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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: wav2vec2_transformer_phonome |
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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_transformer_phonome |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2714 |
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- Wer: 0.5886 |
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- Cer: 0.0707 |
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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: 3e-05 |
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- train_batch_size: 2 |
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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: 4 |
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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: 2000 |
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- training_steps: 26000 |
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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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| 2.0354 | 1.49 | 1000 | 1.5671 | 0.9984 | 0.5492 | |
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| 1.3073 | 2.98 | 2000 | 0.5049 | 0.7604 | 0.1035 | |
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| 1.1054 | 4.46 | 3000 | 0.3268 | 0.6848 | 0.0865 | |
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| 1.066 | 5.95 | 4000 | 0.3185 | 0.6734 | 0.0814 | |
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| 1.0249 | 7.44 | 5000 | 0.3240 | 0.6483 | 0.0796 | |
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| 0.9736 | 8.93 | 6000 | 0.3017 | 0.6206 | 0.0778 | |
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| 0.9367 | 10.42 | 7000 | 0.2813 | 0.6279 | 0.0752 | |
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| 0.8958 | 11.9 | 8000 | 0.2778 | 0.6117 | 0.0763 | |
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| 0.8778 | 13.39 | 9000 | 0.2772 | 0.6393 | 0.0765 | |
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| 0.9016 | 14.88 | 10000 | 0.2768 | 0.6271 | 0.0751 | |
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| 0.8208 | 16.37 | 11000 | 0.3309 | 0.6182 | 0.0759 | |
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| 0.8297 | 17.86 | 12000 | 0.2814 | 0.6011 | 0.0721 | |
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| 0.7533 | 19.35 | 13000 | 0.2674 | 0.6068 | 0.0733 | |
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| 0.7959 | 20.83 | 14000 | 0.2821 | 0.6206 | 0.0736 | |
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| 0.7577 | 22.32 | 15000 | 0.3250 | 0.6206 | 0.0735 | |
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| 0.7456 | 23.81 | 16000 | 0.3078 | 0.5979 | 0.0742 | |
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| 0.7387 | 25.3 | 17000 | 0.3166 | 0.5930 | 0.0720 | |
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| 0.7364 | 26.79 | 18000 | 0.3052 | 0.6141 | 0.0739 | |
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| 0.7136 | 28.27 | 19000 | 0.3026 | 0.6060 | 0.0731 | |
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| 0.7036 | 29.76 | 20000 | 0.2726 | 0.5946 | 0.0720 | |
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| 0.6939 | 31.25 | 21000 | 0.2714 | 0.5930 | 0.0720 | |
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| 0.6985 | 32.74 | 22000 | 0.2722 | 0.5963 | 0.0713 | |
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| 0.677 | 34.23 | 23000 | 0.2799 | 0.6011 | 0.0718 | |
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| 0.7176 | 35.71 | 24000 | 0.2769 | 0.6052 | 0.0710 | |
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| 0.6649 | 37.2 | 25000 | 0.2751 | 0.5987 | 0.0716 | |
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| 0.642 | 38.69 | 26000 | 0.2719 | 0.5963 | 0.0704 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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