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--- |
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language: |
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- nl |
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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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- hf-asr-leaderboard |
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- model_for_talk |
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- nl |
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- robust-speech-event |
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datasets: |
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- common_voice |
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model-index: |
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- name: wav2vec2-large-xls-r-300m-nl |
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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: common_voice |
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type: common_voice |
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args: nl |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 17.17 |
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- name: Test CER |
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type: cer |
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value: 5.13 |
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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: Robust Speech Event - Dev Data |
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type: speech-recognition-community-v2/dev_data |
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args: nl |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 35.76 |
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- name: Test CER |
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type: cer |
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value: 13.99 |
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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: Robust Speech Event - Test Data |
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type: speech-recognition-community-v2/eval_data |
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args: nl |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 37.19 |
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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-xls-r-300m-nl |
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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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It achieves the following results on the test set: |
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- Loss: 0.3923 |
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- Wer: 0.1748 |
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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: 7.5e-05 |
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- train_batch_size: 16 |
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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: 32 |
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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: 50 |
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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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| 1.5787 | 0.89 | 400 | 0.6354 | 0.5643 | |
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| 0.3036 | 1.78 | 800 | 0.3690 | 0.3552 | |
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| 0.188 | 2.67 | 1200 | 0.3239 | 0.2958 | |
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| 0.1434 | 3.56 | 1600 | 0.3093 | 0.2515 | |
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| 0.1245 | 4.44 | 2000 | 0.3024 | 0.2433 | |
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| 0.1095 | 5.33 | 2400 | 0.3249 | 0.2643 | |
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| 0.0979 | 6.22 | 2800 | 0.3191 | 0.2281 | |
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| 0.0915 | 7.11 | 3200 | 0.3152 | 0.2216 | |
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| 0.0829 | 8.0 | 3600 | 0.3419 | 0.2218 | |
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| 0.0777 | 8.89 | 4000 | 0.3432 | 0.2132 | |
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| 0.073 | 9.78 | 4400 | 0.3223 | 0.2131 | |
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| 0.0688 | 10.67 | 4800 | 0.3094 | 0.2152 | |
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| 0.0647 | 11.56 | 5200 | 0.3411 | 0.2152 | |
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| 0.0639 | 12.44 | 5600 | 0.3762 | 0.2135 | |
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| 0.0599 | 13.33 | 6000 | 0.3790 | 0.2137 | |
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| 0.0572 | 14.22 | 6400 | 0.3693 | 0.2118 | |
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| 0.0563 | 15.11 | 6800 | 0.3495 | 0.2139 | |
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| 0.0521 | 16.0 | 7200 | 0.3800 | 0.2023 | |
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| 0.0508 | 16.89 | 7600 | 0.3678 | 0.2033 | |
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| 0.0513 | 17.78 | 8000 | 0.3845 | 0.1987 | |
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| 0.0476 | 18.67 | 8400 | 0.3511 | 0.2037 | |
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| 0.045 | 19.56 | 8800 | 0.3794 | 0.1994 | |
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| 0.044 | 20.44 | 9200 | 0.3525 | 0.2050 | |
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| 0.043 | 21.33 | 9600 | 0.4082 | 0.2007 | |
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| 0.0409 | 22.22 | 10000 | 0.3866 | 0.2004 | |
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| 0.0393 | 23.11 | 10400 | 0.3899 | 0.2008 | |
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| 0.0382 | 24.0 | 10800 | 0.3626 | 0.1951 | |
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| 0.039 | 24.89 | 11200 | 0.3936 | 0.1953 | |
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| 0.0361 | 25.78 | 11600 | 0.4262 | 0.1928 | |
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| 0.0362 | 26.67 | 12000 | 0.3796 | 0.1934 | |
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| 0.033 | 27.56 | 12400 | 0.3616 | 0.1934 | |
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| 0.0321 | 28.44 | 12800 | 0.3742 | 0.1933 | |
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| 0.0325 | 29.33 | 13200 | 0.3582 | 0.1869 | |
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| 0.0309 | 30.22 | 13600 | 0.3717 | 0.1874 | |
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| 0.029 | 31.11 | 14000 | 0.3814 | 0.1894 | |
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| 0.0296 | 32.0 | 14400 | 0.3698 | 0.1877 | |
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| 0.0281 | 32.89 | 14800 | 0.3976 | 0.1899 | |
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| 0.0275 | 33.78 | 15200 | 0.3854 | 0.1858 | |
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| 0.0264 | 34.67 | 15600 | 0.4021 | 0.1889 | |
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| 0.0261 | 35.56 | 16000 | 0.3850 | 0.1830 | |
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| 0.0242 | 36.44 | 16400 | 0.4091 | 0.1878 | |
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| 0.0245 | 37.33 | 16800 | 0.4012 | 0.1846 | |
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| 0.0243 | 38.22 | 17200 | 0.3996 | 0.1833 | |
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| 0.0223 | 39.11 | 17600 | 0.3962 | 0.1815 | |
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| 0.0223 | 40.0 | 18000 | 0.3898 | 0.1832 | |
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| 0.0219 | 40.89 | 18400 | 0.4019 | 0.1822 | |
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| 0.0211 | 41.78 | 18800 | 0.4035 | 0.1809 | |
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| 0.021 | 42.67 | 19200 | 0.3915 | 0.1826 | |
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| 0.0208 | 43.56 | 19600 | 0.3934 | 0.1784 | |
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| 0.0188 | 44.44 | 20000 | 0.3912 | 0.1787 | |
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| 0.0195 | 45.33 | 20400 | 0.3989 | 0.1766 | |
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| 0.0186 | 46.22 | 20800 | 0.3887 | 0.1773 | |
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| 0.0188 | 47.11 | 21200 | 0.3982 | 0.1758 | |
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| 0.0175 | 48.0 | 21600 | 0.3933 | 0.1755 | |
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| 0.0172 | 48.89 | 22000 | 0.3921 | 0.1749 | |
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| 0.0187 | 49.78 | 22400 | 0.3923 | 0.1748 | |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.17.1.dev0 |
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- Tokenizers 0.11.0 |
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