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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-xls-r-300m |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-xls-r-300m-euskera-colab |
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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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config: eu |
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split: test |
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args: eu |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.17522160918337998 |
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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-euskera-colab |
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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 evaluation set: |
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- Loss: 0.1873 |
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- Wer: 0.1752 |
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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: 2 |
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- total_train_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: 500 |
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- num_epochs: 20 |
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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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| 2.5827 | 0.76 | 600 | 0.3985 | 0.5778 | |
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| 0.2963 | 1.51 | 1200 | 0.2492 | 0.4142 | |
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| 0.2122 | 2.27 | 1800 | 0.2132 | 0.3133 | |
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| 0.1686 | 3.03 | 2400 | 0.2078 | 0.2851 | |
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| 0.1373 | 3.79 | 3000 | 0.1910 | 0.2750 | |
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| 0.1254 | 4.54 | 3600 | 0.1850 | 0.2619 | |
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| 0.1137 | 5.3 | 4200 | 0.1874 | 0.2503 | |
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| 0.1008 | 6.06 | 4800 | 0.1857 | 0.2554 | |
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| 0.0934 | 6.81 | 5400 | 0.1844 | 0.2404 | |
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| 0.0876 | 7.57 | 6000 | 0.2001 | 0.2375 | |
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| 0.0801 | 8.33 | 6600 | 0.2036 | 0.2512 | |
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| 0.0732 | 9.09 | 7200 | 0.1921 | 0.2301 | |
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| 0.069 | 9.84 | 7800 | 0.1821 | 0.2330 | |
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| 0.0628 | 10.6 | 8400 | 0.1915 | 0.2249 | |
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| 0.0619 | 11.36 | 9000 | 0.1881 | 0.2113 | |
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| 0.0549 | 12.11 | 9600 | 0.1920 | 0.2076 | |
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| 0.0524 | 12.87 | 10200 | 0.1901 | 0.2079 | |
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| 0.0492 | 13.63 | 10800 | 0.1767 | 0.2020 | |
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| 0.0445 | 14.38 | 11400 | 0.1852 | 0.1933 | |
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| 0.0427 | 15.14 | 12000 | 0.1995 | 0.1994 | |
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| 0.0398 | 15.9 | 12600 | 0.1922 | 0.1932 | |
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| 0.037 | 16.66 | 13200 | 0.1956 | 0.1920 | |
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| 0.0353 | 17.41 | 13800 | 0.1990 | 0.1909 | |
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| 0.0327 | 18.17 | 14400 | 0.1906 | 0.1784 | |
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| 0.0311 | 18.93 | 15000 | 0.1847 | 0.1765 | |
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| 0.0295 | 19.68 | 15600 | 0.1873 | 0.1752 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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