---
language: 
- ur

license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-53-urdu
  results:
  - task: 
      type: automatic-speech-recognition  # Required. Example: automatic-speech-recognition
      name: Urdu Speech Recognition  # Optional. Example: Speech Recognition
    dataset:
      type: common_voice  # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
      name: Urdu  # Required. Example: Common Voice zh-CN
      args: ur         # Optional. Example: zh-CN
    metrics:
      - type: wer    # Required. Example: wer
        value: 100  # Required. Example: 20.90
        name: Test WER    # Optional. Example: Test WER
        args: 
        - learning_rate: 0.0003
        - 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: 10
        - num_epochs: 30
        - mixed_precision_training: Native AMP         # Optional. Example for BLEU: max_order
---

<!-- 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-urdu

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co./facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6772
- 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: 0.0003
- 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: 10
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 11.1125       | 3.33  | 40   | 3.2875          | 1.0 |
| 3.2077        | 6.67  | 80   | 3.1499          | 1.0 |
| 3.1725        | 10.0  | 120  | 3.1484          | 1.0 |
| 3.148         | 13.33 | 160  | 3.0948          | 1.0 |
| 3.1098        | 16.67 | 200  | 3.0897          | 1.0 |
| 3.085         | 20.0  | 240  | 3.0609          | 1.0 |
| 3.0315        | 23.33 | 280  | 2.9636          | 1.0 |
| 2.9038        | 26.67 | 320  | 2.7838          | 1.0 |
| 2.7599        | 30.0  | 360  | 2.6772          | 1.0 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3