--- license: cc-by-nc-4.0 base_model: nguyenvulebinh/wav2vec2-base-vietnamese-250h tags: - generated_from_trainer metrics: - wer model-index: - name: fine-w2v2base-bs16-ep100-lr2e-05-linguistic-rmsnorm-focal_ctc_a0.5_g1.0-0.05_10_0.004_40 results: [] --- # fine-w2v2base-bs16-ep100-lr2e-05-linguistic-rmsnorm-focal_ctc_a0.5_g1.0-0.05_10_0.004_40 This model is a fine-tuned version of [nguyenvulebinh/wav2vec2-base-vietnamese-250h](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0619 - Wer: 0.0997 ## 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: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 64 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 1068.4723 | 0.94 | 50 | 533.5247 | 15.8344 | | 704.929 | 1.89 | 100 | 149.4564 | 0.9983 | | 104.7041 | 2.83 | 150 | 45.2165 | 1.0 | | 57.2527 | 3.77 | 200 | 42.7504 | 1.0 | | 55.1203 | 4.72 | 250 | 41.4518 | 1.0 | | 52.9285 | 5.66 | 300 | 39.8048 | 1.0 | | 51.1361 | 6.6 | 350 | 38.6911 | 1.0 | | 49.1867 | 7.55 | 400 | 37.8983 | 1.0 | | 48.293 | 8.49 | 450 | 37.5179 | 1.0 | | 48.8025 | 9.43 | 500 | 37.2562 | 1.0 | | 48.0603 | 10.38 | 550 | 37.0740 | 1.0 | | 48.0837 | 11.32 | 600 | 37.0175 | 0.9999 | | 45.7671 | 12.26 | 650 | 33.4394 | 0.9620 | | 38.2468 | 13.21 | 700 | 20.5908 | 0.5614 | | 22.0048 | 14.15 | 750 | 9.7715 | 0.2622 | | 13.6453 | 15.09 | 800 | 6.5392 | 0.1925 | | 10.3565 | 16.04 | 850 | 5.1822 | 0.1627 | | 8.4776 | 16.98 | 900 | 4.4310 | 0.1547 | | 7.2782 | 17.92 | 950 | 3.9109 | 0.1441 | | 6.6759 | 18.87 | 1000 | 3.5788 | 0.1371 | | 6.0682 | 19.81 | 1050 | 3.3775 | 0.1336 | | 5.5782 | 20.75 | 1100 | 3.1172 | 0.1222 | | 5.4805 | 21.7 | 1150 | 3.0142 | 0.1225 | | 5.0893 | 22.64 | 1200 | 2.9002 | 0.1234 | | 4.9178 | 23.58 | 1250 | 2.9029 | 0.1257 | | 4.5324 | 24.53 | 1300 | 2.7464 | 0.1149 | | 4.4924 | 25.47 | 1350 | 2.5754 | 0.1104 | | 4.1324 | 26.42 | 1400 | 2.6028 | 0.1099 | | 4.2581 | 27.36 | 1450 | 2.5399 | 0.1049 | | 3.8897 | 28.3 | 1500 | 2.4484 | 0.1062 | | 3.8507 | 29.25 | 1550 | 2.4717 | 0.1081 | | 3.7424 | 30.19 | 1600 | 2.4559 | 0.1114 | | 3.4716 | 31.13 | 1650 | 2.3895 | 0.1043 | | 3.5385 | 32.08 | 1700 | 2.4023 | 0.1079 | | 3.4308 | 33.02 | 1750 | 2.3014 | 0.1022 | | 3.3027 | 33.96 | 1800 | 2.3091 | 0.1054 | | 3.078 | 34.91 | 1850 | 2.2783 | 0.1000 | | 3.1628 | 35.85 | 1900 | 2.2364 | 0.1029 | | 3.1191 | 36.79 | 1950 | 2.1291 | 0.0963 | | 2.9528 | 37.74 | 2000 | 2.1785 | 0.0975 | | 2.9116 | 38.68 | 2050 | 2.1666 | 0.1006 | | 2.7249 | 39.62 | 2100 | 2.1878 | 0.1053 | | 2.7466 | 40.57 | 2150 | 2.1900 | 0.0997 | | 2.6349 | 41.51 | 2200 | 2.1549 | 0.0963 | | 2.6933 | 42.45 | 2250 | 2.1418 | 0.1030 | | 2.5316 | 43.4 | 2300 | 2.1705 | 0.0982 | | 2.5175 | 44.34 | 2350 | 2.1444 | 0.0991 | | 2.5374 | 45.28 | 2400 | 2.1134 | 0.0970 | | 2.4234 | 46.23 | 2450 | 2.1473 | 0.1052 | | 2.318 | 47.17 | 2500 | 2.1129 | 0.1016 | | 2.2632 | 48.11 | 2550 | 2.1011 | 0.0908 | | 2.3666 | 49.06 | 2600 | 2.1168 | 0.0976 | | 2.2127 | 50.0 | 2650 | 2.1183 | 0.0968 | | 2.132 | 50.94 | 2700 | 2.0882 | 0.0943 | | 2.2458 | 51.89 | 2750 | 2.0710 | 0.0934 | | 1.9839 | 52.83 | 2800 | 2.0990 | 0.1026 | | 2.147 | 53.77 | 2850 | 2.0917 | 0.1017 | | 2.1353 | 54.72 | 2900 | 2.1009 | 0.1002 | | 1.9557 | 55.66 | 2950 | 2.1425 | 0.1057 | | 1.8819 | 56.6 | 3000 | 2.1140 | 0.0979 | | 2.0495 | 57.55 | 3050 | 2.1637 | 0.1020 | | 2.027 | 58.49 | 3100 | 2.1385 | 0.1025 | | 1.9783 | 59.43 | 3150 | 2.1003 | 0.1002 | | 1.9553 | 60.38 | 3200 | 2.1139 | 0.1043 | | 1.7827 | 61.32 | 3250 | 2.1029 | 0.0967 | | 1.9633 | 62.26 | 3300 | 2.0796 | 0.0941 | | 1.7306 | 63.21 | 3350 | 2.0947 | 0.1009 | | 1.8145 | 64.15 | 3400 | 2.1027 | 0.1029 | | 1.7772 | 65.09 | 3450 | 2.1160 | 0.1014 | | 1.784 | 66.04 | 3500 | 2.1080 | 0.1038 | | 1.8016 | 66.98 | 3550 | 2.1155 | 0.0991 | | 1.7837 | 67.92 | 3600 | 2.1112 | 0.1004 | | 1.7027 | 68.87 | 3650 | 2.0888 | 0.0955 | | 1.6968 | 69.81 | 3700 | 2.0739 | 0.0977 | | 1.6873 | 70.75 | 3750 | 2.0948 | 0.0972 | | 1.7168 | 71.7 | 3800 | 2.1186 | 0.0989 | | 1.6195 | 72.64 | 3850 | 2.0967 | 0.0969 | | 1.6414 | 73.58 | 3900 | 2.0811 | 0.1018 | | 1.5118 | 74.53 | 3950 | 2.0674 | 0.0987 | | 1.6768 | 75.47 | 4000 | 2.0616 | 0.0959 | | 1.5945 | 76.42 | 4050 | 2.0632 | 0.1009 | | 1.6417 | 77.36 | 4100 | 2.1003 | 0.1040 | | 1.6208 | 78.3 | 4150 | 2.0939 | 0.1023 | | 1.5037 | 79.25 | 4200 | 2.0788 | 0.0998 | | 1.6181 | 80.19 | 4250 | 2.0641 | 0.0955 | | 1.5608 | 81.13 | 4300 | 2.0864 | 0.1023 | | 1.5658 | 82.08 | 4350 | 2.0802 | 0.1000 | | 1.5369 | 83.02 | 4400 | 2.0750 | 0.0984 | | 1.5474 | 83.96 | 4450 | 2.0582 | 0.0976 | | 1.6031 | 84.91 | 4500 | 2.0666 | 0.0998 | | 1.5224 | 85.85 | 4550 | 2.0695 | 0.0984 | | 1.5687 | 86.79 | 4600 | 2.0645 | 0.0972 | | 1.5393 | 87.74 | 4650 | 2.0702 | 0.0995 | | 1.6074 | 88.68 | 4700 | 2.0673 | 0.0975 | | 1.5601 | 89.62 | 4750 | 2.0622 | 0.0991 | | 1.4178 | 90.57 | 4800 | 2.0666 | 0.0998 | | 1.6219 | 91.51 | 4850 | 2.0620 | 0.1004 | | 1.4044 | 92.45 | 4900 | 2.0572 | 0.0990 | | 1.628 | 93.4 | 4950 | 2.0611 | 0.0993 | | 1.5058 | 94.34 | 5000 | 2.0633 | 0.0995 | | 1.4636 | 95.28 | 5050 | 2.0628 | 0.0998 | | 1.5394 | 96.23 | 5100 | 2.0618 | 0.0999 | | 1.4808 | 97.17 | 5150 | 2.0625 | 0.1004 | | 1.5651 | 98.11 | 5200 | 2.0627 | 0.0998 | | 1.499 | 99.06 | 5250 | 2.0618 | 0.0997 | | 1.5463 | 100.0 | 5300 | 2.0619 | 0.0997 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.14.1