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update model card README.md
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README.md
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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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- fleurs
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metrics:
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- wer
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model-index:
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- name: whisper-large-v2-amet
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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: fleurs
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type: fleurs
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config: am_et
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split: validation
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args: am_et
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metrics:
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- name: Wer
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type: wer
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value: 102.94117647058823
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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-v2-amet
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This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the fleurs dataset.
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It achieves the following results on the evaluation set:
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- Loss: 12.2408
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- Wer: 102.9412
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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: 128
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- eval_batch_size: 64
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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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| 0.0 | 1000.0 | 1000 | 8.3822 | 156.0160 |
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| 0.0 | 2000.0 | 2000 | 9.7961 | 110.4278 |
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| 0.0 | 3000.0 | 3000 | 12.0014 | 102.8075 |
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| 0.0 | 4000.0 | 4000 | 12.2633 | 103.3422 |
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| 0.0 | 5000.0 | 5000 | 12.2408 | 102.9412 |
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### Framework versions
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- Transformers 4.26.0.dev0
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- Pytorch 1.13.1+cu117
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- Datasets 2.8.1.dev0
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- Tokenizers 0.13.2
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