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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-large-xlsr-53-Total2e-4_4 |
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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-xlsr-53-Total2e-4_4 |
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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: 0.2474 |
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- Wer: 0.1951 |
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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: 0.0002 |
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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: 500 |
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- num_epochs: 5 |
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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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| 5.5015 | 0.1 | 200 | 2.9261 | 0.9707 | |
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| 2.9197 | 0.2 | 400 | 2.7757 | 0.9707 | |
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| 1.7594 | 0.3 | 600 | 0.6117 | 0.5746 | |
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| 1.0908 | 0.4 | 800 | 0.4673 | 0.4530 | |
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| 0.9441 | 0.5 | 1000 | 0.4142 | 0.4010 | |
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| 0.8688 | 0.6 | 1200 | 0.3909 | 0.3675 | |
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| 0.849 | 0.7 | 1400 | 0.3649 | 0.3360 | |
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| 0.8223 | 0.8 | 1600 | 0.3532 | 0.3334 | |
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| 0.821 | 0.9 | 1800 | 0.3513 | 0.3185 | |
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| 0.7839 | 1.0 | 2000 | 0.3373 | 0.3039 | |
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| 0.714 | 1.1 | 2200 | 0.3210 | 0.2922 | |
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| 0.7129 | 1.2 | 2400 | 0.3216 | 0.2860 | |
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| 0.7076 | 1.3 | 2600 | 0.3279 | 0.2843 | |
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| 0.73 | 1.4 | 2800 | 0.3111 | 0.2662 | |
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| 0.7256 | 1.5 | 3000 | 0.3032 | 0.2625 | |
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| 0.72 | 1.6 | 3200 | 0.3066 | 0.2571 | |
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| 0.6754 | 1.7 | 3400 | 0.2999 | 0.2581 | |
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| 0.6859 | 1.8 | 3600 | 0.2935 | 0.2562 | |
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| 0.6966 | 1.9 | 3800 | 0.2858 | 0.2469 | |
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| 0.6791 | 2.0 | 4000 | 0.2857 | 0.2393 | |
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| 0.6412 | 2.1 | 4200 | 0.2815 | 0.2392 | |
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| 0.6356 | 2.2 | 4400 | 0.2836 | 0.2343 | |
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| 0.6048 | 2.3 | 4600 | 0.2824 | 0.2422 | |
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| 0.6473 | 2.4 | 4800 | 0.2805 | 0.2316 | |
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| 0.659 | 2.5 | 5000 | 0.2775 | 0.2262 | |
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| 0.6412 | 2.6 | 5200 | 0.2729 | 0.2249 | |
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| 0.6167 | 2.7 | 5400 | 0.2719 | 0.2227 | |
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| 0.6226 | 2.8 | 5600 | 0.2661 | 0.2193 | |
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| 0.6168 | 2.9 | 5800 | 0.2615 | 0.2172 | |
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| 0.6145 | 3.0 | 6000 | 0.2608 | 0.2148 | |
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| 0.593 | 3.1 | 6200 | 0.2643 | 0.2123 | |
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| 0.5919 | 3.2 | 6400 | 0.2617 | 0.2131 | |
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| 0.6115 | 3.3 | 6600 | 0.2589 | 0.2114 | |
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| 0.5859 | 3.4 | 6800 | 0.2591 | 0.2100 | |
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| 0.5919 | 3.5 | 7000 | 0.2564 | 0.2103 | |
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| 0.5873 | 3.6 | 7200 | 0.2572 | 0.2074 | |
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| 0.561 | 3.7 | 7400 | 0.2561 | 0.2056 | |
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| 0.5808 | 3.8 | 7600 | 0.2538 | 0.2062 | |
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| 0.5701 | 3.9 | 7800 | 0.2517 | 0.2029 | |
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| 0.5722 | 4.0 | 8000 | 0.2523 | 0.2007 | |
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| 0.5508 | 4.1 | 8200 | 0.2570 | 0.2023 | |
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| 0.5591 | 4.2 | 8400 | 0.2502 | 0.2029 | |
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| 0.5697 | 4.3 | 8600 | 0.2478 | 0.1991 | |
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| 0.5689 | 4.4 | 8800 | 0.2492 | 0.2021 | |
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| 0.5345 | 4.5 | 9000 | 0.2498 | 0.2005 | |
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| 0.5726 | 4.6 | 9200 | 0.2492 | 0.1983 | |
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| 0.5382 | 4.7 | 9400 | 0.2487 | 0.1974 | |
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| 0.5614 | 4.8 | 9600 | 0.2481 | 0.1957 | |
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| 0.5568 | 4.9 | 9800 | 0.2477 | 0.1955 | |
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| 0.5631 | 5.0 | 10000 | 0.2474 | 0.1951 | |
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### Framework versions |
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- Transformers 4.15.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.3 |
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- Tokenizers 0.10.3 |
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