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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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- common_voice_11_0
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metrics:
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- wer
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model-index:
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- name: output_dir
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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: common_voice_11_0
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type: common_voice_11_0
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config: rw
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split: test
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args: rw
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metrics:
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- name: Wer
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type: wer
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value: 43.75236083594792
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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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# output_dir
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_11_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6424
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- Wer: 43.7524
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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: 20
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- eval_batch_size: 20
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 2
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- total_train_batch_size: 40
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- total_eval_batch_size: 40
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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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| 0.7471 | 0.04 | 1000 | 0.9044 | 59.2903 |
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| 0.5987 | 0.08 | 2000 | 0.7523 | 52.0232 |
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| 0.5168 | 0.12 | 3000 | 0.6890 | 47.7610 |
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| 0.5082 | 0.16 | 4000 | 0.6596 | 45.4013 |
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| 0.4748 | 0.2 | 5000 | 0.6424 | 43.7524 |
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### Framework versions
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- Transformers 4.26.0.dev0
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- Pytorch 1.13.1+cu116
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- Datasets 2.7.1.dev0
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- Tokenizers 0.13.2
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