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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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- mozilla-foundation/common_voice_11_0 |
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- generated_from_trainer |
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datasets: |
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- common_voice_11_0 |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-xls-r-300m-tr |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: MOZILLA-FOUNDATION/COMMON_VOICE_11_0 - TR |
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type: common_voice_11_0 |
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config: tr |
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split: test |
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args: 'Config: tr, Training split: train+validation, Eval split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.2862633203284225 |
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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-xls-r-300m-tr |
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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 MOZILLA-FOUNDATION/COMMON_VOICE_11_0 - TR dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3179 |
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- Wer: 0.2863 |
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- Cer: 0.0681 |
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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: 64 |
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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: 500 |
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- num_epochs: 30.0 |
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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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| No log | 0.71 | 400 | 1.7290 | 0.9804 | 0.4797 | |
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| 4.5435 | 1.42 | 800 | 0.4810 | 0.5774 | 0.1450 | |
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| 0.523 | 2.12 | 1200 | 0.3859 | 0.4812 | 0.1156 | |
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| 0.3449 | 2.83 | 1600 | 0.3492 | 0.4498 | 0.1095 | |
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| 0.2814 | 3.54 | 2000 | 0.3660 | 0.4466 | 0.1099 | |
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| 0.2814 | 4.25 | 2400 | 0.3766 | 0.4235 | 0.1043 | |
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| 0.2463 | 4.96 | 2800 | 0.3416 | 0.4119 | 0.1010 | |
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| 0.2296 | 5.66 | 3200 | 0.3322 | 0.4013 | 0.0979 | |
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| 0.2143 | 6.37 | 3600 | 0.3370 | 0.3956 | 0.0972 | |
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| 0.1955 | 7.08 | 4000 | 0.3401 | 0.4033 | 0.0998 | |
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| 0.1955 | 7.79 | 4400 | 0.3375 | 0.3889 | 0.0962 | |
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| 0.1845 | 8.5 | 4800 | 0.3455 | 0.3752 | 0.0923 | |
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| 0.1752 | 9.2 | 5200 | 0.3336 | 0.3718 | 0.0925 | |
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| 0.1705 | 9.91 | 5600 | 0.3145 | 0.3653 | 0.0892 | |
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| 0.1585 | 10.62 | 6000 | 0.3410 | 0.3737 | 0.0922 | |
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| 0.1585 | 11.33 | 6400 | 0.3296 | 0.3664 | 0.0899 | |
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| 0.1474 | 12.04 | 6800 | 0.3492 | 0.3590 | 0.0899 | |
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| 0.1485 | 12.74 | 7200 | 0.3176 | 0.3506 | 0.0867 | |
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| 0.137 | 13.45 | 7600 | 0.3532 | 0.3600 | 0.0890 | |
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| 0.1291 | 14.16 | 8000 | 0.3318 | 0.3571 | 0.0873 | |
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| 0.1291 | 14.87 | 8400 | 0.3353 | 0.3548 | 0.0883 | |
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| 0.1274 | 15.58 | 8800 | 0.3235 | 0.3396 | 0.0823 | |
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| 0.1198 | 16.28 | 9200 | 0.3259 | 0.3389 | 0.0832 | |
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| 0.1164 | 16.99 | 9600 | 0.3263 | 0.3411 | 0.0844 | |
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| 0.1119 | 17.7 | 10000 | 0.3254 | 0.3377 | 0.0824 | |
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| 0.1119 | 18.41 | 10400 | 0.3243 | 0.3331 | 0.0812 | |
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| 0.1054 | 19.12 | 10800 | 0.3223 | 0.3239 | 0.0790 | |
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| 0.1017 | 19.82 | 11200 | 0.3054 | 0.3190 | 0.0774 | |
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| 0.0964 | 20.53 | 11600 | 0.3278 | 0.3237 | 0.0785 | |
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| 0.0903 | 21.24 | 12000 | 0.3167 | 0.3177 | 0.0774 | |
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| 0.0903 | 21.95 | 12400 | 0.3331 | 0.3124 | 0.0766 | |
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| 0.0886 | 22.65 | 12800 | 0.3099 | 0.3089 | 0.0745 | |
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| 0.0836 | 23.36 | 13200 | 0.3171 | 0.3048 | 0.0731 | |
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| 0.0796 | 24.07 | 13600 | 0.3158 | 0.3041 | 0.0733 | |
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| 0.0739 | 24.78 | 14000 | 0.3203 | 0.3003 | 0.0721 | |
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| 0.0739 | 25.49 | 14400 | 0.3138 | 0.2974 | 0.0713 | |
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| 0.0742 | 26.19 | 14800 | 0.3197 | 0.2959 | 0.0711 | |
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| 0.07 | 26.9 | 15200 | 0.3232 | 0.2952 | 0.0703 | |
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| 0.0654 | 27.61 | 15600 | 0.3243 | 0.2939 | 0.0701 | |
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| 0.0631 | 28.32 | 16000 | 0.3213 | 0.2876 | 0.0688 | |
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| 0.0631 | 29.03 | 16400 | 0.3151 | 0.2880 | 0.0685 | |
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| 0.0607 | 29.73 | 16800 | 0.3184 | 0.2867 | 0.0681 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.8.1.dev0 |
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- Tokenizers 0.13.2 |
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