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---
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: unispeech-sat-base-timit-ft
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. -->
# unispeech-sat-base-timit-ft
This model is a fine-tuned version of [microsoft/unispeech-sat-base](https://huggingface.co/microsoft/unispeech-sat-base) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6712
- Wer: 0.4101
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.2582 | 0.69 | 100 | 3.1651 | 1.0 |
| 2.9542 | 1.38 | 200 | 2.9567 | 1.0 |
| 2.9656 | 2.07 | 300 | 2.9195 | 1.0 |
| 2.8946 | 2.76 | 400 | 2.8641 | 1.0 |
| 1.9305 | 3.45 | 500 | 1.7680 | 1.0029 |
| 1.0134 | 4.14 | 600 | 1.0184 | 0.6942 |
| 0.8355 | 4.83 | 700 | 0.7769 | 0.6080 |
| 0.8724 | 5.52 | 800 | 0.7182 | 0.6035 |
| 0.5619 | 6.21 | 900 | 0.6823 | 0.5406 |
| 0.4247 | 6.9 | 1000 | 0.6279 | 0.5237 |
| 0.4257 | 7.59 | 1100 | 0.6056 | 0.5000 |
| 0.5007 | 8.28 | 1200 | 0.5870 | 0.4918 |
| 0.3854 | 8.97 | 1300 | 0.6200 | 0.4804 |
| 0.264 | 9.66 | 1400 | 0.6030 | 0.4600 |
| 0.1989 | 10.34 | 1500 | 0.6049 | 0.4588 |
| 0.3196 | 11.03 | 1600 | 0.5946 | 0.4599 |
| 0.2622 | 11.72 | 1700 | 0.6282 | 0.4422 |
| 0.1697 | 12.41 | 1800 | 0.6559 | 0.4413 |
| 0.1464 | 13.1 | 1900 | 0.6349 | 0.4328 |
| 0.2277 | 13.79 | 2000 | 0.6133 | 0.4284 |
| 0.221 | 14.48 | 2100 | 0.6617 | 0.4219 |
| 0.1391 | 15.17 | 2200 | 0.6705 | 0.4235 |
| 0.112 | 15.86 | 2300 | 0.6207 | 0.4218 |
| 0.1717 | 16.55 | 2400 | 0.6749 | 0.4184 |
| 0.2081 | 17.24 | 2500 | 0.6756 | 0.4169 |
| 0.1244 | 17.93 | 2600 | 0.6750 | 0.4181 |
| 0.0978 | 18.62 | 2700 | 0.6500 | 0.4115 |
| 0.128 | 19.31 | 2800 | 0.6750 | 0.4106 |
| 0.1791 | 20.0 | 2900 | 0.6712 | 0.4101 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
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