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---
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library_name: transformers
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license: apache-2.0
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base_model: openai/whisper-tiny
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tags:
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- generated_from_trainer
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datasets:
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- common_voice_16_1
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metrics:
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- wer
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model-index:
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- name: output1
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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_16_1
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type: common_voice_16_1
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config: ko
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split: test
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args: ko
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metrics:
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- name: Wer
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type: wer
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value: 140.13953488372093
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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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# output1
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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_16_1 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0385
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- Wer: 140.1395
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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: 4000
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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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| 0.0034 | 25.0 | 1000 | 0.9055 | 100.2326 |
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| 0.001 | 50.0 | 2000 | 0.9852 | 113.7674 |
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| 0.0005 | 75.0 | 3000 | 1.0243 | 139.9070 |
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| 0.0004 | 100.0 | 4000 | 1.0385 | 140.1395 |
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### Framework versions
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- Transformers 4.45.0.dev0
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- Pytorch 2.4.1+cpu
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- Datasets 3.0.0
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- Tokenizers 0.19.1
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