---
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-nomiii
  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-nomiii

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    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 4.6675        | 1.6327  | 200   | 2.6799          | 1.0    |
| 1.8836        | 3.2653  | 400   | 0.3203          | 0.6161 |
| 0.3406        | 4.8980  | 600   | 0.0509          | 0.4144 |
| 0.1344        | 6.5306  | 800   | 0.0307          | 0.3704 |
| 0.0919        | 8.1633  | 1000  | 0.0077          | 0.3093 |
| 0.0576        | 9.7959  | 1200  | 0.0027          | 0.3105 |
| 0.0488        | 11.4286 | 1400  | 0.0023          | 0.3093 |
| 0.0497        | 13.0612 | 1600  | 0.0157          | 0.3178 |
| 0.039         | 14.6939 | 1800  | 0.0007          | 0.3068 |
| 0.0338        | 16.3265 | 2000  | 0.0086          | 0.3105 |
| 0.0347        | 17.9592 | 2200  | 0.0020          | 0.3081 |
| 0.0259        | 19.5918 | 2400  | 0.0004          | 0.3081 |
| 0.0254        | 21.2245 | 2600  | 0.0268          | 0.3227 |
| 0.0321        | 22.8571 | 2800  | 0.0093          | 0.3142 |
| 0.0255        | 24.4898 | 3000  | 0.0003          | 0.3105 |
| 0.0222        | 26.1224 | 3200  | 0.0004          | 0.3068 |
| 0.0203        | 27.7551 | 3400  | 0.0106          | 0.3142 |
| 0.0207        | 29.3878 | 3600  | 0.0005          | 0.3068 |
| 0.0177        | 31.0204 | 3800  | 0.0012          | 0.3068 |
| 0.0143        | 32.6531 | 4000  | 0.0002          | 0.3068 |
| 0.0181        | 34.2857 | 4200  | 0.0003          | 0.3068 |
| 0.0142        | 35.9184 | 4400  | 0.0002          | 0.3068 |
| 0.0141        | 37.5510 | 4600  | 0.0003          | 0.3068 |
| 0.0117        | 39.1837 | 4800  | 0.0001          | 0.3068 |
| 0.0115        | 40.8163 | 5000  | 0.0001          | 0.3081 |
| 0.0107        | 42.4490 | 5200  | 0.0002          | 0.3068 |
| 0.0093        | 44.0816 | 5400  | 0.0001          | 0.3068 |
| 0.0111        | 45.7143 | 5600  | 0.0004          | 0.3068 |
| 0.013         | 47.3469 | 5800  | 0.0001          | 0.3068 |
| 0.0115        | 48.9796 | 6000  | 0.0004          | 0.3068 |
| 0.0109        | 50.6122 | 6200  | 0.0001          | 0.3068 |
| 0.0073        | 52.2449 | 6400  | 0.0001          | 0.3068 |
| 0.0058        | 53.8776 | 6600  | 0.0001          | 0.3068 |
| 0.0123        | 55.5102 | 6800  | 0.0001          | 0.3068 |
| 0.0119        | 57.1429 | 7000  | 0.0008          | 0.3081 |
| 0.0108        | 58.7755 | 7200  | 0.0001          | 0.3068 |
| 0.0069        | 60.4082 | 7400  | 0.0001          | 0.3068 |
| 0.0054        | 62.0408 | 7600  | 0.0009          | 0.3081 |
| 0.0066        | 63.6735 | 7800  | 0.0001          | 0.3068 |
| 0.0072        | 65.3061 | 8000  | 0.0001          | 0.3068 |
| 0.0049        | 66.9388 | 8200  | 0.0008          | 0.3068 |
| 0.0054        | 68.5714 | 8400  | 0.0001          | 0.3068 |
| 0.0048        | 70.2041 | 8600  | 0.0001          | 0.3068 |
| 0.0046        | 71.8367 | 8800  | 0.0000          | 0.3068 |
| 0.0034        | 73.4694 | 9000  | 0.0021          | 0.3093 |
| 0.0049        | 75.1020 | 9200  | 0.0000          | 0.3068 |
| 0.0016        | 76.7347 | 9400  | 0.0000          | 0.3068 |
| 0.0039        | 78.3673 | 9600  | 0.0000          | 0.3068 |
| 0.0036        | 80.0    | 9800  | 0.0000          | 0.3068 |
| 0.0036        | 81.6327 | 10000 | 0.0000          | 0.3068 |
| 0.0022        | 83.2653 | 10200 | 0.0006          | 0.3068 |
| 0.0033        | 84.8980 | 10400 | 0.0000          | 0.3081 |
| 0.0027        | 86.5306 | 10600 | 0.0000          | 0.3068 |
| 0.0018        | 88.1633 | 10800 | 0.0000          | 0.3068 |
| 0.0028        | 89.7959 | 11000 | 0.0000          | 0.3068 |
| 0.0019        | 91.4286 | 11200 | 0.0000          | 0.3068 |
| 0.0023        | 93.0612 | 11400 | 0.0000          | 0.3068 |
| 0.0012        | 94.6939 | 11600 | 0.0000          | 0.3068 |
| 0.0017        | 96.3265 | 11800 | 0.0000          | 0.3068 |
| 0.0014        | 97.9592 | 12000 | 0.0000          | 0.3068 |
| 0.0011        | 99.5918 | 12200 | 0.0000          | 0.3068 |


### Framework versions

- Transformers 4.46.0.dev0
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.20.0