update model card README.md
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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- vivos
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metrics:
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- wer
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model-index:
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- name: Wave2Vec2_OV_Vie
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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: vivos
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type: vivos
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config: default
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split: test
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args: default
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metrics:
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- name: Wer
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type: wer
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value: 1.0
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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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# Wave2Vec2_OV_Vie
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the vivos dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.5908
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- Wer: 1.0
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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: 16
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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: 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: 500
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- num_epochs: 15.0
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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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| No log | 0.27 | 100 | 3.9210 | 1.0 |
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| No log | 0.55 | 200 | 3.4375 | 1.0 |
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| No log | 0.82 | 300 | 3.4356 | 1.0 |
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| No log | 1.1 | 400 | 3.4045 | 1.0 |
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| 4.1866 | 1.37 | 500 | 3.4694 | 1.0 |
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| 4.1866 | 1.65 | 600 | 3.6266 | 1.0 |
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| 4.1866 | 1.92 | 700 | 3.5694 | 1.0 |
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| 4.1866 | 2.19 | 800 | 3.5733 | 1.0 |
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| 4.1866 | 2.47 | 900 | 3.6381 | 1.0 |
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| 3.4376 | 2.74 | 1000 | 3.6604 | 1.0 |
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| 3.4376 | 3.02 | 1100 | 3.5868 | 1.0 |
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| 3.4376 | 3.29 | 1200 | 3.4988 | 1.0 |
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| 3.4376 | 3.57 | 1300 | 3.5409 | 1.0 |
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| 3.4376 | 3.84 | 1400 | 3.4883 | 1.0 |
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| 3.4365 | 4.12 | 1500 | 3.6125 | 1.0 |
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| 3.4365 | 4.39 | 1600 | 3.6123 | 1.0 |
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| 3.4365 | 4.66 | 1700 | 3.5978 | 1.0 |
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| 3.4365 | 4.94 | 1800 | 3.5693 | 1.0 |
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| 3.4365 | 5.21 | 1900 | 3.5659 | 1.0 |
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| 3.4339 | 5.49 | 2000 | 3.6234 | 1.0 |
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| 3.4339 | 5.76 | 2100 | 3.5997 | 1.0 |
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| 3.4339 | 6.04 | 2200 | 3.6529 | 1.0 |
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| 3.4339 | 6.31 | 2300 | 3.5780 | 1.0 |
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| 3.4339 | 6.58 | 2400 | 3.5844 | 1.0 |
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| 3.4333 | 6.86 | 2500 | 3.5792 | 1.0 |
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| 3.4333 | 7.13 | 2600 | 3.5468 | 1.0 |
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| 3.4333 | 7.41 | 2700 | 3.5691 | 1.0 |
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| 3.4333 | 7.68 | 2800 | 3.5408 | 1.0 |
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| 3.4333 | 7.96 | 2900 | 3.5482 | 1.0 |
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| 3.4294 | 8.23 | 3000 | 3.6070 | 1.0 |
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| 3.4294 | 8.5 | 3100 | 3.5905 | 1.0 |
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| 3.4294 | 8.78 | 3200 | 3.6018 | 1.0 |
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| 3.4294 | 9.05 | 3300 | 3.6326 | 1.0 |
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| 3.4294 | 9.33 | 3400 | 3.6214 | 1.0 |
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| 3.4293 | 9.6 | 3500 | 3.6372 | 1.0 |
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| 3.4293 | 9.88 | 3600 | 3.6215 | 1.0 |
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| 3.4293 | 10.15 | 3700 | 3.5106 | 1.0 |
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| 3.4293 | 10.43 | 3800 | 3.5066 | 1.0 |
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| 3.4293 | 10.7 | 3900 | 3.5352 | 1.0 |
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| 3.4295 | 10.97 | 4000 | 3.5129 | 1.0 |
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| 3.4295 | 11.25 | 4100 | 3.6384 | 1.0 |
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| 3.4295 | 11.52 | 4200 | 3.6019 | 1.0 |
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| 3.4295 | 11.8 | 4300 | 3.5876 | 1.0 |
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| 3.4295 | 12.07 | 4400 | 3.6207 | 1.0 |
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| 3.4252 | 12.35 | 4500 | 3.5998 | 1.0 |
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| 3.4252 | 12.62 | 4600 | 3.6216 | 1.0 |
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| 3.4252 | 12.89 | 4700 | 3.6073 | 1.0 |
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| 3.4252 | 13.17 | 4800 | 3.5567 | 1.0 |
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| 3.4252 | 13.44 | 4900 | 3.5745 | 1.0 |
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| 3.4274 | 13.72 | 5000 | 3.5738 | 1.0 |
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| 3.4274 | 13.99 | 5100 | 3.5914 | 1.0 |
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| 3.4274 | 14.27 | 5200 | 3.6004 | 1.0 |
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| 3.4274 | 14.54 | 5300 | 3.5968 | 1.0 |
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| 3.4274 | 14.81 | 5400 | 3.5908 | 1.0 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 2.0.1+cu117
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- Datasets 2.13.0
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- Tokenizers 0.13.3
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