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

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: 0.3068

## 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    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 4.6528        | 1.3652  | 200   | 2.5205          | 1.0    |
| 1.5409        | 2.7304  | 400   | 0.2012          | 0.5428 |
| 0.3781        | 4.0956  | 600   | 0.0349          | 0.3839 |
| 0.1391        | 5.4608  | 800   | 0.0140          | 0.3264 |
| 0.1157        | 6.8259  | 1000  | 0.0059          | 0.3093 |
| 0.0752        | 8.1911  | 1200  | 0.0081          | 0.3117 |
| 0.0653        | 9.5563  | 1400  | 0.0072          | 0.3068 |
| 0.0527        | 10.9215 | 1600  | 0.0033          | 0.3117 |
| 0.0542        | 12.2867 | 1800  | 0.0019          | 0.3093 |
| 0.0489        | 13.6519 | 2000  | 0.0148          | 0.3337 |
| 0.0374        | 15.0171 | 2200  | 0.0024          | 0.3093 |
| 0.0412        | 16.3823 | 2400  | 0.0038          | 0.3154 |
| 0.0302        | 17.7474 | 2600  | 0.0005          | 0.3068 |
| 0.0313        | 19.1126 | 2800  | 0.0014          | 0.3105 |
| 0.0274        | 20.4778 | 3000  | 0.0003          | 0.3068 |
| 0.0403        | 21.8430 | 3200  | 0.0010          | 0.3068 |
| 0.0274        | 23.2082 | 3400  | 0.0008          | 0.3068 |
| 0.0264        | 24.5734 | 3600  | 0.0005          | 0.3068 |
| 0.0302        | 25.9386 | 3800  | 0.0022          | 0.3068 |
| 0.0197        | 27.3038 | 4000  | 0.0006          | 0.3068 |
| 0.0187        | 28.6689 | 4200  | 0.0008          | 0.3081 |
| 0.0242        | 30.0341 | 4400  | 0.0003          | 0.3068 |
| 0.0155        | 31.3993 | 4600  | 0.0086          | 0.3068 |
| 0.0217        | 32.7645 | 4800  | 0.0006          | 0.3068 |
| 0.0189        | 34.1297 | 5000  | 0.0004          | 0.3068 |
| 0.0197        | 35.4949 | 5200  | 0.0001          | 0.3068 |
| 0.0211        | 36.8601 | 5400  | 0.0002          | 0.3068 |
| 0.0122        | 38.2253 | 5600  | 0.0003          | 0.3068 |
| 0.017         | 39.5904 | 5800  | 0.0002          | 0.3068 |
| 0.0195        | 40.9556 | 6000  | 0.0005          | 0.3081 |
| 0.0154        | 42.3208 | 6200  | 0.0028          | 0.3093 |
| 0.0081        | 43.6860 | 6400  | 0.0046          | 0.3117 |
| 0.0155        | 45.0512 | 6600  | 0.0005          | 0.3068 |
| 0.014         | 46.4164 | 6800  | 0.0004          | 0.3081 |
| 0.0113        | 47.7816 | 7000  | 0.0002          | 0.3068 |
| 0.0122        | 49.1468 | 7200  | 0.0001          | 0.3068 |
| 0.0105        | 50.5119 | 7400  | 0.0000          | 0.3068 |
| 0.0075        | 51.8771 | 7600  | 0.0000          | 0.3068 |
| 0.01          | 53.2423 | 7800  | 0.0001          | 0.3068 |
| 0.0077        | 54.6075 | 8000  | 0.0002          | 0.3068 |
| 0.0076        | 55.9727 | 8200  | 0.0001          | 0.3068 |
| 0.0097        | 57.3379 | 8400  | 0.0010          | 0.3081 |
| 0.0083        | 58.7031 | 8600  | 0.0000          | 0.3068 |
| 0.0097        | 60.0683 | 8800  | 0.0000          | 0.3068 |
| 0.0091        | 61.4334 | 9000  | 0.0001          | 0.3068 |
| 0.0059        | 62.7986 | 9200  | 0.0003          | 0.3081 |
| 0.0055        | 64.1638 | 9400  | 0.0006          | 0.3081 |
| 0.0073        | 65.5290 | 9600  | 0.0001          | 0.3081 |
| 0.0068        | 66.8942 | 9800  | 0.0005          | 0.3081 |
| 0.0057        | 68.2594 | 10000 | 0.0001          | 0.3068 |
| 0.0054        | 69.6246 | 10200 | 0.0006          | 0.3081 |
| 0.0049        | 70.9898 | 10400 | 0.0000          | 0.3068 |
| 0.0044        | 72.3549 | 10600 | 0.0004          | 0.3081 |
| 0.0065        | 73.7201 | 10800 | 0.0001          | 0.3068 |
| 0.0026        | 75.0853 | 11000 | 0.0000          | 0.3068 |
| 0.003         | 76.4505 | 11200 | 0.0000          | 0.3068 |
| 0.0038        | 77.8157 | 11400 | 0.0000          | 0.3068 |
| 0.0023        | 79.1809 | 11600 | 0.0000          | 0.3068 |
| 0.0034        | 80.5461 | 11800 | 0.0000          | 0.3068 |
| 0.0022        | 81.9113 | 12000 | 0.0000          | 0.3068 |
| 0.0024        | 83.2765 | 12200 | 0.0000          | 0.3068 |
| 0.0027        | 84.6416 | 12400 | 0.0000          | 0.3068 |
| 0.0015        | 86.0068 | 12600 | 0.0000          | 0.3068 |
| 0.0021        | 87.3720 | 12800 | 0.0000          | 0.3068 |
| 0.0037        | 88.7372 | 13000 | 0.0000          | 0.3068 |
| 0.0019        | 90.1024 | 13200 | 0.0000          | 0.3068 |
| 0.0016        | 91.4676 | 13400 | 0.0000          | 0.3068 |
| 0.0025        | 92.8328 | 13600 | 0.0000          | 0.3081 |
| 0.0029        | 94.1980 | 13800 | 0.0000          | 0.3068 |
| 0.0023        | 95.5631 | 14000 | 0.0000          | 0.3068 |
| 0.0017        | 96.9283 | 14200 | 0.0000          | 0.3068 |
| 0.0018        | 98.2935 | 14400 | 0.0000          | 0.3068 |
| 0.0016        | 99.6587 | 14600 | 0.0000          | 0.3068 |


### Framework versions

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