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--- |
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library_name: transformers |
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language: |
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- ur |
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license: apache-2.0 |
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base_model: GogetaBlueMUI/whisper-medium-ur-v3 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- fsicoli/common_voice_19_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Medium Ur - Your Name |
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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 19.0 |
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type: fsicoli/common_voice_19_0 |
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config: ur |
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split: test |
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args: 'config: ur, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 25.0787058744725 |
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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 Ur - Your Name |
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This model is a fine-tuned version of [GogetaBlueMUI/whisper-medium-ur-v3](https://huggingface.co/GogetaBlueMUI/whisper-medium-ur-v3) 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: 0.3692 |
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- Wer: 25.0787 |
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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: 3e-06 |
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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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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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 | |
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|:-------------:|:------:|:----:|:---------------:|:-------:| |
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| 0.1648 | 0.3279 | 250 | 0.3832 | 28.1711 | |
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| 0.1748 | 0.6557 | 500 | 0.3737 | 30.1650 | |
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| 0.1887 | 0.9836 | 750 | 0.3587 | 24.8532 | |
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| 0.132 | 1.3108 | 1000 | 0.3692 | 25.0787 | |
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### Framework versions |
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- Transformers 4.49.0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.4.1 |
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- Tokenizers 0.21.0 |
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