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--- |
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license: apache-2.0 |
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base_model: facebook/hubert-large-ll60k |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: hubert_large_emodb |
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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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# hubert_large_emodb |
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This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9789 |
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- Uar: 0.8800 |
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- Acc: 0.8897 |
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For the test Set: |
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- UAR: 0.805 |
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- 0.845 |
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FI scores: |
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labels: ['anger', 'happiness', 'sadness', 'neutral'] |
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Result per class (F1 score): [0.84, 0.364, 1.0, 1.0] |
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## Model description |
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This model is to predict one of four emotion categories: 'anger', 'happiness', 'sadness', 'neutral' |
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## Intended uses & limitations |
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How to use: |
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``` |
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from transformers import pipeline |
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pipe = pipeline("audio-classification", model="Bagus/hubert_large_emodb") |
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pipe('file.wav') |
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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: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Uar | Acc | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
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| No log | 0.15 | 1 | 1.3865 | 0.25 | 0.1985 | |
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| No log | 0.31 | 2 | 1.3794 | 0.25 | 0.1985 | |
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| No log | 0.46 | 3 | 1.3745 | 0.25 | 0.1985 | |
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| No log | 0.62 | 4 | 1.3684 | 0.3227 | 0.3162 | |
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| No log | 0.77 | 5 | 1.3592 | 0.4722 | 0.5809 | |
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| No log | 0.92 | 6 | 1.3487 | 0.3981 | 0.5221 | |
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| 1.4402 | 1.08 | 7 | 1.3406 | 0.4444 | 0.5588 | |
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| 1.4402 | 1.23 | 8 | 1.3359 | 0.5278 | 0.625 | |
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| 1.4402 | 1.38 | 9 | 1.3305 | 0.5418 | 0.6324 | |
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| 1.4402 | 1.54 | 10 | 1.3228 | 0.5790 | 0.6544 | |
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| 1.4402 | 1.69 | 11 | 1.3078 | 0.6392 | 0.6985 | |
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| 1.4402 | 1.85 | 12 | 1.2832 | 0.6577 | 0.7132 | |
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| 1.4402 | 2.0 | 13 | 1.2445 | 0.6670 | 0.7206 | |
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| 1.0783 | 2.15 | 14 | 1.2087 | 0.6715 | 0.7279 | |
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| 1.0783 | 2.31 | 15 | 1.1857 | 0.6579 | 0.7059 | |
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| 1.0783 | 2.46 | 16 | 1.1746 | 0.6488 | 0.6912 | |
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| 1.0783 | 2.62 | 17 | 1.1666 | 0.6397 | 0.6765 | |
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| 1.0783 | 2.77 | 18 | 1.1393 | 0.6443 | 0.6838 | |
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| 1.0783 | 2.92 | 19 | 1.1079 | 0.6810 | 0.7279 | |
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| 0.9255 | 3.08 | 20 | 1.0908 | 0.7271 | 0.7721 | |
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| 0.9255 | 3.23 | 21 | 1.0786 | 0.7131 | 0.7647 | |
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| 0.9255 | 3.38 | 22 | 1.0697 | 0.6574 | 0.7279 | |
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| 0.9255 | 3.54 | 23 | 1.0711 | 0.6111 | 0.6912 | |
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| 0.9255 | 3.69 | 24 | 1.0651 | 0.6389 | 0.7132 | |
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| 0.9255 | 3.85 | 25 | 1.0596 | 0.6481 | 0.7206 | |
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| 0.9255 | 4.0 | 26 | 1.0566 | 0.6667 | 0.7353 | |
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| 0.6547 | 4.15 | 27 | 1.0562 | 0.6667 | 0.7353 | |
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| 0.6547 | 4.31 | 28 | 1.0553 | 0.7222 | 0.7794 | |
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| 0.6547 | 4.46 | 29 | 1.0549 | 0.7316 | 0.7794 | |
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| 0.6547 | 4.62 | 30 | 1.0546 | 0.7456 | 0.7868 | |
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| 0.6547 | 4.77 | 31 | 1.0516 | 0.7549 | 0.7941 | |
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| 0.6547 | 4.92 | 32 | 1.0428 | 0.7456 | 0.7868 | |
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| 0.7058 | 5.08 | 33 | 1.0312 | 0.7502 | 0.7941 | |
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| 0.7058 | 5.23 | 34 | 1.0235 | 0.7594 | 0.8015 | |
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| 0.7058 | 5.38 | 35 | 1.0143 | 0.7732 | 0.8162 | |
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| 0.7058 | 5.54 | 36 | 1.0079 | 0.7963 | 0.8382 | |
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| 0.7058 | 5.69 | 37 | 1.0049 | 0.7963 | 0.8382 | |
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| 0.7058 | 5.85 | 38 | 1.0051 | 0.7778 | 0.8235 | |
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| 0.7058 | 6.0 | 39 | 1.0066 | 0.7593 | 0.8088 | |
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| 0.4919 | 6.15 | 40 | 1.0119 | 0.7407 | 0.7941 | |
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| 0.4919 | 6.31 | 41 | 1.0172 | 0.7222 | 0.7794 | |
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| 0.4919 | 6.46 | 42 | 1.0191 | 0.7130 | 0.7721 | |
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| 0.4919 | 6.62 | 43 | 1.0175 | 0.7130 | 0.7721 | |
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| 0.4919 | 6.77 | 44 | 1.0144 | 0.7222 | 0.7794 | |
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| 0.4919 | 6.92 | 45 | 1.0094 | 0.7222 | 0.7794 | |
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| 0.5048 | 7.08 | 46 | 1.0050 | 0.7593 | 0.8088 | |
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| 0.5048 | 7.23 | 47 | 0.9984 | 0.7870 | 0.8309 | |
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| 0.5048 | 7.38 | 48 | 0.9948 | 0.7778 | 0.8235 | |
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| 0.5048 | 7.54 | 49 | 0.9917 | 0.7825 | 0.8235 | |
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| 0.5048 | 7.69 | 50 | 0.9884 | 0.8195 | 0.8529 | |
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| 0.5048 | 7.85 | 51 | 0.9846 | 0.8242 | 0.8529 | |
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| 0.5048 | 8.0 | 52 | 0.9827 | 0.8152 | 0.8382 | |
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| 0.4133 | 8.15 | 53 | 0.9816 | 0.8337 | 0.8529 | |
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| 0.4133 | 8.31 | 54 | 0.9812 | 0.8522 | 0.8676 | |
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| 0.4133 | 8.46 | 55 | 0.9810 | 0.8522 | 0.8676 | |
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| 0.4133 | 8.62 | 56 | 0.9810 | 0.8707 | 0.8824 | |
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| 0.4133 | 8.77 | 57 | 0.9806 | 0.8800 | 0.8897 | |
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| 0.4133 | 8.92 | 58 | 0.9796 | 0.8800 | 0.8897 | |
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| 0.4717 | 9.08 | 59 | 0.9793 | 0.8800 | 0.8897 | |
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| 0.4717 | 9.23 | 60 | 0.9789 | 0.8800 | 0.8897 | |
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### Framework versions |
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- Transformers 4.32.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.13.3 |
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