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--- |
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license: apache-2.0 |
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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: sew-d-small-100k-ft-timit-2 |
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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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# sew-d-small-100k-ft-timit-2 |
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This model is a fine-tuned version of [asapp/sew-d-small-100k](https://huggingface.co/asapp/sew-d-small-100k) on the TIMIT_ASR - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7357 |
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- Wer: 0.7935 |
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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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| 4.1554 | 0.69 | 100 | 4.0531 | 1.0 | |
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| 2.9584 | 1.38 | 200 | 2.9775 | 1.0 | |
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| 2.9355 | 2.07 | 300 | 2.9412 | 1.0 | |
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| 2.9048 | 2.76 | 400 | 2.9143 | 1.0 | |
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| 2.8568 | 3.45 | 500 | 2.8786 | 1.0 | |
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| 2.7248 | 4.14 | 600 | 2.7553 | 0.9833 | |
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| 2.6124 | 4.83 | 700 | 2.5874 | 1.0511 | |
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| 2.5463 | 5.52 | 800 | 2.4630 | 1.0883 | |
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| 2.3302 | 6.21 | 900 | 2.3948 | 1.0651 | |
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| 2.0669 | 6.9 | 1000 | 2.2228 | 0.9920 | |
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| 2.1991 | 7.59 | 1100 | 2.0815 | 0.9185 | |
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| 2.293 | 8.28 | 1200 | 2.0229 | 0.8674 | |
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| 2.0366 | 8.97 | 1300 | 1.9590 | 0.9165 | |
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| 1.767 | 9.66 | 1400 | 1.9129 | 0.8125 | |
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| 1.6222 | 10.34 | 1500 | 1.8868 | 0.8259 | |
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| 2.173 | 11.03 | 1600 | 1.8691 | 0.8661 | |
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| 1.8614 | 11.72 | 1700 | 1.8388 | 0.8250 | |
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| 1.5928 | 12.41 | 1800 | 1.8528 | 0.7772 | |
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| 1.5978 | 13.1 | 1900 | 1.8002 | 0.7892 | |
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| 1.9886 | 13.79 | 2000 | 1.7848 | 0.8448 | |
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| 1.8042 | 14.48 | 2100 | 1.7819 | 0.8156 | |
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| 1.5488 | 15.17 | 2200 | 1.7615 | 0.8228 | |
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| 1.4468 | 15.86 | 2300 | 1.7565 | 0.7946 | |
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| 1.8153 | 16.55 | 2400 | 1.7537 | 0.8341 | |
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| 1.77 | 17.24 | 2500 | 1.7527 | 0.7958 | |
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| 1.4742 | 17.93 | 2600 | 1.7592 | 0.7850 | |
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| 1.4088 | 18.62 | 2700 | 1.7421 | 0.8149 | |
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| 1.7066 | 19.31 | 2800 | 1.7382 | 0.7977 | |
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| 1.7068 | 20.0 | 2900 | 1.7357 | 0.7935 | |
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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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