---
library_name: transformers
language:
- ur
license: apache-2.0
base_model: openai/whisper-medium
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: 27.720097349677363
---

<!-- 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 [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 19.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3564
- Wer: 27.7201

## 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: 150
- training_steps: 1500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.3965        | 0.6557 | 500  | 0.3952          | 30.0288 |
| 0.3086        | 1.3108 | 1000 | 0.3665          | 27.9635 |
| 0.2877        | 1.9666 | 1500 | 0.3564          | 27.7201 |


### Framework versions

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