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w2v-bert-2.0-lg-cv-1hr-v2

Automatic Speech Recognition
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wav2vec2-bert
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  ---
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  library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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  ---
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  library_name: transformers
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+ license: mit
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+ base_model: facebook/w2v-bert-2.0
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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: w2v-bert-2.0-lg-cv-1hr-v2
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+ results: []
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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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+ # w2v-bert-2.0-lg-cv-1hr-v2
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+
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+ This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.8417
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+ - Model Preparation Time: 0.0129
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+ - Wer: 0.9997
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+ - Cer: 0.9914
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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: 1e-05
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+ - train_batch_size: 8
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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: 16
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_ratio: 0.01
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+ - num_epochs: 100
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Wer | Cer |
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+ |:-------------:|:-------:|:----:|:---------------:|:----------------------:|:------:|:------:|
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+ | 15.3055 | 0.9859 | 35 | 12.2381 | 0.0129 | 1.0 | 1.0 |
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+ | 9.6208 | 2.0 | 71 | 8.3440 | 0.0129 | 1.0 | 1.0 |
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+ | 8.5028 | 2.9859 | 106 | 7.9784 | 0.0129 | 1.0 | 1.0 |
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+ | 7.9601 | 4.0 | 142 | 7.7040 | 0.0129 | 1.0 | 1.0 |
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+ | 7.9111 | 4.9859 | 177 | 7.4474 | 0.0129 | 1.0 | 1.0 |
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+ | 7.4259 | 6.0 | 213 | 7.1874 | 0.0129 | 1.0 | 1.0 |
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+ | 7.3711 | 6.9859 | 248 | 6.9404 | 0.0129 | 1.0 | 1.0 |
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+ | 6.9121 | 8.0 | 284 | 6.6929 | 0.0129 | 1.0 | 1.0 |
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+ | 6.8465 | 8.9859 | 319 | 6.4528 | 0.0129 | 1.0 | 1.0 |
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+ | 6.4091 | 10.0 | 355 | 6.2112 | 0.0129 | 1.0 | 1.0 |
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+ | 6.3427 | 10.9859 | 390 | 5.9794 | 0.0129 | 1.0 | 1.0 |
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+ | 5.9281 | 12.0 | 426 | 5.7489 | 0.0129 | 1.0 | 1.0 |
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+ | 5.861 | 12.9859 | 461 | 5.5291 | 0.0129 | 1.0 | 1.0 |
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+ | 5.4728 | 14.0 | 497 | 5.3136 | 0.0129 | 1.0 | 1.0 |
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+ | 5.4055 | 14.9859 | 532 | 5.1116 | 0.0129 | 1.0 | 1.0 |
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+ | 5.05 | 16.0 | 568 | 4.9106 | 0.0129 | 1.0 | 1.0 |
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+ | 4.9891 | 16.9859 | 603 | 4.7271 | 0.0129 | 1.0 | 1.0 |
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+ | 4.6647 | 18.0 | 639 | 4.5480 | 0.0129 | 1.0 | 1.0 |
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+ | 4.6156 | 18.9859 | 674 | 4.3846 | 0.0129 | 1.0 | 1.0 |
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+ | 4.3257 | 20.0 | 710 | 4.2293 | 0.0129 | 1.0 | 1.0 |
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+ | 4.2913 | 20.9859 | 745 | 4.0908 | 0.0129 | 1.0 | 1.0 |
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+ | 4.0311 | 22.0 | 781 | 3.9577 | 0.0129 | 1.0 | 1.0 |
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+ | 4.0132 | 22.9859 | 816 | 3.8405 | 0.0129 | 1.0 | 1.0 |
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+ | 3.7827 | 24.0 | 852 | 3.7315 | 0.0129 | 1.0 | 1.0 |
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+ | 3.7818 | 24.9859 | 887 | 3.6348 | 0.0129 | 1.0 | 1.0 |
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+ | 3.581 | 26.0 | 923 | 3.5459 | 0.0129 | 1.0 | 1.0 |
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+ | 3.5949 | 26.9859 | 958 | 3.4699 | 0.0129 | 1.0 | 1.0 |
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+ | 3.4195 | 28.0 | 994 | 3.3998 | 0.0129 | 1.0 | 1.0 |
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+ | 3.4464 | 28.9859 | 1029 | 3.3396 | 0.0129 | 1.0 | 1.0 |
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+ | 3.2914 | 30.0 | 1065 | 3.2848 | 0.0129 | 1.0 | 1.0 |
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+ | 3.3323 | 30.9859 | 1100 | 3.2404 | 0.0129 | 1.0 | 1.0 |
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+ | 3.1943 | 32.0 | 1136 | 3.1985 | 0.0129 | 1.0 | 1.0 |
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+ | 3.2449 | 32.9859 | 1171 | 3.1625 | 0.0129 | 1.0 | 1.0 |
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+ | 3.1197 | 34.0 | 1207 | 3.1302 | 0.0129 | 1.0 | 1.0 |
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+ | 3.1765 | 34.9859 | 1242 | 3.1066 | 0.0129 | 1.0 | 1.0 |
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+ | 3.0618 | 36.0 | 1278 | 3.0819 | 0.0129 | 1.0 | 1.0 |
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+ | 3.1256 | 36.9859 | 1313 | 3.0686 | 0.0129 | 1.0 | 1.0 |
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+ | 3.0218 | 38.0 | 1349 | 3.0477 | 0.0129 | 1.0 | 1.0 |
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+ | 3.09 | 38.9859 | 1384 | 3.0354 | 0.0129 | 1.0 | 1.0 |
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+ | 2.9895 | 40.0 | 1420 | 3.0255 | 0.0129 | 1.0 | 1.0 |
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+ | 3.0632 | 40.9859 | 1455 | 3.0127 | 0.0129 | 1.0 | 1.0 |
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+ | 2.9671 | 42.0 | 1491 | 3.0028 | 0.0129 | 1.0 | 1.0 |
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+ | 3.0415 | 42.9859 | 1526 | 2.9959 | 0.0129 | 1.0 | 1.0 |
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+ | 2.9499 | 44.0 | 1562 | 2.9881 | 0.0129 | 1.0 | 1.0 |
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+ | 3.0269 | 44.9859 | 1597 | 2.9858 | 0.0129 | 1.0 | 1.0 |
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+ | 2.9369 | 46.0 | 1633 | 2.9776 | 0.0129 | 1.0 | 1.0 |
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+ | 3.0154 | 46.9859 | 1668 | 2.9727 | 0.0129 | 1.0 | 1.0 |
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+ | 2.9269 | 48.0 | 1704 | 2.9696 | 0.0129 | 1.0 | 1.0 |
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+ | 3.0057 | 48.9859 | 1739 | 2.9655 | 0.0129 | 1.0 | 1.0 |
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+ | 2.9185 | 50.0 | 1775 | 2.9613 | 0.0129 | 1.0 | 1.0 |
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+ | 2.9982 | 50.9859 | 1810 | 2.9593 | 0.0129 | 1.0 | 1.0 |
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+ | 2.9112 | 52.0 | 1846 | 2.9555 | 0.0129 | 1.0 | 1.0 |
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+ | 2.9912 | 52.9859 | 1881 | 2.9532 | 0.0129 | 1.0 | 1.0 |
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+ | 2.9047 | 54.0 | 1917 | 2.9496 | 0.0129 | 1.0 | 1.0 |
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+ | 2.9844 | 54.9859 | 1952 | 2.9486 | 0.0129 | 1.0 | 1.0 |
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+ | 2.8984 | 56.0 | 1988 | 2.9454 | 0.0129 | 1.0 | 1.0 |
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+ | 2.9786 | 56.9859 | 2023 | 2.9435 | 0.0129 | 1.0 | 1.0 |
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+ | 2.8928 | 58.0 | 2059 | 2.9391 | 0.0129 | 1.0 | 1.0 |
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+ | 2.9716 | 58.9859 | 2094 | 2.9357 | 0.0129 | 1.0 | 1.0 |
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+ | 2.8834 | 60.0 | 2130 | 2.9296 | 0.0129 | 1.0 | 1.0 |
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+ | 2.9603 | 60.9859 | 2165 | 2.9241 | 0.0129 | 1.0 | 1.0 |
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+ | 2.87 | 62.0 | 2201 | 2.9152 | 0.0129 | 1.0 | 1.0 |
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+ | 2.9421 | 62.9859 | 2236 | 2.9050 | 0.0129 | 1.0 | 1.0 |
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+ | 2.8491 | 64.0 | 2272 | 2.8932 | 0.0129 | 1.0 | 1.0 |
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+ | 2.9179 | 64.9859 | 2307 | 2.8783 | 0.0129 | 1.0 | 1.0 |
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+ | 2.8239 | 66.0 | 2343 | 2.8657 | 0.0129 | 1.0 | 0.9974 |
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+ | 2.8902 | 66.9859 | 2378 | 2.8543 | 0.0129 | 1.0 | 0.9963 |
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+ | 2.7972 | 68.0 | 2414 | 2.8407 | 0.0129 | 1.0 | 0.9955 |
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+ | 2.8628 | 68.9859 | 2449 | 2.8276 | 0.0129 | 1.0 | 0.9936 |
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+ | 2.7694 | 70.0 | 2485 | 2.8108 | 0.0129 | 1.0 | 0.9945 |
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+ | 2.831 | 70.9859 | 2520 | 2.7947 | 0.0129 | 0.9996 | 0.9919 |
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+ | 2.735 | 72.0 | 2556 | 2.7773 | 0.0129 | 0.9998 | 0.9888 |
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+ | 2.7981 | 72.9859 | 2591 | 2.7636 | 0.0129 | 0.9998 | 0.9870 |
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+ | 2.7062 | 74.0 | 2627 | 2.7507 | 0.0129 | 0.9998 | 0.9846 |
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+ | 2.7699 | 74.9859 | 2662 | 2.7373 | 0.0129 | 0.9998 | 0.9849 |
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+ | 2.6797 | 76.0 | 2698 | 2.7237 | 0.0129 | 0.9996 | 0.9818 |
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+ | 2.7434 | 76.9859 | 2733 | 2.7133 | 0.0129 | 1.0 | 0.9806 |
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+ | 2.6558 | 78.0 | 2769 | 2.7024 | 0.0129 | 0.9996 | 0.9779 |
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+ | 2.7204 | 78.9859 | 2804 | 2.6910 | 0.0129 | 0.9998 | 0.9763 |
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+ | 2.6344 | 80.0 | 2840 | 2.6817 | 0.0129 | 0.9998 | 0.9727 |
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+ | 2.7002 | 80.9859 | 2875 | 2.6726 | 0.0129 | 0.9998 | 0.9690 |
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+ | 2.6166 | 82.0 | 2911 | 2.6645 | 0.0129 | 0.9998 | 0.9655 |
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+ | 2.6827 | 82.9859 | 2946 | 2.6571 | 0.0129 | 1.0 | 0.9599 |
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+ | 2.6014 | 84.0 | 2982 | 2.6503 | 0.0129 | 1.0 | 0.9549 |
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+ | 2.6693 | 84.9859 | 3017 | 2.6444 | 0.0129 | 1.0 | 0.9497 |
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+ | 2.5889 | 86.0 | 3053 | 2.6391 | 0.0129 | 1.0 | 0.9434 |
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+ | 2.6577 | 86.9859 | 3088 | 2.6350 | 0.0129 | 1.0 | 0.9354 |
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+ | 2.5795 | 88.0 | 3124 | 2.6305 | 0.0129 | 1.0 | 0.9290 |
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+ | 2.6494 | 88.9859 | 3159 | 2.6275 | 0.0129 | 1.0 | 0.9249 |
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+ | 2.5731 | 90.0 | 3195 | 2.6248 | 0.0129 | 1.0 | 0.9217 |
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+ | 2.6435 | 90.9859 | 3230 | 2.6222 | 0.0129 | 1.0 | 0.9140 |
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+ | 2.5678 | 92.0 | 3266 | 2.6206 | 0.0129 | 1.0 | 0.9128 |
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+ | 2.6399 | 92.9859 | 3301 | 2.6193 | 0.0129 | 1.0 | 0.9088 |
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+ | 2.5653 | 94.0 | 3337 | 2.6183 | 0.0129 | 1.0 | 0.9070 |
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+ | 2.6379 | 94.9859 | 3372 | 2.6177 | 0.0129 | 1.0 | 0.9043 |
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+ | 2.5642 | 96.0 | 3408 | 2.6175 | 0.0129 | 1.0 | 0.9052 |
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+ | 2.6369 | 96.9859 | 3443 | 2.6173 | 0.0129 | 1.0 | 0.9040 |
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+ | 2.5639 | 98.0 | 3479 | 2.6173 | 0.0129 | 1.0 | 0.9043 |
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+ | 2.5974 | 98.5915 | 3500 | 2.6173 | 0.0129 | 1.0 | 0.9044 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.2
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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