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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- automatic-speech-recognition |
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- NbAiLab/NPSC |
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- robust-speech-event |
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- false |
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- nb-NO |
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- hf-asr-leaderboard |
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datasets: |
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- NbAiLab/NPSC |
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language: |
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- nb-NO |
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model-index: |
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- name: wav2vec2-xls-r-1b-npsc-bokmaal |
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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: NPSC |
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type: NbAiLab/NPSC |
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args: 16K_mp3_bokmaal |
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metrics: |
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- name: "Test (Bokm\xE5l) WER" |
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type: wer |
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value: 0.07901700231893541 |
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- name: "Test (Bokm\xE5l) CER" |
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type: cer |
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value: 0.029734583252347752 |
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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-npsc |
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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 [NbAiLab/NPSC (16K_mp3_bokmaal)](https://huggingface.co./datasets/NbAiLab/NPSC/viewer/16K_mp3_bokmaal/train) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1598 |
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- WER: 0.0966 |
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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: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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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: 15.0 |
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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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| 0.8361 | 0.32 | 500 | 0.6304 | 0.4970 | |
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| 0.5703 | 0.64 | 1000 | 0.3195 | 0.2775 | |
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| 0.5451 | 0.97 | 1500 | 0.2700 | 0.2246 | |
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| 0.47 | 1.29 | 2000 | 0.2564 | 0.2329 | |
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| 0.4063 | 1.61 | 2500 | 0.2459 | 0.2099 | |
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| 0.374 | 1.93 | 3000 | 0.2175 | 0.1894 | |
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| 0.3297 | 2.26 | 3500 | 0.2036 | 0.1755 | |
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| 0.3145 | 2.58 | 4000 | 0.1957 | 0.1757 | |
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| 0.3989 | 2.9 | 4500 | 0.1923 | 0.1723 | |
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| 0.271 | 3.22 | 5000 | 0.1889 | 0.1649 | |
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| 0.2758 | 3.55 | 5500 | 0.1768 | 0.1588 | |
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| 0.2683 | 3.87 | 6000 | 0.1720 | 0.1534 | |
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| 0.2341 | 4.19 | 6500 | 0.1689 | 0.1471 | |
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| 0.2316 | 4.51 | 7000 | 0.1706 | 0.1405 | |
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| 0.2383 | 4.84 | 7500 | 0.1637 | 0.1426 | |
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| 0.2148 | 5.16 | 8000 | 0.1584 | 0.1347 | |
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| 0.2085 | 5.48 | 8500 | 0.1601 | 0.1387 | |
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| 0.2944 | 5.8 | 9000 | 0.1566 | 0.1294 | |
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| 0.1944 | 6.13 | 9500 | 0.1494 | 0.1271 | |
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| 0.1853 | 6.45 | 10000 | 0.1561 | 0.1247 | |
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| 0.235 | 6.77 | 10500 | 0.1461 | 0.1215 | |
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| 0.2286 | 7.09 | 11000 | 0.1447 | 0.1167 | |
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| 0.1781 | 7.41 | 11500 | 0.1502 | 0.1199 | |
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| 0.1714 | 7.74 | 12000 | 0.1425 | 0.1179 | |
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| 0.1725 | 8.06 | 12500 | 0.1427 | 0.1173 | |
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| 0.143 | 8.38 | 13000 | 0.1448 | 0.1142 | |
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| 0.154 | 8.7 | 13500 | 0.1392 | 0.1104 | |
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| 0.1447 | 9.03 | 14000 | 0.1404 | 0.1094 | |
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| 0.1471 | 9.35 | 14500 | 0.1404 | 0.1088 | |
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| 0.1479 | 9.67 | 15000 | 0.1414 | 0.1133 | |
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| 0.1607 | 9.99 | 15500 | 0.1458 | 0.1171 | |
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| 0.166 | 10.32 | 16000 | 0.1652 | 0.1264 | |
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| 0.188 | 10.64 | 16500 | 0.1713 | 0.1322 | |
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| 0.1461 | 10.96 | 17000 | 0.1423 | 0.1111 | |
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| 0.1289 | 11.28 | 17500 | 0.1388 | 0.1097 | |
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| 0.1273 | 11.61 | 18000 | 0.1438 | 0.1074 | |
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| 0.1317 | 11.93 | 18500 | 0.1312 | 0.1066 | |
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| 0.1448 | 12.25 | 19000 | 0.1446 | 0.1042 | |
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| 0.1424 | 12.57 | 19500 | 0.1386 | 0.1015 | |
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| 0.1392 | 12.89 | 20000 | 0.1379 | 0.1005 | |
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| 0.1408 | 13.22 | 20500 | 0.1408 | 0.0992 | |
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| 0.1239 | 13.54 | 21000 | 0.1338 | 0.0968 | |
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| 0.1244 | 13.86 | 21500 | 0.1335 | 0.0957 | |
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| 0.1254 | 14.18 | 22000 | 0.1382 | 0.0950 | |
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| 0.1597 | 14.51 | 22500 | 0.1544 | 0.0970 | |
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| 0.1566 | 14.83 | 23000 | 0.1589 | 0.0963 | |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.2+cu113 |
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- Datasets 1.18.3.dev0 |
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- Tokenizers 0.11.0 |
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