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---
license: apache-2.0
base_model: hughlan1214/SER_wav2vec2-large-xlsr-53_240304_fine-tuned1.1
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned2.0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SER_wav2vec2-large-xlsr-53_240304_fine-tuned_2
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.
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.
This achieves the following results on the evaluation set:
- Loss: 1.0601
- Accuracy: 0.6731
- Precision: 0.6761
- Recall: 0.6794
- F1: 0.6738
## Model description
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.
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.
```python
emotions = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']
```
## Intended uses & limitations
More information needed
## Training and evaluation data
70/30 of entire dataset.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8904 | 1.0 | 1048 | 1.1923 | 0.5773 | 0.6162 | 0.5563 | 0.5494 |
| 1.1394 | 2.0 | 2096 | 1.0143 | 0.6071 | 0.6481 | 0.6189 | 0.6057 |
| 0.9373 | 3.0 | 3144 | 1.0585 | 0.6126 | 0.6296 | 0.6254 | 0.6119 |
| 0.7405 | 4.0 | 4192 | 0.9580 | 0.6514 | 0.6732 | 0.6562 | 0.6576 |
| 1.1638 | 5.0 | 5240 | 0.9940 | 0.6486 | 0.6485 | 0.6627 | 0.6435 |
| 0.6741 | 6.0 | 6288 | 1.0307 | 0.6628 | 0.6710 | 0.6711 | 0.6646 |
| 0.604 | 7.0 | 7336 | 1.0248 | 0.6667 | 0.6678 | 0.6751 | 0.6682 |
| 0.6835 | 8.0 | 8384 | 1.0396 | 0.6722 | 0.6803 | 0.6790 | 0.6743 |
| 0.5421 | 9.0 | 9432 | 1.0493 | 0.6714 | 0.6765 | 0.6785 | 0.6736 |
| 0.5728 | 10.0 | 10480 | 1.0601 | 0.6731 | 0.6761 | 0.6794 | 0.6738 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1
- Datasets 2.17.1
- Tokenizers 0.15.2
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