---
language:
  - sr
license: apache-2.0
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_16_1
metrics:
  - wer
base_model: openai/whisper-large-v3
model-index:
  - name: Whisper Large v3 Sr - Slavko Djogic
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Common Voice 16.1
          type: mozilla-foundation/common_voice_16_1
          args: 'Config: sr'
        metrics:
          - type: wer
            value: 17.2694
            name: Wer
---

<!-- 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-large-v3-sr

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3961
- Wer: 17.2694

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0498        | 4.81  | 1000 | 0.2004          | 20.1799 |
| 0.0042        | 9.62  | 2000 | 0.3225          | 18.2395 |
| 0.0001        | 14.42 | 3000 | 0.3799          | 17.2694 |
| 0.0001        | 19.23 | 4000 | 0.3961          | 17.2694 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1