--- 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_a0.25_g0.5-0.05_10_0.004_40 results: [] --- # fine-w2v2base-bs16-ep100-lr2e-05-linguistic-rmsnorm-focal_ctc_a0.25_g0.5-0.05_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: 1.0890 - Wer: 0.0978 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 522.579 | 0.94 | 50 | 251.0458 | 14.9639 | | 317.1515 | 1.89 | 100 | 84.8688 | 0.9971 | | 57.5912 | 2.83 | 150 | 24.7802 | 1.0 | | 28.4209 | 3.77 | 200 | 21.7164 | 1.0 | | 27.1215 | 4.72 | 250 | 21.1519 | 1.0 | | 26.1663 | 5.66 | 300 | 20.5749 | 1.0 | | 25.4374 | 6.6 | 350 | 20.1536 | 1.0 | | 24.5548 | 7.55 | 400 | 19.6697 | 1.0 | | 24.1548 | 8.49 | 450 | 19.5588 | 1.0 | | 24.4262 | 9.43 | 500 | 19.4870 | 1.0 | | 24.0949 | 10.38 | 550 | 19.5979 | 1.0 | | 24.1762 | 11.32 | 600 | 20.2140 | 1.0 | | 23.2554 | 12.26 | 650 | 20.0865 | 1.0 | | 22.7304 | 13.21 | 700 | 19.4624 | 0.9999 | | 22.0028 | 14.15 | 750 | 17.8907 | 0.9991 | | 20.0064 | 15.09 | 800 | 11.9386 | 0.7020 | | 12.6884 | 16.04 | 850 | 5.5360 | 0.3161 | | 7.2843 | 16.98 | 900 | 3.4633 | 0.2105 | | 5.2335 | 17.92 | 950 | 2.6680 | 0.1726 | | 4.2601 | 18.87 | 1000 | 2.1859 | 0.1505 | | 3.6512 | 19.81 | 1050 | 1.9664 | 0.1471 | | 3.2164 | 20.75 | 1100 | 1.7849 | 0.1351 | | 3.0286 | 21.7 | 1150 | 1.6425 | 0.1313 | | 2.776 | 22.64 | 1200 | 1.5509 | 0.1323 | | 2.5805 | 23.58 | 1250 | 1.5048 | 0.1281 | | 2.372 | 24.53 | 1300 | 1.4450 | 0.1169 | | 2.3566 | 25.47 | 1350 | 1.3800 | 0.1136 | | 2.137 | 26.42 | 1400 | 1.3534 | 0.1164 | | 2.1112 | 27.36 | 1450 | 1.3263 | 0.1132 | | 1.9889 | 28.3 | 1500 | 1.3027 | 0.1091 | | 1.9183 | 29.25 | 1550 | 1.2998 | 0.1117 | | 1.8744 | 30.19 | 1600 | 1.2637 | 0.1093 | | 1.75 | 31.13 | 1650 | 1.2712 | 0.1059 | | 1.7865 | 32.08 | 1700 | 1.2368 | 0.1092 | | 1.6976 | 33.02 | 1750 | 1.2081 | 0.1039 | | 1.6891 | 33.96 | 1800 | 1.2146 | 0.1064 | | 1.5919 | 34.91 | 1850 | 1.2082 | 0.1080 | | 1.5751 | 35.85 | 1900 | 1.2008 | 0.1033 | | 1.5628 | 36.79 | 1950 | 1.1641 | 0.1024 | | 1.4812 | 37.74 | 2000 | 1.2022 | 0.1054 | | 1.4784 | 38.68 | 2050 | 1.1667 | 0.1025 | | 1.4142 | 39.62 | 2100 | 1.1611 | 0.1054 | | 1.3841 | 40.57 | 2150 | 1.1252 | 0.0979 | | 1.3636 | 41.51 | 2200 | 1.1582 | 0.1022 | | 1.3526 | 42.45 | 2250 | 1.1616 | 0.1080 | | 1.2923 | 43.4 | 2300 | 1.1714 | 0.1045 | | 1.2576 | 44.34 | 2350 | 1.1561 | 0.1035 | | 1.2791 | 45.28 | 2400 | 1.1193 | 0.1006 | | 1.2104 | 46.23 | 2450 | 1.1346 | 0.1026 | | 1.1839 | 47.17 | 2500 | 1.1126 | 0.1009 | | 1.1314 | 48.11 | 2550 | 1.1136 | 0.0996 | | 1.1772 | 49.06 | 2600 | 1.1369 | 0.1029 | | 1.1137 | 50.0 | 2650 | 1.1157 | 0.1012 | | 1.1125 | 50.94 | 2700 | 1.1241 | 0.1015 | | 1.1536 | 51.89 | 2750 | 1.1277 | 0.1012 | | 1.0589 | 52.83 | 2800 | 1.1413 | 0.1142 | | 1.1234 | 53.77 | 2850 | 1.1188 | 0.1034 | | 1.1047 | 54.72 | 2900 | 1.1186 | 0.1068 | | 0.9979 | 55.66 | 2950 | 1.1079 | 0.1007 | | 0.9788 | 56.6 | 3000 | 1.0918 | 0.0939 | | 1.009 | 57.55 | 3050 | 1.1172 | 0.1024 | | 0.9942 | 58.49 | 3100 | 1.1139 | 0.0990 | | 0.9602 | 59.43 | 3150 | 1.1063 | 0.1017 | | 0.9813 | 60.38 | 3200 | 1.1151 | 0.1047 | | 0.9112 | 61.32 | 3250 | 1.0930 | 0.0970 | | 0.9705 | 62.26 | 3300 | 1.0990 | 0.0993 | | 0.8753 | 63.21 | 3350 | 1.1053 | 0.1039 | | 0.9259 | 64.15 | 3400 | 1.0978 | 0.0984 | | 0.8877 | 65.09 | 3450 | 1.1047 | 0.0987 | | 0.9111 | 66.04 | 3500 | 1.0937 | 0.1009 | | 0.9103 | 66.98 | 3550 | 1.0963 | 0.0998 | | 0.9031 | 67.92 | 3600 | 1.0969 | 0.1024 | | 0.876 | 68.87 | 3650 | 1.0920 | 0.0964 | | 0.8722 | 69.81 | 3700 | 1.0868 | 0.0958 | | 0.8751 | 70.75 | 3750 | 1.0880 | 0.0966 | | 0.8816 | 71.7 | 3800 | 1.0879 | 0.0974 | | 0.8488 | 72.64 | 3850 | 1.0898 | 0.0974 | | 0.8327 | 73.58 | 3900 | 1.0848 | 0.0978 | | 0.7818 | 74.53 | 3950 | 1.0878 | 0.0957 | | 0.8569 | 75.47 | 4000 | 1.0838 | 0.0997 | | 0.8078 | 76.42 | 4050 | 1.0725 | 0.0983 | | 0.8557 | 77.36 | 4100 | 1.0776 | 0.1000 | | 0.8361 | 78.3 | 4150 | 1.0857 | 0.0978 | | 0.7911 | 79.25 | 4200 | 1.0816 | 0.0953 | | 0.8146 | 80.19 | 4250 | 1.0816 | 0.0969 | | 0.8237 | 81.13 | 4300 | 1.0928 | 0.1005 | | 0.7944 | 82.08 | 4350 | 1.0918 | 0.0965 | | 0.8108 | 83.02 | 4400 | 1.0946 | 0.0968 | | 0.7892 | 83.96 | 4450 | 1.0921 | 0.0968 | | 0.8261 | 84.91 | 4500 | 1.0867 | 0.0975 | | 0.7909 | 85.85 | 4550 | 1.0858 | 0.0964 | | 0.804 | 86.79 | 4600 | 1.0832 | 0.0966 | | 0.7981 | 87.74 | 4650 | 1.0888 | 0.0984 | | 0.7975 | 88.68 | 4700 | 1.0890 | 0.0986 | | 0.7966 | 89.62 | 4750 | 1.0862 | 0.0962 | | 0.7295 | 90.57 | 4800 | 1.0895 | 0.0968 | | 0.8447 | 91.51 | 4850 | 1.0907 | 0.0981 | | 0.7192 | 92.45 | 4900 | 1.0872 | 0.0967 | | 0.8368 | 93.4 | 4950 | 1.0875 | 0.0971 | | 0.7808 | 94.34 | 5000 | 1.0887 | 0.0977 | | 0.76 | 95.28 | 5050 | 1.0896 | 0.0978 | | 0.7858 | 96.23 | 5100 | 1.0896 | 0.0974 | | 0.766 | 97.17 | 5150 | 1.0894 | 0.0978 | | 0.7899 | 98.11 | 5200 | 1.0898 | 0.0978 | | 0.784 | 99.06 | 5250 | 1.0889 | 0.0978 | | 0.801 | 100.0 | 5300 | 1.0890 | 0.0978 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.14.1