---
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: w2v2-bert-urdu
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: ur
      split: test[:100]
      args: ur
    metrics:
    - type: wer
      value: 0.3300546448087432
      name: Wer
---

<!-- 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. -->

# w2v2-bert-urdu

This model was trained from scratch on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4246
- Wer: 0.3301

## 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: 5e-06
- 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: 100
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.8145        | 0.1695 | 50   | 0.4620          | 0.3421 |
| 0.4364        | 0.3390 | 100  | 0.3969          | 0.2874 |
| 0.418         | 0.5085 | 150  | 0.3697          | 0.2820 |
| 0.402         | 0.6780 | 200  | 0.3627          | 0.2842 |
| 0.3698        | 0.8475 | 250  | 0.3314          | 0.2710 |
| 0.3779        | 1.0169 | 300  | 0.3292          | 0.2852 |
| 0.3167        | 1.1864 | 350  | 0.3230          | 0.2820 |
| 0.3578        | 1.3559 | 400  | 0.3825          | 0.2940 |
| 0.4189        | 1.5254 | 450  | 0.4225          | 0.3104 |
| 0.4803        | 1.6949 | 500  | 0.4248          | 0.3311 |
| 0.4612        | 1.8644 | 550  | 0.4246          | 0.3301 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1