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---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: hubert-base-timit-demo-google-colab-ft30ep_v4
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. -->
# hubert-base-timit-demo-google-colab-ft35ep
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the timit-asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4602
- Wer: 0.3466
## 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: 8
- eval_batch_size: 8
- 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.825 | 0.87 | 500 | 2.9521 | 1.0 |
| 2.431 | 1.73 | 1000 | 0.9760 | 0.8013 |
| 1.0089 | 2.6 | 1500 | 0.5934 | 0.5968 |
| 0.6859 | 3.46 | 2000 | 0.5132 | 0.5356 |
| 0.5302 | 4.33 | 2500 | 0.4506 | 0.4894 |
| 0.44 | 5.19 | 3000 | 0.4340 | 0.4670 |
| 0.3926 | 6.06 | 3500 | 0.4506 | 0.4528 |
| 0.3326 | 6.92 | 4000 | 0.4197 | 0.4486 |
| 0.2937 | 7.79 | 4500 | 0.4093 | 0.4193 |
| 0.2568 | 8.65 | 5000 | 0.4098 | 0.4229 |
| 0.2473 | 9.52 | 5500 | 0.4090 | 0.4141 |
| 0.2233 | 10.38 | 6000 | 0.4152 | 0.4125 |
| 0.2108 | 11.25 | 6500 | 0.4586 | 0.4189 |
| 0.2086 | 12.11 | 7000 | 0.4284 | 0.3969 |
| 0.1858 | 12.98 | 7500 | 0.4028 | 0.3946 |
| 0.1641 | 13.84 | 8000 | 0.4679 | 0.4002 |
| 0.1686 | 14.71 | 8500 | 0.4441 | 0.3936 |
| 0.1489 | 15.57 | 9000 | 0.4897 | 0.3828 |
| 0.1541 | 16.44 | 9500 | 0.4953 | 0.3783 |
| 0.1417 | 17.3 | 10000 | 0.4500 | 0.3758 |
| 0.1428 | 18.17 | 10500 | 0.4533 | 0.3796 |
| 0.1306 | 19.03 | 11000 | 0.4474 | 0.3792 |
| 0.1185 | 19.9 | 11500 | 0.4762 | 0.3743 |
| 0.1081 | 20.76 | 12000 | 0.4770 | 0.3699 |
| 0.1253 | 21.63 | 12500 | 0.4749 | 0.3629 |
| 0.1087 | 22.49 | 13000 | 0.4577 | 0.3534 |
| 0.1172 | 23.36 | 13500 | 0.4819 | 0.3525 |
| 0.1086 | 24.22 | 14000 | 0.4709 | 0.3623 |
| 0.089 | 25.09 | 14500 | 0.4852 | 0.3544 |
| 0.086 | 25.95 | 15000 | 0.4602 | 0.3555 |
| 0.086 | 26.82 | 15500 | 0.4861 | 0.3497 |
| 0.086 | 27.68 | 16000 | 0.4527 | 0.3473 |
| 0.0919 | 28.55 | 16500 | 0.4607 | 0.3487 |
| 0.0792 | 29.41 | 17000 | 0.4602 | 0.3466 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
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