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exp20-M04-both
This model is a fine-tuned version of yongjian/wav2vec2-large-a on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.3358
- Wer: 1.1374
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
37.2423 | 0.34 | 500 | 3.2577 | 1.0 |
3.1334 | 0.68 | 1000 | 3.0084 | 1.0 |
2.9616 | 1.02 | 1500 | 2.9946 | 1.0 |
2.8067 | 1.36 | 2000 | 2.6228 | 1.3130 |
2.732 | 1.7 | 2500 | 2.5059 | 1.5013 |
2.3673 | 2.04 | 3000 | 2.1828 | 1.4911 |
2.1378 | 2.38 | 3500 | 2.2066 | 1.4911 |
1.9853 | 2.72 | 4000 | 1.9877 | 1.4580 |
1.8574 | 3.06 | 4500 | 1.8850 | 1.4656 |
1.7085 | 3.4 | 5000 | 1.9121 | 1.4606 |
1.6161 | 3.74 | 5500 | 2.1036 | 1.4326 |
1.5304 | 4.08 | 6000 | 1.9807 | 1.4478 |
1.3531 | 4.42 | 6500 | 2.0211 | 1.4656 |
1.3269 | 4.77 | 7000 | 1.9231 | 1.3893 |
1.2312 | 5.11 | 7500 | 2.2652 | 1.4097 |
1.1161 | 5.45 | 8000 | 1.9543 | 1.4529 |
1.0305 | 5.79 | 8500 | 2.1463 | 1.4071 |
0.9403 | 6.13 | 9000 | 3.7872 | 1.4071 |
0.8723 | 6.47 | 9500 | 2.8466 | 1.4326 |
0.8752 | 6.81 | 10000 | 2.2215 | 1.3766 |
0.7774 | 7.15 | 10500 | 2.0462 | 1.3257 |
0.74 | 7.49 | 11000 | 2.1928 | 1.3333 |
0.7371 | 7.83 | 11500 | 2.8058 | 1.3410 |
0.7075 | 8.17 | 12000 | 2.3100 | 1.3308 |
0.6746 | 8.51 | 12500 | 2.6284 | 1.2875 |
0.6233 | 8.85 | 13000 | 2.2268 | 1.3003 |
0.7172 | 9.19 | 13500 | 2.1980 | 1.2926 |
0.5697 | 9.53 | 14000 | 2.1950 | 1.2468 |
0.5691 | 9.87 | 14500 | 2.1819 | 1.2316 |
0.5062 | 10.21 | 15000 | 2.1426 | 1.2621 |
0.4818 | 10.55 | 15500 | 2.2259 | 1.2545 |
0.5083 | 10.89 | 16000 | 2.1764 | 1.2214 |
0.3901 | 11.23 | 16500 | 2.2412 | 1.2341 |
0.4275 | 11.57 | 17000 | 2.3781 | 1.2290 |
0.4225 | 11.91 | 17500 | 2.1578 | 1.2443 |
0.4106 | 12.25 | 18000 | 2.5651 | 1.2341 |
0.3933 | 12.59 | 18500 | 2.1819 | 1.2265 |
0.3821 | 12.93 | 19000 | 2.0564 | 1.1934 |
0.3584 | 13.27 | 19500 | 2.5475 | 1.2290 |
0.3468 | 13.61 | 20000 | 2.5857 | 1.1781 |
0.3984 | 13.96 | 20500 | 2.2383 | 1.2239 |
0.308 | 14.3 | 21000 | 2.4947 | 1.2137 |
0.3356 | 14.64 | 21500 | 2.6563 | 1.2163 |
0.3406 | 14.98 | 22000 | 2.3337 | 1.2061 |
0.3297 | 15.32 | 22500 | 2.2793 | 1.1908 |
0.3028 | 15.66 | 23000 | 2.6462 | 1.1654 |
0.3226 | 16.0 | 23500 | 2.3785 | 1.1705 |
0.2605 | 16.34 | 24000 | 2.7212 | 1.1858 |
0.2669 | 16.68 | 24500 | 3.0365 | 1.2087 |
0.2967 | 17.02 | 25000 | 2.4898 | 1.1934 |
0.2547 | 17.36 | 25500 | 2.4020 | 1.1832 |
0.2779 | 17.7 | 26000 | 2.5558 | 1.1705 |
0.2341 | 18.04 | 26500 | 2.9406 | 1.1934 |
0.2304 | 18.38 | 27000 | 3.1528 | 1.1603 |
0.226 | 18.72 | 27500 | 3.0001 | 1.2163 |
0.2319 | 19.06 | 28000 | 3.0117 | 1.1603 |
0.1836 | 19.4 | 28500 | 2.8332 | 1.1858 |
0.2085 | 19.74 | 29000 | 2.8757 | 1.1603 |
0.2383 | 20.08 | 29500 | 3.2235 | 1.1934 |
0.2006 | 20.42 | 30000 | 3.0189 | 1.1603 |
0.1722 | 20.76 | 30500 | 2.8001 | 1.1527 |
0.1955 | 21.1 | 31000 | 3.0401 | 1.1578 |
0.1839 | 21.44 | 31500 | 3.2621 | 1.1578 |
0.1592 | 21.78 | 32000 | 3.1740 | 1.1552 |
0.1835 | 22.12 | 32500 | 3.3974 | 1.1934 |
0.197 | 22.46 | 33000 | 2.8283 | 1.1425 |
0.1788 | 22.8 | 33500 | 3.1983 | 1.1705 |
0.169 | 23.14 | 34000 | 3.1978 | 1.1425 |
0.1649 | 23.49 | 34500 | 3.1829 | 1.1552 |
0.1431 | 23.83 | 35000 | 3.0528 | 1.1272 |
0.1384 | 24.17 | 35500 | 3.3792 | 1.1196 |
0.1234 | 24.51 | 36000 | 3.3988 | 1.1425 |
0.1552 | 24.85 | 36500 | 3.1008 | 1.1170 |
0.124 | 25.19 | 37000 | 2.9486 | 1.1374 |
0.1439 | 25.53 | 37500 | 3.1028 | 1.1323 |
0.1612 | 25.87 | 38000 | 3.0209 | 1.1043 |
0.1456 | 26.21 | 38500 | 2.9466 | 1.1323 |
0.1333 | 26.55 | 39000 | 3.1298 | 1.1221 |
0.1368 | 26.89 | 39500 | 3.1051 | 1.1272 |
0.1263 | 27.23 | 40000 | 3.2888 | 1.1298 |
0.1198 | 27.57 | 40500 | 3.0984 | 1.1298 |
0.1202 | 27.91 | 41000 | 3.1653 | 1.1374 |
0.1252 | 28.25 | 41500 | 3.3016 | 1.1552 |
0.1177 | 28.59 | 42000 | 3.2566 | 1.1349 |
0.1072 | 28.93 | 42500 | 3.3303 | 1.1425 |
0.1497 | 29.27 | 43000 | 3.2549 | 1.1399 |
0.1089 | 29.61 | 43500 | 3.3121 | 1.1374 |
0.0936 | 29.95 | 44000 | 3.3358 | 1.1374 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.13.2