559 | 7.32 | 35000 | 0.5608 | 0.2611 | |
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| 0.1604 | 7.43 | 35500 | 0.5477 | 0.2611 | |
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| 0.162 | 7.53 | 36000 | 0.5257 | 0.2559 | |
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| 0.1579 | 7.63 | 36500 | 0.5398 | 0.2570 | |
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| 0.162 | 7.74 | 37000 | 0.5566 | 0.2605 | |
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| 0.2351 | 7.84 | 37500 | 0.5371 | 0.2564 | |
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| 0.1566 | 7.95 | 38000 | 0.5507 | 0.2565 | |
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| 0.1515 | 8.05 | 38500 | 0.5640 | 0.2544 | |
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| 0.1459 | 8.16 | 39000 | 0.5739 | 0.2523 | |
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| 0.1463 | 8.26 | 39500 | 0.5596 | 0.2522 | |
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| 0.1466 | 8.37 | 40000 | 0.5522 | 0.2537 | |
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| 0.2372 | 8.47 | 40500 | 0.5567 | 0.2520 | |
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| 0.1488 | 8.58 | 41000 | 0.5546 | 0.2506 | |
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| 0.1492 | 8.68 | 41500 | 0.5533 | 0.2518 | |
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| 0.1454 | 8.78 | 42000 | 0.5488 | 0.2508 | |
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| 0.148 | 8.89 | 42500 | 0.5635 | 0.2526 | |
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| 0.1424 | 8.99 | 43000 | 0.5513 | 0.2509 | |
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| 0.1356 | 9.1 | 43500 | 0.5534 | 0.2527 | |
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| 0.1346 | 9.2 | 44000 | 0.5735 | 0.2497 | |
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| 0.1346 | 9.31 | 44500 | 0.5710 | 0.2489 | |
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| 0.1401 | 9.41 | 45000 | 0.5561 | 0.2491 | |
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| 0.2212 | 9.52 | 45500 | 0.5564 | 0.2482 | |
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| 0.1369 | 9.62 | 46000 | 0.5658 | 0.2484 | |
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| 0.1323 | 9.73 | 46500 | 0.5582 | 0.2495 | |
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| 0.1369 | 9.83 | 47000 | 0.5560 | 0.2503 | |
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| 0.1368 | 9.94 | 47500 | 0.5552 | 0.2489 | |
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| 0.1333 | 10.04 | 48000 | 0.5953 | 0.2491 | |
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| 0.1305 | 10.14 | 48500 | 0.5818 | 0.2520 | |
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| 0.1316 | 10.25 | 49000 | 0.5773 | 0.2506 | |
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| 0.1334 | 10.35 | 49500 | 0.5882 | 0.2485 | |
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| 0.1351 | 10.46 | 50000 | 0.5750 | 0.2483 | |
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| 0.1337 | 10.56 | 50500 | 0.5910 | 0.2486 | |
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| 0.2241 | 10.67 | 51000 | 0.5732 | 0.2491 | |
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| 0.1327 | 10.77 | 51500 | 0.5839 | 0.2493 | |
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| 0.1364 | 10.88 | 52000 | 0.5724 | 0.2464 | |
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| 0.1305 | 10.98 | 52500 | 0.5758 | 0.2468 | |
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| 0.128 | 11.09 | 53000 | 0.5811 | 0.2482 | |
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| 0.1267 | 11.19 | 53500 | 0.5903 | 0.2483 | |
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| 0.1262 | 11.29 | 54000 | 0.5792 | 0.2483 | |
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| 0.1291 | 11.4 | 54500 | 0.5735 | 0.2497 | |
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| 0.1228 | 11.5 | 55000 | 0.5920 | 0.2494 | |
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| 0.1249 | 11.61 | 55500 | 0.5907 | 0.2488 | |
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| 0.1266 | 11.71 | 56000 | 0.5786 | 0.2486 | |
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| 0.1235 | 11.82 | 56500 | 0.5790 | 0.2473 | |
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| 0.1243 | 11.92 | 57000 | 0.5787 | 0.2470 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.0 |
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- Tokenizers 0.12.1 |
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