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--- |
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language: |
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- tr |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- common_voice |
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- generated_from_trainer |
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- xls_r_repro_common_voice_tr |
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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-common_voice-tr-ft |
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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-common_voice-tr-ft |
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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 - TR dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4179 |
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- Wer: 0.3071 |
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- Cer: 0.0736 |
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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: 0.0005 |
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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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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 64 |
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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: 100.0 |
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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 | Cer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
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| 0.7638 | 9.09 | 500 | 0.4763 | 0.5313 | 0.1333 | |
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| 0.5739 | 18.18 | 1000 | 0.4007 | 0.4357 | 0.1099 | |
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| 0.4343 | 27.27 | 1500 | 0.3819 | 0.4060 | 0.1012 | |
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| 0.4401 | 36.36 | 2000 | 0.3991 | 0.3954 | 0.1001 | |
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| 0.2647 | 45.45 | 2500 | 0.3901 | 0.3689 | 0.0914 | |
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| 0.2656 | 54.55 | 3000 | 0.4284 | 0.3463 | 0.0852 | |
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| 0.2586 | 63.64 | 3500 | 0.4084 | 0.3297 | 0.0804 | |
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| 0.2041 | 72.73 | 4000 | 0.3907 | 0.3193 | 0.0781 | |
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| 0.4265 | 81.82 | 4500 | 0.4265 | 0.3120 | 0.0755 | |
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| 0.2041 | 90.91 | 5000 | 0.4240 | 0.3071 | 0.0736 | |
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### Framework versions |
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- Transformers 4.13.0.dev0 |
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- Pytorch 1.9.0+cu111 |
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- Datasets 1.15.2.dev0 |
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- Tokenizers 0.10.3 |
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