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README.md
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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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model-index:
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- name: wav2vec2-base-Toronto_emotional_speech_set
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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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# 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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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-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
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