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badrex / xlsr-he-adap-de
Model's Last Updated: June 07 2024
automatic-speech-recognition
Introduction of xlsr-he-adap-de
Model Details of xlsr-he-adap-de
xlsr-he-adap-de
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:
- Loss: 1.1481
- Wer: 0.5333
- Cer: 0.1968
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 3.697 | 0.8368 | 100 | 3.7808 | 1.0 | 1.0 |
| 3.1842 | 1.6736 | 200 | 3.5119 | 1.0 | 1.0 |
| 3.3472 | 2.5105 | 300 | 3.4405 | 1.0 | 1.0 |
| 1.6876 | 3.3473 | 400 | 2.0791 | 0.9664 | 0.5128 |
| 1.1828 | 4.1841 | 500 | 1.5367 | 0.8851 | 0.3903 |
| 0.9151 | 5.0209 | 600 | 1.2217 | 0.8210 | 0.3806 |
| 0.7117 | 5.8577 | 700 | 1.0726 | 0.7824 | 0.3500 |
| 0.809 | 6.6946 | 800 | 1.1018 | 0.8094 | 0.3451 |
| 0.9377 | 7.5314 | 900 | 0.9955 | 0.7438 | 0.3255 |
| 0.5836 | 8.3682 | 1000 | 0.9658 | 0.7605 | 0.3209 |
| 0.5226 | 9.2050 | 1100 | 0.9701 | 0.7316 | 0.3125 |
| 0.4732 | 10.0418 | 1200 | 0.9576 | 0.7636 | 0.2993 |
| 0.5439 | 10.8787 | 1300 | 0.9689 | 0.7743 | 0.2976 |
| 0.3479 | 11.7155 | 1400 | 1.0207 | 0.7026 | 0.2813 |
| 0.4111 | 12.5523 | 1500 | 1.0051 | 0.6873 | 0.2725 |
| 0.2865 | 13.3891 | 1600 | 0.9566 | 0.7087 | 0.2716 |
| 0.3942 | 14.2259 | 1700 | 1.0009 | 0.6929 | 0.2730 |
| 0.3058 | 15.0628 | 1800 | 0.9195 | 0.6695 | 0.2583 |
| 0.2141 | 15.8996 | 1900 | 0.9707 | 0.6523 | 0.2532 |
| 0.4893 | 16.7364 | 2000 | 1.0019 | 0.6772 | 0.2548 |
| 0.2922 | 17.5732 | 2100 | 1.0317 | 0.6721 | 0.2645 |
| 0.3056 | 18.4100 | 2200 | 1.0440 | 0.6385 | 0.2595 |
| 0.3616 | 19.2469 | 2300 | 1.1057 | 0.6406 | 0.2516 |
| 0.271 | 20.0837 | 2400 | 1.1302 | 0.6411 | 0.2532 |
| 0.2183 | 20.9205 | 2500 | 1.2060 | 0.6050 | 0.2513 |
| 0.3128 | 21.7573 | 2600 | 1.1261 | 0.6436 | 0.2522 |
| 0.1602 | 22.5941 | 2700 | 1.1014 | 0.6141 | 0.2394 |
| 0.2255 | 23.4310 | 2800 | 1.2601 | 0.6009 | 0.2480 |
| 0.3142 | 24.2678 | 2900 | 1.0729 | 0.6151 | 0.2410 |
| 0.1815 | 25.1046 | 3000 | 1.0396 | 0.6111 | 0.2314 |
| 0.2507 | 25.9414 | 3100 | 1.1343 | 0.5760 | 0.2236 |
| 0.151 | 26.7782 | 3200 | 1.1477 | 0.6263 | 0.2382 |
| 0.1531 | 27.6151 | 3300 | 1.0935 | 0.5984 | 0.2281 |
| 0.1943 | 28.4519 | 3400 | 1.0250 | 0.5689 | 0.2150 |
| 0.2592 | 29.2887 | 3500 | 1.0309 | 0.5780 | 0.2115 |
| 0.2394 | 30.1255 | 3600 | 1.0363 | 0.5735 | 0.2176 |
| 0.2146 | 30.9623 | 3700 | 1.0521 | 0.5582 | 0.2098 |
| 0.1629 | 31.7992 | 3800 | 1.0586 | 0.5816 | 0.2116 |
| 0.099 | 32.6360 | 3900 | 1.0348 | 0.5643 | 0.2100 |
| 0.1748 | 33.4728 | 4000 | 1.0983 | 0.5841 | 0.2147 |
| 0.1143 | 34.3096 | 4100 | 1.0979 | 0.5567 | 0.2059 |
| 0.1364 | 35.1464 | 4200 | 1.1404 | 0.5663 | 0.2094 |
| 0.1552 | 35.9833 | 4300 | 1.0805 | 0.5628 | 0.2085 |
| 0.1121 | 36.8201 | 4400 | 1.1262 | 0.5628 | 0.2061 |
| 0.1051 | 37.6569 | 4500 | 1.1390 | 0.5425 | 0.2059 |
| 0.1384 | 38.4937 | 4600 | 1.1252 | 0.5394 | 0.2016 |
| 0.1268 | 39.3305 | 4700 | 1.1607 | 0.5552 | 0.2068 |
| 0.1233 | 40.1674 | 4800 | 1.1776 | 0.5618 | 0.2072 |
| 0.2489 | 41.0042 | 4900 | 1.1335 | 0.5399 | 0.1977 |
| 0.1468 | 41.8410 | 5000 | 1.1419 | 0.5404 | 0.1964 |
| 0.1148 | 42.6778 | 5100 | 1.1404 | 0.5455 | 0.2008 |
| 0.1415 | 43.5146 | 5200 | 1.1149 | 0.5425 | 0.2005 |
| 0.1358 | 44.3515 | 5300 | 1.1354 | 0.5430 | 0.2013 |
| 0.1231 | 45.1883 | 5400 | 1.1457 | 0.5374 | 0.1999 |
| 0.0898 | 46.0251 | 5500 | 1.1218 | 0.5343 | 0.1989 |
| 0.1271 | 46.8619 | 5600 | 1.1404 | 0.5353 | 0.1977 |
| 0.1467 | 47.6987 | 5700 | 1.1765 | 0.5318 | 0.1961 |
| 0.1757 | 48.5356 | 5800 | 1.1517 | 0.5292 | 0.1973 |
| 0.1471 | 49.3724 | 5900 | 1.1481 | 0.5333 | 0.1968 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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Updated:March 28 2026
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