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---
language:
- tr
license: apache-2.0
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
base_model: openai/whisper-medium
model-index:
- name: Whisper Medium TR - Emre Tasar
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: test[:10%]
args: 'config: tr, split: test'
metrics:
- type: wer
value: 18.51
name: Wer
---
# Whisper Medium TR
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.211673
- Wer: 18.51
## Model description
This model is the openai whisper medium transformer adapted for Turkish audio to text transcription. This model has weight decay set to 0.1 to cope with overfitting.
## Intended uses & limitations
The model is available through its [HuggingFace web app](https://huggingface.co/spaces/emre/emre-whisper-medium-turkish-2)
## Training and evaluation data
Data used for training is the initial 10% of train and validation of [Turkish Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/tr/train) 11.0 from Mozilla Foundation.
Weight decay showed to have slightly better result also on the evaluation dataset.
## Training procedure
After loading the pre trained model, it has been trained on the dataset.
### 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
- weight_decay: 0.1
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2 |