---
library_name: transformers
language:
- tr
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Large TR - BBS
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: tr
      split: None
      args: 'config: tr, split: test'
    metrics:
    - type: wer
      value: 13.832450369680386
      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 TR - BBS

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2229
- Wer: 13.8325

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.1339        | 0.8857 | 1000 | 0.1939          | 16.2543 |
| 0.0687        | 1.7715 | 2000 | 0.1906          | 15.3810 |
| 0.0341        | 2.6572 | 3000 | 0.1922          | 15.0570 |
| 0.0101        | 3.5430 | 4000 | 0.2055          | 14.4457 |
| 0.0025        | 4.4287 | 5000 | 0.2229          | 13.8325 |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.4