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--- |
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language: |
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- fr |
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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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- generated_from_trainer |
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- robust-speech-event |
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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-300m-fr |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice 8.0 fr |
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type: mozilla-foundation/common_voice_8_0 |
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args: fr |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 36.81 |
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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: fr |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 35.55 |
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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: fr |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 39.94 |
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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-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2388 |
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- Wer: 0.3681 |
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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: 0.0001 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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: 1500 |
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- num_epochs: 2.0 |
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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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| 4.3748 | 0.07 | 500 | 3.8784 | 1.0 | |
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| 2.8068 | 0.14 | 1000 | 2.8289 | 0.9826 | |
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| 1.6698 | 0.22 | 1500 | 0.8811 | 0.7127 | |
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| 1.3488 | 0.29 | 2000 | 0.5166 | 0.5369 | |
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| 1.2239 | 0.36 | 2500 | 0.4105 | 0.4741 | |
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| 1.1537 | 0.43 | 3000 | 0.3585 | 0.4448 | |
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| 1.1184 | 0.51 | 3500 | 0.3336 | 0.4292 | |
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| 1.0968 | 0.58 | 4000 | 0.3195 | 0.4180 | |
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| 1.0737 | 0.65 | 4500 | 0.3075 | 0.4141 | |
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| 1.0677 | 0.72 | 5000 | 0.3015 | 0.4089 | |
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| 1.0462 | 0.8 | 5500 | 0.2971 | 0.4077 | |
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| 1.0392 | 0.87 | 6000 | 0.2870 | 0.3997 | |
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| 1.0178 | 0.94 | 6500 | 0.2805 | 0.3963 | |
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| 0.992 | 1.01 | 7000 | 0.2748 | 0.3935 | |
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| 1.0197 | 1.09 | 7500 | 0.2691 | 0.3884 | |
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| 1.0056 | 1.16 | 8000 | 0.2682 | 0.3889 | |
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| 0.9826 | 1.23 | 8500 | 0.2647 | 0.3868 | |
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| 0.9815 | 1.3 | 9000 | 0.2603 | 0.3832 | |
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| 0.9717 | 1.37 | 9500 | 0.2561 | 0.3807 | |
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| 0.9605 | 1.45 | 10000 | 0.2523 | 0.3783 | |
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| 0.96 | 1.52 | 10500 | 0.2494 | 0.3788 | |
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| 0.9442 | 1.59 | 11000 | 0.2478 | 0.3760 | |
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| 0.9564 | 1.66 | 11500 | 0.2454 | 0.3733 | |
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| 0.9436 | 1.74 | 12000 | 0.2439 | 0.3747 | |
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| 0.938 | 1.81 | 12500 | 0.2411 | 0.3716 | |
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| 0.9353 | 1.88 | 13000 | 0.2397 | 0.3698 | |
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| 0.9271 | 1.95 | 13500 | 0.2388 | 0.3681 | |
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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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