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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-base |
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tags: |
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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: wav2vec2-base-timit-demo-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-base-timit-demo-colab |
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5532 |
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- Wer: 0.3373 |
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- Cer: 0.1112 |
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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: 0.0001 |
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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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- 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: 1000 |
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- num_epochs: 30 |
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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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| 0.1293 | 1.0 | 500 | 0.3918 | 0.3677 | 0.1170 | |
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| 0.133 | 2.01 | 1000 | 0.4392 | 0.3797 | 0.1234 | |
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| 0.1473 | 3.01 | 1500 | 0.4959 | 0.3914 | 0.1267 | |
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| 0.1373 | 4.02 | 2000 | 0.4781 | 0.3851 | 0.1260 | |
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| 0.1259 | 5.02 | 2500 | 0.4473 | 0.3810 | 0.1237 | |
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| 0.1123 | 6.02 | 3000 | 0.5314 | 0.3774 | 0.1243 | |
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| 0.1086 | 7.03 | 3500 | 0.4231 | 0.3801 | 0.1228 | |
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| 0.0956 | 8.03 | 4000 | 0.5203 | 0.3734 | 0.1236 | |
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| 0.0839 | 9.04 | 4500 | 0.5310 | 0.3750 | 0.1227 | |
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| 0.0778 | 10.04 | 5000 | 0.5279 | 0.3793 | 0.1257 | |
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| 0.0772 | 11.04 | 5500 | 0.4969 | 0.3792 | 0.1265 | |
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| 0.072 | 12.05 | 6000 | 0.5489 | 0.3701 | 0.1239 | |
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| 0.0678 | 13.05 | 6500 | 0.5123 | 0.3669 | 0.1207 | |
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| 0.067 | 14.06 | 7000 | 0.4969 | 0.3663 | 0.1192 | |
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| 0.061 | 15.06 | 7500 | 0.4742 | 0.3664 | 0.1212 | |
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| 0.0575 | 16.06 | 8000 | 0.5304 | 0.3643 | 0.1194 | |
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| 0.0574 | 17.07 | 8500 | 0.4936 | 0.3729 | 0.1218 | |
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| 0.0474 | 18.07 | 9000 | 0.5363 | 0.3601 | 0.1185 | |
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| 0.0447 | 19.08 | 9500 | 0.5347 | 0.3552 | 0.1177 | |
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| 0.0372 | 20.08 | 10000 | 0.5372 | 0.3519 | 0.1157 | |
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| 0.0325 | 21.08 | 10500 | 0.5455 | 0.3525 | 0.1159 | |
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| 0.0309 | 22.09 | 11000 | 0.5193 | 0.3514 | 0.1146 | |
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| 0.0314 | 23.09 | 11500 | 0.5402 | 0.3494 | 0.1160 | |
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| 0.0272 | 24.1 | 12000 | 0.5309 | 0.3457 | 0.1129 | |
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| 0.0238 | 25.1 | 12500 | 0.5490 | 0.3447 | 0.1132 | |
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| 0.0217 | 26.1 | 13000 | 0.5702 | 0.3406 | 0.1117 | |
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| 0.0225 | 27.11 | 13500 | 0.5575 | 0.3414 | 0.1116 | |
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| 0.0189 | 28.11 | 14000 | 0.5572 | 0.3391 | 0.1115 | |
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| 0.0179 | 29.12 | 14500 | 0.5532 | 0.3373 | 0.1112 | |
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### Framework versions |
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- Transformers 4.32.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 1.18.3 |
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- Tokenizers 0.13.3 |
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