---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: gopdataset_base_fadam
  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. -->

# gopdataset_base_fadam

This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1001
- Wer: 0.1662

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.9033        | 1.05  | 500   | 3.0006          | 1.0    |
| 2.1655        | 2.11  | 1000  | 0.3024          | 0.3575 |
| 0.582         | 3.16  | 1500  | 0.2045          | 0.2566 |
| 0.3618        | 4.21  | 2000  | 0.1377          | 0.2188 |
| 0.3297        | 5.26  | 2500  | 0.1551          | 0.2232 |
| 0.2847        | 6.32  | 3000  | 0.1742          | 0.2486 |
| 0.2488        | 7.37  | 3500  | 0.2328          | 0.2036 |
| 0.1996        | 8.42  | 4000  | 0.1379          | 0.2079 |
| 0.2165        | 9.47  | 4500  | 0.1183          | 0.1924 |
| 0.189         | 10.53 | 5000  | 0.1295          | 0.1956 |
| 0.1817        | 11.58 | 5500  | 0.1198          | 0.1888 |
| 0.1682        | 12.63 | 6000  | 0.1270          | 0.1887 |
| 0.1246        | 13.68 | 6500  | 0.1211          | 0.1867 |
| 0.1442        | 14.74 | 7000  | 0.1301          | 0.1805 |
| 0.1732        | 15.79 | 7500  | 0.1107          | 0.1801 |
| 0.1142        | 16.84 | 8000  | 0.1096          | 0.1848 |
| 0.1485        | 17.89 | 8500  | 0.1075          | 0.1790 |
| 0.1037        | 18.95 | 9000  | 0.1109          | 0.1778 |
| 0.1155        | 20.0  | 9500  | 0.1120          | 0.1736 |
| 0.1162        | 21.05 | 10000 | 0.1053          | 0.1740 |
| 0.0874        | 22.11 | 10500 | 0.1157          | 0.1739 |
| 0.0797        | 23.16 | 11000 | 0.1128          | 0.1735 |
| 0.0726        | 24.21 | 11500 | 0.1089          | 0.1745 |
| 0.0691        | 25.26 | 12000 | 0.1084          | 0.1696 |
| 0.0677        | 26.32 | 12500 | 0.1059          | 0.1696 |
| 0.0582        | 27.37 | 13000 | 0.1065          | 0.1696 |
| 0.0669        | 28.42 | 13500 | 0.1001          | 0.1701 |
| 0.0956        | 29.47 | 14000 | 0.1017          | 0.1687 |


### Framework versions

- Transformers 4.17.0
- Pytorch 2.5.1+cu121
- Datasets 1.18.3
- Tokenizers 0.20.3