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makhataei
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emotion_recog

Transformers
PyTorch
wav2vec2
Speech-Emotion-Recognition
Generated from Trainer
Inference Endpoints
Model card Files Community
makhataei commited on
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End of training

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  2. pytorch_model.bin +1 -1
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - Speech Emotion Recognition
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: Wav2vec2-xlsr-Shemo
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+ results: []
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+ ---
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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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+
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+ # Wav2vec2-xlsr-Shemo
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+
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+ This model is a fine-tuned version of [ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition](https://huggingface.co/ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition) on the minoosh/shEMO dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.9168
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+ - Accuracy: 0.7267
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.003
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 16
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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: 30
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 1.1825 | 1.0 | 150 | 1.1383 | 0.6267 |
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+ | 1.3392 | 2.0 | 300 | 1.4398 | 0.5533 |
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+ | 1.2058 | 3.0 | 450 | 1.1194 | 0.6300 |
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+ | 1.0984 | 4.0 | 600 | 1.2049 | 0.6200 |
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+ | 1.0033 | 5.0 | 750 | 1.0080 | 0.6500 |
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+ | 0.9694 | 6.0 | 900 | 0.9878 | 0.6367 |
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+ | 0.8506 | 7.0 | 1050 | 0.8965 | 0.7033 |
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+ | 0.8068 | 8.0 | 1200 | 0.9359 | 0.6833 |
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+ | 0.7674 | 9.0 | 1350 | 1.1235 | 0.6333 |
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+ | 0.7817 | 10.0 | 1500 | 0.8682 | 0.6900 |
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+ | 0.7172 | 11.0 | 1650 | 0.8289 | 0.7067 |
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+ | 0.6989 | 12.0 | 1800 | 0.9318 | 0.7000 |
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+ | 0.6127 | 13.0 | 1950 | 0.8712 | 0.6967 |
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+ | 0.6311 | 14.0 | 2100 | 0.8965 | 0.7133 |
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+ | 0.5901 | 15.0 | 2250 | 0.9008 | 0.7267 |
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+ | 0.5667 | 16.0 | 2400 | 1.0093 | 0.7200 |
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+ | 0.5652 | 17.0 | 2550 | 0.9032 | 0.7300 |
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+ | 0.565 | 18.0 | 2700 | 0.9317 | 0.7267 |
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+ | 0.5705 | 19.0 | 2850 | 1.0134 | 0.7133 |
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+ | 0.4984 | 20.0 | 3000 | 0.9432 | 0.7367 |
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+ | 0.5207 | 21.0 | 3150 | 0.9368 | 0.6933 |
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+ | 0.5005 | 22.0 | 3300 | 0.9746 | 0.7033 |
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+ | 0.5055 | 23.0 | 3450 | 1.0437 | 0.7133 |
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+ | 0.4867 | 24.0 | 3600 | 1.0052 | 0.7067 |
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+ | 0.5315 | 25.0 | 3750 | 0.9689 | 0.7200 |
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+ | 0.4755 | 26.0 | 3900 | 0.8962 | 0.7367 |
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+ | 0.5083 | 27.0 | 4050 | 0.9319 | 0.7300 |
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+ | 0.4661 | 28.0 | 4200 | 0.9301 | 0.7233 |
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+ | 0.4536 | 29.0 | 4350 | 0.9370 | 0.7267 |
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+ | 0.4693 | 30.0 | 4500 | 0.9168 | 0.7267 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.29.2
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+ - Pytorch 2.0.1+cu117
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+ - Datasets 2.12.0
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+ - Tokenizers 0.13.3
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