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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_7_0 |
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- sv |
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- generated_from_trainer |
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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_7_0 |
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model-index: |
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- name: XLS-R-300M-voxrex - 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: Common Voice 7 |
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type: mozilla-foundation/common_voice_7_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: 18.89 |
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- name: Test CER |
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type: cer |
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value: 6.63 |
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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: Robust Speech Event - Dev Data |
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type: Robust Speech Event - 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: 30.65 |
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- name: Test CER |
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type: cer |
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value: 13.56 |
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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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# |
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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_7_0 - SV-SE dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2201 |
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- Wer: 0.1778 |
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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: 7.5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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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: 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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| 3.1522 | 1.45 | 500 | 3.1290 | 1.0 | |
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| 2.9576 | 2.91 | 1000 | 2.9633 | 1.0 | |
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| 1.9853 | 4.36 | 1500 | 0.8902 | 0.6104 | |
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| 1.5867 | 5.81 | 2000 | 0.4793 | 0.3664 | |
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| 1.4608 | 7.27 | 2500 | 0.3816 | 0.3095 | |
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| 1.3496 | 8.72 | 3000 | 0.3415 | 0.2783 | |
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| 1.3058 | 10.17 | 3500 | 0.3072 | 0.2519 | |
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| 1.2533 | 11.63 | 4000 | 0.2877 | 0.2381 | |
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| 1.2535 | 13.08 | 4500 | 0.2791 | 0.2320 | |
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| 1.2273 | 14.53 | 5000 | 0.2726 | 0.2282 | |
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| 1.2083 | 15.99 | 5500 | 0.2638 | 0.2212 | |
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| 1.1606 | 17.44 | 6000 | 0.2531 | 0.2174 | |
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| 1.1545 | 18.89 | 6500 | 0.2468 | 0.2109 | |
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| 1.1344 | 20.35 | 7000 | 0.2494 | 0.2050 | |
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| 1.1173 | 21.8 | 7500 | 0.2447 | 0.1980 | |
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| 1.1081 | 23.26 | 8000 | 0.2428 | 0.1998 | |
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| 1.1023 | 24.71 | 8500 | 0.2329 | 0.1951 | |
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| 1.0923 | 26.16 | 9000 | 0.2388 | 0.1962 | |
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| 1.0798 | 27.61 | 9500 | 0.2363 | 0.1944 | |
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| 1.0769 | 29.07 | 10000 | 0.2342 | 0.1913 | |
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| 1.0672 | 30.52 | 10500 | 0.2250 | 0.1875 | |
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| 1.0735 | 31.97 | 11000 | 0.2305 | 0.1874 | |
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| 1.0628 | 33.43 | 11500 | 0.2291 | 0.1851 | |
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| 1.0451 | 34.88 | 12000 | 0.2263 | 0.1856 | |
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| 1.0299 | 36.34 | 12500 | 0.2257 | 0.1834 | |
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| 1.0368 | 37.79 | 13000 | 0.2230 | 0.1808 | |
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| 1.0322 | 39.24 | 13500 | 0.2231 | 0.1833 | |
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| 1.0451 | 40.7 | 14000 | 0.2197 | 0.1817 | |
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| 1.0304 | 42.15 | 14500 | 0.2241 | 0.1813 | |
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| 1.0102 | 43.6 | 15000 | 0.2233 | 0.1795 | |
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| 1.0135 | 45.06 | 15500 | 0.2200 | 0.1794 | |
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| 1.014 | 46.51 | 16000 | 0.2207 | 0.1779 | |
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| 1.0071 | 47.96 | 16500 | 0.2205 | 0.1784 | |
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| 0.9729 | 49.42 | 17000 | 0.2204 | 0.1777 | |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.17.1.dev0 |
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- Tokenizers 0.11.0 |
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