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--- |
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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: wav2vec2-large-xls-r-300m-sinhala-aug-data-with-original-split-part3 |
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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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# wav2vec2-large-xls-r-300m-sinhala-aug-data-with-original-split-part3 |
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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: 0.0246 |
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- Wer: 0.0367 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 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: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 21 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 5.8396 | 0.27 | 400 | 0.9752 | 0.8374 | |
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| 0.6967 | 0.54 | 800 | 0.3812 | 0.5935 | |
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| 0.4806 | 0.81 | 1200 | 0.3368 | 0.4757 | |
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| 0.3996 | 1.09 | 1600 | 0.1994 | 0.3143 | |
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| 0.3497 | 1.36 | 2000 | 0.1684 | 0.2564 | |
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| 0.3372 | 1.63 | 2400 | 0.1586 | 0.2419 | |
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| 0.312 | 1.9 | 2800 | 0.1413 | 0.2227 | |
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| 0.2797 | 2.17 | 3200 | 0.1466 | 0.2273 | |
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| 0.2554 | 2.44 | 3600 | 0.1543 | 0.2366 | |
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| 0.2613 | 2.71 | 4000 | 0.1450 | 0.2326 | |
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| 0.2399 | 2.99 | 4400 | 0.1238 | 0.2030 | |
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| 0.2125 | 3.26 | 4800 | 0.0989 | 0.1610 | |
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| 0.2144 | 3.53 | 5200 | 0.0984 | 0.1612 | |
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| 0.212 | 3.8 | 5600 | 0.0876 | 0.1507 | |
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| 0.1964 | 4.07 | 6000 | 0.1017 | 0.1753 | |
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| 0.1814 | 4.34 | 6400 | 0.0967 | 0.1654 | |
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| 0.1772 | 4.61 | 6800 | 0.0956 | 0.1631 | |
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| 0.1748 | 4.88 | 7200 | 0.0870 | 0.1483 | |
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| 0.1706 | 5.16 | 7600 | 0.0771 | 0.1306 | |
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| 0.1545 | 5.43 | 8000 | 0.0653 | 0.1199 | |
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| 0.1627 | 5.7 | 8400 | 0.0600 | 0.1103 | |
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| 0.1541 | 5.97 | 8800 | 0.0589 | 0.1068 | |
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| 0.1382 | 6.24 | 9200 | 0.0710 | 0.1231 | |
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| 0.1397 | 6.51 | 9600 | 0.0651 | 0.1248 | |
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| 0.1345 | 6.78 | 10000 | 0.0670 | 0.1194 | |
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| 0.1281 | 7.06 | 10400 | 0.0541 | 0.1006 | |
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| 0.1315 | 7.33 | 10800 | 0.0559 | 0.1062 | |
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| 0.1234 | 7.6 | 11200 | 0.0528 | 0.0970 | |
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| 0.1248 | 7.87 | 11600 | 0.0448 | 0.0865 | |
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| 0.115 | 8.14 | 12000 | 0.0546 | 0.0994 | |
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| 0.1143 | 8.41 | 12400 | 0.0595 | 0.1086 | |
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| 0.1169 | 8.68 | 12800 | 0.0485 | 0.0874 | |
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| 0.1165 | 8.96 | 13200 | 0.0524 | 0.0977 | |
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| 0.1035 | 9.23 | 13600 | 0.0445 | 0.0837 | |
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| 0.1017 | 9.5 | 14000 | 0.0413 | 0.0792 | |
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| 0.109 | 9.77 | 14400 | 0.0420 | 0.0833 | |
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| 0.1018 | 10.04 | 14800 | 0.0454 | 0.0823 | |
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| 0.0929 | 10.31 | 15200 | 0.0429 | 0.0786 | |
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| 0.0956 | 10.58 | 15600 | 0.0403 | 0.0772 | |
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| 0.0986 | 10.85 | 16000 | 0.0468 | 0.0906 | |
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| 0.0941 | 11.13 | 16400 | 0.0362 | 0.0694 | |
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| 0.0845 | 11.4 | 16800 | 0.0387 | 0.0702 | |
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| 0.0955 | 11.67 | 17200 | 0.0351 | 0.0627 | |
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| 0.089 | 11.94 | 17600 | 0.0361 | 0.0675 | |
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| 0.0806 | 12.21 | 18000 | 0.0381 | 0.0685 | |
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| 0.0803 | 12.48 | 18400 | 0.0370 | 0.0675 | |
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| 0.0839 | 12.75 | 18800 | 0.0333 | 0.0619 | |
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| 0.0834 | 13.03 | 19200 | 0.0334 | 0.0577 | |
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| 0.0779 | 13.3 | 19600 | 0.0358 | 0.0621 | |
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| 0.0773 | 13.57 | 20000 | 0.0330 | 0.0565 | |
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| 0.0717 | 13.84 | 20400 | 0.0350 | 0.0625 | |
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| 0.0737 | 14.11 | 20800 | 0.0355 | 0.0603 | |
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| 0.075 | 14.38 | 21200 | 0.0361 | 0.0626 | |
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| 0.0715 | 14.65 | 21600 | 0.0314 | 0.0575 | |
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| 0.0722 | 14.93 | 22000 | 0.0310 | 0.0575 | |
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| 0.0666 | 15.2 | 22400 | 0.0314 | 0.0559 | |
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| 0.0672 | 15.47 | 22800 | 0.0307 | 0.0535 | |
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| 0.0664 | 15.74 | 23200 | 0.0315 | 0.0552 | |
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| 0.0678 | 16.01 | 23600 | 0.0312 | 0.0548 | |
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| 0.0616 | 16.28 | 24000 | 0.0315 | 0.0527 | |
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| 0.0644 | 16.55 | 24400 | 0.0269 | 0.0481 | |
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| 0.062 | 16.82 | 24800 | 0.0308 | 0.0513 | |
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| 0.0584 | 17.1 | 25200 | 0.0294 | 0.0502 | |
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| 0.0563 | 17.37 | 25600 | 0.0294 | 0.0492 | |
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| 0.0547 | 17.64 | 26000 | 0.0281 | 0.0452 | |
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| 0.056 | 17.91 | 26400 | 0.0279 | 0.0451 | |
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| 0.0572 | 18.18 | 26800 | 0.0293 | 0.0460 | |
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| 0.0544 | 18.45 | 27200 | 0.0283 | 0.0464 | |
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| 0.052 | 18.72 | 27600 | 0.0274 | 0.0438 | |
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| 0.0533 | 19.0 | 28000 | 0.0264 | 0.0413 | |
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| 0.046 | 19.27 | 28400 | 0.0276 | 0.0412 | |
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| 0.0498 | 19.54 | 28800 | 0.0282 | 0.0419 | |
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| 0.0454 | 19.81 | 29200 | 0.0279 | 0.0417 | |
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| 0.0483 | 20.08 | 29600 | 0.0260 | 0.0396 | |
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| 0.0447 | 20.35 | 30000 | 0.0267 | 0.0418 | |
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| 0.0424 | 20.62 | 30400 | 0.0249 | 0.0373 | |
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| 0.0409 | 20.9 | 30800 | 0.0246 | 0.0367 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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