---
license: apache-2.0
base_model: Talha/URDU-ASR
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-53-ur-cv13
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_13_0
      type: common_voice_13_0
      config: ur
      split: test
      args: ur
    metrics:
    - name: Wer
      type: wer
      value: 0.398777515571202
---

<!-- 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-large-xlsr-53-ur-cv13

This model is a fine-tuned version of [Talha/URDU-ASR](https://huggingface.co./Talha/URDU-ASR) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4323
- Wer: 0.3988
- Cer: 0.1932

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.8043        | 1.46  | 250  | 0.5256          | 0.4023 | 0.1949 |
| 0.5435        | 2.92  | 500  | 0.4381          | 0.3965 | 0.1961 |
| 0.4827        | 4.39  | 750  | 0.4323          | 0.3988 | 0.1932 |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1