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--- |
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language: |
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- es |
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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_8_0 |
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- generated_from_trainer |
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- es |
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- robust-speech-event |
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- hf-asr-leaderboard |
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datasets: |
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- mozilla-foundation/common_voice_8_0 |
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model-index: |
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- name: xls-r-es-test |
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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 8.0 |
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type: mozilla-foundation/common_voice_8_0 |
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args: es |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 12.62 |
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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: es |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 36.08 |
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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: es |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 39.19 |
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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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# xls-r-es-test |
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ES dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1304 |
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- WER: 0.1261 |
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- CER: 0.035 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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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: 2000 |
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- num_epochs: 10.0 |
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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 | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 2.9613 | 0.07 | 500 | 2.9647 | 1.0 | |
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| 2.604 | 0.14 | 1000 | 1.8300 | 0.9562 | |
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| 1.177 | 0.21 | 1500 | 0.3652 | 0.3077 | |
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| 1.0745 | 0.28 | 2000 | 0.2707 | 0.2504 | |
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| 1.0103 | 0.35 | 2500 | 0.2338 | 0.2157 | |
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| 0.9858 | 0.42 | 3000 | 0.2321 | 0.2129 | |
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| 0.974 | 0.49 | 3500 | 0.2164 | 0.2031 | |
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| 0.9699 | 0.56 | 4000 | 0.2078 | 0.1970 | |
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| 0.9513 | 0.63 | 4500 | 0.2173 | 0.2139 | |
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| 0.9657 | 0.7 | 5000 | 0.2050 | 0.1979 | |
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| 0.9484 | 0.77 | 5500 | 0.2008 | 0.1919 | |
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| 0.9317 | 0.84 | 6000 | 0.2012 | 0.1911 | |
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| 0.9366 | 0.91 | 6500 | 0.2024 | 0.1976 | |
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| 0.9242 | 0.98 | 7000 | 0.2062 | 0.2028 | |
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| 0.9138 | 1.05 | 7500 | 0.1924 | 0.1863 | |
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| 0.921 | 1.12 | 8000 | 0.1935 | 0.1836 | |
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| 0.9117 | 1.19 | 8500 | 0.1887 | 0.1815 | |
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| 0.9064 | 1.26 | 9000 | 0.1909 | 0.1839 | |
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| 0.9118 | 1.32 | 9500 | 0.1869 | 0.1830 | |
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| 0.9121 | 1.39 | 10000 | 0.1863 | 0.1802 | |
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| 0.9048 | 1.46 | 10500 | 0.1845 | 0.1791 | |
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| 0.8955 | 1.53 | 11000 | 0.1863 | 0.1774 | |
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| 0.8947 | 1.6 | 11500 | 0.1907 | 0.1814 | |
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| 0.9073 | 1.67 | 12000 | 0.1892 | 0.1853 | |
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| 0.8927 | 1.74 | 12500 | 0.1821 | 0.1750 | |
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| 0.8732 | 1.81 | 13000 | 0.1815 | 0.1768 | |
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| 0.8761 | 1.88 | 13500 | 0.1822 | 0.1749 | |
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| 0.8751 | 1.95 | 14000 | 0.1789 | 0.1715 | |
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| 0.8889 | 2.02 | 14500 | 0.1819 | 0.1791 | |
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| 0.8864 | 2.09 | 15000 | 0.1826 | 0.1794 | |
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| 0.886 | 2.16 | 15500 | 0.1788 | 0.1776 | |
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| 0.8915 | 2.23 | 16000 | 0.1756 | 0.1719 | |
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| 0.8689 | 2.3 | 16500 | 0.1769 | 0.1711 | |
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| 0.879 | 2.37 | 17000 | 0.1777 | 0.1739 | |
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| 0.8692 | 2.44 | 17500 | 0.1765 | 0.1705 | |
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| 0.8504 | 2.51 | 18000 | 0.1699 | 0.1652 | |
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| 0.8728 | 2.58 | 18500 | 0.1705 | 0.1694 | |
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| 0.8523 | 2.65 | 19000 | 0.1674 | 0.1645 | |
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| 0.8513 | 2.72 | 19500 | 0.1661 | 0.1611 | |
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| 0.8498 | 2.79 | 20000 | 0.1660 | 0.1631 | |
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| 0.8432 | 2.86 | 20500 | 0.1636 | 0.1610 | |
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| 0.8492 | 2.93 | 21000 | 0.1708 | 0.1688 | |
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| 0.8561 | 3.0 | 21500 | 0.1663 | 0.1604 | |
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| 0.842 | 3.07 | 22000 | 0.1690 | 0.1625 | |
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| 0.857 | 3.14 | 22500 | 0.1642 | 0.1605 | |
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| 0.8518 | 3.21 | 23000 | 0.1626 | 0.1585 | |
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| 0.8506 | 3.28 | 23500 | 0.1651 | 0.1605 | |
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| 0.8394 | 3.35 | 24000 | 0.1647 | 0.1585 | |
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| 0.8431 | 3.42 | 24500 | 0.1632 | 0.1573 | |
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| 0.8566 | 3.49 | 25000 | 0.1614 | 0.1550 | |
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| 0.8534 | 3.56 | 25500 | 0.1645 | 0.1589 | |
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| 0.8386 | 3.63 | 26000 | 0.1632 | 0.1582 | |
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| 0.8357 | 3.7 | 26500 | 0.1631 | 0.1556 | |
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| 0.8299 | 3.77 | 27000 | 0.1612 | 0.1550 | |
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| 0.8421 | 3.84 | 27500 | 0.1602 | 0.1552 | |
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| 0.8375 | 3.91 | 28000 | 0.1592 | 0.1537 | |
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| 0.8328 | 3.97 | 28500 | 0.1587 | 0.1537 | |
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| 0.8155 | 4.04 | 29000 | 0.1587 | 0.1520 | |
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| 0.8335 | 4.11 | 29500 | 0.1624 | 0.1556 | |
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| 0.8138 | 4.18 | 30000 | 0.1581 | 0.1547 | |
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| 0.8195 | 4.25 | 30500 | 0.1560 | 0.1507 | |
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| 0.8092 | 4.32 | 31000 | 0.1561 | 0.1534 | |
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| 0.8191 | 4.39 | 31500 | 0.1549 | 0.1493 | |
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| 0.8008 | 4.46 | 32000 | 0.1540 | 0.1493 | |
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| 0.8138 | 4.53 | 32500 | 0.1544 | 0.1493 | |
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| 0.8173 | 4.6 | 33000 | 0.1553 | 0.1511 | |
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| 0.8081 | 4.67 | 33500 | 0.1541 | 0.1484 | |
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| 0.8192 | 4.74 | 34000 | 0.1560 | 0.1506 | |
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| 0.8068 | 4.81 | 34500 | 0.1540 | 0.1503 | |
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| 0.8105 | 4.88 | 35000 | 0.1529 | 0.1483 | |
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| 0.7976 | 4.95 | 35500 | 0.1507 | 0.1451 | |
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| 0.8143 | 5.02 | 36000 | 0.1505 | 0.1462 | |
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| 0.8053 | 5.09 | 36500 | 0.1517 | 0.1476 | |
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| 0.785 | 5.16 | 37000 | 0.1526 | 0.1478 | |
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| 0.7936 | 5.23 | 37500 | 0.1489 | 0.1421 | |
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| 0.807 | 5.3 | 38000 | 0.1483 | 0.1420 | |
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| 0.8092 | 5.37 | 38500 | 0.1481 | 0.1435 | |
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| 0.793 | 5.44 | 39000 | 0.1503 | 0.1438 | |
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| 0.814 | 5.51 | 39500 | 0.1495 | 0.1480 | |
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| 0.807 | 5.58 | 40000 | 0.1472 | 0.1424 | |
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| 0.7913 | 5.65 | 40500 | 0.1471 | 0.1422 | |
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| 0.7844 | 5.72 | 41000 | 0.1473 | 0.1422 | |
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| 0.7888 | 5.79 | 41500 | 0.1445 | 0.1385 | |
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| 0.7806 | 5.86 | 42000 | 0.1435 | 0.1394 | |
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| 0.7773 | 5.93 | 42500 | 0.1461 | 0.1424 | |
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| 0.786 | 6.0 | 43000 | 0.1450 | 0.1413 | |
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| 0.7784 | 6.07 | 43500 | 0.1463 | 0.1424 | |
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| 0.7937 | 6.14 | 44000 | 0.1438 | 0.1386 | |
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| 0.7738 | 6.21 | 44500 | 0.1437 | 0.1383 | |
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| 0.7728 | 6.28 | 45000 | 0.1424 | 0.1371 | |
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| 0.7681 | 6.35 | 45500 | 0.1416 | 0.1376 | |
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| 0.776 | 6.42 | 46000 | 0.1415 | 0.1380 | |
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| 0.7773 | 6.49 | 46500 | 0.1416 | 0.1371 | |
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| 0.7692 | 6.56 | 47000 | 0.1398 | 0.1345 | |
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| 0.7642 | 6.62 | 47500 | 0.1381 | 0.1341 | |
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| 0.7692 | 6.69 | 48000 | 0.1392 | 0.1334 | |
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| 0.7667 | 6.76 | 48500 | 0.1392 | 0.1348 | |
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| 0.7712 | 6.83 | 49000 | 0.1398 | 0.1333 | |
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| 0.7628 | 6.9 | 49500 | 0.1392 | 0.1344 | |
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| 0.7622 | 6.97 | 50000 | 0.1377 | 0.1329 | |
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| 0.7639 | 7.04 | 50500 | 0.1361 | 0.1316 | |
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| 0.742 | 7.11 | 51000 | 0.1376 | 0.1327 | |
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| 0.7526 | 7.18 | 51500 | 0.1387 | 0.1342 | |
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| 0.7606 | 7.25 | 52000 | 0.1363 | 0.1316 | |
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| 0.7626 | 7.32 | 52500 | 0.1365 | 0.1313 | |
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| 0.752 | 7.39 | 53000 | 0.1354 | 0.1309 | |
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| 0.7562 | 7.46 | 53500 | 0.1362 | 0.1312 | |
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| 0.7557 | 7.53 | 54000 | 0.1358 | 0.1325 | |
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| 0.7588 | 7.6 | 54500 | 0.1343 | 0.1311 | |
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| 0.7485 | 7.67 | 55000 | 0.1346 | 0.1301 | |
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| 0.7466 | 7.74 | 55500 | 0.1354 | 0.1314 | |
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| 0.7558 | 7.81 | 56000 | 0.1359 | 0.1325 | |
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| 0.7578 | 7.88 | 56500 | 0.1363 | 0.1334 | |
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| 0.7411 | 7.95 | 57000 | 0.1346 | 0.1301 | |
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| 0.7478 | 8.02 | 57500 | 0.1355 | 0.1305 | |
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| 0.7451 | 8.09 | 58000 | 0.1349 | 0.1302 | |
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| 0.7383 | 8.16 | 58500 | 0.1349 | 0.1294 | |
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| 0.7482 | 8.23 | 59000 | 0.1341 | 0.1293 | |
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| 0.742 | 8.3 | 59500 | 0.1338 | 0.1296 | |
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| 0.7343 | 8.37 | 60000 | 0.1348 | 0.1307 | |
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| 0.7385 | 8.44 | 60500 | 0.1324 | 0.1282 | |
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| 0.7567 | 8.51 | 61000 | 0.1334 | 0.1281 | |
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| 0.7342 | 8.58 | 61500 | 0.1338 | 0.1289 | |
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| 0.7401 | 8.65 | 62000 | 0.1331 | 0.1285 | |
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| 0.7362 | 8.72 | 62500 | 0.1329 | 0.1283 | |
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| 0.7241 | 8.79 | 63000 | 0.1323 | 0.1277 | |
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| 0.7244 | 8.86 | 63500 | 0.1317 | 0.1269 | |
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| 0.7274 | 8.93 | 64000 | 0.1308 | 0.1260 | |
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| 0.7411 | 9.0 | 64500 | 0.1309 | 0.1256 | |
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| 0.7255 | 9.07 | 65000 | 0.1316 | 0.1265 | |
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| 0.7406 | 9.14 | 65500 | 0.1315 | 0.1270 | |
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| 0.7418 | 9.21 | 66000 | 0.1315 | 0.1269 | |
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| 0.7301 | 9.27 | 66500 | 0.1315 | 0.1273 | |
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| 0.7248 | 9.34 | 67000 | 0.1323 | 0.1274 | |
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| 0.7423 | 9.41 | 67500 | 0.1309 | 0.1267 | |
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| 0.7152 | 9.48 | 68000 | 0.1312 | 0.1271 | |
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| 0.7295 | 9.55 | 68500 | 0.1306 | 0.1262 | |
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| 0.7231 | 9.62 | 69000 | 0.1308 | 0.1263 | |
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| 0.7344 | 9.69 | 69500 | 0.1313 | 0.1267 | |
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| 0.7264 | 9.76 | 70000 | 0.1305 | 0.1263 | |
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| 0.7309 | 9.83 | 70500 | 0.1303 | 0.1262 | |
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| 0.73 | 9.9 | 71000 | 0.1303 | 0.1261 | |
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| 0.7353 | 9.97 | 71500 | 0.1304 | 0.1260 | |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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