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--- |
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language: |
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- sr |
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license: apache-2.0 |
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base_model: openai/whisper-large-v3 |
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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_13_0 |
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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 Large v3 Sr |
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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 13 |
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type: mozilla-foundation/common_voice_13_0 |
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config: sr |
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split: test |
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args: sr |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.05560382276281494 |
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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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# UPDATE |
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Use an updated fine tunned version [Sagicc/whisper-large-v3-sr-cmb](https://huggingface.co/Sagicc/whisper-large-v3-sr-cmb) with new 50+ hours of dataset. |
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# Whisper Large v3 Sr |
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on Serbian Mozilla/Common Voice 13 and Google/Fleurs datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1628 |
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- Wer Ortho: 0.1635 |
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- Wer: 0.0556 |
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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: 2 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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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: 50 |
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- training_steps: 1500 |
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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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| 0.0567 | 1.34 | 500 | 0.1512 | 0.1676 | 0.0717 | |
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| 0.0256 | 2.67 | 1000 | 0.1482 | 0.1585 | 0.0610 | |
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| 0.0114 | 4.01 | 1500 | 0.1628 | 0.1635 | 0.0556 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
