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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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datasets: |
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- common_voice |
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model-index: |
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- name: wav2vec2-large-xls-r-300m-tr-colab |
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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-tr-colab |
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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 the common_voice dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4316 |
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- Wer: 0.2905 |
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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: 16 |
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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: 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: 500 |
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- num_epochs: 100 |
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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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| 3.9953 | 3.67 | 400 | 0.7024 | 0.7226 | |
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| 0.4046 | 7.34 | 800 | 0.4342 | 0.5343 | |
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| 0.201 | 11.01 | 1200 | 0.4396 | 0.5290 | |
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| 0.1513 | 14.68 | 1600 | 0.4319 | 0.4108 | |
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| 0.1285 | 18.35 | 2000 | 0.4422 | 0.3864 | |
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| 0.1086 | 22.02 | 2400 | 0.4568 | 0.3796 | |
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| 0.0998 | 25.69 | 2800 | 0.4687 | 0.3732 | |
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| 0.0863 | 29.36 | 3200 | 0.4726 | 0.3803 | |
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| 0.0809 | 33.03 | 3600 | 0.4479 | 0.3601 | |
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| 0.0747 | 36.7 | 4000 | 0.4624 | 0.3525 | |
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| 0.0692 | 40.37 | 4400 | 0.4366 | 0.3435 | |
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| 0.0595 | 44.04 | 4800 | 0.4204 | 0.3510 | |
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| 0.0584 | 47.71 | 5200 | 0.4202 | 0.3402 | |
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| 0.0545 | 51.38 | 5600 | 0.4366 | 0.3343 | |
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| 0.0486 | 55.05 | 6000 | 0.4492 | 0.3678 | |
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| 0.0444 | 58.72 | 6400 | 0.4471 | 0.3301 | |
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| 0.0406 | 62.39 | 6800 | 0.4382 | 0.3318 | |
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| 0.0341 | 66.06 | 7200 | 0.4295 | 0.3258 | |
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| 0.0297 | 69.72 | 7600 | 0.4336 | 0.3205 | |
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| 0.0295 | 73.39 | 8000 | 0.4240 | 0.3199 | |
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| 0.0261 | 77.06 | 8400 | 0.4316 | 0.3143 | |
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| 0.0247 | 80.73 | 8800 | 0.4300 | 0.3165 | |
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| 0.0207 | 84.4 | 9200 | 0.4380 | 0.3111 | |
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| 0.0203 | 88.07 | 9600 | 0.4218 | 0.2998 | |
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| 0.0174 | 91.74 | 10000 | 0.4271 | 0.2973 | |
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| 0.015 | 95.41 | 10400 | 0.4330 | 0.2939 | |
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| 0.0144 | 99.08 | 10800 | 0.4316 | 0.2905 | |
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### Framework versions |
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- Transformers 4.23.1 |
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- Pytorch 1.12.1+cu102 |
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- Datasets 2.5.2 |
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- Tokenizers 0.13.1 |
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