transformers safetensors wav2vec2 audio-classification generated_from_trainer base_model:facebook/wav2vec2-large-xlsr-53 license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: SER_wav2vec2-large-xlsr-53_fine-tuned_1.0
  results: []
---

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# SER_wav2vec2-large-xlsr-53_240303

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) 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.

It achieves the following results on the evaluation set:
- Loss: 1.7923
- Accuracy: 0.2408
- Precision: 0.2324
- Recall: 0.2466
- F1: 0.2226


## For a better performance version, please refer to 

[hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned2.0](https://huggingface.co/hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned2.0)


## 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

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.9297        | 1.0   | 101  | 1.9452          | 0.1233   | 0.0306    | 0.1468 | 0.0454 |
| 1.9114        | 2.0   | 202  | 1.9115          | 0.1773   | 0.1501    | 0.1803 | 0.1323 |
| 1.7863        | 3.0   | 303  | 1.8564          | 0.2081   | 0.1117    | 0.2193 | 0.1336 |
| 1.8439        | 4.0   | 404  | 1.8590          | 0.2042   | 0.2196    | 0.2156 | 0.1755 |
| 1.9361        | 5.0   | 505  | 1.8375          | 0.2081   | 0.2617    | 0.2213 | 0.1573 |
| 1.7572        | 6.0   | 606  | 1.8081          | 0.2100   | 0.2018    | 0.2214 | 0.1841 |
| 1.6715        | 7.0   | 707  | 1.8131          | 0.2389   | 0.2263    | 0.2442 | 0.2129 |
| 1.6687        | 8.0   | 808  | 1.7923          | 0.2408   | 0.2324    | 0.2466 | 0.2226 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.2.1
- Datasets 2.17.1
- Tokenizers 0.15.2
