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--- |
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language: en |
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tags: |
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- automatic-speech-recognition |
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- timit_asr |
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- generated_from_trainer |
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datasets: |
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- timit_asr |
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model-index: |
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- name: sat-base |
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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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# sat-base |
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This model is a fine-tuned version of [microsoft/unispeech-sat-base](https://huggingface.co./microsoft/unispeech-sat-base) on the TIMIT_ASR - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7014 |
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- Wer: 0.5374 |
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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: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 1 |
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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: 1000 |
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- num_epochs: 20.0 |
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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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| 6.9958 | 0.69 | 100 | 6.7171 | 1.0 | |
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| 3.0453 | 1.38 | 200 | 3.0374 | 1.0 | |
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| 2.9989 | 2.07 | 300 | 2.9807 | 1.0 | |
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| 2.969 | 2.76 | 400 | 2.9579 | 1.0 | |
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| 2.903 | 3.45 | 500 | 2.9072 | 1.0 | |
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| 2.8565 | 4.14 | 600 | 2.8804 | 1.0 | |
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| 2.8195 | 4.83 | 700 | 2.7916 | 1.0 | |
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| 2.3134 | 5.52 | 800 | 2.1456 | 1.0004 | |
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| 1.5475 | 6.21 | 900 | 1.4663 | 0.9549 | |
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| 1.1295 | 6.9 | 1000 | 1.1140 | 0.7227 | |
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| 1.0181 | 7.59 | 1100 | 0.9258 | 0.6497 | |
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| 1.0252 | 8.28 | 1200 | 0.8430 | 0.6255 | |
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| 0.835 | 8.97 | 1300 | 0.8063 | 0.6032 | |
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| 0.662 | 9.66 | 1400 | 0.7595 | 0.5931 | |
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| 0.5558 | 10.34 | 1500 | 0.7322 | 0.5819 | |
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| 0.7596 | 11.03 | 1600 | 0.7120 | 0.5708 | |
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| 0.6169 | 11.72 | 1700 | 0.7073 | 0.5606 | |
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| 0.4565 | 12.41 | 1800 | 0.7124 | 0.5586 | |
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| 0.4554 | 13.1 | 1900 | 0.6880 | 0.5501 | |
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| 0.6216 | 13.79 | 2000 | 0.6783 | 0.5494 | |
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| 0.5393 | 14.48 | 2100 | 0.7067 | 0.5499 | |
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| 0.4095 | 15.17 | 2200 | 0.7014 | 0.5438 | |
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| 0.3551 | 15.86 | 2300 | 0.7000 | 0.5426 | |
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| 0.5112 | 16.55 | 2400 | 0.6866 | 0.5426 | |
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| 0.5139 | 17.24 | 2500 | 0.7134 | 0.5446 | |
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| 0.3638 | 17.93 | 2600 | 0.7130 | 0.5434 | |
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| 0.3327 | 18.62 | 2700 | 0.6980 | 0.5377 | |
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| 0.4385 | 19.31 | 2800 | 0.7017 | 0.5390 | |
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| 0.4986 | 20.0 | 2900 | 0.7014 | 0.5374 | |
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### Framework versions |
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- Transformers 4.12.0.dev0 |
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- Pytorch 1.8.1 |
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- Datasets 1.14.1.dev0 |
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- Tokenizers 0.10.3 |
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