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This model is a fine-tuned version of openai/whisper-small on the EmoTa (TamilSER-DB) dataset. It achieves the following results on the evaluation set: Loss: 3.0859 Accuracy: 0.2556 F1 Macro: 0.2109
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This model is a fine-tuned version of openai/whisper-small on the EmoTa (TamilSER-DB) dataset. It achieves the following results on the evaluation set:
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1 fonteVerificado 7 de ago.
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|---|---|---|---|---|---|
| No log | 1.0 | 24 | 3.3423 | 0.1944 | 0.0664 |
| 3.2189 | 2.0 | 48 | 3.2316 | 0.2444 | 0.1409 |
| 3.1391 | 3.0 | 72 | 3.1735 | 0.2333 | 0.1118 |
| 3.0224 | 4.0 | 96 | 3.1014 | 0.2556 | 0.1987 |
| 2.9275 |
--- library_name: transformers language: - ta license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - accuracy model-index: - name: whisper-small-ta-emotion results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-ta-emotion This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the EmoTa (TamilSER-DB) dataset. It achieves the following results on the evaluation set: - Loss: 3.0859 - Accuracy: 0.2556 - F1 Macro: 0.2109 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 0.1 - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | No log | 1.0 | 24 | 3.3423 | 0.1944 | 0.0664 | | 3.2189 | 2.0 | 48 | 3.2316 | 0.2444 | 0.1409 | | 3.1391 | 3.0 | 72 | 3.1735 | 0.2333 | 0.1118 | | 3.0224 | 4.0 | 96 | 3.1014 | 0.2556 | 0.1987 | | 2.9275 | 5.0 | 120 | 3.2727 | 0.1889 | 0.1405 | | 2.8341 | 6.0 | 144 | 3.0972 | 0.2611 | 0.1392 | | 2.7619 | 7.0 | 168 | 3.0267 | 0.2 | 0.1597 | | 2.7071 | 8.0 | 192 | 3.0764 | 0.2444 | 0.2072 | | 2.6381 | 9.0 | 216 | 3.0706 | 0.2278 | 0.1613 | | 2.5599 | 10.0 | 240 | 3.1087 | 0.25 | 0.1716 | | 2.5374 | 11.0 | 264 | 3.0905 | 0.2611 | 0.2099 | | 2.4927 | 12.0 | 288 | 3.0859 | 0.2556 | 0.2109 | ### Framework versions - Transformers 5.12.1 - Pytorch 2.10.0+cu128 - Datasets 5.0.0 - Tokenizers 0.22.2
Source context: 7 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo Venky0411/whisper-small-ta-emotion
| 5.0 |
| 120 |
| 3.2727 |
| 0.1889 |
| 0.1405 |
| 2.8341 | 6.0 | 144 | 3.0972 | 0.2611 | 0.1392 |
| 2.7619 | 7.0 | 168 | 3.0267 | 0.2 | 0.1597 |
| 2.7071 | 8.0 | 192 | 3.0764 | 0.2444 | 0.2072 |
| 2.6381 | 9.0 | 216 | 3.0706 | 0.2278 | 0.1613 |
| 2.5599 | 10.0 | 240 | 3.1087 | 0.25 | 0.1716 |
| 2.5374 | 11.0 | 264 | 3.0905 | 0.2611 | 0.2099 |
| 2.4927 | 12.0 | 288 | 3.0859 | 0.2556 | 0.2109 |
