---
language:
- tr
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Large V3 tr Fleurs - Chee Li
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Google Fleurs
      type: google/fleurs
      config: tr_tr
      split: None
      args: 'config: tr split: test'
    metrics:
    - name: Wer
      type: wer
      value: 649.9222153080274
---

<!-- 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 tr Fleurs - Chee Li

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the Google Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1432
- Wer: 649.9222

## 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      |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.0466        | 2.7933  | 500  | 0.1060          | 147.9932 |
| 0.006         | 5.5866  | 1000 | 0.1208          | 481.1605 |
| 0.0017        | 8.3799  | 1500 | 0.1291          | 602.0769 |
| 0.0012        | 11.1732 | 2000 | 0.1288          | 627.3647 |
| 0.0002        | 13.9665 | 2500 | 0.1382          | 641.4203 |
| 0.0001        | 16.7598 | 3000 | 0.1411          | 647.7520 |
| 0.0001        | 19.5531 | 3500 | 0.1426          | 642.9294 |
| 0.0001        | 22.3464 | 4000 | 0.1432          | 649.9222 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1