---
language:
- tr
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Medium Tr - Can K V2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 17.0
      type: mozilla-foundation/common_voice_17_0
      config: tr
      split: test
      args: 'config: tr, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 15.4185472196202
---

<!-- 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 Medium Tr - Can K V2

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2285
- Wer: 15.4185

## 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: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 12000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.203         | 0.3448 | 1000  | 0.2255          | 19.4192 |
| 0.1602        | 0.6895 | 2000  | 0.2142          | 18.0448 |
| 0.0814        | 1.0343 | 3000  | 0.2087          | 17.5338 |
| 0.0761        | 1.3791 | 4000  | 0.2060          | 17.1558 |
| 0.0734        | 1.7238 | 5000  | 0.1998          | 16.5052 |
| 0.0335        | 2.0686 | 6000  | 0.2073          | 16.7283 |
| 0.0344        | 2.4134 | 7000  | 0.2066          | 15.9091 |
| 0.0338        | 2.7581 | 8000  | 0.2023          | 15.3709 |
| 0.0099        | 3.1029 | 9000  | 0.2211          | 15.6331 |
| 0.0097        | 3.4477 | 10000 | 0.2254          | 15.6008 |
| 0.0096        | 3.7924 | 11000 | 0.2254          | 15.3334 |
| 0.0022        | 4.1372 | 12000 | 0.2285          | 15.4185 |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1