🚀 [Update] Engine Script for SER

src/ASR-with-Speech-Sentiment-Analysis-Text-Summarizer/Speech_Sentiment_Analysis/engine.py

@@ -0,0 +1,64 @@
+import numpy as np
+import joblib
+import librosa
+import torch
+
+from sklearn.preprocessing import StandardScaler
+from neuralnet.model import HybridModel
+from feature import getMELspectrogram, splitIntoChunks 
+
+
+EMOTIONS = {
+    1: 'neutral', 
+    2: 'calm', 
+    3: 'happy', 
+    4: 'sad', 
+    5: 'angry', 
+    6: 'fear', 
+    7: 'disgust', 
+    0: 'surprise'
+}
+
+scaler = StandardScaler()
+model = HybridModel(len(EMOTIONS))
+model.load_state_dict(torch.load("model/speech_sentiment.pt", map_location=torch.device('cpu')))
+SAMPLE_RATE = 48000
+scaler = joblib.load('model/scaler.pkl')
+
+def process_audio(audio_file_path):
+    global scaler
+    chunked_spec = []
+
+    # Load audio file
+    audio, sample_rate = librosa.load(audio_file_path, sr=SAMPLE_RATE, duration=3)
+    signal = np.zeros((int(SAMPLE_RATE * 3),))
+    signal[:len(audio)] = audio
+    mel_spectrogram = getMELspectrogram(signal, SAMPLE_RATE)
+    chunks = splitIntoChunks(mel_spectrogram, win_size=128, stride=64)
+
+    chunked_spec.append(chunks)
+    chunks = np.stack(chunked_spec, axis=0)
+    chunks = np.expand_dims(chunks, axis=2)
+
+    # Reshape the chunks
+    chunks = np.reshape(chunks, newshape=(1, -1)) 
+    chunks_scaled = scaler.transform(chunks)
+    chunks_scaled = np.reshape(chunks_scaled, newshape=(1, 7, 1, 128, 128))  
+
+    # Convert to tensor for model input
+    chunks_tensor = torch.tensor(chunks_scaled).float()
+
+    # Model inference
+    with torch.inference_mode():
+        model.eval()
+        _, output_softmax, _ = model(chunks_tensor)
+        predictions = torch.argmax(output_softmax, dim=1)
+        print(predictions)
+        predicted_emotion = EMOTIONS[predictions.item()]
+
+        print(f"Predicted Emotion: {predicted_emotion}")
+        return predicted_emotion
+
+file_path = "fear.wav"
+process_audio(file_path)
+