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---
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library_name: transformers
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language:
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- ug
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license: apache-2.0
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base_model: openai/whisper-small
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tags:
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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 Small ug - Sanchit Gandhi
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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
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type: mozilla-foundation/common_voice_11_0
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config: ug
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split: test
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args: ug
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metrics:
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- name: Wer
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type: wer
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value: 34.25947275382369
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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 Small ug - Sanchit Gandhi
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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 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3966
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- Wer Ortho: 39.5518
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- Wer: 34.2595
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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: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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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: constant_with_warmup
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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 Ortho | Wer |
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|:-------------:|:-------:|:----:|:---------------:|:---------:|:-------:|
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| 0.0842 | 3.9216 | 1000 | 0.2856 | 44.0534 | 37.9371 |
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| 0.0173 | 7.8431 | 2000 | 0.3364 | 40.8646 | 34.9089 |
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| 0.0087 | 11.7647 | 3000 | 0.3656 | 39.9169 | 34.3824 |
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| 0.0065 | 15.6863 | 4000 | 0.3966 | 39.5518 | 34.2595 |
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| 0.0068 | 19.6078 | 5000 | 0.3997 | 39.5790 | 34.3210 |
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### Framework versions
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- Transformers 4.45.2
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- Pytorch 1.12.0+cu113
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- Datasets 3.0.2
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- Tokenizers 0.20.1
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