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fine-w2v2base-bs16-ep100-lr2e-05-linguistic-rmsnorm-focal_ctc_a0.5_g0.5-0.05_10_0.004_40

Transformers
PyTorch
wav2vec2
Generated from Trainer
Inference Endpoints
Model card Files Community
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  2. pytorch_model.bin +1 -1
README.md ADDED
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+ ---
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+ license: cc-by-nc-4.0
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+ base_model: nguyenvulebinh/wav2vec2-base-vietnamese-250h
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - wer
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+ model-index:
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+ - name: fine-w2v2base-bs16-ep100-lr2e-05-linguistic-rmsnorm-focal_ctc_a0.5_g0.5-0.05_10_0.004_40
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+ results: []
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+ ---
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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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+
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+ # fine-w2v2base-bs16-ep100-lr2e-05-linguistic-rmsnorm-focal_ctc_a0.5_g0.5-0.05_10_0.004_40
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+
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+ 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.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.1651
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+ - Wer: 0.0985
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - distributed_type: multi-GPU
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+ - num_devices: 4
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+ - total_train_batch_size: 64
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+ - total_eval_batch_size: 32
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+ - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 100
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Wer |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------:|
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+ | 1068.6505 | 0.94 | 50 | 527.9987 | 15.8840 |
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+ | 924.9194 | 1.89 | 100 | 365.2998 | 15.7290 |
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+ | 268.1159 | 2.83 | 150 | 45.3076 | 1.0 |
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+ | 56.3914 | 3.77 | 200 | 42.2653 | 1.0 |
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+ | 54.5992 | 4.72 | 250 | 41.2272 | 1.0 |
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+ | 52.7823 | 5.66 | 300 | 40.2126 | 1.0 |
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+ | 51.1032 | 6.6 | 350 | 39.6254 | 1.0 |
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+ | 49.2081 | 7.55 | 400 | 38.7989 | 1.0 |
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+ | 48.3538 | 8.49 | 450 | 38.5792 | 1.0 |
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+ | 48.8615 | 9.43 | 500 | 38.4622 | 1.0 |
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+ | 48.1912 | 10.38 | 550 | 38.1422 | 1.0 |
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+ | 48.3589 | 11.32 | 600 | 38.6144 | 1.0 |
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+ | 46.5985 | 12.26 | 650 | 39.6394 | 1.0473 |
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+ | 45.5769 | 13.21 | 700 | 37.7580 | 0.9992 |
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+ | 44.1749 | 14.15 | 750 | 36.0692 | 0.9991 |
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+ | 41.8932 | 15.09 | 800 | 27.9404 | 0.9316 |
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+ | 29.8551 | 16.04 | 850 | 14.1211 | 0.3930 |
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+ | 16.9135 | 16.98 | 900 | 7.9824 | 0.2228 |
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+ | 11.5569 | 17.92 | 950 | 5.8073 | 0.1693 |
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+ | 9.1965 | 18.87 | 1000 | 4.6891 | 0.1577 |
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+ | 7.6846 | 19.81 | 1050 | 4.0938 | 0.1444 |
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+ | 6.6186 | 20.75 | 1100 | 3.7074 | 0.1337 |
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+ | 6.1733 | 21.7 | 1150 | 3.3769 | 0.1279 |
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+ | 5.5833 | 22.64 | 1200 | 3.1933 | 0.1288 |
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+ | 5.1097 | 23.58 | 1250 | 3.0785 | 0.1232 |
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+ | 4.8098 | 24.53 | 1300 | 3.0687 | 0.1210 |
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+ | 4.784 | 25.47 | 1350 | 2.7771 | 0.1152 |
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+ | 4.3574 | 26.42 | 1400 | 2.7347 | 0.1200 |
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+ | 4.2972 | 27.36 | 1450 | 2.6853 | 0.1147 |
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+ | 4.1072 | 28.3 | 1500 | 2.5680 | 0.1185 |
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+ | 3.9651 | 29.25 | 1550 | 2.5938 | 0.1200 |
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+ | 4.0325 | 30.19 | 1600 | 2.5324 | 0.1180 |
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+ | 3.6586 | 31.13 | 1650 | 2.5847 | 0.1113 |
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+ | 3.7213 | 32.08 | 1700 | 2.5886 | 0.1116 |
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+ | 3.4746 | 33.02 | 1750 | 2.4285 | 0.1005 |
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+ | 3.3572 | 33.96 | 1800 | 2.4607 | 0.1073 |
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+ | 3.2202 | 34.91 | 1850 | 2.4459 | 0.1103 |
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+ | 3.2437 | 35.85 | 1900 | 2.3630 | 0.1027 |
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+ | 3.1303 | 36.79 | 1950 | 2.3281 | 0.1025 |
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+ | 3.0037 | 37.74 | 2000 | 2.3129 | 0.1019 |
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+ | 3.0523 | 38.68 | 2050 | 2.2962 | 0.0988 |
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+ | 2.8943 | 39.62 | 2100 | 2.3238 | 0.1021 |
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+ | 2.8502 | 40.57 | 2150 | 2.3549 | 0.1044 |
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+ | 2.7045 | 41.51 | 2200 | 2.3680 | 0.1018 |
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+ | 2.7291 | 42.45 | 2250 | 2.4172 | 0.1129 |
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+ | 2.6162 | 43.4 | 2300 | 2.3216 | 0.1018 |
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+ | 2.5643 | 44.34 | 2350 | 2.2663 | 0.0979 |
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+ | 2.5842 | 45.28 | 2400 | 2.2408 | 0.0986 |
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+ | 2.4498 | 46.23 | 2450 | 2.2695 | 0.1017 |
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+ | 2.4177 | 47.17 | 2500 | 2.2029 | 0.0980 |
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+ | 2.3297 | 48.11 | 2550 | 2.2254 | 0.0938 |
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+ | 2.3637 | 49.06 | 2600 | 2.2551 | 0.1011 |
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+ | 2.2528 | 50.0 | 2650 | 2.2350 | 0.1012 |
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+ | 2.2221 | 50.94 | 2700 | 2.2253 | 0.0968 |
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+ | 2.3083 | 51.89 | 2750 | 2.2426 | 0.0958 |
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+ | 2.0585 | 52.83 | 2800 | 2.2169 | 0.0972 |
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+ | 2.2349 | 53.77 | 2850 | 2.2151 | 0.1004 |
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+ | 2.1969 | 54.72 | 2900 | 2.2562 | 0.1024 |
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+ | 2.0415 | 55.66 | 2950 | 2.2862 | 0.1027 |
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+ | 2.0126 | 56.6 | 3000 | 2.2167 | 0.1015 |
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+ | 2.1 | 57.55 | 3050 | 2.2360 | 0.1024 |
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+ | 2.0739 | 58.49 | 3100 | 2.2198 | 0.1056 |
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+ | 1.9875 | 59.43 | 3150 | 2.1716 | 0.0987 |
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+ | 2.0259 | 60.38 | 3200 | 2.2143 | 0.0999 |
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+ | 1.8519 | 61.32 | 3250 | 2.1837 | 0.0958 |
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+ | 1.9733 | 62.26 | 3300 | 2.1865 | 0.1008 |
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+ | 1.8496 | 63.21 | 3350 | 2.2045 | 0.1054 |
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+ | 1.9354 | 64.15 | 3400 | 2.1783 | 0.1002 |
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+ | 1.8247 | 65.09 | 3450 | 2.1670 | 0.0989 |
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+ | 1.8418 | 66.04 | 3500 | 2.1823 | 0.0993 |
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+ | 1.8259 | 66.98 | 3550 | 2.1875 | 0.0990 |
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+ | 1.8458 | 67.92 | 3600 | 2.2048 | 0.1000 |
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+ | 1.7796 | 68.87 | 3650 | 2.2019 | 0.0975 |
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+ | 1.7931 | 69.81 | 3700 | 2.1673 | 0.0955 |
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+ | 1.789 | 70.75 | 3750 | 2.1924 | 0.0985 |
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+ | 1.8166 | 71.7 | 3800 | 2.1839 | 0.0964 |
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+ | 1.692 | 72.64 | 3850 | 2.1771 | 0.0950 |
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+ | 1.6898 | 73.58 | 3900 | 2.1621 | 0.0944 |
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+ | 1.5916 | 74.53 | 3950 | 2.1718 | 0.0973 |
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+ | 1.7778 | 75.47 | 4000 | 2.1617 | 0.0973 |
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+ | 1.6884 | 76.42 | 4050 | 2.1566 | 0.0982 |
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+ | 1.7182 | 77.36 | 4100 | 2.1699 | 0.0968 |
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+ | 1.6774 | 78.3 | 4150 | 2.1849 | 0.0964 |
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+ | 1.5921 | 79.25 | 4200 | 2.1785 | 0.0962 |
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+ | 1.7108 | 80.19 | 4250 | 2.1642 | 0.0981 |
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+ | 1.7039 | 81.13 | 4300 | 2.1836 | 0.0997 |
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+ | 1.6068 | 82.08 | 4350 | 2.1924 | 0.1002 |
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+ | 1.6267 | 83.02 | 4400 | 2.1808 | 0.0979 |
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+ | 1.6209 | 83.96 | 4450 | 2.1808 | 0.0976 |
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+ | 1.6989 | 84.91 | 4500 | 2.1661 | 0.0976 |
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+ | 1.6126 | 85.85 | 4550 | 2.1738 | 0.0988 |
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+ | 1.6623 | 86.79 | 4600 | 2.1695 | 0.0979 |
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+ | 1.637 | 87.74 | 4650 | 2.1702 | 0.0989 |
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+ | 1.63 | 88.68 | 4700 | 2.1631 | 0.0973 |
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+ | 1.6153 | 89.62 | 4750 | 2.1659 | 0.0985 |
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+ | 1.4989 | 90.57 | 4800 | 2.1691 | 0.0991 |
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+ | 1.7316 | 91.51 | 4850 | 2.1688 | 0.0986 |
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+ | 1.4623 | 92.45 | 4900 | 2.1635 | 0.0980 |
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+ | 1.6932 | 93.4 | 4950 | 2.1671 | 0.0986 |
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+ | 1.5762 | 94.34 | 5000 | 2.1678 | 0.0990 |
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+ | 1.5346 | 95.28 | 5050 | 2.1654 | 0.0984 |
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+ | 1.6015 | 96.23 | 5100 | 2.1667 | 0.0986 |
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+ | 1.5609 | 97.17 | 5150 | 2.1653 | 0.0982 |
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+ | 1.6414 | 98.11 | 5200 | 2.1648 | 0.0983 |
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+ | 1.581 | 99.06 | 5250 | 2.1666 | 0.0987 |
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+ | 1.6469 | 100.0 | 5300 | 2.1651 | 0.0985 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.34.0
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+ - Pytorch 2.0.1
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+ - Datasets 2.14.5
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+ - Tokenizers 0.14.1
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