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--- |
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language: |
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- pt |
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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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- robust-speech-event |
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- pt |
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- hf-asr-leaderboard |
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datasets: |
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- common_voice |
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model-index: |
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- name: wav2vec2-large-xls-r-300m-pt-cv |
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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 6 |
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type: common_voice |
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args: pt |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 24.29 |
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- name: Test CER |
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type: cer |
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value: 7.51 |
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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: 55.72 |
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- name: Test CER |
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type: cer |
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value: 21.82 |
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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: pt |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 47.88 |
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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: pt |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 50.78 |
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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-large-xls-r-300m-pt-cv |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3418 |
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- Wer: 0.3581 |
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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: 0.0003 |
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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: 2 |
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- total_train_batch_size: 16 |
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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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- num_epochs: 5 |
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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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| 10.9035 | 0.2 | 100 | 4.2750 | 1.0 | |
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| 3.3275 | 0.41 | 200 | 3.0334 | 1.0 | |
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| 3.0016 | 0.61 | 300 | 2.9494 | 1.0 | |
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| 2.1874 | 0.82 | 400 | 1.4355 | 0.8721 | |
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| 1.09 | 1.02 | 500 | 0.9987 | 0.7165 | |
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| 0.8251 | 1.22 | 600 | 0.7886 | 0.6406 | |
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| 0.6927 | 1.43 | 700 | 0.6753 | 0.5801 | |
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| 0.6143 | 1.63 | 800 | 0.6300 | 0.5509 | |
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| 0.5451 | 1.84 | 900 | 0.5586 | 0.5156 | |
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| 0.5003 | 2.04 | 1000 | 0.5493 | 0.5027 | |
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| 0.3712 | 2.24 | 1100 | 0.5271 | 0.4872 | |
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| 0.3486 | 2.45 | 1200 | 0.4953 | 0.4817 | |
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| 0.3498 | 2.65 | 1300 | 0.4619 | 0.4538 | |
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| 0.3112 | 2.86 | 1400 | 0.4570 | 0.4387 | |
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| 0.3013 | 3.06 | 1500 | 0.4437 | 0.4147 | |
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| 0.2136 | 3.27 | 1600 | 0.4176 | 0.4124 | |
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| 0.2131 | 3.47 | 1700 | 0.4281 | 0.4194 | |
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| 0.2099 | 3.67 | 1800 | 0.3864 | 0.3949 | |
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| 0.1925 | 3.88 | 1900 | 0.3926 | 0.3913 | |
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| 0.1709 | 4.08 | 2000 | 0.3764 | 0.3804 | |
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| 0.1406 | 4.29 | 2100 | 0.3787 | 0.3742 | |
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| 0.1342 | 4.49 | 2200 | 0.3645 | 0.3693 | |
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| 0.1305 | 4.69 | 2300 | 0.3463 | 0.3625 | |
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| 0.1298 | 4.9 | 2400 | 0.3418 | 0.3581 | |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.13.3 |
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- Tokenizers 0.10.3 |
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