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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: openai/whisper-small
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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: ps_af
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split: test
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args: ps_af
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metrics:
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- name: Wer
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type: wer
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value: 89.14951573849879
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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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# openai/whisper-small
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the fleurs dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.3860
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- Wer: 89.1495
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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: 3e-07
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- train_batch_size: 32
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 64
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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: 10
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- training_steps: 400
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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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| 1.9754 | 14.29 | 100 | 1.9261 | 240.8596 |
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| 1.5323 | 28.57 | 200 | 1.5718 | 168.5608 |
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| 1.338 | 42.86 | 300 | 1.4249 | 96.6480 |
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| 1.282 | 57.14 | 400 | 1.3860 | 89.1495 |
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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 0.13.2
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