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--- |
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language: |
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- et |
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license: apache-2.0 |
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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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- et |
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- robust-speech-event |
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- generated_from_trainer |
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- hf-asr-leaderboard |
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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-1B - Estonian |
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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 8 |
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type: mozilla-foundation/common_voice_8_0 |
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args: et |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 52.47 |
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- name: Test CER |
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type: cer |
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value: 12.59 |
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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: 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: 61.02 |
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- name: Test CER |
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type: cer |
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value: 21.08 |
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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: speech-recognition-community-v2/dev_data |
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args: et |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 59.23 |
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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 - Test Data |
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type: speech-recognition-community-v2/eval_data |
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args: et |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 69.08 |
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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 [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ET dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8824 |
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- Wer: 0.5246 |
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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: 7e-05 |
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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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- 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: 500 |
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- training_steps: 25000 |
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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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| 1.0296 | 2.79 | 500 | 0.8106 | 0.8029 | |
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| 0.9339 | 5.59 | 1000 | 0.7419 | 0.7932 | |
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| 0.8925 | 8.38 | 1500 | 0.7137 | 0.7706 | |
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| 0.8484 | 11.17 | 2000 | 0.7020 | 0.7677 | |
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| 0.7521 | 13.97 | 2500 | 0.7043 | 0.7375 | |
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| 0.719 | 16.76 | 3000 | 0.6617 | 0.7428 | |
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| 0.656 | 19.55 | 3500 | 0.6388 | 0.7202 | |
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| 0.6085 | 22.35 | 4000 | 0.6211 | 0.6960 | |
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| 0.5598 | 25.14 | 4500 | 0.6132 | 0.6644 | |
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| 0.4969 | 27.93 | 5000 | 0.6065 | 0.6521 | |
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| 0.4638 | 30.73 | 5500 | 0.6978 | 0.6577 | |
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| 0.4385 | 33.52 | 6000 | 0.5994 | 0.6565 | |
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| 0.396 | 36.31 | 6500 | 0.6170 | 0.6258 | |
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| 0.3861 | 39.11 | 7000 | 0.6486 | 0.6217 | |
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| 0.3602 | 41.9 | 7500 | 0.6508 | 0.6115 | |
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| 0.3251 | 44.69 | 8000 | 0.7022 | 0.6253 | |
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| 0.3197 | 47.49 | 8500 | 0.7706 | 0.6215 | |
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| 0.3013 | 50.28 | 9000 | 0.6419 | 0.5999 | |
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| 0.2813 | 53.07 | 9500 | 0.6908 | 0.5959 | |
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| 0.286 | 55.87 | 10000 | 0.7151 | 0.5916 | |
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| 0.2645 | 58.66 | 10500 | 0.7181 | 0.5860 | |
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| 0.2535 | 61.45 | 11000 | 0.7877 | 0.5979 | |
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| 0.247 | 64.25 | 11500 | 0.8199 | 0.6129 | |
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| 0.2412 | 67.04 | 12000 | 0.7679 | 0.5884 | |
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| 0.2404 | 69.83 | 12500 | 0.7266 | 0.5816 | |
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| 0.2293 | 72.63 | 13000 | 0.7928 | 0.5795 | |
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| 0.2176 | 75.42 | 13500 | 0.7916 | 0.5846 | |
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| 0.2143 | 78.21 | 14000 | 0.7954 | 0.5765 | |
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| 0.2185 | 81.01 | 14500 | 0.8317 | 0.5907 | |
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| 0.2057 | 83.8 | 15000 | 0.8016 | 0.5851 | |
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| 0.1895 | 86.59 | 15500 | 0.8080 | 0.5679 | |
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| 0.1883 | 89.39 | 16000 | 0.8103 | 0.5712 | |
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| 0.1802 | 92.18 | 16500 | 0.8383 | 0.5644 | |
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| 0.1826 | 94.97 | 17000 | 0.8799 | 0.5657 | |
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| 0.1717 | 97.77 | 17500 | 0.8620 | 0.5709 | |
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| 0.1701 | 100.56 | 18000 | 0.8717 | 0.5662 | |
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| 0.1623 | 103.35 | 18500 | 0.8534 | 0.5594 | |
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| 0.158 | 106.15 | 19000 | 0.8595 | 0.5546 | |
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| 0.1508 | 108.94 | 19500 | 0.8574 | 0.5545 | |
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| 0.142 | 111.73 | 20000 | 0.8671 | 0.5537 | |
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| 0.1395 | 114.53 | 20500 | 0.8436 | 0.5525 | |
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| 0.1373 | 117.32 | 21000 | 0.8808 | 0.5482 | |
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| 0.1338 | 120.11 | 21500 | 0.9024 | 0.5418 | |
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| 0.1278 | 122.91 | 22000 | 0.9143 | 0.5409 | |
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| 0.1207 | 125.7 | 22500 | 0.8917 | 0.5358 | |
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| 0.1203 | 128.49 | 23000 | 0.9041 | 0.5341 | |
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| 0.1083 | 131.28 | 23500 | 0.8884 | 0.5341 | |
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| 0.1147 | 134.08 | 24000 | 0.8910 | 0.5255 | |
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| 0.1129 | 136.87 | 24500 | 0.8826 | 0.5241 | |
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| 0.1029 | 139.66 | 25000 | 0.8824 | 0.5246 | |
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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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