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--- |
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license: apache-2.0 |
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base_model: hughlan1214/SER_wav2vec2-large-xlsr-53_240304_fine-tuned1.1 |
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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: hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned2.0 |
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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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# SER_wav2vec2-large-xlsr-53_240304_fine-tuned_2 |
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This model is a fine-tuned version of [hughlan1214/SER_wav2vec2-large-xlsr-53_240304_fine-tuned1.1](https://huggingface.co/hughlan1214/SER_wav2vec2-large-xlsr-53_240304_fine-tuned1.1) on a [Speech Emotion Recognition (en)](https://www.kaggle.com/datasets/dmitrybabko/speech-emotion-recognition-en) dataset. |
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This dataset includes the 4 most popular datasets in English: Crema, Ravdess, Savee, and Tess, containing a total of over 12,000 .wav audio files. Each of these four datasets includes 6 to 8 different emotional labels. |
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This achieves the following results on the evaluation set: |
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- Loss: 1.0601 |
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- Accuracy: 0.6731 |
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- Precision: 0.6761 |
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- Recall: 0.6794 |
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- F1: 0.6738 |
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## Model description |
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The model was obtained through feature extraction using [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) and underwent several rounds of fine-tuning. It predicts the 7 types of emotions contained in speech, aiming to lay the foundation for subsequent use of human micro-expressions on the visual level and context semantics under LLMS to infer user emotions in real-time. |
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Although the model was trained on purely English datasets, post-release testing showed that it also performs well in predicting emotions in Chinese and French, demonstrating the powerful cross-linguistic capability of the [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) pre-trained model. |
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```python |
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emotions = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise'] |
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``` |
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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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70/30 of entire dataset. |
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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: 8 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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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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| 0.8904 | 1.0 | 1048 | 1.1923 | 0.5773 | 0.6162 | 0.5563 | 0.5494 | |
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| 1.1394 | 2.0 | 2096 | 1.0143 | 0.6071 | 0.6481 | 0.6189 | 0.6057 | |
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| 0.9373 | 3.0 | 3144 | 1.0585 | 0.6126 | 0.6296 | 0.6254 | 0.6119 | |
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| 0.7405 | 4.0 | 4192 | 0.9580 | 0.6514 | 0.6732 | 0.6562 | 0.6576 | |
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| 1.1638 | 5.0 | 5240 | 0.9940 | 0.6486 | 0.6485 | 0.6627 | 0.6435 | |
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| 0.6741 | 6.0 | 6288 | 1.0307 | 0.6628 | 0.6710 | 0.6711 | 0.6646 | |
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| 0.604 | 7.0 | 7336 | 1.0248 | 0.6667 | 0.6678 | 0.6751 | 0.6682 | |
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| 0.6835 | 8.0 | 8384 | 1.0396 | 0.6722 | 0.6803 | 0.6790 | 0.6743 | |
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| 0.5421 | 9.0 | 9432 | 1.0493 | 0.6714 | 0.6765 | 0.6785 | 0.6736 | |
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| 0.5728 | 10.0 | 10480 | 1.0601 | 0.6731 | 0.6761 | 0.6794 | 0.6738 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.2.1 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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