---
library_name: transformers
language:
- ur
license: apache-2.0
base_model: GogetaBlueMUI/whisper-medium-ur-v3
tags:
- generated_from_trainer
datasets:
- fsicoli/common_voice_19_0
metrics:
- wer
model-index:
- name: Whisper Medium Ur - Your Name
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 19.0
      type: fsicoli/common_voice_19_0
      config: ur
      split: test
      args: 'config: ur, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 25.0787058744725
---

<!-- 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. -->

# Whisper Medium Ur - Your Name

This model is a fine-tuned version of [GogetaBlueMUI/whisper-medium-ur-v3](https://huggingface.co/GogetaBlueMUI/whisper-medium-ur-v3) on the Common Voice 19.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3692
- Wer: 25.0787

## 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: 3e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.1648        | 0.3279 | 250  | 0.3832          | 28.1711 |
| 0.1748        | 0.6557 | 500  | 0.3737          | 30.1650 |
| 0.1887        | 0.9836 | 750  | 0.3587          | 24.8532 |
| 0.132         | 1.3108 | 1000 | 0.3692          | 25.0787 |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.5.1+cu121
- Datasets 3.4.1
- Tokenizers 0.21.0