---
license: apache-2.0
base_model: facebook/wav2vec2-base-960h
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-urdufinetuned
  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. -->

# wav2vec2-urdufinetuned

This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6089
- Wer: 1.0

## 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: 0.001
- train_batch_size: 10
- 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: 100
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 10.903        | 0.06  | 100  | 3.7302          | 1.0 |
| 3.6693        | 0.11  | 200  | 3.6193          | 1.0 |
| 3.6908        | 0.17  | 300  | 3.6678          | 1.0 |
| 3.6565        | 0.22  | 400  | 3.6365          | 1.0 |
| 3.6348        | 0.28  | 500  | 3.6443          | 1.0 |
| 3.6878        | 0.33  | 600  | 3.6583          | 1.0 |
| 3.572         | 0.39  | 700  | 3.6304          | 1.0 |
| 3.6749        | 0.44  | 800  | 3.6420          | 1.0 |
| 3.6872        | 0.5   | 900  | 3.6469          | 1.0 |
| 3.6594        | 0.56  | 1000 | 3.6278          | 1.0 |
| 3.6131        | 0.61  | 1100 | 3.6169          | 1.0 |
| 3.5748        | 0.67  | 1200 | 3.6234          | 1.0 |
| 3.6181        | 0.72  | 1300 | 3.6494          | 1.0 |
| 3.6164        | 0.78  | 1400 | 3.6248          | 1.0 |
| 3.6688        | 0.83  | 1500 | 3.6610          | 1.0 |
| 4.1978        | 0.89  | 1600 | 3.6903          | 1.0 |
| 3.7485        | 0.94  | 1700 | 3.6275          | 1.0 |
| 3.649         | 1.0   | 1800 | 3.6139          | 1.0 |
| 3.5834        | 1.06  | 1900 | 3.6161          | 1.0 |
| 3.6338        | 1.11  | 2000 | 3.6647          | 1.0 |
| 3.5427        | 1.17  | 2100 | 3.6129          | 1.0 |
| 3.6117        | 1.22  | 2200 | 3.6084          | 1.0 |
| 3.6726        | 1.28  | 2300 | 3.6149          | 1.0 |
| 3.6278        | 1.33  | 2400 | 3.6342          | 1.0 |
| 3.6746        | 1.39  | 2500 | 3.6102          | 1.0 |
| 3.574         | 1.44  | 2600 | 3.7048          | 1.0 |
| 3.5892        | 1.5   | 2700 | 3.6126          | 1.0 |
| 3.6575        | 1.56  | 2800 | 3.6163          | 1.0 |
| 3.592         | 1.61  | 2900 | 3.6610          | 1.0 |
| 3.6506        | 1.67  | 3000 | 3.6127          | 1.0 |
| 3.5823        | 1.72  | 3100 | 3.6071          | 1.0 |
| 3.6674        | 1.78  | 3200 | 3.6032          | 1.0 |
| 3.6017        | 1.83  | 3300 | 3.6236          | 1.0 |
| 3.5865        | 1.89  | 3400 | 3.6208          | 1.0 |
| 3.646         | 1.94  | 3500 | 3.6074          | 1.0 |
| 3.6042        | 2.0   | 3600 | 3.6442          | 1.0 |
| 3.56          | 2.06  | 3700 | 3.6076          | 1.0 |
| 3.6241        | 2.11  | 3800 | 3.6051          | 1.0 |
| 3.6245        | 2.17  | 3900 | 3.6074          | 1.0 |
| 3.5764        | 2.22  | 4000 | 3.6238          | 1.0 |
| 3.6168        | 2.28  | 4100 | 3.6192          | 1.0 |
| 3.6143        | 2.33  | 4200 | 3.6093          | 1.0 |
| 3.613         | 2.39  | 4300 | 3.6123          | 1.0 |
| 3.6178        | 2.44  | 4400 | 3.6135          | 1.0 |
| 3.6234        | 2.5   | 4500 | 3.6161          | 1.0 |
| 3.5833        | 2.56  | 4600 | 3.6064          | 1.0 |
| 3.5759        | 2.61  | 4700 | 3.6077          | 1.0 |
| 3.6747        | 2.67  | 4800 | 3.6123          | 1.0 |
| 3.5914        | 2.72  | 4900 | 3.6041          | 1.0 |
| 3.6342        | 2.78  | 5000 | 3.6208          | 1.0 |
| 3.5883        | 2.83  | 5100 | 3.6056          | 1.0 |
| 3.5563        | 2.89  | 5200 | 3.6159          | 1.0 |
| 3.6213        | 2.94  | 5300 | 3.6173          | 1.0 |
| 3.6507        | 3.0   | 5400 | 3.6031          | 1.0 |
| 3.549         | 3.06  | 5500 | 3.6371          | 1.0 |
| 3.5712        | 3.11  | 5600 | 3.6049          | 1.0 |
| 3.5731        | 3.17  | 5700 | 3.6273          | 1.0 |
| 3.6232        | 3.22  | 5800 | 3.6012          | 1.0 |
| 3.6406        | 3.28  | 5900 | 3.6020          | 1.0 |
| 3.6456        | 3.33  | 6000 | 3.6015          | 1.0 |
| 3.6268        | 3.39  | 6100 | 3.6047          | 1.0 |
| 3.6286        | 3.44  | 6200 | 3.6023          | 1.0 |
| 3.609         | 3.5   | 6300 | 3.6053          | 1.0 |
| 3.6256        | 3.56  | 6400 | 3.6040          | 1.0 |
| 3.5537        | 3.61  | 6500 | 3.6075          | 1.0 |
| 3.5214        | 3.67  | 6600 | 3.6055          | 1.0 |
| 3.6031        | 3.72  | 6700 | 3.6156          | 1.0 |
| 3.6624        | 3.78  | 6800 | 3.6037          | 1.0 |
| 3.5813        | 3.83  | 6900 | 3.6030          | 1.0 |
| 3.6514        | 3.89  | 7000 | 3.6043          | 1.0 |
| 3.5535        | 3.94  | 7100 | 3.6091          | 1.0 |
| 3.5954        | 4.0   | 7200 | 3.6089          | 1.0 |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1