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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: saq-20s_asr-scr_w2v2-base_002 |
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results: [] |
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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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# saq-20s_asr-scr_w2v2-base_002 |
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5036 |
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- Per: 0.1541 |
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- Pcc: 0.6677 |
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- Ctc Loss: 0.5422 |
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- Mse Loss: 0.9427 |
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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: 16 |
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- eval_batch_size: 1 |
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- seed: 2222 |
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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: 2226 |
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- training_steps: 22260 |
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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 | Per | Pcc | Ctc Loss | Mse Loss | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:--------:|:--------:| |
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| 16.9349 | 3.0 | 2226 | 4.6257 | 0.9983 | 0.6397 | 3.7753 | 0.9152 | |
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| 4.358 | 6.0 | 4452 | 4.3728 | 0.9983 | 0.6743 | 3.7449 | 0.7973 | |
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| 3.976 | 9.0 | 6678 | 4.2399 | 0.9983 | 0.6928 | 3.6699 | 0.8195 | |
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| 2.9839 | 12.0 | 8904 | 2.3433 | 0.3730 | 0.6740 | 1.5100 | 0.8973 | |
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| 1.2641 | 15.0 | 11130 | 1.7650 | 0.2095 | 0.6732 | 0.7985 | 0.9498 | |
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| 0.8466 | 18.0 | 13356 | 1.5664 | 0.1818 | 0.6642 | 0.6611 | 0.8872 | |
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| 0.6752 | 21.0 | 15582 | 1.5958 | 0.1708 | 0.6690 | 0.6012 | 0.9664 | |
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| 0.5802 | 24.0 | 17808 | 1.7719 | 0.1651 | 0.6737 | 0.5668 | 1.1474 | |
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| 0.5266 | 27.0 | 20034 | 1.6479 | 0.1577 | 0.6707 | 0.5482 | 1.0587 | |
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| 0.4851 | 30.0 | 22260 | 1.5036 | 0.1541 | 0.6677 | 0.5422 | 0.9427 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.0.1 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.2 |
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