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--- |
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library_name: transformers |
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language: |
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- sq |
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license: mit |
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base_model: openai/whisper-large-v3-turbo |
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tags: |
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- generated_from_trainer |
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datasets: |
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- Kushtrim/common_voice_19_sq |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Large V3 Turbo SQ |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: Common Voice 19.0 |
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type: Kushtrim/common_voice_19_sq |
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args: 'config: sq, split: test' |
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metrics: |
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- type: wer |
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value: 23.96274909042358 |
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name: Wer |
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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 Large V3 Turbo SQ |
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This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Common Voice 19.0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3161 |
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- Wer: 23.9627 |
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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: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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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: 500 |
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- num_epochs: 4 |
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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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| 0.5057 | 0.5112 | 500 | 0.5311 | 39.0968 | |
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| 0.3303 | 1.0225 | 1000 | 0.4321 | 34.5439 | |
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| 0.3165 | 1.5337 | 1500 | 0.3782 | 31.1893 | |
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| 0.1799 | 2.0450 | 2000 | 0.3470 | 27.7212 | |
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| 0.1945 | 2.5562 | 2500 | 0.3320 | 26.4628 | |
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| 0.1277 | 3.0675 | 3000 | 0.3235 | 24.8606 | |
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| 0.1502 | 3.5787 | 3500 | 0.3161 | 23.9627 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.1 |
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