<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
xlsr-syntesized-turkish-8-hour-llr-2
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3058
- Wer: 0.2940
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0025
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
3.8595 | 0.26 | 100 | 3.0079 | 1.0 |
2.6103 | 0.52 | 200 | 1.6661 | 1.0 |
1.0733 | 0.78 | 300 | 0.8518 | 0.9716 |
0.7436 | 1.04 | 400 | 0.4824 | 0.6979 |
0.5821 | 1.3 | 500 | 0.4184 | 0.6479 |
0.5916 | 1.56 | 600 | 0.4041 | 0.6042 |
0.5099 | 1.82 | 700 | 0.3637 | 0.6122 |
0.4787 | 2.08 | 800 | 0.3393 | 0.5328 |
0.4304 | 2.34 | 900 | 0.3145 | 0.4870 |
0.4366 | 2.6 | 1000 | 0.3054 | 0.4718 |
0.4144 | 2.86 | 1100 | 0.3118 | 0.4915 |
0.4303 | 3.12 | 1200 | 0.3303 | 0.4612 |
0.3768 | 3.39 | 1300 | 0.3169 | 0.4844 |
0.39 | 3.65 | 1400 | 0.2931 | 0.4351 |
0.3717 | 3.91 | 1500 | 0.2824 | 0.4303 |
0.3396 | 4.17 | 1600 | 0.2606 | 0.3929 |
0.341 | 4.43 | 1700 | 0.2845 | 0.4712 |
0.3427 | 4.69 | 1800 | 0.2820 | 0.4452 |
0.3285 | 4.95 | 1900 | 0.2577 | 0.3957 |
0.3011 | 5.21 | 2000 | 0.2486 | 0.3717 |
0.2923 | 5.47 | 2100 | 0.2482 | 0.3990 |
0.298 | 5.73 | 2200 | 0.2333 | 0.3858 |
0.2939 | 5.99 | 2300 | 0.2435 | 0.3900 |
0.285 | 6.25 | 2400 | 0.2884 | 0.4040 |
0.2763 | 6.51 | 2500 | 0.2518 | 0.3880 |
0.2764 | 6.77 | 2600 | 0.2429 | 0.3832 |
0.2711 | 7.03 | 2700 | 0.2408 | 0.3729 |
0.2457 | 7.29 | 2800 | 0.2270 | 0.3799 |
0.2428 | 7.55 | 2900 | 0.2354 | 0.3761 |
0.2457 | 7.81 | 3000 | 0.2309 | 0.3640 |
0.2413 | 8.07 | 3100 | 0.2408 | 0.3426 |
0.224 | 8.33 | 3200 | 0.2233 | 0.3452 |
0.2295 | 8.59 | 3300 | 0.2324 | 0.3320 |
0.2316 | 8.85 | 3400 | 0.2329 | 0.3434 |
0.2156 | 9.11 | 3500 | 0.2506 | 0.3493 |
0.2057 | 9.38 | 3600 | 0.2416 | 0.3475 |
0.2091 | 9.64 | 3700 | 0.2421 | 0.3413 |
0.2105 | 9.9 | 3800 | 0.2557 | 0.3514 |
0.2086 | 10.16 | 3900 | 0.2281 | 0.3365 |
0.1848 | 10.42 | 4000 | 0.2267 | 0.3214 |
0.203 | 10.68 | 4100 | 0.2292 | 0.3598 |
0.1863 | 10.94 | 4200 | 0.2242 | 0.3527 |
0.1732 | 11.2 | 4300 | 0.2426 | 0.3359 |
0.1774 | 11.46 | 4400 | 0.2313 | 0.3438 |
0.1699 | 11.72 | 4500 | 0.2326 | 0.3343 |
0.171 | 11.98 | 4600 | 0.2360 | 0.3124 |
0.1691 | 12.24 | 4700 | 0.2566 | 0.3322 |
0.1632 | 12.5 | 4800 | 0.2556 | 0.2985 |
0.1637 | 12.76 | 4900 | 0.2536 | 0.3161 |
0.1619 | 13.02 | 5000 | 0.2418 | 0.3493 |
0.1476 | 13.28 | 5100 | 0.2419 | 0.3159 |
0.1542 | 13.54 | 5200 | 0.2742 | 0.3214 |
0.1481 | 13.8 | 5300 | 0.2454 | 0.2971 |
0.1446 | 14.06 | 5400 | 0.2520 | 0.2910 |
0.137 | 14.32 | 5500 | 0.2470 | 0.2980 |
0.1365 | 14.58 | 5600 | 0.2563 | 0.3008 |
0.1369 | 14.84 | 5700 | 0.2620 | 0.3033 |
0.1285 | 15.1 | 5800 | 0.2735 | 0.2861 |
0.1266 | 15.36 | 5900 | 0.2816 | 0.2948 |
0.1285 | 15.62 | 6000 | 0.2842 | 0.2890 |
0.1281 | 15.89 | 6100 | 0.2715 | 0.2889 |
0.1264 | 16.15 | 6200 | 0.2779 | 0.2954 |
0.1178 | 16.41 | 6300 | 0.2944 | 0.3113 |
0.1252 | 16.67 | 6400 | 0.2837 | 0.2901 |
0.119 | 16.93 | 6500 | 0.2861 | 0.3111 |
0.1203 | 17.19 | 6600 | 0.2785 | 0.3054 |
0.1108 | 17.45 | 6700 | 0.2823 | 0.3101 |
0.11 | 17.71 | 6800 | 0.2871 | 0.2926 |
0.11 | 17.97 | 6900 | 0.2803 | 0.2995 |
0.1065 | 18.23 | 7000 | 0.2920 | 0.2947 |
0.1047 | 18.49 | 7100 | 0.3047 | 0.2972 |
0.1123 | 18.75 | 7200 | 0.2945 | 0.2978 |
0.1159 | 19.01 | 7300 | 0.2999 | 0.2976 |
0.1061 | 19.27 | 7400 | 0.2954 | 0.2945 |
0.1011 | 19.53 | 7500 | 0.3024 | 0.2969 |
0.1007 | 19.79 | 7600 | 0.3058 | 0.2940 |
Framework versions
- Transformers 4.26.0
- Pytorch 2.1.0+cu118
- Datasets 2.9.0
- Tokenizers 0.13.3