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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model_index:
name: wav2vec-english-speech-emotion-recognition
---
# Speech Emotion Recognition By Fine-Tuning Wav2Vec 2.0
The model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) for a Speech Emotion Recognition (SER) task.
Several datasets were used the fine-tune the original model:
- Surrey Audio-Visual Expressed Emotion [(SAVEE)](http://kahlan.eps.surrey.ac.uk/savee/Database.html) - 480 audio files from 4 male actors
- Ryerson Audio-Visual Database of Emotional Speech and Song [(RAVDESS)](https://zenodo.org/record/1188976) - 1440 audio files from 24 professional actors (12 female, 12 male)
- Toronto emotional speech set [(TESS)](https://tspace.library.utoronto.ca/handle/1807/24487) - 2800 audio files from 2 female actors
7 labels/emotions were used as classification labels
```python
emotions = ['angry' 'disgust' 'fear' 'happy' 'neutral' 'sad' 'surprise']
```
It achieves the following results on the evaluation set:
- Loss: 0.104075
- Accuracy: 0.97463
## Model Usage
```bash
pip install transformers librosa torch
```
```python
from transformers import *
import librosa
import torch
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("r-f/wav2vec-english-speech-emotion-recognition")
model = Wav2Vec2ForCTC.from_pretrained("r-f/wav2vec-english-speech-emotion-recognition")
def predict_emotion(audio_path):
audio, rate = librosa.load(audio_path, sr=16000)
inputs = feature_extractor(audio, sampling_rate=rate, return_tensors="pt", padding=True)
with torch.no_grad():
outputs = model(inputs.input_values)
predictions = torch.nn.functional.softmax(outputs.logits.mean(dim=1), dim=-1) # Average over sequence length
predicted_label = torch.argmax(predictions, dim=-1)
emotion = model.config.id2label[predicted_label.item()]
return emotion
emotion = predict_emotion("example_audio.wav")
print(f"Predicted emotion: {emotion}")
>> Predicted emotion: angry
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- eval_steps: 500
- seed: 42
- gradient_accumulation_steps: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 4
- max_steps=7500
- save_steps: 1500
### Training results
| Step | Training Loss | Validation Loss | Accuracy |
| ---- | ------------- | --------------- | -------- |
| 500 | 1.8124 | 1.365212 | 0.486258 |
| 1000 | 0.8872 | 0.773145 | 0.79704 |
| 1500 | 0.7035 | 0.574954 | 0.852008 |
| 2000 | 0.6879 | 1.286738 | 0.775899 |
| 2500 | 0.6498 | 0.697455 | 0.832981 |
| 3000 | 0.5696 | 0.33724 | 0.892178 |
| 3500 | 0.4218 | 0.307072 | 0.911205 |
| 4000 | 0.3088 | 0.374443 | 0.930233 |
| 4500 | 0.2688 | 0.260444 | 0.936575 |
| 5000 | 0.2973 | 0.302985 | 0.92389 |
| 5500 | 0.1765 | 0.165439 | 0.961945 |
| 6000 | 0.1475 | 0.170199 | 0.961945 |
| 6500 | 0.1274 | 0.15531 | 0.966173 |
| 7000 | 0.0699 | 0.103882 | 0.976744 |
| 7500 | 0.083 | 0.104075 | 0.97463 | |