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DewiBrynJones
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wav2vec2-xlsr-53-ft-btb-cy

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
wav2vec2
DewiBrynJones/banc-trawsgrifiadau-bangor-normalized
Generated from Trainer
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  1. README.md +31 -114
  2. model.safetensors +1 -1
  3. runs/May10_18-59-42_09e070d6a7b1/events.out.tfevents.1715364171.09e070d6a7b1.452.0 +2 -2
README.md CHANGED
@@ -2,8 +2,6 @@
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  license: apache-2.0
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  base_model: facebook/wav2vec2-large-xlsr-53
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  tags:
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- - automatic-speech-recognition
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- - DewiBrynJones/banc-trawsgrifiadau-bangor-normalized
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  - generated_from_trainer
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  metrics:
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  - wer
@@ -17,10 +15,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # wav2vec2-xlsr-53-ft-btb-cy
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- This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the DEWIBRYNJONES/BANC-TRAWSGRIFIADAU-BANGOR-NORMALIZED - DEFAULT dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.9669
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- - Wer: 0.6125
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  ## Model description
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@@ -48,119 +46,38 @@ The following hyperparameters were used during training:
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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: 15.0
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  - mixed_precision_training: Native AMP
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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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- | No log | 0.1414 | 100 | 3.7427 | 1.0 |
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- | No log | 0.2829 | 200 | 2.9179 | 1.0 |
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- | No log | 0.4243 | 300 | 2.8036 | 1.0 |
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- | No log | 0.5658 | 400 | 1.2196 | 0.8934 |
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- | 3.574 | 0.7072 | 500 | 0.9860 | 0.7276 |
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- | 3.574 | 0.8487 | 600 | 0.8392 | 0.6504 |
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- | 3.574 | 0.9901 | 700 | 0.7804 | 0.6029 |
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- | 3.574 | 1.1315 | 800 | 0.6122 | 0.4909 |
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- | 3.574 | 1.2730 | 900 | 0.5901 | 0.4883 |
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- | 0.811 | 1.4144 | 1000 | 0.5500 | 0.4508 |
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- | 0.811 | 1.5559 | 1100 | 0.5232 | 0.4142 |
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- | 0.811 | 1.6973 | 1200 | 0.5186 | 0.4065 |
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- | 0.811 | 1.8388 | 1300 | 0.4953 | 0.3929 |
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- | 0.811 | 1.9802 | 1400 | 0.4880 | 0.3928 |
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- | 0.6459 | 2.1216 | 1500 | 0.4645 | 0.3692 |
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- | 0.6459 | 2.2631 | 1600 | 0.4666 | 0.3586 |
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- | 0.6459 | 2.4045 | 1700 | 0.4502 | 0.3593 |
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- | 0.6459 | 2.5460 | 1800 | 0.4528 | 0.3638 |
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- | 0.6459 | 2.6874 | 1900 | 0.4665 | 0.3926 |
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- | 0.5306 | 2.8289 | 2000 | 0.4329 | 0.3505 |
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- | 0.5306 | 2.9703 | 2100 | 0.4245 | 0.3374 |
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- | 0.5306 | 3.1117 | 2200 | 0.4377 | 0.3340 |
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- | 0.5306 | 3.2532 | 2300 | 0.4272 | 0.3337 |
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- | 0.5306 | 3.3946 | 2400 | 0.4335 | 0.3326 |
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- | 0.4628 | 3.5361 | 2500 | 0.4268 | 0.3275 |
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- | 0.4628 | 3.6775 | 2600 | 0.4502 | 0.3409 |
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- | 0.4628 | 3.8190 | 2700 | 0.6345 | 0.4390 |
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- | 0.4628 | 3.9604 | 2800 | 1.0203 | 0.6403 |
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- | 0.4628 | 4.1018 | 2900 | 1.2208 | 0.7922 |
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- | 0.8685 | 4.2433 | 3000 | 1.1018 | 0.7387 |
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- | 0.8685 | 4.3847 | 3100 | 1.2497 | 0.8062 |
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- | 0.8685 | 4.5262 | 3200 | 1.6165 | 0.9616 |
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- | 0.8685 | 4.6676 | 3300 | 1.4655 | 0.9217 |
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- | 0.8685 | 4.8091 | 3400 | 1.0288 | 0.7465 |
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- | 1.3918 | 4.9505 | 3500 | 0.9067 | 0.5948 |
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- | 1.3918 | 5.0919 | 3600 | 0.9486 | 0.6353 |
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- | 1.3918 | 5.2334 | 3700 | 0.8674 | 0.5428 |
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- | 1.3918 | 5.3748 | 3800 | 0.9403 | 0.5793 |
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- | 1.3918 | 5.5163 | 3900 | 0.9481 | 0.5764 |
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- | 1.0402 | 5.6577 | 4000 | 1.0176 | 0.8257 |
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- | 1.0402 | 5.7992 | 4100 | 0.9857 | 0.6343 |
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- | 1.0402 | 5.9406 | 4200 | 1.3289 | 0.9014 |
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- | 1.0402 | 6.0820 | 4300 | 2.0891 | 0.7125 |
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- | 1.0402 | 6.2235 | 4400 | 1.2563 | 0.7696 |
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- | 1.2886 | 6.3649 | 4500 | 1.1441 | 0.6927 |
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- | 1.2886 | 6.5064 | 4600 | 1.0626 | 0.6573 |
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- | 1.2886 | 6.6478 | 4700 | 0.9997 | 0.6423 |
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- | 1.2886 | 6.7893 | 4800 | 0.9814 | 0.6380 |
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- | 1.2886 | 6.9307 | 4900 | 1.0955 | 0.7651 |
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- | 1.0984 | 7.0721 | 5000 | 0.9213 | 0.5883 |
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- | 1.0984 | 7.2136 | 5100 | 0.8885 | 0.5933 |
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- | 1.0984 | 7.3550 | 5200 | 0.9001 | 0.5899 |
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- | 1.0984 | 7.4965 | 5300 | 0.8784 | 0.5859 |
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- | 1.0984 | 7.6379 | 5400 | 0.9072 | 0.5898 |
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- | 0.9659 | 7.7793 | 5500 | 0.8812 | 0.5841 |
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- | 0.9659 | 7.9208 | 5600 | 0.8912 | 0.5855 |
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- | 0.9659 | 8.0622 | 5700 | 0.8816 | 0.5807 |
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- | 0.9659 | 8.2037 | 5800 | 0.8914 | 0.5803 |
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- | 0.9659 | 8.3451 | 5900 | 0.8956 | 0.5810 |
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- | 0.9679 | 8.4866 | 6000 | 0.9162 | 0.5780 |
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- | 0.9679 | 8.6280 | 6100 | 0.9409 | 0.5810 |
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- | 0.9679 | 8.7694 | 6200 | 0.9371 | 0.5781 |
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- | 0.9679 | 8.9109 | 6300 | 0.9417 | 0.5790 |
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- | 0.9679 | 9.0523 | 6400 | 0.9664 | 0.5784 |
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- | 1.0241 | 9.1938 | 6500 | 0.9720 | 0.5775 |
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- | 1.0241 | 9.3352 | 6600 | 0.9841 | 0.5784 |
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- | 1.0241 | 9.4767 | 6700 | 0.9574 | 0.5887 |
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- | 1.0241 | 9.6181 | 6800 | 1.0725 | 0.6068 |
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- | 1.0241 | 9.7595 | 6900 | 1.0362 | 0.6000 |
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- | 1.0797 | 9.9010 | 7000 | 1.0117 | 0.5914 |
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- | 1.0797 | 10.0424 | 7100 | 0.9563 | 0.6058 |
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- | 1.0797 | 10.1839 | 7200 | 0.9664 | 0.5978 |
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- | 1.0797 | 10.3253 | 7300 | 1.0209 | 0.6022 |
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- | 1.0797 | 10.4668 | 7400 | 0.9849 | 0.5975 |
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- | 1.0701 | 10.6082 | 7500 | 0.9719 | 0.6057 |
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- | 1.0701 | 10.7496 | 7600 | 0.9670 | 0.6123 |
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- | 1.0701 | 10.8911 | 7700 | 0.9669 | 0.6125 |
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- | 1.0701 | 11.0325 | 7800 | 0.9669 | 0.6125 |
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- | 1.0701 | 11.1740 | 7900 | 0.9669 | 0.6125 |
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- | 1.0518 | 11.3154 | 8000 | 0.9669 | 0.6125 |
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- | 1.0518 | 11.4569 | 8100 | 0.9669 | 0.6125 |
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- | 1.0518 | 11.5983 | 8200 | 0.9669 | 0.6125 |
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- | 1.0518 | 11.7397 | 8300 | 0.9669 | 0.6125 |
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- | 1.0518 | 11.8812 | 8400 | 0.9669 | 0.6125 |
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- | 1.0594 | 12.0226 | 8500 | 0.9669 | 0.6125 |
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- | 1.0594 | 12.1641 | 8600 | 0.9669 | 0.6125 |
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- | 1.0594 | 12.3055 | 8700 | 0.9669 | 0.6125 |
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- | 1.0594 | 12.4470 | 8800 | 0.9669 | 0.6125 |
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- | 1.0594 | 12.5884 | 8900 | 0.9669 | 0.6125 |
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- | 1.0584 | 12.7298 | 9000 | 0.9669 | 0.6125 |
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- | 1.0584 | 12.8713 | 9100 | 0.9669 | 0.6125 |
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- | 1.0584 | 13.0127 | 9200 | 0.9669 | 0.6125 |
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- | 1.0584 | 13.1542 | 9300 | 0.9669 | 0.6125 |
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- | 1.0584 | 13.2956 | 9400 | 0.9669 | 0.6125 |
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- | 1.0556 | 13.4371 | 9500 | 0.9669 | 0.6125 |
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- | 1.0556 | 13.5785 | 9600 | 0.9669 | 0.6125 |
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- | 1.0556 | 13.7199 | 9700 | 0.9669 | 0.6125 |
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- | 1.0556 | 13.8614 | 9800 | 0.9669 | 0.6125 |
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- | 1.0556 | 14.0028 | 9900 | 0.9669 | 0.6125 |
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- | 1.0511 | 14.1443 | 10000 | 0.9669 | 0.6125 |
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- | 1.0511 | 14.2857 | 10100 | 0.9669 | 0.6125 |
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- | 1.0511 | 14.4272 | 10200 | 0.9669 | 0.6125 |
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- | 1.0511 | 14.5686 | 10300 | 0.9669 | 0.6125 |
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- | 1.0511 | 14.7100 | 10400 | 0.9669 | 0.6125 |
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- | 1.0585 | 14.8515 | 10500 | 0.9669 | 0.6125 |
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- | 1.0585 | 14.9929 | 10600 | 0.9669 | 0.6125 |
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  ### Framework versions
 
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  license: apache-2.0
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  base_model: facebook/wav2vec2-large-xlsr-53
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  tags:
 
 
5
  - generated_from_trainer
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  metrics:
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  - wer
 
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  # wav2vec2-xlsr-53-ft-btb-cy
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+ This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3847
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+ - Wer: 0.2967
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  ## Model description
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46
  - 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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+ - training_steps: 2500
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  - mixed_precision_training: Native AMP
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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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+ | No log | 0.1414 | 100 | 3.7464 | 1.0 |
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+ | No log | 0.2829 | 200 | 2.9399 | 1.0 |
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+ | No log | 0.4243 | 300 | 2.5961 | 0.9991 |
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+ | No log | 0.5658 | 400 | 1.1619 | 0.7905 |
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+ | 3.5448 | 0.7072 | 500 | 0.9466 | 0.6898 |
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+ | 3.5448 | 0.8487 | 600 | 0.7895 | 0.6111 |
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+ | 3.5448 | 0.9901 | 700 | 0.6820 | 0.5379 |
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+ | 3.5448 | 1.1315 | 800 | 0.6039 | 0.4724 |
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+ | 3.5448 | 1.2730 | 900 | 0.5631 | 0.4675 |
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+ | 0.7808 | 1.4144 | 1000 | 0.5279 | 0.4291 |
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+ | 0.7808 | 1.5559 | 1100 | 0.5024 | 0.3994 |
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+ | 0.7808 | 1.6973 | 1200 | 0.4895 | 0.3895 |
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+ | 0.7808 | 1.8388 | 1300 | 0.4596 | 0.3696 |
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+ | 0.7808 | 1.9802 | 1400 | 0.4473 | 0.3611 |
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+ | 0.6005 | 2.1216 | 1500 | 0.4332 | 0.3474 |
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+ | 0.6005 | 2.2631 | 1600 | 0.4269 | 0.3418 |
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+ | 0.6005 | 2.4045 | 1700 | 0.4155 | 0.3361 |
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+ | 0.6005 | 2.5460 | 1800 | 0.4121 | 0.3214 |
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+ | 0.6005 | 2.6874 | 1900 | 0.4145 | 0.3366 |
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+ | 0.4666 | 2.8289 | 2000 | 0.3939 | 0.3114 |
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+ | 0.4666 | 2.9703 | 2100 | 0.3889 | 0.3081 |
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+ | 0.4666 | 3.1117 | 2200 | 0.3909 | 0.3064 |
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+ | 0.4666 | 3.2532 | 2300 | 0.3874 | 0.3015 |
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+ | 0.4666 | 3.3946 | 2400 | 0.3869 | 0.2983 |
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+ | 0.3805 | 3.5361 | 2500 | 0.3847 | 0.2967 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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