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update model card README.md
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README.md
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---
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language:
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- pt
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license: apache-2.0
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tags:
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- whisper-event
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- generated_from_trainer
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datasets:
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- mozilla-foundation/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: Whisper Tiny PT
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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: mozilla-foundation/common_voice_11_0
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config: pt
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split: test
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args: pt
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metrics:
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- name: Wer
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type: wer
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value: 29.984364755131892
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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 Tiny PT
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) 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: 0.6077
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- Wer: 29.9844
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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: 64
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- eval_batch_size: 8
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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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| 0.4143 | 1.04 | 500 | 0.5325 | 32.7399 |
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| 0.2693 | 3.03 | 1000 | 0.4718 | 29.4867 |
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| 0.1724 | 5.01 | 1500 | 0.4758 | 28.7218 |
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| 0.0849 | 7.0 | 2000 | 0.5070 | 29.2211 |
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| 0.0659 | 8.04 | 2500 | 0.5223 | 29.3169 |
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| 0.0539 | 10.03 | 3000 | 0.5402 | 30.1458 |
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| 0.0376 | 12.02 | 3500 | 0.5755 | 29.9995 |
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| 0.0217 | 14.0 | 4000 | 0.6067 | 29.6565 |
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| 0.0168 | 15.04 | 4500 | 0.6082 | 29.8162 |
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| 0.0205 | 17.03 | 5000 | 0.6077 | 29.9844 |
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### Framework versions
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- Transformers 4.26.0.dev0
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- Pytorch 1.13.0+cu116
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- Datasets 2.7.1.dev0
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- Tokenizers 0.13.2
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