README.md

---
language: 
- ur

license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
datasets:
- common_voice
metrics:
- wer
- cer
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: 66.2  # 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: 200
        - num_epochs: 50
        - mixed_precision_training: Native AMP         # Optional. Example for BLEU: max_order
      - type: cer    # Required. Example: wer
        value: 31.7  # Required. Example: 20.90
        name: Test CER    # 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: 200
        - num_epochs: 50
        - mixed_precision_training: Native AMP         # Optional. Example for BLEU: max_order
---

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# wav2vec2-large-xlsr-53-urdu

This model is a fine-tuned version of [m3hrdadfi/wav2vec2-large-xlsr-persian-v3](https://huggingface.co./m3hrdadfi/wav2vec2-large-xlsr-persian-v3) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5727
- Wer: 0.6620
- Cer: 0.3166


More information needed
The training and valid dataset is 0.58 hours. It was hard to train any model on lower number of so I decided to take Persian checkpoint and finetune the XLSR model.  

## Training procedure
Trained on m3hrdadfi/wav2vec2-large-xlsr-persian-v3 due to lesser number of samples. Persian and Urdu are quite similar. 

### 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: 200
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 2.9707        | 8.33  | 100  | 1.2689          | 0.8463 | 0.4373 |
| 0.746         | 16.67 | 200  | 1.2370          | 0.7214 | 0.3486 |
| 0.3719        | 25.0  | 300  | 1.3885          | 0.6908 | 0.3381 |
| 0.2411        | 33.33 | 400  | 1.4780          | 0.6690 | 0.3186 |
| 0.1841        | 41.67 | 500  | 1.5557          | 0.6629 | 0.3241 |
| 0.165         | 50.0  | 600  | 1.5727          | 0.6620 | 0.3166 |


### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3