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--- |
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language: |
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- uk |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- common_voice |
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- generated_from_trainer |
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datasets: |
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- common_voice |
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model-index: |
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- name: wav2vec2-xls-r-300m-uk |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice uk |
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type: common_voice |
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args: uk |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 27.99 |
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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-uk |
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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 common_voice dataset. |
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Notebook for training is located in this repository: [https://github.com/robinhad/wav2vec2-xls-r-ukrainian](https://github.com/robinhad/wav2vec2-xls-r-ukrainian). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4165 |
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- Wer: 0.2799 |
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- Cer: 0.0601 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 20 |
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- total_train_batch_size: 160 |
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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: 500 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |
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|:-------------:|:------:|:-----:|:------:|:---------------:|:------:| |
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| 4.3982 | 9.3 | 400 | 0.1437 | 0.5218 | 0.6507 | |
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| 0.229 | 18.6 | 800 | 0.0848 | 0.3679 | 0.4048 | |
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| 0.1054 | 27.9 | 1200 | 0.0778 | 0.3813 | 0.3670 | |
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| 0.0784 | 37.21 | 1600 | 0.0747 | 0.3839 | 0.3550 | |
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| 0.066 | 46.51 | 2000 | 0.0736 | 0.3970 | 0.3443 | |
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| 0.0603 | 55.8 | 2400 | 0.0722 | 0.3702 | 0.3393 | |
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| 0.0539 | 65.11 | 2800 | 0.0724 | 0.3762 | 0.3388 | |
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| 0.0497 | 74.41 | 3200 | 0.0713 | 0.3623 | 0.3414 | |
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| 0.0432 | 83.71 | 3600 | 0.0725 | 0.3847 | 0.3346 | |
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| 0.0438 | 93.02 | 4000 | 0.0750 | 0.4058 | 0.3393 | |
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| 0.0413 | 102.32 | 4400 | 0.0727 | 0.3957 | 0.3363 | |
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| 0.039 | 111.62 | 4800 | 0.0718 | 0.3865 | 0.3330 | |
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| 0.0356 | 120.92 | 5200 | 0.0711 | 0.3860 | 0.3319 | |
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| 0.0336 | 130.23 | 5600 | 0.0700 | 0.3902 | 0.3242 | |
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| 0.034 | 139.53 | 6000 | 0.0732 | 0.3930 | 0.3337 | |
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| 0.0273 | 148.83 | 6400 | 0.0748 | 0.3912 | 0.3375 | |
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| 0.027 | 158.14 | 6800 | 0.0752 | 0.4266 | 0.3434 | |
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| 0.028 | 167.44 | 7200 | 0.0708 | 0.3895 | 0.3227 | |
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| 0.0241 | 176.73 | 7600 | 0.0727 | 0.3967 | 0.3294 | |
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| 0.0241 | 186.05 | 8000 | 0.0712 | 0.4058 | 0.3255 | |
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| 0.0209 | 195.34 | 8400 | 0.0702 | 0.4102 | 0.3233 | |
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| 0.0206 | 204.64 | 8800 | 0.0699 | 0.4075 | 0.3194 | |
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| 0.0172 | 213.94 | 9200 | 0.0695 | 0.4222 | 0.3191 | |
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| 0.0166 | 223.25 | 9600 | 0.0678 | 0.3860 | 0.3135 | |
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| 0.0156 | 232.55 | 10000 | 0.0677 | 0.4035 | 0.3117 | |
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| 0.0149 | 241.85 | 10400 | 0.0677 | 0.3951 | 0.3087 | |
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| 0.0142 | 251.16 | 10800 | 0.0674 | 0.3972 | 0.3097 | |
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| 0.0134 | 260.46 | 11200 | 0.0675 | 0.4069 | 0.3111 | |
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| 0.0116 | 269.76 | 11600 | 0.0697 | 0.4189 | 0.3161 | |
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| 0.0119 | 279.07 | 12000 | 0.0648 | 0.3902 | 0.3008 | |
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| 0.0098 | 288.37 | 12400 | 0.0652 | 0.4095 | 0.3002 | |
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| 0.0091 | 297.67 | 12800 | 0.0644 | 0.3892 | 0.2990 | |
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| 0.0094 | 306.96 | 13200 | 0.0647 | 0.4026 | 0.2983 | |
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| 0.0081 | 316.28 | 13600 | 0.0646 | 0.4303 | 0.2978 | |
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| 0.0079 | 325.57 | 14000 | 0.0643 | 0.4044 | 0.2980 | |
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| 0.0072 | 334.87 | 14400 | 0.0655 | 0.3828 | 0.2999 | |
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| 0.0081 | 344.18 | 14800 | 0.0668 | 0.4108 | 0.3046 | |
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| 0.0088 | 353.48 | 15200 | 0.0654 | 0.4019 | 0.2993 | |
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| 0.0088 | 362.78 | 15600 | 0.0681 | 0.4073 | 0.3091 | |
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| 0.0079 | 372.09 | 16000 | 0.0667 | 0.4204 | 0.3055 | |
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| 0.0072 | 381.39 | 16400 | 0.0656 | 0.4030 | 0.3028 | |
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| 0.0073 | 390.69 | 16800 | 0.0677 | 0.4032 | 0.3081 | |
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| 0.0069 | 399.99 | 17200 | 0.0669 | 0.4130 | 0.3021 | |
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| 0.0063 | 409.3 | 17600 | 0.0651 | 0.4072 | 0.2979 | |
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| 0.0059 | 418.6 | 18000 | 0.0640 | 0.4110 | 0.2969 | |
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| 0.0056 | 427.9 | 18400 | 0.0647 | 0.4229 | 0.2995 | |
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| 0.005 | 437.21 | 18800 | 0.0624 | 0.4118 | 0.2885 | |
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| 0.0046 | 446.51 | 19200 | 0.0615 | 0.4111 | 0.2841 | |
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| 0.0043 | 455.8 | 19600 | 0.0616 | 0.4071 | 0.2850 | |
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| 0.0038 | 465.11 | 20000 | 0.0624 | 0.4268 | 0.2867 | |
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| 0.0035 | 474.41 | 20400 | 0.0605 | 0.4117 | 0.2820 | |
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| 0.0035 | 483.71 | 20800 | 0.0602 | 0.4155 | 0.2819 | |
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| 0.0034 | 493.02 | 21200 | 0.0601 | 0.4165 | 0.2799 | |
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### Framework versions |
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- Transformers 4.14.1 |
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- Pytorch 1.10.0 |
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- Datasets 1.16.1 |
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- Tokenizers 0.10.3 |
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