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--- |
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license: apache-2.0 |
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base_model: honzapucalek/p6_commonvoice_16_1 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- honzapucalek/p6_impaired_v2 |
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metrics: |
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- wer |
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model-index: |
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- name: p6_commonvoice_impaired |
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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: honzapucalek/p6_impaired_v2 cs |
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type: honzapucalek/p6_impaired_v2 |
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config: cs |
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split: test |
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args: cs |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.41563311220074994 |
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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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# p6_commonvoice_impaired |
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This model is a fine-tuned version of [honzapucalek/p6_commonvoice_16_1](https://huggingface.co./honzapucalek/p6_commonvoice_16_1) on the honzapucalek/p6_impaired_v2 cs dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3476 |
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- Wer: 0.4156 |
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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: 1e-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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- 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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- training_steps: 5000 |
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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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| 0.0078 | 14.93 | 1000 | 1.1002 | 0.4759 | |
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| 0.0007 | 29.85 | 2000 | 1.2277 | 0.4332 | |
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| 0.0012 | 44.78 | 3000 | 1.1811 | 0.4381 | |
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| 0.0001 | 59.7 | 4000 | 1.3258 | 0.4200 | |
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| 0.0001 | 74.63 | 5000 | 1.3476 | 0.4156 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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