---
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