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app.py

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+import numpy as np
+import pickle
+import librosa
+from tensorflow.keras.models import Sequential, model_from_json
+import gradio as gr
+
+json_file = open('/content/drive/MyDrive/Project/Audio/CNN_model.json', 'r')
+loaded_model_json = json_file.read()
+json_file.close()
+loaded_model = model_from_json(loaded_model_json)
+# load weights into new model
+loaded_model.load_weights("/content/drive/MyDrive/Project/Audio/best_model1_weights.h5")
+
+
+with open('/content/drive/MyDrive/Project/Audio/scaler2.pickle', 'rb') as f:
+    scaler2 = pickle.load(f)
+
+with open('/content/drive/MyDrive/Project/Audio/encoder2.pickle', 'rb') as f:
+    encoder2 = pickle.load(f)
+
+
+def zcr(data,frame_length,hop_length):
+    zcr=librosa.feature.zero_crossing_rate(data,frame_length=frame_length,hop_length=hop_length)
+    return np.squeeze(zcr)
+def rmse(data,frame_length=2048,hop_length=512):
+    rmse=librosa.feature.rms(y=data,frame_length=frame_length,hop_length=hop_length)
+    return np.squeeze(rmse)
+def mfcc(data,sr,frame_length=2048,hop_length=512,flatten:bool=True):
+    mfcc=librosa.feature.mfcc(y=data,sr=sr)
+    return np.squeeze(mfcc.T)if not flatten else np.ravel(mfcc.T)
+
+def extract_features(data,sr=22050,frame_length=2048,hop_length=512):
+    result=np.array([])
+
+    result=np.hstack((result,
+                      zcr(data,frame_length,hop_length),
+                      rmse(data,frame_length,hop_length),
+                      mfcc(data,sr,frame_length,hop_length)
+                     ))
+    return result
+
+def get_predict_feat(path):
+    d, s_rate= librosa.load(path, duration=2.5, offset=0.6)
+    res=extract_features(d)
+    result=np.array(res)
+    result=np.reshape(result,newshape=(1,2376))
+    i_result = scaler2.transform(result)
+    final_result=np.expand_dims(i_result, axis=2)
+
+    return final_result
+
+emotions1={1:'Neutral', 2:'Calm', 3:'Happy', 4:'Sad', 5:'Angry', 6:'Fear', 7:'Disgust',8:'Surprise'}
+def prediction(path1):
+    res = get_predict_feat(path1)
+    predictions = loaded_model.predict(res)
+    predicted_class = predictions.argmax(axis=1)[0] + 1  # Convert from 0-based indexing to emotion labels
+    predicted_emotion = emotions1[predicted_class]  # Get the corresponding emotion label
+    return predicted_emotion[0]
+
+gr.Interface(fn=prediction, inputs="audio", outputs="text").launch()

requirements.txt

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+transformers