README.md

---
language: 
- ur

license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
- cer
model-index:
- name: wav2vec2-urdu-V8-Abid
  results:
  - task: 
      type: automatic-speech-recognition  # Required. Example: automatic-speech-recognition
      name: Speech Recognition  # Optional. Example: Speech Recognition
    dataset:
      type: common_voice  # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
      name: Common Voice ur  # Required. Example: Common Voice zh-CN
      args: ur         # Optional. Example: zh-CN
    metrics:
      - type: wer    # Required. Example: wer
        value: 47.41 # Required. Example: 20.90
        name: Test WER    # Optional. Example: Test WER
        args: 
        - learning_rate: 0.00007
        - train_batch_size: 64
        - eval_batch_size: 8
        - seed: 42
        - gradient_accumulation_steps: 4
        - total_train_batch_size: 128
        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
        - lr_scheduler_type: linear
        - lr_scheduler_warmup_steps: 100
        - num_epochs: 100
        - mixed_precision_training: Native AMP        # Optional. Example for BLEU: max_order
      - type: cer    # Required. Example: wer
        value: 25.01  # Required. Example: 20.90
        name: Test CER    # Optional. Example: Test WER
        args: 
        - learning_rate: 0.00007
        - train_batch_size: 64
        - eval_batch_size: 8
        - seed: 42
        - gradient_accumulation_steps: 4
        - total_train_batch_size: 128
        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
        - lr_scheduler_type: linear
        - lr_scheduler_warmup_steps: 100
        - num_epochs: 100
        - mixed_precision_training: Native AMP

---

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# wav2vec2-60-Urdu-V8

This model is a fine-tuned version of [kingabzpro/wav2vec2-urdu](https://huggingface.co./kingabzpro/wav2vec2-urdu) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9192
- Wer: 0.4741
- Cer: 0.2504


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 2.8836        | 16.62 | 50   | 4.7827          | 0.5011 | 0.2625 |
| 0.6992        | 33.31 | 100  | 3.5358          | 0.4882 | 0.2537 |
| 0.6321        | 49.92 | 150  | 4.9054          | 0.4774 | 0.2519 |
| 0.4669        | 66.62 | 200  | 5.9508          | 0.4719 | 0.2513 |
| 0.3119        | 83.31 | 250  | 5.5791          | 0.4745 | 0.2508 |
| 0.2788        | 99.92 | 300  | 4.9192          | 0.4741 | 0.2504 |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0