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---
license: apache-2.0
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: distilhubert-timit
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-timit
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3601
- Wer: 0.6776
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.4447 | 0.69 | 100 | 4.9546 | 1.0 |
| 2.9499 | 1.38 | 200 | 2.9519 | 1.0 |
| 2.8989 | 2.07 | 300 | 2.8624 | 1.0 |
| 2.2076 | 2.76 | 400 | 2.1089 | 1.0008 |
| 1.4186 | 3.45 | 500 | 1.4112 | 0.9165 |
| 0.9951 | 4.14 | 600 | 1.1378 | 0.7701 |
| 0.9754 | 4.83 | 700 | 1.0152 | 0.7274 |
| 0.9364 | 5.52 | 800 | 0.9619 | 0.7011 |
| 0.6557 | 6.21 | 900 | 0.9144 | 0.6868 |
| 0.5681 | 6.9 | 1000 | 0.8899 | 0.6683 |
| 0.66 | 7.59 | 1100 | 0.8992 | 0.6654 |
| 0.6144 | 8.28 | 1200 | 0.9299 | 0.6898 |
| 0.4099 | 8.97 | 1300 | 0.9510 | 0.6674 |
| 0.3384 | 9.66 | 1400 | 0.9598 | 0.6612 |
| 0.3163 | 10.34 | 1500 | 0.9954 | 0.6612 |
| 0.4204 | 11.03 | 1600 | 1.0164 | 0.6607 |
| 0.1932 | 11.72 | 1700 | 1.0637 | 0.6658 |
| 0.1449 | 12.41 | 1800 | 1.1190 | 0.6652 |
| 0.1803 | 13.1 | 1900 | 1.1260 | 0.6689 |
| 0.328 | 13.79 | 2000 | 1.2186 | 0.6751 |
| 0.0838 | 14.48 | 2100 | 1.2591 | 0.6909 |
| 0.0766 | 15.17 | 2200 | 1.2529 | 0.6780 |
| 0.0956 | 15.86 | 2300 | 1.2537 | 0.6668 |
| 0.2339 | 16.55 | 2400 | 1.3210 | 0.6797 |
| 0.0431 | 17.24 | 2500 | 1.3241 | 0.6781 |
| 0.0508 | 17.93 | 2600 | 1.3184 | 0.6683 |
| 0.0616 | 18.62 | 2700 | 1.3728 | 0.6889 |
| 0.1608 | 19.31 | 2800 | 1.3572 | 0.6771 |
| 0.0378 | 20.0 | 2900 | 1.3601 | 0.6776 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
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