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import tensorflow as tf
import tensorflow.keras as keras
import numpy as np
import librosa
import random
#import tensorflow_addons
SAMPLE_RATE = 22050
class _Emotion_spotting_service():
model = None
#instance = None
mapping = [' amazement', ' solemnity', ' tenderness',
' nostalgia', ' calmness', ' power',
' joyfulness', ' tension',' sadness']
def __init__(self,model_path):
self.model = keras.models.load_model(model_path)
def predict(self,file_path):
log_spectrogram = self.preprocess(file_path)
X = np.array(log_spectrogram).astype("float32")
X = np.expand_dims(X, axis=0)
# Do predictions
num_predictions = self.model.predict(X)
prediction = np.argmax(num_predictions)
predicted_keyword = self.mapping[prediction]
return predicted_keyword
# Split audio into 10 second excerpts
# Attain log spectrogram with following parameters
# sample rate = 22050, n_fft = 2048, hop_length = 512
# output, 1024*431
def preprocess(self,file_path):
signal, sr = librosa.load(file_path,sr=SAMPLE_RATE)
signal_normalized = librosa.util.normalize(signal)
len_to_check = 10 * 22050
# If audio is less than 10 seconds, we pad it with zeroes
# If audio is more than 10 seconds, we split into segments and randomly choose one
if len(signal_normalized) < len_to_check:
num_zeros = len_to_check - len(signal_normalized)
signal_normalized = signal_normalized + [0] * num_zeros
elif len(signal_normalized) > len_to_check:
num_segments = len(signal_normalized)//len_to_check
segments = []
for i in range(num_segments):
start = i * len_to_check
end = start + len_to_check
if len(signal[start:end]) != len_to_check:
continue
else:
segments.append(signal[start:end])
signal_normalized = random.choice(segments)
stft = librosa.stft(signal_normalized, n_fft=2048,hop_length=512)[:-1]
spectrogram = np.abs(stft)
log_spectrogram = librosa.amplitude_to_db(spectrogram)
return log_spectrogram
# def Emotion_spotting_service():
# if _Emotion_spotting_service.instance == None:
# _Emotion_spotting_service.instance = _Emotion_spotting_service()
# _Emotion_spotting_service.model = keras.models.load_model("ERM.h5")
# return _Emotion_spotting_service.instance
# if __name__ == "__main__":
# emotion_service = _Emotion_spotting_service("emotion_model.h5")
# predicted_word = emotion_service.predict("10.mp3")
# print(predicted_word) |