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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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- PolyAI/minds14
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metrics:
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- wer
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model-index:
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- name: whisper-tiny-en-finetune-minds14
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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: PolyAI/minds14
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type: PolyAI/minds14
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config: en-US
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split: train[450:]
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args: en-US
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metrics:
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- name: Wer
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type: wer
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value: 0.3382526564344746
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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-en-finetune-minds14
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6541
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- Wer Ortho: 0.3399
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- Wer: 0.3383
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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: 8
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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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: 50
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- training_steps: 1000
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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 Ortho | Wer |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
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| 0.3136 | 3.57 | 100 | 0.4883 | 0.3640 | 0.3524 |
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| 0.0417 | 7.14 | 200 | 0.5146 | 0.3560 | 0.3442 |
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| 0.0066 | 10.71 | 300 | 0.5736 | 0.3411 | 0.3353 |
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| 0.0017 | 14.29 | 400 | 0.6040 | 0.3455 | 0.3418 |
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| 0.0013 | 17.86 | 500 | 0.6226 | 0.3393 | 0.3365 |
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| 0.0009 | 21.43 | 600 | 0.6352 | 0.3393 | 0.3365 |
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| 0.0007 | 25.0 | 700 | 0.6436 | 0.3399 | 0.3371 |
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| 0.0006 | 28.57 | 800 | 0.6492 | 0.3399 | 0.3383 |
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| 0.0006 | 32.14 | 900 | 0.6530 | 0.3399 | 0.3383 |
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| 0.0006 | 35.71 | 1000 | 0.6541 | 0.3399 | 0.3383 |
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### Framework versions
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- Transformers 4.29.2
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- Pytorch 1.13.1+cu117
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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