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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-batch12-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: 23.92914653784219
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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-batch12-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: 0.4240
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- Wer: 23.9291
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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: 12
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- eval_batch_size: 6
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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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| 0.3536 | 1.45 | 1000 | 0.4083 | 26.1882 |
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| 0.2171 | 2.9 | 2000 | 0.3794 | 24.4813 |
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| 0.0604 | 4.35 | 3000 | 0.3954 | 24.5595 |
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| 0.0531 | 5.81 | 4000 | 0.4079 | 23.7727 |
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| 0.0245 | 7.26 | 5000 | 0.4240 | 23.9291 |
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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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