---
language:
- tr
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17
metrics:
- wer
model-index:
- name: 'Whisper Large v2 TR '
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 13
      type: mozilla-foundation/common_voice_17
      config: tr
      split: None
      args: tr
    metrics:
    - name: Wer
      type: wer
      value: 9.018929438770417
---

<!-- 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 v2 TR 

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1568
- Wer: 9.0189

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.1437        | 0.9997 | 1450 | 0.1550          | 9.9787 |
| 0.0766        | 2.0    | 2901 | 0.1470          | 9.3616 |
| 0.0371        | 2.9990 | 4350 | 0.1568          | 9.0189 |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.17.1
- Tokenizers 0.19.1