---
language:
- ur
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Tiny Ur - 3
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 17.0
      type: mozilla-foundation/common_voice_17_0
      config: ur
      split: None
      args: 'config: ur, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 16.033947800693557
pipeline_tag: automatic-speech-recognition
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Tiny Ur - 3

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1247
- Wer: 16.0339

## Model description

Whisper Tiny Urdu ASR Model
This Whisper Tiny model has been fine-tuned on the Common Voice 17 dataset, which includes over 55 hours of Urdu speech data. The model was trained twice to optimize its performance:

First Training: The model was trained on the training set and evaluated on the test set for 20 epochs.
Second Training: The model was retrained on the combined train and validation sets, with the test set used for validation, also for 20 epochs.
Despite being the smallest variant in its family, this model achieves state-of-the-art performance for Urdu ASR tasks. It is specifically designed for deployment on small devices, offering an excellent balance between efficiency and accuracy.

Intended Use:


## Intended uses & limitations

This model is particularly suited for applications on edge devices with limited computational resources. Additionally, it can be converted to a FasterWhisper model using the CTranslate2 library, allowing for even faster inference on devices with lower processing power.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0057        | 10.1351 | 1500 | 0.1443          | 18.1511 |
| 0.0005        | 20.2703 | 3000 | 0.1247          | 16.0339 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1