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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-large-xlsr-53 |
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tags: |
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- automatic-speech-recognition |
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- ./sample_speech.py |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: en-xlsr |
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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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# en-xlsr |
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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 ./SAMPLE_SPEECH.PY - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4835 |
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- Cer: 0.1119 |
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- Wer: 0.2446 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- total_eval_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: 10 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Cer | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
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| 2.9534 | 0.22 | 100 | 2.9533 | 1.0 | 1.0 | |
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| 2.933 | 0.44 | 200 | 2.9231 | 1.0 | 1.0 | |
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| 2.904 | 0.65 | 300 | 2.8851 | 1.0 | 1.0 | |
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| 2.3607 | 0.87 | 400 | 2.1546 | 0.6799 | 0.9976 | |
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| 1.1725 | 1.09 | 500 | 0.9899 | 0.2665 | 0.6191 | |
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| 0.9865 | 1.31 | 600 | 0.8060 | 0.2126 | 0.5064 | |
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| 0.8959 | 1.53 | 700 | 0.7131 | 0.1980 | 0.4607 | |
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| 0.7743 | 1.74 | 800 | 0.6663 | 0.1799 | 0.4370 | |
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| 0.7805 | 1.96 | 900 | 0.6159 | 0.1683 | 0.3997 | |
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| 0.6562 | 2.18 | 1000 | 0.6186 | 0.1537 | 0.3705 | |
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| 0.6223 | 2.4 | 1100 | 0.5698 | 0.1496 | 0.3552 | |
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| 0.5627 | 2.62 | 1200 | 0.5555 | 0.1446 | 0.3372 | |
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| 0.5476 | 2.84 | 1300 | 0.5435 | 0.1416 | 0.3307 | |
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| 0.5002 | 3.05 | 1400 | 0.5304 | 0.1436 | 0.3393 | |
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| 0.5174 | 3.27 | 1500 | 0.5377 | 0.1485 | 0.3357 | |
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| 0.4745 | 3.49 | 1600 | 0.5289 | 0.1340 | 0.3132 | |
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| 0.5239 | 3.71 | 1700 | 0.5112 | 0.1395 | 0.3239 | |
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| 0.5115 | 3.93 | 1800 | 0.5079 | 0.1342 | 0.3094 | |
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| 0.4471 | 4.14 | 1900 | 0.5131 | 0.1301 | 0.2965 | |
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| 0.4455 | 4.36 | 2000 | 0.5015 | 0.1278 | 0.2931 | |
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| 0.4199 | 4.58 | 2100 | 0.4954 | 0.1299 | 0.2962 | |
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| 0.4699 | 4.8 | 2200 | 0.4827 | 0.1268 | 0.2890 | |
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| 0.3521 | 5.02 | 2300 | 0.4857 | 0.1217 | 0.2782 | |
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| 0.3976 | 5.23 | 2400 | 0.4936 | 0.1231 | 0.2802 | |
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| 0.365 | 5.45 | 2500 | 0.4906 | 0.1221 | 0.2774 | |
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| 0.3857 | 5.67 | 2600 | 0.4843 | 0.1202 | 0.2757 | |
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| 0.3578 | 5.89 | 2700 | 0.4857 | 0.1196 | 0.2708 | |
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| 0.3298 | 6.11 | 2800 | 0.4867 | 0.1197 | 0.2689 | |
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| 0.3099 | 6.32 | 2900 | 0.4924 | 0.1237 | 0.2770 | |
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| 0.3606 | 6.54 | 3000 | 0.4851 | 0.1189 | 0.2684 | |
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| 0.3807 | 6.76 | 3100 | 0.4700 | 0.1196 | 0.2656 | |
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| 0.3286 | 6.98 | 3200 | 0.4770 | 0.1205 | 0.2730 | |
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| 0.3318 | 7.2 | 3300 | 0.4845 | 0.1166 | 0.2579 | |
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| 0.2936 | 7.42 | 3400 | 0.4909 | 0.1159 | 0.2570 | |
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| 0.3119 | 7.63 | 3500 | 0.4899 | 0.1150 | 0.2539 | |
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| 0.3142 | 7.85 | 3600 | 0.4782 | 0.1143 | 0.2550 | |
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| 0.2935 | 8.07 | 3700 | 0.4885 | 0.1153 | 0.2527 | |
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| 0.2805 | 8.29 | 3800 | 0.4906 | 0.1143 | 0.2529 | |
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| 0.254 | 8.51 | 3900 | 0.4822 | 0.1144 | 0.2538 | |
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| 0.2855 | 8.72 | 4000 | 0.4852 | 0.1123 | 0.2476 | |
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| 0.2661 | 8.94 | 4100 | 0.4847 | 0.1132 | 0.2496 | |
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| 0.2524 | 9.16 | 4200 | 0.4900 | 0.1116 | 0.2442 | |
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| 0.253 | 9.38 | 4300 | 0.4888 | 0.1120 | 0.2458 | |
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| 0.2591 | 9.6 | 4400 | 0.4813 | 0.1125 | 0.2458 | |
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| 0.2583 | 9.81 | 4500 | 0.4844 | 0.1114 | 0.2435 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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