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--- |
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language: |
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- uk |
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license: mit |
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tags: |
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- automatic-speech-recognition |
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- common_voice |
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- generated_from_trainer |
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datasets: |
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- common_voice |
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model-index: |
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- name: wav2vec2-xls-r-300m-uk |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice uk |
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type: common_voice |
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args: uk |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 12.22 |
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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-300m-uk |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0927 |
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- Wer: 0.1222 |
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- Cer: 0.0204 |
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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: 3e-05 |
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- train_batch_size: 40 |
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- eval_batch_size: 40 |
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- seed: 42 |
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- gradient_accumulation_steps: 6 |
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- total_train_batch_size: 240 |
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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: 100 |
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- num_epochs: 100 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:------:|:---------------:|:------:| |
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| 9.0008 | 1.68 | 200 | 1.0 | 3.7590 | 1.0 | |
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| 3.4972 | 3.36 | 400 | 1.0 | 3.3933 | 1.0 | |
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| 3.3432 | 5.04 | 600 | 1.0 | 3.2617 | 1.0 | |
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| 3.2421 | 6.72 | 800 | 1.0 | 3.0712 | 1.0 | |
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| 1.9839 | 7.68 | 1000 | 0.1400 | 0.7204 | 0.6561 | |
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| 0.8017 | 9.36 | 1200 | 0.0766 | 0.3734 | 0.4159 | |
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| 0.5554 | 11.04 | 1400 | 0.0583 | 0.2621 | 0.3237 | |
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| 0.4309 | 12.68 | 1600 | 0.0486 | 0.2085 | 0.2753 | |
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| 0.3697 | 14.36 | 1800 | 0.0421 | 0.1746 | 0.2427 | |
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| 0.3293 | 16.04 | 2000 | 0.0388 | 0.1597 | 0.2243 | |
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| 0.2934 | 17.72 | 2200 | 0.0358 | 0.1428 | 0.2083 | |
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| 0.2704 | 19.4 | 2400 | 0.0333 | 0.1326 | 0.1949 | |
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| 0.2547 | 21.08 | 2600 | 0.0322 | 0.1255 | 0.1882 | |
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| 0.2366 | 22.76 | 2800 | 0.0309 | 0.1211 | 0.1815 | |
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| 0.2183 | 24.44 | 3000 | 0.0294 | 0.1159 | 0.1727 | |
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| 0.2115 | 26.13 | 3200 | 0.0280 | 0.1117 | 0.1661 | |
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| 0.1968 | 27.8 | 3400 | 0.0274 | 0.1063 | 0.1622 | |
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| 0.1922 | 29.48 | 3600 | 0.0269 | 0.1082 | 0.1598 | |
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| 0.1847 | 31.17 | 3800 | 0.0260 | 0.1061 | 0.1550 | |
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| 0.1715 | 32.84 | 4000 | 0.0252 | 0.1014 | 0.1496 | |
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| 0.1689 | 34.53 | 4200 | 0.0250 | 0.1012 | 0.1492 | |
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| 0.1655 | 36.21 | 4400 | 0.0243 | 0.0999 | 0.1450 | |
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| 0.1585 | 37.88 | 4600 | 0.0239 | 0.0967 | 0.1432 | |
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| 0.1492 | 39.57 | 4800 | 0.0237 | 0.0978 | 0.1421 | |
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| 0.1491 | 41.25 | 5000 | 0.0236 | 0.0963 | 0.1412 | |
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| 0.1453 | 42.93 | 5200 | 0.0230 | 0.0979 | 0.1373 | |
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| 0.1386 | 44.61 | 5400 | 0.0227 | 0.0959 | 0.1353 | |
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| 0.1387 | 46.29 | 5600 | 0.0226 | 0.0927 | 0.1355 | |
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| 0.1329 | 47.97 | 5800 | 0.0224 | 0.0951 | 0.1341 | |
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| 0.1295 | 49.65 | 6000 | 0.0219 | 0.0950 | 0.1306 | |
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| 0.1287 | 51.33 | 6200 | 0.0216 | 0.0937 | 0.1290 | |
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| 0.1277 | 53.02 | 6400 | 0.0215 | 0.0963 | 0.1294 | |
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| 0.1201 | 54.69 | 6600 | 0.0213 | 0.0959 | 0.1282 | |
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| 0.1199 | 56.38 | 6800 | 0.0215 | 0.0944 | 0.1286 | |
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| 0.1221 | 58.06 | 7000 | 0.0209 | 0.0938 | 0.1249 | |
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| 0.1145 | 59.68 | 7200 | 0.0208 | 0.0941 | 0.1254 | |
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| 0.1143 | 61.36 | 7400 | 0.0209 | 0.0941 | 0.1249 | |
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| 0.1143 | 63.04 | 7600 | 0.0209 | 0.0940 | 0.1248 | |
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| 0.1137 | 64.72 | 7800 | 0.0205 | 0.0931 | 0.1234 | |
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| 0.1125 | 66.4 | 8000 | 0.0204 | 0.0927 | 0.1222 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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