---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: xlsr-aiish-nomo
  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. -->

# xlsr-aiish-nomo

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Wer: 0.3105

## 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: 0.0004
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 132
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 4.5816        | 1.7167  | 200   | 2.5505          | 1.0    |
| 1.5977        | 3.4335  | 400   | 0.1411          | 0.5024 |
| 0.2835        | 5.1502  | 600   | 0.0321          | 0.3716 |
| 0.1323        | 6.8670  | 800   | 0.0247          | 0.3166 |
| 0.0826        | 8.5837  | 1000  | 0.0093          | 0.3289 |
| 0.0816        | 10.3004 | 1200  | 0.0032          | 0.3105 |
| 0.0543        | 12.0172 | 1400  | 0.0022          | 0.3130 |
| 0.0516        | 13.7339 | 1600  | 0.0045          | 0.3105 |
| 0.0399        | 15.4506 | 1800  | 0.0028          | 0.3130 |
| 0.0368        | 17.1674 | 2000  | 0.0099          | 0.3252 |
| 0.032         | 18.8841 | 2200  | 0.0007          | 0.3105 |
| 0.0349        | 20.6009 | 2400  | 0.0152          | 0.3240 |
| 0.025         | 22.3176 | 2600  | 0.0004          | 0.3105 |
| 0.0267        | 24.0343 | 2800  | 0.0003          | 0.3105 |
| 0.0279        | 25.7511 | 3000  | 0.0003          | 0.3105 |
| 0.0231        | 27.4678 | 3200  | 0.0014          | 0.3142 |
| 0.0311        | 29.1845 | 3400  | 0.0005          | 0.3105 |
| 0.0209        | 30.9013 | 3600  | 0.0015          | 0.3105 |
| 0.0175        | 32.6180 | 3800  | 0.0023          | 0.3105 |
| 0.0158        | 34.3348 | 4000  | 0.0002          | 0.3105 |
| 0.0189        | 36.0515 | 4200  | 0.0004          | 0.3105 |
| 0.0137        | 37.7682 | 4400  | 0.0001          | 0.3105 |
| 0.0138        | 39.4850 | 4600  | 0.0002          | 0.3105 |
| 0.0141        | 41.2017 | 4800  | 0.0002          | 0.3105 |
| 0.0147        | 42.9185 | 5000  | 0.0011          | 0.3105 |
| 0.013         | 44.6352 | 5200  | 0.0020          | 0.3130 |
| 0.0204        | 46.3519 | 5400  | 0.0011          | 0.3154 |
| 0.0131        | 48.0687 | 5600  | 0.0018          | 0.3117 |
| 0.0109        | 49.7854 | 5800  | 0.0053          | 0.3105 |
| 0.0135        | 51.5021 | 6000  | 0.0038          | 0.3068 |
| 0.0112        | 53.2189 | 6200  | 0.0003          | 0.3081 |
| 0.0075        | 54.9356 | 6400  | 0.0001          | 0.3081 |
| 0.0116        | 56.6524 | 6600  | 0.0001          | 0.3068 |
| 0.0048        | 58.3691 | 6800  | 0.0013          | 0.3093 |
| 0.0077        | 60.0858 | 7000  | 0.0000          | 0.3081 |
| 0.009         | 61.8026 | 7200  | 0.0002          | 0.3081 |
| 0.0043        | 63.5193 | 7400  | 0.0001          | 0.3081 |
| 0.0043        | 65.2361 | 7600  | 0.0017          | 0.3093 |
| 0.0063        | 66.9528 | 7800  | 0.0000          | 0.3081 |
| 0.0051        | 68.6695 | 8000  | 0.0000          | 0.3081 |
| 0.0062        | 70.3863 | 8200  | 0.0001          | 0.3081 |
| 0.0027        | 72.1030 | 8400  | 0.0000          | 0.3081 |
| 0.0043        | 73.8197 | 8600  | 0.0000          | 0.3068 |
| 0.0031        | 75.5365 | 8800  | 0.0000          | 0.3081 |
| 0.004         | 77.2532 | 9000  | 0.0001          | 0.3081 |
| 0.0045        | 78.9700 | 9200  | 0.0000          | 0.3093 |
| 0.0049        | 80.6867 | 9400  | 0.0000          | 0.3081 |
| 0.0026        | 82.4034 | 9600  | 0.0001          | 0.3093 |
| 0.0025        | 84.1202 | 9800  | 0.0000          | 0.3081 |
| 0.0029        | 85.8369 | 10000 | 0.0000          | 0.3093 |
| 0.0025        | 87.5536 | 10200 | 0.0000          | 0.3105 |
| 0.0029        | 89.2704 | 10400 | 0.0000          | 0.3105 |
| 0.0018        | 90.9871 | 10600 | 0.0000          | 0.3105 |
| 0.0027        | 92.7039 | 10800 | 0.0000          | 0.3105 |
| 0.0025        | 94.4206 | 11000 | 0.0000          | 0.3105 |
| 0.0014        | 96.1373 | 11200 | 0.0000          | 0.3105 |
| 0.0021        | 97.8541 | 11400 | 0.0000          | 0.3105 |
| 0.0016        | 99.5708 | 11600 | 0.0000          | 0.3105 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1