---
library_name: transformers
language:
- ps
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- pairsys/open_asr
metrics:
- wer
model-index:
- name: Whisper Small Pashto
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Open ASR
      type: pairsys/open_asr
      args: 'config: pashto'
    metrics:
    - name: Wer
      type: wer
      value: 34.475374732334046
---

<!-- 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 Small Pashto

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Open ASR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7846
- Wer: 34.4754

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.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: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0112        | 17.8571 | 1000 | 0.6265          | 38.1462 |
| 0.0023        | 35.7143 | 2000 | 0.7230          | 35.0260 |
| 0.0006        | 53.5714 | 3000 | 0.7555          | 34.7201 |
| 0.0001        | 71.4286 | 4000 | 0.7708          | 34.9342 |
| 0.0001        | 89.2857 | 5000 | 0.7846          | 34.4754 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3