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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- audiofolder |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: wav2vec2-base-Toronto_emotional_speech_set |
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results: [] |
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language: |
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- en |
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pipeline_tag: audio-classification |
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--- |
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# wav2vec2-base-Toronto_emotional_speech_set |
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4925 |
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- Accuracy: 0.8804 |
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- Weighted f1: 0.8837 |
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- Micro f1: 0.8804 |
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- Macro f1: 0.8822 |
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- Weighted recall: 0.8804 |
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- Micro recall: 0.8804 |
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- Macro recall: 0.8757 |
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- Weighted precision: 0.9044 |
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- Micro precision: 0.8804 |
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- Macro precision: 0.9059 |
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## Model description |
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This model classifies the emotion when someone speaks in audio sample. |
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For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Audio-Projects/Emotion%20Detection/Toronto%20Emotional%20Speech%20Set%20(TESS)/Toronto%20Emotional%20Speech%20Set%20(TESS).ipynb |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://www.kaggle.com/datasets/ejlok1/toronto-emotional-speech-set-tess |
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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-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
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| 1.9517 | 0.97 | 17 | 1.9432 | 0.2411 | 0.1338 | 0.2411 | 0.1201 | 0.2411 | 0.2411 | 0.2168 | 0.1161 | 0.2411 | 0.1049 | |
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| 1.9517 | 2.0 | 35 | 1.9036 | 0.3375 | 0.3037 | 0.3375 | 0.3082 | 0.3375 | 0.3375 | 0.3533 | 0.5364 | 0.3375 | 0.5379 | |
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| 1.9517 | 2.97 | 52 | 1.6629 | 0.4518 | 0.4020 | 0.4518 | 0.3936 | 0.4518 | 0.4518 | 0.4503 | 0.6751 | 0.4518 | 0.6555 | |
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| 1.9517 | 4.0 | 70 | 1.2026 | 0.7357 | 0.7121 | 0.7357 | 0.6989 | 0.7357 | 0.7357 | 0.7240 | 0.7903 | 0.7357 | 0.7848 | |
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| 1.9517 | 4.97 | 87 | 0.8458 | 0.8839 | 0.8796 | 0.8839 | 0.8767 | 0.8839 | 0.8839 | 0.8845 | 0.8874 | 0.8839 | 0.8807 | |
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| 1.9517 | 6.0 | 105 | 0.6493 | 0.8946 | 0.8939 | 0.8946 | 0.8914 | 0.8946 | 0.8946 | 0.8937 | 0.9049 | 0.8946 | 0.9014 | |
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| 1.9517 | 6.97 | 122 | 0.5149 | 0.9089 | 0.9046 | 0.9089 | 0.8989 | 0.9089 | 0.9089 | 0.8957 | 0.9275 | 0.9089 | 0.9327 | |
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| 1.9517 | 8.0 | 140 | 0.3814 | 0.9536 | 0.9531 | 0.9536 | 0.9501 | 0.9536 | 0.9536 | 0.9474 | 0.9577 | 0.9536 | 0.9583 | |
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| 1.9517 | 8.97 | 157 | 0.5627 | 0.85 | 0.8459 | 0.85 | 0.8402 | 0.85 | 0.85 | 0.8378 | 0.9100 | 0.85 | 0.9160 | |
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| 1.9517 | 10.0 | 175 | 0.4702 | 0.8911 | 0.8861 | 0.8911 | 0.8854 | 0.8911 | 0.8911 | 0.8938 | 0.9021 | 0.8911 | 0.8967 | |
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| 1.9517 | 10.97 | 192 | 0.3362 | 0.9393 | 0.9376 | 0.9393 | 0.9361 | 0.9393 | 0.9393 | 0.9399 | 0.9402 | 0.9393 | 0.9365 | |
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| 1.9517 | 12.0 | 210 | 0.3808 | 0.9179 | 0.9181 | 0.9179 | 0.9176 | 0.9179 | 0.9179 | 0.9180 | 0.9251 | 0.9179 | 0.9235 | |
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| 1.9517 | 12.97 | 227 | 0.4546 | 0.9036 | 0.9045 | 0.9036 | 0.9024 | 0.9036 | 0.9036 | 0.8988 | 0.9151 | 0.9036 | 0.9157 | |
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| 1.9517 | 14.0 | 245 | 0.5065 | 0.8786 | 0.8826 | 0.8786 | 0.8813 | 0.8786 | 0.8786 | 0.8742 | 0.9040 | 0.8786 | 0.9055 | |
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| 1.9517 | 14.57 | 255 | 0.4925 | 0.8804 | 0.8837 | 0.8804 | 0.8822 | 0.8804 | 0.8804 | 0.8757 | 0.9044 | 0.8804 | 0.9059 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 2.0.0 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
