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--- |
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library_name: transformers |
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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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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: speech-emotion-recognition-with-openai-whisper-large-v3 |
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results: [] |
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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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# speech-emotion-recognition-with-openai-whisper-large-v3 |
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the [RAVDESS](https://zenodo.org/records/1188976#.XsAXemgzaUk), [SAVEE](https://www.kaggle.com/datasets/ejlok1/surrey-audiovisual-expressed-emotion-savee/data), [TESS](https://tspace.library.utoronto.ca/handle/1807/24487), and [URDU](https://www.kaggle.com/datasets/bitlord/urdu-language-speech-dataset) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5008 |
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- Accuracy: 0.9199 |
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- Precision: 0.9230 |
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- Recall: 0.9199 |
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- F1: 0.9198 |
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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: 5e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 5 |
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- total_train_batch_size: 10 |
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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_ratio: 0.1 |
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- num_epochs: 25 |
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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 | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.4948 | 0.9995 | 394 | 0.4911 | 0.8286 | 0.8449 | 0.8286 | 0.8302 | |
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| 0.6271 | 1.9990 | 788 | 0.5307 | 0.8225 | 0.8559 | 0.8225 | 0.8277 | |
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| 0.2364 | 2.9985 | 1182 | 0.5076 | 0.8692 | 0.8727 | 0.8692 | 0.8684 | |
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| 0.0156 | 3.9980 | 1576 | 0.5669 | 0.8732 | 0.8868 | 0.8732 | 0.8745 | |
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| 0.2305 | 5.0 | 1971 | 0.4578 | 0.9108 | 0.9142 | 0.9108 | 0.9114 | |
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| 0.0112 | 5.9995 | 2365 | 0.4701 | 0.9108 | 0.9159 | 0.9108 | 0.9114 | |
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| 0.0013 | 6.9990 | 2759 | 0.5232 | 0.9138 | 0.9204 | 0.9138 | 0.9137 | |
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| 0.1894 | 7.9985 | 3153 | 0.5008 | 0.9199 | 0.9230 | 0.9199 | 0.9198 | |
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| 0.0877 | 8.9980 | 3547 | 0.5517 | 0.9138 | 0.9152 | 0.9138 | 0.9138 | |
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| 0.1471 | 10.0 | 3942 | 0.5856 | 0.8895 | 0.9002 | 0.8895 | 0.8915 | |
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| 0.0026 | 10.9995 | 4336 | 0.8334 | 0.8773 | 0.8949 | 0.8773 | 0.8770 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.0 |
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- Tokenizers 0.19.1 |
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