Create README.md
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README.md
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---
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library_name: transformers
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license: mit
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base_model: openai/whisper-large-v3-turbo
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tags:
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- generated_from_trainer
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datasets:
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- fsicoli/common_voice_18_0
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metrics:
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- wer
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model-index:
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- name: Whisper Turbo Train
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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 18.0
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type: fsicoli/common_voice_18_0
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split: None
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metrics:
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- name: Wer
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type: wer
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value: 15.246076710047603
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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 Turbo Train
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This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Common Voice 18.0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1156
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- Wer: 15.2461
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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: 32
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- eval_batch_size: 32
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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: 1000
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- training_steps: 8000
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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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| 0.3715 | 0.4257 | 1000 | 0.3457 | 40.4692 |
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| 0.251 | 0.8514 | 2000 | 0.2181 | 27.7065 |
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| 0.1569 | 1.2771 | 3000 | 0.1814 | 24.1533 |
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| 0.1436 | 1.7029 | 4000 | 0.1531 | 20.3812 |
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| 0.0931 | 2.1286 | 5000 | 0.1374 | 18.4662 |
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| 0.0891 | 2.5543 | 6000 | 0.1252 | 16.9349 |
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| 0.0738 | 2.9800 | 7000 | 0.1199 | 15.5610 |
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| 0.0544 | 3.4057 | 8000 | 0.1156 | 15.2461 |
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### Framework versions
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- Transformers 4.45.1
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- Pytorch 2.1.0
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- Datasets 3.0.1
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- Tokenizers 0.20.0
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