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README.md
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---
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language:
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- uz
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license: apache-2.0
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base_model: openai/whisper-medium
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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_17_0
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metrics:
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- wer
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model-index:
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- name: Whisper Medium UZB
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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 17.0
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type: mozilla-foundation/common_voice_17_0
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config: uz
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split: None
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args: 'config: uz, split: test'
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metrics:
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- name: Wer
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type: wer
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value: 31.77905998468049
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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 Medium UZB
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 17.0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2859
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- Wer: 31.7790
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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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Founder: Rifat Mamayusupov
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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: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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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: 4000
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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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| 0.5187 | 0.5392 | 1000 | 0.4935 | 44.1403 |
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| 0.3423 | 1.0785 | 2000 | 0.4008 | 37.6948 |
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| 0.3018 | 1.6177 | 3000 | 0.3739 | 36.3575 |
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| 0.2401 | 2.1569 | 4000 | 0.2821 | 31.7791 |
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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