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badrex / xlsr-128upper-sorbian
Model's Last Updated: July 05 2024
automatic-speech-recognition
Introduction of xlsr-128upper-sorbian
Model Details of xlsr-128upper-sorbian
xlsr-128upper-sorbian
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: 0.7625
- Wer: 0.5044
- Cer: 0.1106
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: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 3.8489 | 3.9216 | 100 | 4.0479 | 1.0 | 1.0 |
| 3.1996 | 7.8431 | 200 | 3.2124 | 0.9804 | 0.9850 |
| 2.3527 | 11.7647 | 300 | 2.4026 | 1.0 | 0.6858 |
| 0.4424 | 15.6863 | 400 | 0.7917 | 0.7418 | 0.1910 |
| 0.2617 | 19.6078 | 500 | 0.7624 | 0.6804 | 0.1696 |
| 0.1421 | 23.5294 | 600 | 0.7839 | 0.6582 | 0.1579 |
| 0.097 | 27.4510 | 700 | 0.8322 | 0.6316 | 0.1495 |
| 0.0459 | 31.3725 | 800 | 0.8119 | 0.6171 | 0.1446 |
| 0.0668 | 35.2941 | 900 | 0.8534 | 0.6418 | 0.1535 |
| 0.0627 | 39.2157 | 1000 | 0.8256 | 0.6019 | 0.1397 |
| 0.0454 | 43.1373 | 1100 | 0.7747 | 0.5994 | 0.1363 |
| 0.04 | 47.0588 | 1200 | 0.8046 | 0.5810 | 0.1321 |
| 0.0563 | 50.9804 | 1300 | 0.7910 | 0.5797 | 0.1325 |
| 0.039 | 54.9020 | 1400 | 0.7370 | 0.5595 | 0.1265 |
| 0.0254 | 58.8235 | 1500 | 0.7395 | 0.5418 | 0.1188 |
| 0.0211 | 62.7451 | 1600 | 0.7582 | 0.5430 | 0.1209 |
| 0.0218 | 66.6667 | 1700 | 0.7123 | 0.5051 | 0.1121 |
| 0.0206 | 70.5882 | 1800 | 0.7912 | 0.5297 | 0.1165 |
| 0.0155 | 74.5098 | 1900 | 0.7671 | 0.5367 | 0.1183 |
| 0.0242 | 78.4314 | 2000 | 0.7926 | 0.5418 | 0.1170 |
| 0.0081 | 82.3529 | 2100 | 0.7817 | 0.5373 | 0.1221 |
| 0.0087 | 86.2745 | 2200 | 0.7989 | 0.5285 | 0.1165 |
| 0.0088 | 90.1961 | 2300 | 0.7523 | 0.5165 | 0.1141 |
| 0.0173 | 94.1176 | 2400 | 0.7646 | 0.5038 | 0.1108 |
| 0.0217 | 98.0392 | 2500 | 0.7625 | 0.5044 | 0.1106 |
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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