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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: facebook/wav2vec2-large-xlsr-53 |
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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-facebook-wav2vec2-large-xlsr-53 |
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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-facebook-wav2vec2-large-xlsr-53 |
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4989 |
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- Accuracy: 0.9168 |
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- Precision: 0.9209 |
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- Recall: 0.9168 |
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- F1: 0.9166 |
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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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| 1.9343 | 0.9995 | 394 | 1.9277 | 0.2505 | 0.1425 | 0.2505 | 0.1691 | |
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| 1.7944 | 1.9990 | 788 | 1.6446 | 0.4574 | 0.5759 | 0.4574 | 0.4213 | |
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| 1.4601 | 2.9985 | 1182 | 1.3242 | 0.5953 | 0.6183 | 0.5953 | 0.5709 | |
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| 1.0551 | 3.9980 | 1576 | 1.0764 | 0.6623 | 0.6659 | 0.6623 | 0.6447 | |
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| 0.8934 | 5.0 | 1971 | 0.9209 | 0.7059 | 0.7172 | 0.7059 | 0.6825 | |
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| 1.1156 | 5.9995 | 2365 | 0.8292 | 0.7465 | 0.7635 | 0.7465 | 0.7442 | |
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| 0.6307 | 6.9990 | 2759 | 0.6439 | 0.8043 | 0.8090 | 0.8043 | 0.8020 | |
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| 0.774 | 7.9985 | 3153 | 0.6666 | 0.7921 | 0.8117 | 0.7921 | 0.7916 | |
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| 0.5537 | 8.9980 | 3547 | 0.5111 | 0.8245 | 0.8268 | 0.8245 | 0.8205 | |
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| 0.3762 | 10.0 | 3942 | 0.5506 | 0.8306 | 0.8390 | 0.8306 | 0.8296 | |
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| 0.716 | 10.9995 | 4336 | 0.5499 | 0.8276 | 0.8465 | 0.8276 | 0.8268 | |
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| 0.5372 | 11.9990 | 4730 | 0.5463 | 0.8377 | 0.8606 | 0.8377 | 0.8404 | |
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| 0.3746 | 12.9985 | 5124 | 0.4758 | 0.8611 | 0.8714 | 0.8611 | 0.8597 | |
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| 0.4317 | 13.9980 | 5518 | 0.4438 | 0.8742 | 0.8843 | 0.8742 | 0.8756 | |
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| 0.2104 | 15.0 | 5913 | 0.4426 | 0.8803 | 0.8864 | 0.8803 | 0.8806 | |
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| 0.3193 | 15.9995 | 6307 | 0.4741 | 0.8671 | 0.8751 | 0.8671 | 0.8683 | |
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| 0.3445 | 16.9990 | 6701 | 0.3850 | 0.9037 | 0.9047 | 0.9037 | 0.9038 | |
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| 0.2777 | 17.9985 | 7095 | 0.4802 | 0.8834 | 0.8923 | 0.8834 | 0.8836 | |
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| 0.4406 | 18.9980 | 7489 | 0.4053 | 0.9047 | 0.9096 | 0.9047 | 0.9043 | |
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| 0.1707 | 20.0 | 7884 | 0.4434 | 0.9067 | 0.9129 | 0.9067 | 0.9069 | |
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| 0.2138 | 20.9995 | 8278 | 0.5051 | 0.9037 | 0.9155 | 0.9037 | 0.9053 | |
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| 0.1812 | 21.9990 | 8672 | 0.4238 | 0.8955 | 0.9007 | 0.8955 | 0.8953 | |
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| 0.3639 | 22.9985 | 9066 | 0.4021 | 0.9138 | 0.9182 | 0.9138 | 0.9143 | |
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| 0.3193 | 23.9980 | 9460 | 0.4989 | 0.9168 | 0.9209 | 0.9168 | 0.9166 | |
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| 0.2067 | 24.9873 | 9850 | 0.4959 | 0.8976 | 0.9032 | 0.8976 | 0.8975 | |
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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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