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README.md
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---
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library_name: transformers
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language:
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- dv
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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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- google/fleurs
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metrics:
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- wer
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model-index:
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- name: Whisper Small Dv - 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: fleurs_voice
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type: google/fleurs
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metrics:
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- name: Wer
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type: wer
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value: 33.078149920255186
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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 Dv - 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 fleurs_voice dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6240
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- Wer Ortho: 34.0069
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- Wer: 33.0781
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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: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: constant_with_warmup
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- lr_scheduler_warmup_steps: 50
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- training_steps: 500
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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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| 0.1015 | 3.4722 | 500 | 0.6240 | 34.0069 | 33.0781 |
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### Framework versions
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- Transformers 5.12.0
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- Pytorch 2.11.0+cu128
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- Datasets 4.0.0
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- Tokenizers 0.22.2
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