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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-plus-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-plus-timit-ft |
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This model is a fine-tuned version of [microsoft/unispeech-sat-base-plus](https://huggingface.co/microsoft/unispeech-sat-base-plus) on the TIMIT_ASR - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6549 |
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- Wer: 0.4051 |
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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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| 3.3838 | 0.69 | 100 | 3.2528 | 1.0 | |
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| 2.9608 | 1.38 | 200 | 2.9682 | 1.0 | |
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| 2.9574 | 2.07 | 300 | 2.9346 | 1.0 | |
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| 2.8555 | 2.76 | 400 | 2.7612 | 1.0 | |
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| 1.7418 | 3.45 | 500 | 1.5732 | 0.9857 | |
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| 0.9606 | 4.14 | 600 | 1.0014 | 0.7052 | |
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| 0.8334 | 4.83 | 700 | 0.7691 | 0.6161 | |
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| 0.852 | 5.52 | 800 | 0.7169 | 0.5997 | |
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| 0.5707 | 6.21 | 900 | 0.6821 | 0.5527 | |
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| 0.4235 | 6.9 | 1000 | 0.6078 | 0.5140 | |
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| 0.4357 | 7.59 | 1100 | 0.5927 | 0.4982 | |
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| 0.5004 | 8.28 | 1200 | 0.5814 | 0.4826 | |
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| 0.3757 | 8.97 | 1300 | 0.5951 | 0.4643 | |
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| 0.2579 | 9.66 | 1400 | 0.5990 | 0.4581 | |
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| 0.2087 | 10.34 | 1500 | 0.5864 | 0.4488 | |
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| 0.3155 | 11.03 | 1600 | 0.5836 | 0.4464 | |
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| 0.2701 | 11.72 | 1700 | 0.6045 | 0.4348 | |
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| 0.172 | 12.41 | 1800 | 0.6494 | 0.4344 | |
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| 0.1529 | 13.1 | 1900 | 0.5915 | 0.4241 | |
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| 0.2411 | 13.79 | 2000 | 0.6156 | 0.4246 | |
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| 0.2348 | 14.48 | 2100 | 0.6363 | 0.4206 | |
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| 0.1429 | 15.17 | 2200 | 0.6394 | 0.4161 | |
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| 0.1151 | 15.86 | 2300 | 0.6186 | 0.4167 | |
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| 0.1723 | 16.55 | 2400 | 0.6498 | 0.4124 | |
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| 0.1997 | 17.24 | 2500 | 0.6541 | 0.4076 | |
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| 0.1297 | 17.93 | 2600 | 0.6546 | 0.4117 | |
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| 0.101 | 18.62 | 2700 | 0.6471 | 0.4075 | |
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| 0.1272 | 19.31 | 2800 | 0.6586 | 0.4065 | |
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| 0.1901 | 20.0 | 2900 | 0.6549 | 0.4051 | |
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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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