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--- |
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language: |
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- tr |
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license: apache-2.0 |
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datasets: |
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- mozilla-foundation/common_voice_11_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Medium TR - Emre Tasar |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice 11.0 |
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type: mozilla-foundation/common_voice_11_0 |
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config: tr |
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split: test[:10%] |
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args: 'config: tr, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 18.51 |
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--- |
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# Whisper TMedium TR |
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.211673 |
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- Wer: 18.51 |
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## Model description |
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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. |
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## Intended uses & limitations |
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The model is available through its [HuggingFace web app](https://huggingface.co/spaces/emre/emre-whisper-medium-turkish-2) |
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## Training and evaluation data |
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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. |
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Weight decay showed to have slightly better result also on the evaluation dataset. |
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## Training procedure |
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After loading the pre trained model, it has been trained on the dataset. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 4000 |
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- mixed_precision_training: Native AMP |
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- weight_decay: 0.1 |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
