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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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- audiofolder
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metrics:
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- wer
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model-index:
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- name: whisper-base-300v2
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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: audiofolder
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type: audiofolder
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config: default
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split: test
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args: default
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metrics:
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- name: Wer
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type: wer
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value: 86.48648648648648
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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-base-300v2
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0748
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- Wer Ortho: 86.4865
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- Wer: 86.4865
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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: 16
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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: constant_with_warmup
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- lr_scheduler_warmup_steps: 30
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- training_steps: 300
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
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| 0.129 | 20.0 | 60 | 1.0256 | 72.9730 | 72.9730 |
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| 0.0005 | 40.0 | 120 | 1.0519 | 81.0811 | 81.0811 |
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| 0.0001 | 60.0 | 180 | 1.0650 | 86.4865 | 86.4865 |
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| 0.0001 | 80.0 | 240 | 1.0748 | 86.4865 | 86.4865 |
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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