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r-f/
wav2vec-english-speech-emotion-recognition

Name: wav2vec-english-speech-emotion-recognition
Availability: InStock

transformers

pytorch

wav2vec2

automatic-speech-recognition

generated_from_trainer

apache-2.0

endpoints_compatible

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Speech Emotion Recognition By Fine-Tuning Wav2Vec 2.0

The model is a fine-tuned version of 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) - 480 audio files from 4 male actors
Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) - 1440 audio files from 24 professional actors (12 female, 12 male)
Toronto emotional speech set (TESS) - 2800 audio files from 2 female actors

7 labels/emotions were used as classification labels

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

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