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---
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base_model: openai/whisper-small
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datasets:
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- common_voice_17_0
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library_name: transformers
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license: apache-2.0
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metrics:
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- wer
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tags:
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- generated_from_trainer
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model-index:
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- name: whisper-small-tr
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results:
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- task:
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type: automatic-speech-recognition
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name: Automatic Speech Recognition
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dataset:
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name: common_voice_17_0
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type: common_voice_17_0
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config: tr
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split: test
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args: tr
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metrics:
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- type: wer
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value: 20.088563399472026
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name: Wer
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# whisper-small-tr
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_17_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2316
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- Wer: 20.0886
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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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: 5000
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:------:|:----:|:---------------:|:-------:|
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| 0.2284 | 0.3447 | 1000 | 0.2814 | 23.9819 |
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| 0.1906 | 0.6894 | 2000 | 0.2606 | 22.5598 |
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| 0.0945 | 1.0341 | 3000 | 0.2472 | 21.1990 |
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| 0.0871 | 1.3788 | 4000 | 0.2405 | 20.6744 |
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| 0.0823 | 1.7235 | 5000 | 0.2316 | 20.0886 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.0+cu124
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- Datasets 3.0.0
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- Tokenizers 0.19.1
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