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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: xlsr-wav2vec2-3 |
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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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# xlsr-wav2vec2-3 |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4201 |
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- Wer: 0.3998 |
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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: 800 |
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- num_epochs: 30 |
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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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| 5.0117 | 0.68 | 400 | 3.0284 | 0.9999 | |
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| 2.6502 | 1.35 | 800 | 1.0868 | 0.9374 | |
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| 0.9362 | 2.03 | 1200 | 0.5216 | 0.6491 | |
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| 0.6675 | 2.7 | 1600 | 0.4744 | 0.5837 | |
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| 0.5799 | 3.38 | 2000 | 0.4400 | 0.5802 | |
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| 0.5196 | 4.05 | 2400 | 0.4266 | 0.5314 | |
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| 0.4591 | 4.73 | 2800 | 0.3808 | 0.5190 | |
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| 0.4277 | 5.41 | 3200 | 0.3987 | 0.5036 | |
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| 0.4125 | 6.08 | 3600 | 0.3902 | 0.5040 | |
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| 0.3797 | 6.76 | 4000 | 0.4105 | 0.5025 | |
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| 0.3606 | 7.43 | 4400 | 0.3975 | 0.4823 | |
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| 0.3554 | 8.11 | 4800 | 0.3733 | 0.4747 | |
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| 0.3373 | 8.78 | 5200 | 0.3737 | 0.4726 | |
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| 0.3252 | 9.46 | 5600 | 0.3795 | 0.4736 | |
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| 0.3192 | 10.14 | 6000 | 0.3935 | 0.4736 | |
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| 0.3012 | 10.81 | 6400 | 0.3974 | 0.4648 | |
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| 0.2972 | 11.49 | 6800 | 0.4497 | 0.4724 | |
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| 0.2873 | 12.16 | 7200 | 0.4645 | 0.4843 | |
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| 0.2849 | 12.84 | 7600 | 0.4461 | 0.4709 | |
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| 0.274 | 13.51 | 8000 | 0.4002 | 0.4695 | |
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| 0.2709 | 14.19 | 8400 | 0.4188 | 0.4627 | |
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| 0.2619 | 14.86 | 8800 | 0.3987 | 0.4646 | |
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| 0.2545 | 15.54 | 9200 | 0.4083 | 0.4668 | |
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| 0.2477 | 16.22 | 9600 | 0.4525 | 0.4728 | |
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| 0.2455 | 16.89 | 10000 | 0.4148 | 0.4515 | |
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| 0.2281 | 17.57 | 10400 | 0.4304 | 0.4514 | |
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| 0.2267 | 18.24 | 10800 | 0.4077 | 0.4446 | |
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| 0.2136 | 18.92 | 11200 | 0.4209 | 0.4445 | |
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| 0.2032 | 19.59 | 11600 | 0.4543 | 0.4534 | |
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| 0.1999 | 20.27 | 12000 | 0.4184 | 0.4373 | |
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| 0.1898 | 20.95 | 12400 | 0.4044 | 0.4424 | |
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| 0.1846 | 21.62 | 12800 | 0.4098 | 0.4288 | |
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| 0.1796 | 22.3 | 13200 | 0.4047 | 0.4262 | |
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| 0.1715 | 22.97 | 13600 | 0.4077 | 0.4189 | |
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| 0.1641 | 23.65 | 14000 | 0.4162 | 0.4248 | |
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| 0.1615 | 24.32 | 14400 | 0.4392 | 0.4222 | |
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| 0.1575 | 25.0 | 14800 | 0.4296 | 0.4185 | |
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| 0.1456 | 25.68 | 15200 | 0.4363 | 0.4129 | |
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| 0.1461 | 26.35 | 15600 | 0.4305 | 0.4124 | |
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| 0.1422 | 27.03 | 16000 | 0.4237 | 0.4086 | |
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| 0.1378 | 27.7 | 16400 | 0.4294 | 0.4051 | |
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| 0.1326 | 28.38 | 16800 | 0.4311 | 0.4051 | |
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| 0.1286 | 29.05 | 17200 | 0.4153 | 0.3992 | |
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| 0.1283 | 29.73 | 17600 | 0.4201 | 0.3998 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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