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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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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: 8.72 |
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- name: Test CER |
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type: cer |
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value: 3.05 |
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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: 19.67 |
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- name: Test CER |
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type: cer |
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value: 8.94 |
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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 the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1595 |
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- Wer: 0.1200 |
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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_ratio: 0.25 |
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- num_epochs: 100.0 |
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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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| 3.0418 | 5.49 | 500 | 3.0176 | 1.0 | |
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| 1.1819 | 10.98 | 1000 | 0.2562 | 0.2168 | |
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| 1.0032 | 16.48 | 1500 | 0.1746 | 0.1546 | |
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| 0.9077 | 21.97 | 2000 | 0.1600 | 0.1339 | |
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| 0.8687 | 27.47 | 2500 | 0.1647 | 0.1378 | |
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| 0.8081 | 32.96 | 3000 | 0.1608 | 0.1353 | |
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| 0.7923 | 38.46 | 3500 | 0.1534 | 0.1277 | |
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| 0.7349 | 43.95 | 4000 | 0.1546 | 0.1303 | |
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| 0.7199 | 49.45 | 4500 | 0.1617 | 0.1277 | |
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| 0.7028 | 54.94 | 5000 | 0.1572 | 0.1287 | |
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| 0.6912 | 60.44 | 5500 | 0.1560 | 0.1249 | |
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| 0.6492 | 65.93 | 6000 | 0.1542 | 0.1260 | |
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| 0.6407 | 71.43 | 6500 | 0.1605 | 0.1240 | |
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| 0.6222 | 76.92 | 7000 | 0.1577 | 0.1219 | |
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| 0.6039 | 82.42 | 7500 | 0.1645 | 0.1249 | |
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| 0.5928 | 87.91 | 8000 | 0.1590 | 0.1214 | |
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| 0.6022 | 93.4 | 8500 | 0.1597 | 0.1213 | |
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| 0.5814 | 98.9 | 9000 | 0.1599 | 0.1199 | |
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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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