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---
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language:
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- nn
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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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- norwegian-parliament
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metrics:
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- wer
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model-index:
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- name: whisper-medium-nn-v3
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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: Stortingskorpuset
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type: norwegian-parliament
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config: default
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split: validation
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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: 11.337582785573966
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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-medium-nn-v3
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Stortingskorpuset dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2116
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- Wer: 11.3376
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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: 0.0001
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 16
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|
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| 0.4413 | 0.25 | 2000 | 0.4447 | 26.7707 |
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| 0.1945 | 1.1 | 4000 | 0.3042 | 17.8344 |
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| 0.1013 | 1.35 | 6000 | 0.2421 | 14.2138 |
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| 0.0308 | 2.2 | 8000 | 0.2116 | 11.3376 |
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### Framework versions
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- Transformers 4.27.4
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- Pytorch 2.0.0+cu117
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- Datasets 2.11.0
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- Tokenizers 0.13.2
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