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--- |
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language: |
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- fr |
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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_9_0 |
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- generated_from_trainer |
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- hf-asr-leaderboard |
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- robust-speech-event |
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datasets: |
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- mozilla-foundation/common_voice_9_0 |
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model-index: |
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- name: wav2vec2-xls-r-1b-ft |
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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 9 |
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type: mozilla-foundation/common_voice_9_0 |
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args: fr |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 12.72 |
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- name: Test CER |
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type: cer |
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value: 3.78 |
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- name: Test WER (+LM) |
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type: wer |
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value: 10.60 |
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- name: Test CER (+LM) |
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type: cer |
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value: 3.41 |
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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-1b-ft |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - FR dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1430 |
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- Wer: 0.1245 |
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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: 0.0001 |
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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: 8 |
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- total_train_batch_size: 128 |
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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_ratio: 0.1 |
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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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| 0.9229 | 0.14 | 500 | 0.5049 | 0.4008 | |
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| 0.3823 | 0.28 | 1000 | 0.2831 | 0.2297 | |
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| 0.3079 | 0.42 | 1500 | 0.2385 | 0.1951 | |
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| 0.2899 | 0.55 | 2000 | 0.2273 | 0.1978 | |
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| 0.2795 | 0.69 | 2500 | 0.2329 | 0.1983 | |
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| 0.2863 | 0.83 | 3000 | 0.2289 | 0.1991 | |
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| 0.3063 | 0.97 | 3500 | 0.2370 | 0.2046 | |
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| 0.2766 | 1.11 | 4000 | 0.2322 | 0.2021 | |
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| 0.2749 | 1.25 | 4500 | 0.2332 | 0.2055 | |
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| 0.2769 | 1.39 | 5000 | 0.2322 | 0.2035 | |
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| 0.2628 | 1.53 | 5500 | 0.2242 | 0.1948 | |
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| 0.2614 | 1.66 | 6000 | 0.2303 | 0.1962 | |
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| 0.2547 | 1.8 | 6500 | 0.2238 | 0.1920 | |
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| 0.2458 | 1.94 | 7000 | 0.2186 | 0.1894 | |
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| 0.231 | 2.08 | 7500 | 0.2169 | 0.1895 | |
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| 0.2309 | 2.22 | 8000 | 0.2131 | 0.1870 | |
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| 0.2258 | 2.36 | 8500 | 0.2133 | 0.1818 | |
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| 0.2278 | 2.5 | 9000 | 0.2176 | 0.1878 | |
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| 0.2263 | 2.63 | 9500 | 0.2030 | 0.1813 | |
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| 0.2262 | 2.77 | 10000 | 0.2077 | 0.1824 | |
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| 0.2228 | 2.91 | 10500 | 0.2115 | 0.1840 | |
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| 0.2118 | 3.05 | 11000 | 0.2093 | 0.1782 | |
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| 0.2073 | 3.19 | 11500 | 0.2004 | 0.1756 | |
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| 0.2015 | 3.33 | 12000 | 0.1988 | 0.1748 | |
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| 0.214 | 3.47 | 12500 | 0.2088 | 0.1816 | |
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| 0.2075 | 3.61 | 13000 | 0.1976 | 0.1746 | |
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| 0.2039 | 3.74 | 13500 | 0.1958 | 0.1744 | |
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| 0.2003 | 3.88 | 14000 | 0.1931 | 0.1693 | |
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| 0.1886 | 4.02 | 14500 | 0.1964 | 0.1686 | |
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| 0.1943 | 4.16 | 15000 | 0.1986 | 0.1746 | |
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| 0.1919 | 4.3 | 15500 | 0.1957 | 0.1700 | |
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| 0.1857 | 4.44 | 16000 | 0.1907 | 0.1671 | |
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| 0.1834 | 4.58 | 16500 | 0.1877 | 0.1641 | |
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| 0.18 | 4.71 | 17000 | 0.1828 | 0.1600 | |
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| 0.1774 | 4.85 | 17500 | 0.1863 | 0.1605 | |
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| 0.1755 | 4.99 | 18000 | 0.1833 | 0.1595 | |
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| 0.1692 | 5.13 | 18500 | 0.1814 | 0.1569 | |
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| 0.1674 | 5.27 | 19000 | 0.1819 | 0.1566 | |
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| 0.1664 | 5.41 | 19500 | 0.1805 | 0.1572 | |
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| 0.1677 | 5.55 | 20000 | 0.1803 | 0.1560 | |
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| 0.1637 | 5.68 | 20500 | 0.1750 | 0.1525 | |
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| 0.1628 | 5.82 | 21000 | 0.1774 | 0.1532 | |
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| 0.1645 | 5.96 | 21500 | 0.1744 | 0.1527 | |
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| 0.1551 | 6.1 | 22000 | 0.1778 | 0.1543 | |
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| 0.1505 | 6.24 | 22500 | 0.1754 | 0.1528 | |
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| 0.1499 | 6.38 | 23000 | 0.1743 | 0.1500 | |
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| 0.1491 | 6.52 | 23500 | 0.1684 | 0.1473 | |
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| 0.1477 | 6.66 | 24000 | 0.1661 | 0.1472 | |
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| 0.1456 | 6.79 | 24500 | 0.1654 | 0.1440 | |
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| 0.1415 | 6.93 | 25000 | 0.1654 | 0.1448 | |
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| 0.136 | 7.07 | 25500 | 0.1616 | 0.1407 | |
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| 0.132 | 7.21 | 26000 | 0.1625 | 0.1410 | |
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| 0.1323 | 7.35 | 26500 | 0.1604 | 0.1404 | |
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| 0.1338 | 7.49 | 27000 | 0.1574 | 0.1386 | |
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| 0.13 | 7.63 | 27500 | 0.1576 | 0.1384 | |
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| 0.1291 | 7.76 | 28000 | 0.1551 | 0.1366 | |
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| 0.1277 | 7.9 | 28500 | 0.1542 | 0.1356 | |
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| 0.1241 | 8.04 | 29000 | 0.1545 | 0.1350 | |
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| 0.1198 | 8.18 | 29500 | 0.1536 | 0.1322 | |
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| 0.1204 | 8.32 | 30000 | 0.1547 | 0.1337 | |
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| 0.1195 | 8.46 | 30500 | 0.1494 | 0.1309 | |
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| 0.1169 | 8.6 | 31000 | 0.1490 | 0.1300 | |
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| 0.1159 | 8.74 | 31500 | 0.1485 | 0.1305 | |
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| 0.1142 | 8.87 | 32000 | 0.1479 | 0.1292 | |
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| 0.1087 | 9.01 | 32500 | 0.1471 | 0.1284 | |
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| 0.1076 | 9.15 | 33000 | 0.1467 | 0.1270 | |
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| 0.1078 | 9.29 | 33500 | 0.1467 | 0.1270 | |
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| 0.1073 | 9.43 | 34000 | 0.1447 | 0.1256 | |
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| 0.108 | 9.57 | 34500 | 0.1447 | 0.1257 | |
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| 0.106 | 9.71 | 35000 | 0.1438 | 0.1255 | |
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| 0.1052 | 9.84 | 35500 | 0.1428 | 0.1247 | |
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| 0.1044 | 9.98 | 36000 | 0.1430 | 0.1245 | |
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### Framework versions |
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- Transformers 4.22.0.dev0 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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