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Model_G_ALL_Wav2Vec2

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Inference Endpoints
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+ ---
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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: Model_G_ALL_Wav2Vec2
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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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+ # Model_G_ALL_Wav2Vec2
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+
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+ This model was trained from scratch on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7956
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+ - Wer: 0.1972
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+ - Cer: 0.0811
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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: 0.0003
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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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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 32
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 30
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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 | Cer |
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+ |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
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+ | 0.8395 | 0.67 | 400 | 0.5656 | 0.3303 | 0.1236 |
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+ | 0.3196 | 1.34 | 800 | 0.5007 | 0.2922 | 0.1071 |
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+ | 0.2491 | 2.01 | 1200 | 0.5008 | 0.2830 | 0.1056 |
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+ | 0.2012 | 2.68 | 1600 | 0.5177 | 0.2689 | 0.1003 |
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+ | 0.1882 | 3.35 | 2000 | 0.5517 | 0.2622 | 0.0986 |
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+ | 0.1811 | 4.02 | 2400 | 0.5225 | 0.2543 | 0.0980 |
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+ | 0.1648 | 4.69 | 2800 | 0.5504 | 0.2477 | 0.0948 |
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+ | 0.1451 | 5.36 | 3200 | 0.5181 | 0.2346 | 0.0908 |
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+ | 0.149 | 6.04 | 3600 | 0.5204 | 0.2375 | 0.0941 |
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+ | 0.1328 | 6.71 | 4000 | 0.5780 | 0.2375 | 0.0920 |
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+ | 0.1256 | 7.38 | 4400 | 0.5406 | 0.2443 | 0.0960 |
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+ | 0.1193 | 8.05 | 4800 | 0.5489 | 0.2317 | 0.0928 |
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+ | 0.1099 | 8.72 | 5200 | 0.5864 | 0.2363 | 0.0925 |
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+ | 0.1125 | 9.39 | 5600 | 0.5749 | 0.2267 | 0.0902 |
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+ | 0.1044 | 10.06 | 6000 | 0.5698 | 0.2279 | 0.0905 |
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+ | 0.0925 | 10.73 | 6400 | 0.6051 | 0.2337 | 0.0933 |
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+ | 0.0951 | 11.4 | 6800 | 0.6785 | 0.2286 | 0.0907 |
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+ | 0.0926 | 12.07 | 7200 | 0.5937 | 0.2337 | 0.0919 |
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+ | 0.0838 | 12.74 | 7600 | 0.5918 | 0.2233 | 0.0893 |
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+ | 0.0775 | 13.41 | 8000 | 0.5642 | 0.2227 | 0.0888 |
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+ | 0.0742 | 14.08 | 8400 | 0.5927 | 0.2249 | 0.0898 |
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+ | 0.0687 | 14.75 | 8800 | 0.6647 | 0.2265 | 0.0900 |
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+ | 0.0685 | 15.42 | 9200 | 0.7438 | 0.2164 | 0.0885 |
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+ | 0.0645 | 16.09 | 9600 | 0.6351 | 0.2128 | 0.0858 |
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+ | 0.0582 | 16.76 | 10000 | 0.6164 | 0.2169 | 0.0878 |
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+ | 0.0604 | 17.44 | 10400 | 0.6327 | 0.2146 | 0.0867 |
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+ | 0.0557 | 18.11 | 10800 | 0.6790 | 0.2148 | 0.0879 |
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+ | 0.0552 | 18.78 | 11200 | 0.6859 | 0.2101 | 0.0848 |
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+ | 0.0474 | 19.45 | 11600 | 0.6648 | 0.2071 | 0.0847 |
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+ | 0.048 | 20.12 | 12000 | 0.7172 | 0.2136 | 0.0873 |
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+ | 0.0475 | 20.79 | 12400 | 0.6451 | 0.2058 | 0.0845 |
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+ | 0.041 | 21.46 | 12800 | 0.6826 | 0.2074 | 0.0839 |
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+ | 0.0405 | 22.13 | 13200 | 0.6738 | 0.2110 | 0.0842 |
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+ | 0.0355 | 22.8 | 13600 | 0.7020 | 0.2050 | 0.0839 |
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+ | 0.0325 | 23.47 | 14000 | 0.7085 | 0.2117 | 0.0854 |
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+ | 0.0308 | 24.14 | 14400 | 0.7418 | 0.2077 | 0.0854 |
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+ | 0.0321 | 24.81 | 14800 | 0.7371 | 0.2051 | 0.0840 |
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+ | 0.0274 | 25.48 | 15200 | 0.7611 | 0.2082 | 0.0848 |
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+ | 0.0287 | 26.15 | 15600 | 0.7208 | 0.2021 | 0.0836 |
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+ | 0.0253 | 26.82 | 16000 | 0.7432 | 0.2025 | 0.0831 |
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+ | 0.0256 | 27.49 | 16400 | 0.7435 | 0.2011 | 0.0824 |
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+ | 0.0243 | 28.16 | 16800 | 0.7543 | 0.1991 | 0.0818 |
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+ | 0.0241 | 28.83 | 17200 | 0.7676 | 0.1986 | 0.0814 |
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+ | 0.0204 | 29.51 | 17600 | 0.7956 | 0.1972 | 0.0811 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.31.0
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+ - Pytorch 2.0.1+cu117
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+ - Datasets 1.18.3
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+ - Tokenizers 0.13.3