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README.md
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license:
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---
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- ncc_s
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metrics:
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- wer
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model-index:
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- name: whisper-large-nob-ncc-s-lr5e-6
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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: ncc_s
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type: ncc_s
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config: 'no'
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split: validation
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args: 'no'
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metrics:
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- name: Wer
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type: wer
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value: 12.058465286236297
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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-large-nob-ncc-s-lr5e-6
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This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the ncc_s dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2784
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- Wer: 12.0585
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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: 5e-06
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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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- 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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| 0.6755 | 0.2 | 1000 | 0.3108 | 14.3118 |
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| 0.673 | 0.4 | 2000 | 0.3004 | 13.4592 |
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| 0.6378 | 0.6 | 3000 | 0.2865 | 13.0024 |
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| 0.5776 | 0.8 | 4000 | 0.2809 | 12.6675 |
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| 0.5962 | 1.0 | 5000 | 0.2784 | 12.0585 |
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### Framework versions
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- Transformers 4.26.0.dev0
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- Pytorch 1.13.0+cu117
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- Datasets 2.7.1.dev0
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- Tokenizers 0.11.0
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