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--- |
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language: |
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- sv-SE |
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license: cc0-1.0 |
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tags: |
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- automatic-speech-recognition |
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- mozilla-foundation/common_voice_8_0 |
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- generated_from_trainer |
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- sv |
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- robust-speech-event |
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- model_for_talk |
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datasets: |
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- mozilla-foundation/common_voice_8_0 |
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- marinone94/nst_sv |
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model-index: |
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- name: XLS-R-300M - Swedish |
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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: mozilla-foundation/common_voice_8_0 |
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type: mozilla-foundation/common_voice_8_0 |
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args: sv-SE |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 12.68 |
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- name: Test CER |
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type: cer |
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value: 3.79 |
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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: speech-recognition-community-v2/dev_data |
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type: speech-recognition-community-v2/dev_data |
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args: sv |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 27.55 |
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- name: Test CER |
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type: cer |
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value: 9.79 |
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--- |
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# |
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This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) on 2 epochs of the MARINONE94/NST_SV - SV dataset (80% random split with seed 42 as the dataset for now has only the "train" split), and then on 50 epochs of the the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset ("train+validation" split). |
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See run.sh to have a complete overview of all the training steps. |
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NOTE: the first training for now didn't work as expected, so it might be useless or even degrade performance. Further investigation and development is needed. |
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It achieves the following results on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE "test" set, without any language model: |
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- Loss: 0.1497 |
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- Wer: 0.1261 |
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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: 0.00025 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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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_steps: 2000 |
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- num_epochs: 50.0 |
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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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| 3.3533 | 1.1 | 100 | 3.2807 | 1.0 | |
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| 3.1709 | 2.2 | 200 | 3.1325 | 1.0 | |
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| 3.0573 | 3.3 | 300 | 3.0615 | 1.0 | |
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| 3.0314 | 4.39 | 400 | 3.0990 | 1.0 | |
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| 3.0129 | 5.49 | 500 | 3.0400 | 1.0 | |
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| 2.9964 | 6.59 | 600 | 2.9990 | 1.0 | |
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| 2.9602 | 7.69 | 700 | 2.9620 | 1.0 | |
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| 2.8756 | 8.79 | 800 | 2.7302 | 1.0 | |
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| 2.2931 | 9.89 | 900 | 1.5058 | 0.9776 | |
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| 1.8427 | 10.98 | 1000 | 0.9155 | 0.7832 | |
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| 1.4286 | 12.09 | 1100 | 0.4075 | 0.3796 | |
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| 1.2229 | 13.19 | 1200 | 0.2893 | 0.2652 | |
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| 1.1106 | 14.28 | 1300 | 0.2469 | 0.2254 | |
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| 1.0663 | 15.38 | 1400 | 0.2219 | 0.1973 | |
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| 1.0667 | 16.48 | 1500 | 0.2129 | 0.1894 | |
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| 1.0193 | 17.58 | 1600 | 0.1991 | 0.1789 | |
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| 0.9816 | 18.68 | 1700 | 0.1940 | 0.1801 | |
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| 0.9814 | 19.78 | 1800 | 0.1860 | 0.1667 | |
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| 0.9787 | 20.87 | 1900 | 0.1888 | 0.1642 | |
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| 0.9699 | 21.97 | 2000 | 0.1875 | 0.1704 | |
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| 0.9616 | 23.08 | 2100 | 0.1802 | 0.1617 | |
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| 0.9378 | 24.17 | 2200 | 0.1793 | 0.1577 | |
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| 0.888 | 25.27 | 2300 | 0.1764 | 0.1545 | |
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| 0.8942 | 26.37 | 2400 | 0.1674 | 0.1492 | |
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| 0.8701 | 27.47 | 2500 | 0.1739 | 0.1512 | |
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| 0.8555 | 28.57 | 2600 | 0.1690 | 0.1446 | |
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| 0.8513 | 29.67 | 2700 | 0.1649 | 0.1477 | |
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| 0.8659 | 30.77 | 2800 | 0.1637 | 0.1422 | |
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| 0.8419 | 31.86 | 2900 | 0.1614 | 0.1397 | |
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| 0.8491 | 32.96 | 3000 | 0.1595 | 0.1401 | |
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| 0.8395 | 34.07 | 3100 | 0.1607 | 0.1376 | |
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| 0.83 | 35.16 | 3200 | 0.1538 | 0.1379 | |
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| 0.7835 | 36.26 | 3300 | 0.1602 | 0.1408 | |
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| 0.7703 | 37.36 | 3400 | 0.1601 | 0.1369 | |
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| 0.7474 | 38.46 | 3500 | 0.1514 | 0.1342 | |
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| 0.7719 | 39.56 | 3600 | 0.1593 | 0.1353 | |
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| 0.7638 | 40.66 | 3700 | 0.1536 | 0.1338 | |
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| 0.771 | 41.75 | 3800 | 0.1531 | 0.1317 | |
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| 0.7594 | 42.85 | 3900 | 0.1498 | 0.1288 | |
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| 0.7383 | 43.95 | 4000 | 0.1527 | 0.1300 | |
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| 0.7565 | 45.05 | 4100 | 0.1482 | 0.1289 | |
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| 0.7697 | 46.15 | 4200 | 0.1495 | 0.1272 | |
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| 0.7194 | 47.25 | 4300 | 0.1493 | 0.1269 | |
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| 0.7479 | 48.35 | 4400 | 0.1490 | 0.1276 | |
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| 0.7132 | 49.45 | 4500 | 0.1501 | 0.1265 | |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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