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--- |
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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: unispeech-sat-base-timit-ft |
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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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# unispeech-sat-base-timit-ft |
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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: 0.6712 |
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- Wer: 0.4101 |
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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: 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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| 3.2582 | 0.69 | 100 | 3.1651 | 1.0 | |
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| 2.9542 | 1.38 | 200 | 2.9567 | 1.0 | |
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| 2.9656 | 2.07 | 300 | 2.9195 | 1.0 | |
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| 2.8946 | 2.76 | 400 | 2.8641 | 1.0 | |
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| 1.9305 | 3.45 | 500 | 1.7680 | 1.0029 | |
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| 1.0134 | 4.14 | 600 | 1.0184 | 0.6942 | |
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| 0.8355 | 4.83 | 700 | 0.7769 | 0.6080 | |
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| 0.8724 | 5.52 | 800 | 0.7182 | 0.6035 | |
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| 0.5619 | 6.21 | 900 | 0.6823 | 0.5406 | |
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| 0.4247 | 6.9 | 1000 | 0.6279 | 0.5237 | |
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| 0.4257 | 7.59 | 1100 | 0.6056 | 0.5000 | |
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| 0.5007 | 8.28 | 1200 | 0.5870 | 0.4918 | |
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| 0.3854 | 8.97 | 1300 | 0.6200 | 0.4804 | |
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| 0.264 | 9.66 | 1400 | 0.6030 | 0.4600 | |
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| 0.1989 | 10.34 | 1500 | 0.6049 | 0.4588 | |
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| 0.3196 | 11.03 | 1600 | 0.5946 | 0.4599 | |
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| 0.2622 | 11.72 | 1700 | 0.6282 | 0.4422 | |
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| 0.1697 | 12.41 | 1800 | 0.6559 | 0.4413 | |
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| 0.1464 | 13.1 | 1900 | 0.6349 | 0.4328 | |
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| 0.2277 | 13.79 | 2000 | 0.6133 | 0.4284 | |
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| 0.221 | 14.48 | 2100 | 0.6617 | 0.4219 | |
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| 0.1391 | 15.17 | 2200 | 0.6705 | 0.4235 | |
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| 0.112 | 15.86 | 2300 | 0.6207 | 0.4218 | |
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| 0.1717 | 16.55 | 2400 | 0.6749 | 0.4184 | |
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| 0.2081 | 17.24 | 2500 | 0.6756 | 0.4169 | |
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| 0.1244 | 17.93 | 2600 | 0.6750 | 0.4181 | |
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| 0.0978 | 18.62 | 2700 | 0.6500 | 0.4115 | |
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| 0.128 | 19.31 | 2800 | 0.6750 | 0.4106 | |
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| 0.1791 | 20.0 | 2900 | 0.6712 | 0.4101 | |
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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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