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README.md
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---
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license: apache-2.0
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base_model: openai/whisper-base
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tags:
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- generated_from_trainer
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datasets:
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- common_voice_9_0
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metrics:
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- wer
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model-index:
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- name: cv9-special-batch8-base
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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_9_0
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type: common_voice_9_0
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config: id
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split: test
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args: id
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metrics:
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- name: Wer
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type: wer
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value: 23.501265240395675
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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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# cv9-special-batch8-base
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the common_voice_9_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3893
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- Wer: 23.5013
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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: 8
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- eval_batch_size: 4
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|
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| 0.4648 | 0.97 | 1000 | 0.4258 | 27.6236 |
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| 0.2992 | 1.94 | 2000 | 0.3831 | 24.4444 |
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| 0.1597 | 2.9 | 3000 | 0.3778 | 23.6163 |
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| 0.1137 | 3.87 | 4000 | 0.3793 | 23.1930 |
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| 0.0632 | 4.84 | 5000 | 0.3893 | 23.5013 |
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### Framework versions
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- Transformers 4.31.0.dev0
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- Pytorch 2.0.1+cu117
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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