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Priyanship
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indicwav2vec_outputs

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
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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: indicwav2vec_outputs
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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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+ # indicwav2vec_outputs
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+
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+ This model was trained from scratch on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: nan
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+ - Cer: 1.0
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+ - Wer: 1.0
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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: 0.0001
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 1011
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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: 2000
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+ - num_epochs: 35.0
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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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+ | 3.8795 | 0.3028 | 500 | 3.7869 | 0.9860 | 1.0 |
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+ | 1.8805 | 0.6057 | 1000 | 2.0423 | 0.4124 | 0.6416 |
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+ | 1.5823 | 0.9085 | 1500 | 1.7622 | 0.3701 | 0.5792 |
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+ | 2.2702 | 1.2114 | 2000 | 2.0595 | 0.5233 | 0.8442 |
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+ | 2.7429 | 1.5142 | 2500 | 2.9181 | 0.8706 | 0.9792 |
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+ | 3.1077 | 1.8171 | 3000 | 3.0393 | 0.9061 | 0.9898 |
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+ | 2.9896 | 2.1199 | 3500 | 2.8581 | 0.8528 | 0.9778 |
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+ | 3.2643 | 2.4228 | 4000 | 3.0456 | 0.8025 | 0.9649 |
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+ | 3.6542 | 2.7256 | 4500 | 3.4606 | 0.8008 | 0.9658 |
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+ | 3.7622 | 3.0285 | 5000 | 3.6476 | 0.8315 | 0.9835 |
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+ | 3.8614 | 3.3313 | 5500 | 3.8326 | 0.8628 | 0.9924 |
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+ | 3.9769 | 3.6342 | 6000 | 4.0055 | 0.8808 | 0.9953 |
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+ | 4.1241 | 3.9370 | 6500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1261 | 4.2399 | 7000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1009 | 4.5427 | 7500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1698 | 4.8455 | 8000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.129 | 5.1484 | 8500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1413 | 5.4512 | 9000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.122 | 5.7541 | 9500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1652 | 6.0569 | 10000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1801 | 6.3598 | 10500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.092 | 6.6626 | 11000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.0204 | 6.9655 | 11500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1036 | 7.2683 | 12000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1918 | 7.5712 | 12500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1059 | 7.8740 | 13000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.0833 | 8.1769 | 13500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1278 | 8.4797 | 14000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1365 | 8.7826 | 14500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1201 | 9.0854 | 15000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1476 | 9.3882 | 15500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.0935 | 9.6911 | 16000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1109 | 9.9939 | 16500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1389 | 10.2968 | 17000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.0907 | 10.5996 | 17500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.0825 | 10.9025 | 18000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1094 | 11.2053 | 18500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.0689 | 11.5082 | 19000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.0984 | 11.8110 | 19500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.0569 | 12.1139 | 20000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1462 | 12.4167 | 20500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1554 | 12.7196 | 21000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.2207 | 13.0224 | 21500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1518 | 13.3253 | 22000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1521 | 13.6281 | 22500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1367 | 13.9310 | 23000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.0904 | 14.2338 | 23500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.0813 | 14.5366 | 24000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1001 | 14.8395 | 24500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1333 | 15.1423 | 25000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.0785 | 15.4452 | 25500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1651 | 15.7480 | 26000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.0987 | 16.0509 | 26500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1327 | 16.3537 | 27000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.1128 | 16.6566 | 27500 | 4.1374 | 0.8920 | 0.9965 |
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+ | 4.0694 | 16.9594 | 28000 | 4.1374 | 0.8920 | 0.9965 |
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+ | 5.946 | 17.2623 | 28500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 17.5651 | 29000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 17.8680 | 29500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 18.1708 | 30000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 18.4737 | 30500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 18.7765 | 31000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 19.0793 | 31500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 19.3822 | 32000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 19.6850 | 32500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 19.9879 | 33000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 20.2907 | 33500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 20.5936 | 34000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 20.8964 | 34500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 21.1993 | 35000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 21.5021 | 35500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 21.8050 | 36000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 22.1078 | 36500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 22.4107 | 37000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 22.7135 | 37500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 23.0164 | 38000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 23.3192 | 38500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 23.6220 | 39000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 23.9249 | 39500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 24.2277 | 40000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 24.5306 | 40500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 24.8334 | 41000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 25.1363 | 41500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 25.4391 | 42000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 25.7420 | 42500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 26.0448 | 43000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 26.3477 | 43500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 26.6505 | 44000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 26.9534 | 44500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 27.2562 | 45000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 27.5591 | 45500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 27.8619 | 46000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 28.1647 | 46500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 28.4676 | 47000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 28.7704 | 47500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 29.0733 | 48000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 29.3761 | 48500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 29.6790 | 49000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 29.9818 | 49500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 30.2847 | 50000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 30.5875 | 50500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 30.8904 | 51000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 31.1932 | 51500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 31.4961 | 52000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 31.7989 | 52500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 32.1018 | 53000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 32.4046 | 53500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 32.7075 | 54000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 33.0103 | 54500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 33.3131 | 55000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 33.6160 | 55500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 33.9188 | 56000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 34.2217 | 56500 | nan | 1.0 | 1.0 |
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+ | 0.0 | 34.5245 | 57000 | nan | 1.0 | 1.0 |
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+ | 0.0 | 34.8274 | 57500 | nan | 1.0 | 1.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.43.1
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+ - Pytorch 2.4.0
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1