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--- |
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language: |
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- ro |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- generated_from_trainer |
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- hf-asr-leaderboard |
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- mozilla-foundation/common_voice_7_0 |
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- robust-speech-event |
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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: wav2vec2-xls-r-1b-ro |
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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 7.0 |
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type: mozilla-foundation/common_voice_7_0 |
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args: ro |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 99.99 |
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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: ro |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 99.98 |
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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: ro |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 99.99 |
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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-ro |
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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_7_0 - RO dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1113 |
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- Wer: 0.4770 |
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- Cer: 0.0306 |
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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: 3e-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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- 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 | Cer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| |
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| 0.7844 | 1.67 | 1500 | 0.3412 | 0.8600 | 0.0940 | |
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| 0.7272 | 3.34 | 3000 | 0.1926 | 0.6409 | 0.0527 | |
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| 0.6924 | 5.02 | 4500 | 0.1413 | 0.5722 | 0.0401 | |
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| 0.6327 | 6.69 | 6000 | 0.1252 | 0.5366 | 0.0371 | |
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| 0.6363 | 8.36 | 7500 | 0.1235 | 0.5741 | 0.0389 | |
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| 0.6238 | 10.03 | 9000 | 0.1180 | 0.5542 | 0.0362 | |
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| 0.6018 | 11.71 | 10500 | 0.1192 | 0.5694 | 0.0369 | |
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| 0.583 | 13.38 | 12000 | 0.1216 | 0.5772 | 0.0385 | |
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| 0.5643 | 15.05 | 13500 | 0.1195 | 0.5419 | 0.0371 | |
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| 0.5399 | 16.72 | 15000 | 0.1240 | 0.5224 | 0.0370 | |
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| 0.5529 | 18.39 | 16500 | 0.1174 | 0.5555 | 0.0367 | |
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| 0.5246 | 20.07 | 18000 | 0.1097 | 0.5047 | 0.0339 | |
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| 0.4936 | 21.74 | 19500 | 0.1225 | 0.5189 | 0.0382 | |
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| 0.4629 | 23.41 | 21000 | 0.1142 | 0.5047 | 0.0344 | |
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| 0.4463 | 25.08 | 22500 | 0.1168 | 0.4887 | 0.0339 | |
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| 0.4671 | 26.76 | 24000 | 0.1119 | 0.5073 | 0.0338 | |
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| 0.4359 | 28.43 | 25500 | 0.1206 | 0.5479 | 0.0363 | |
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| 0.4225 | 30.1 | 27000 | 0.1122 | 0.5170 | 0.0345 | |
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| 0.4038 | 31.77 | 28500 | 0.1159 | 0.5032 | 0.0343 | |
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| 0.4271 | 33.44 | 30000 | 0.1116 | 0.5126 | 0.0339 | |
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| 0.3867 | 35.12 | 31500 | 0.1101 | 0.4937 | 0.0327 | |
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| 0.3674 | 36.79 | 33000 | 0.1142 | 0.4940 | 0.0330 | |
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| 0.3607 | 38.46 | 34500 | 0.1106 | 0.5145 | 0.0327 | |
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| 0.3651 | 40.13 | 36000 | 0.1172 | 0.4921 | 0.0317 | |
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| 0.3268 | 41.81 | 37500 | 0.1093 | 0.4830 | 0.0310 | |
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| 0.3345 | 43.48 | 39000 | 0.1131 | 0.4760 | 0.0314 | |
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| 0.3236 | 45.15 | 40500 | 0.1132 | 0.4864 | 0.0317 | |
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| 0.312 | 46.82 | 42000 | 0.1124 | 0.4861 | 0.0315 | |
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| 0.3106 | 48.49 | 43500 | 0.1116 | 0.4745 | 0.0306 | |
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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 1.18.2.dev0 |
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- Tokenizers 0.11.0 |
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