---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-small-fa
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: fa
      split: test
      args: fa
    metrics:
    - name: Wer
      type: wer
      value: 35.497333642476235
---

<!-- 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-fa

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9258
- Wer: 35.4973

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 100000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step   | Validation Loss | Wer     |
|:-------------:|:-------:|:------:|:---------------:|:-------:|
| 0.0193        | 8.1103  | 20000  | 0.5349          | 36.7125 |
| 0.0046        | 16.2206 | 40000  | 0.6839          | 36.0033 |
| 0.0018        | 24.3309 | 60000  | 0.7936          | 36.2543 |
| 0.0003        | 32.4412 | 80000  | 0.8729          | 35.9406 |
| 0.0           | 40.5515 | 100000 | 0.9258          | 35.4973 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0