---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hubert-base-libri-demo-feature_extractor_frozen_v2
  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_frozen_v2

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: 0.1202
- Wer: 0.1115

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.401         | 1.12  | 500   | 3.5086          | 1.0    |
| 2.8748        | 2.24  | 1000  | 3.3953          | 1.0    |
| 2.2716        | 3.36  | 1500  | 0.7177          | 0.6110 |
| 0.5536        | 4.48  | 2000  | 0.2387          | 0.2692 |
| 0.2897        | 5.61  | 2500  | 0.1593          | 0.1946 |
| 0.2077        | 6.73  | 3000  | 0.1401          | 0.1558 |
| 0.1778        | 7.85  | 3500  | 0.1225          | 0.1423 |
| 0.1639        | 8.97  | 4000  | 0.1156          | 0.1342 |
| 0.1478        | 10.09 | 4500  | 0.1186          | 0.1290 |
| 0.1146        | 11.21 | 5000  | 0.1131          | 0.1244 |
| 0.1172        | 12.33 | 5500  | 0.1189          | 0.1235 |
| 0.0925        | 13.45 | 6000  | 0.1175          | 0.1214 |
| 0.092         | 14.57 | 6500  | 0.1224          | 0.1194 |
| 0.0865        | 15.7  | 7000  | 0.1160          | 0.1196 |
| 0.0786        | 16.82 | 7500  | 0.1151          | 0.1152 |
| 0.0743        | 17.94 | 8000  | 0.1124          | 0.1153 |
| 0.0739        | 19.06 | 8500  | 0.1214          | 0.1146 |
| 0.0774        | 20.18 | 9000  | 0.1219          | 0.1143 |
| 0.0667        | 21.3  | 9500  | 0.1188          | 0.1129 |
| 0.0661        | 22.42 | 10000 | 0.1176          | 0.1123 |
| 0.0606        | 23.54 | 10500 | 0.1201          | 0.1118 |
| 0.0584        | 24.66 | 11000 | 0.1202          | 0.1115 |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1
- Datasets 2.12.1.dev0
- Tokenizers 0.13.3