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ctaguchi
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ssc-bew-model

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: facebook/wav2vec2-xls-r-300m
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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: ssc-bew-model
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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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+ # ssc-bew-model
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+
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+ This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.2698
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+ - Cer: 0.4050
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+ - Wer: 0.9687
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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: 16
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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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 100
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+ - num_epochs: 30
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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 | Cer | Wer |
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+ |:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
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+ | 4.8123 | 0.4484 | 100 | 3.0133 | 0.9969 | 1.0 |
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+ | 3.1265 | 0.8969 | 200 | 2.9396 | 0.9969 | 1.0 |
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+ | 3.119 | 1.3453 | 300 | 2.8454 | 0.9969 | 1.0 |
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+ | 3.0907 | 1.7937 | 400 | 2.8643 | 0.9969 | 1.0 |
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+ | 3.0756 | 2.2422 | 500 | 2.9632 | 0.9969 | 1.0 |
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+ | 2.9941 | 2.6906 | 600 | 2.8723 | 0.9969 | 1.0 |
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+ | 2.8902 | 3.1390 | 700 | 2.6961 | 0.9892 | 1.0 |
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+ | 2.7428 | 3.5874 | 800 | 2.4343 | 0.8631 | 0.9999 |
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+ | 2.5463 | 4.0359 | 900 | 2.3476 | 0.7009 | 1.0 |
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+ | 2.3062 | 4.4843 | 1000 | 2.1183 | 0.6930 | 1.0 |
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+ | 2.2073 | 4.9327 | 1100 | 2.0210 | 0.7117 | 1.0 |
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+ | 2.0765 | 5.3812 | 1200 | 2.0935 | 0.6305 | 0.9960 |
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+ | 2.031 | 5.8296 | 1300 | 1.9194 | 0.6221 | 0.9994 |
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+ | 1.9854 | 6.2780 | 1400 | 1.8179 | 0.5811 | 0.9999 |
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+ | 1.8907 | 6.7265 | 1500 | 1.7837 | 0.5585 | 0.9998 |
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+ | 1.8584 | 7.1749 | 1600 | 1.8593 | 0.5447 | 0.9931 |
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+ | 1.7839 | 7.6233 | 1700 | 1.8705 | 0.5141 | 1.0417 |
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+ | 1.785 | 8.0717 | 1800 | 1.7395 | 0.5033 | 0.9858 |
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+ | 1.6759 | 8.5202 | 1900 | 1.7391 | 0.4723 | 0.9718 |
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+ | 1.7218 | 8.9686 | 2000 | 1.7175 | 0.4940 | 0.9723 |
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+ | 1.609 | 9.4170 | 2100 | 1.7110 | 0.4781 | 0.9772 |
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+ | 1.5811 | 9.8655 | 2200 | 1.7124 | 0.4658 | 0.9843 |
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+ | 1.5396 | 10.3139 | 2300 | 1.6364 | 0.4590 | 0.9560 |
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+ | 1.5133 | 10.7623 | 2400 | 1.6455 | 0.4580 | 0.9590 |
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+ | 1.4639 | 11.2108 | 2500 | 1.7604 | 0.4770 | 0.9661 |
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+ | 1.4046 | 11.6592 | 2600 | 1.7846 | 0.4839 | 1.0466 |
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+ | 1.3616 | 12.1076 | 2700 | 1.6502 | 0.4380 | 0.9452 |
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+ | 1.3035 | 12.5561 | 2800 | 1.6649 | 0.4458 | 0.9521 |
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+ | 1.3431 | 13.0045 | 2900 | 1.6487 | 0.4386 | 0.9449 |
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+ | 1.2072 | 13.4529 | 3000 | 1.6000 | 0.4358 | 0.9402 |
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+ | 1.1978 | 13.9013 | 3100 | 1.6768 | 0.4380 | 0.9613 |
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+ | 1.1108 | 14.3498 | 3200 | 1.7206 | 0.4302 | 0.9412 |
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+ | 1.1131 | 14.7982 | 3300 | 1.6861 | 0.4315 | 0.9715 |
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+ | 1.0874 | 15.2466 | 3400 | 1.5880 | 0.4253 | 0.9327 |
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+ | 1.0333 | 15.6951 | 3500 | 1.5706 | 0.4154 | 0.9273 |
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+ | 0.9514 | 16.1435 | 3600 | 1.7136 | 0.4228 | 0.9538 |
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+ | 0.9313 | 16.5919 | 3700 | 1.8036 | 0.4289 | 1.0255 |
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+ | 0.9902 | 17.0404 | 3800 | 1.6053 | 0.4120 | 0.9242 |
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+ | 0.8452 | 17.4888 | 3900 | 1.5672 | 0.4104 | 0.9327 |
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+ | 0.872 | 17.9372 | 4000 | 1.6529 | 0.4131 | 0.9536 |
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+ | 0.7782 | 18.3857 | 4100 | 1.8549 | 0.4384 | 1.0661 |
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+ | 0.7984 | 18.8341 | 4200 | 1.8437 | 0.4153 | 1.0265 |
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+ | 0.7615 | 19.2825 | 4300 | 1.7319 | 0.4127 | 0.9696 |
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+ | 0.7246 | 19.7309 | 4400 | 1.7560 | 0.4035 | 0.9411 |
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+ | 0.698 | 20.1794 | 4500 | 1.8200 | 0.4254 | 0.9628 |
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+ | 0.6601 | 20.6278 | 4600 | 1.8046 | 0.4220 | 0.9428 |
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+ | 0.6534 | 21.0762 | 4700 | 1.9306 | 0.4108 | 0.9498 |
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+ | 0.6144 | 21.5247 | 4800 | 1.8637 | 0.4024 | 0.9563 |
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+ | 0.6418 | 21.9731 | 4900 | 1.9459 | 0.4060 | 0.9565 |
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+ | 0.5814 | 22.4215 | 5000 | 1.9776 | 0.4344 | 1.0098 |
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+ | 0.577 | 22.8700 | 5100 | 2.0336 | 0.4109 | 0.9729 |
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+ | 0.5315 | 23.3184 | 5200 | 2.1168 | 0.4078 | 0.9863 |
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+ | 0.5363 | 23.7668 | 5300 | 2.0074 | 0.4066 | 0.9515 |
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+ | 0.5649 | 24.2152 | 5400 | 2.0267 | 0.4149 | 0.9734 |
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+ | 0.5181 | 24.6637 | 5500 | 1.9906 | 0.4076 | 0.9469 |
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+ | 0.4848 | 25.1121 | 5600 | 2.1771 | 0.4014 | 0.9471 |
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+ | 0.4804 | 25.5605 | 5700 | 2.0896 | 0.3972 | 0.9382 |
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+ | 0.5048 | 26.0090 | 5800 | 2.1047 | 0.3946 | 0.9295 |
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+ | 0.4581 | 26.4574 | 5900 | 2.1378 | 0.4016 | 0.9482 |
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+ | 0.4616 | 26.9058 | 6000 | 2.1853 | 0.4071 | 0.9466 |
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+ | 0.4075 | 27.3543 | 6100 | 2.2196 | 0.3982 | 0.9473 |
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+ | 0.4338 | 27.8027 | 6200 | 2.1815 | 0.4027 | 0.9511 |
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+ | 0.4475 | 28.2511 | 6300 | 2.2522 | 0.4019 | 0.9564 |
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+ | 0.4115 | 28.6996 | 6400 | 2.2593 | 0.4024 | 0.9580 |
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+ | 0.3882 | 29.1480 | 6500 | 2.2502 | 0.4072 | 0.9666 |
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+ | 0.4021 | 29.5964 | 6600 | 2.2698 | 0.4050 | 0.9687 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.57.2
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+ - Pytorch 2.9.1+cu128
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+ - Datasets 3.6.0
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+ - Tokenizers 0.22.0
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