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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: distilhubert-timit |
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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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# distilhubert-timit |
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This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the TIMIT_ASR - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3601 |
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- Wer: 0.6776 |
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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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| 5.4447 | 0.69 | 100 | 4.9546 | 1.0 | |
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| 2.9499 | 1.38 | 200 | 2.9519 | 1.0 | |
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| 2.8989 | 2.07 | 300 | 2.8624 | 1.0 | |
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| 2.2076 | 2.76 | 400 | 2.1089 | 1.0008 | |
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| 1.4186 | 3.45 | 500 | 1.4112 | 0.9165 | |
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| 0.9951 | 4.14 | 600 | 1.1378 | 0.7701 | |
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| 0.9754 | 4.83 | 700 | 1.0152 | 0.7274 | |
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| 0.9364 | 5.52 | 800 | 0.9619 | 0.7011 | |
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| 0.6557 | 6.21 | 900 | 0.9144 | 0.6868 | |
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| 0.5681 | 6.9 | 1000 | 0.8899 | 0.6683 | |
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| 0.66 | 7.59 | 1100 | 0.8992 | 0.6654 | |
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| 0.6144 | 8.28 | 1200 | 0.9299 | 0.6898 | |
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| 0.4099 | 8.97 | 1300 | 0.9510 | 0.6674 | |
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| 0.3384 | 9.66 | 1400 | 0.9598 | 0.6612 | |
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| 0.3163 | 10.34 | 1500 | 0.9954 | 0.6612 | |
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| 0.4204 | 11.03 | 1600 | 1.0164 | 0.6607 | |
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| 0.1932 | 11.72 | 1700 | 1.0637 | 0.6658 | |
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| 0.1449 | 12.41 | 1800 | 1.1190 | 0.6652 | |
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| 0.1803 | 13.1 | 1900 | 1.1260 | 0.6689 | |
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| 0.328 | 13.79 | 2000 | 1.2186 | 0.6751 | |
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| 0.0838 | 14.48 | 2100 | 1.2591 | 0.6909 | |
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| 0.0766 | 15.17 | 2200 | 1.2529 | 0.6780 | |
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| 0.0956 | 15.86 | 2300 | 1.2537 | 0.6668 | |
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| 0.2339 | 16.55 | 2400 | 1.3210 | 0.6797 | |
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| 0.0431 | 17.24 | 2500 | 1.3241 | 0.6781 | |
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| 0.0508 | 17.93 | 2600 | 1.3184 | 0.6683 | |
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| 0.0616 | 18.62 | 2700 | 1.3728 | 0.6889 | |
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| 0.1608 | 19.31 | 2800 | 1.3572 | 0.6771 | |
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| 0.0378 | 20.0 | 2900 | 1.3601 | 0.6776 | |
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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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