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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hubert-base-libri-demo-feature_extractor_not_frozen_v3_25epochs_check
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-libri-demo-feature_extractor_not_frozen_v3_25epochs_check
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1231
- Wer: 0.1112
## 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: 0.00015
- train_batch_size: 64
- 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: 3000
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.3342 | 1.12 | 500 | 3.4935 | 1.0000 |
| 2.8802 | 2.24 | 1000 | 3.5637 | 1.0000 |
| 2.1866 | 3.36 | 1500 | 0.7219 | 0.6232 |
| 0.6141 | 4.48 | 2000 | 0.2954 | 0.3238 |
| 0.3328 | 5.61 | 2500 | 0.1810 | 0.2212 |
| 0.2251 | 6.73 | 3000 | 0.1377 | 0.1640 |
| 0.1861 | 7.85 | 3500 | 0.1270 | 0.1473 |
| 0.1671 | 8.97 | 4000 | 0.1173 | 0.1372 |
| 0.1496 | 10.09 | 4500 | 0.1218 | 0.1322 |
| 0.117 | 11.21 | 5000 | 0.1180 | 0.1268 |
| 0.1182 | 12.33 | 5500 | 0.1255 | 0.1257 |
| 0.0948 | 13.45 | 6000 | 0.1215 | 0.1221 |
| 0.0935 | 14.57 | 6500 | 0.1233 | 0.1217 |
| 0.0873 | 15.7 | 7000 | 0.1124 | 0.1209 |
| 0.0798 | 16.82 | 7500 | 0.1172 | 0.1185 |
| 0.0752 | 17.94 | 8000 | 0.1197 | 0.1171 |
| 0.0747 | 19.06 | 8500 | 0.1252 | 0.1171 |
| 0.0775 | 20.18 | 9000 | 0.1209 | 0.1149 |
| 0.0665 | 21.3 | 9500 | 0.1180 | 0.1133 |
| 0.0657 | 22.42 | 10000 | 0.1240 | 0.1122 |
| 0.0606 | 23.54 | 10500 | 0.1222 | 0.1110 |
| 0.0581 | 24.66 | 11000 | 0.1231 | 0.1112 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1
- Datasets 2.12.1.dev0
- Tokenizers 0.13.3
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