Demo 버전 업로드

+import cv2
+import numpy as np
+import pyaudio
+import librosa
+import librosa.display
+import matplotlib.pyplot as plt
+import time
+import tensorflow as tf
+import tensorflow.keras as keras
+
+CATEGORES = ["angry", "happy", "lonely", "sad"]
+
+model = keras.load_model("yout model path")
+
+rate = 16000
+chunk_size = rate // 4
+
+"""
+    생성된 데이터를 모델에 맞게 변경함
+    @param filepath(str) : path where your image file is
+    @return array that has reshaped for predict
+"""
+def prepare(filepath):
+    img_array = cv2.imread(filepath, cv2.IMREAD_COLOR)
+    new_array = cv2.resize(img_array, (62, 78))
+    return new_array.reshape(-1, 62, 78, 1)
+
+p = pyaudio.PyAudio()
+stream = p.open(format=pyaudio.paFloat32,
+                channels=1,
+                rate=rate,
+                input=True,
+                input_device_index=1,
+                frames_per_buffer=chunk_size)
+
+
+frames = []
+
+plt.figure(figsize=(10, 4))
+do_melspec = librosa.feature.melspectrogram
+pwr_to_db = librosa.core.power_to_db
+
+while True:
+
+    start = time.time()
+
+    data = stream.read(chunk_size)
+    data = np.fromstring(data, dtype=np.float32)
+
+    melspec = do_melspec(y=data, sr=rate, n_mels=128, fmax=4000)
+    norm_melspec = pwr_to_db(melspec, ref=np.max)
+
+    frames.append(norm_melspec)
+    
+    if len(frames) == 20:
+
+        
+        stack = np.hstack(frames)
+        librosa.display.specshow(stack, fmax=4000)
+        plt.savefig('your save path' + '.jpg', dpi=300)
+        prediction = model.predict([prepare('path where your image is')])
+        print(CATEGORES[int(prediction[0][0])])
+        plt.draw()
+        plt.pause(0.0001)
+        plt.clf()
+        #break
+        frames.pop(0)
+
+
+    t = time.time() - start