Maël
105 linhas
3.9 KiB
Python
105 linhas
3.9 KiB
Python
import pickle
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from AudioLibrary.AudioSignal import *
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from AudioLibrary.AudioFeatures import *
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class AudioEmotionRecognition:
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def __init__(self, model_path):
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# Load classifier
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self._clf = pickle.load(open(os.path.join(model_path, 'MODEL_CLF.p'), 'rb'))
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# Load features parameters
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self._features_param = pickle.load(open(os.path.join(model_path, 'MODEL_PARAM.p'), 'rb'))
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# Load feature scaler parametrs (mean and std)
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self._features_mean, self._features_std = pickle.load(open(os.path.join(model_path, 'MODEL_SCALER.p'), 'rb'))
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# Load PCA
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self._pca = pickle.load(open(os.path.join(model_path, 'MODEL_PCA.p'), 'rb'))
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# Load label encoder
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self._encoder = pickle.load(open(os.path.join(model_path, 'MODEL_ENCODER.p'), 'rb'))
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'''
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Function to scale audio features
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'''
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def scale_features(self, features):
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# Scaled features
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scaled_features = (features - self._features_mean) / self._features_std
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# Return scaled features
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return scaled_features
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'''
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Function to predict speech emotion from an audio signals
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'''
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def predict_emotion(self, audio_signal, predict_proba=False, decode=True):
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# Extract audio features
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audio_features = AudioFeatures(audio_signal, float(self._features_param.get("win_size")),
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float(self._features_param.get("win_step")))
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features, features_names = audio_features.global_feature_extraction(stats=self._features_param.get("stats"),
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features_list=self._features_param.get(
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"features_list"),
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nb_mfcc=self._features_param.get("nb_mfcc"),
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diff=self._features_param.get("diff"))
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# Scale features
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features = self.scale_features(features)
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# Apply feature dimension reduction
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if self._features_param.get("PCA") is True:
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features = self._pca.transform(features)
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# Make prediction
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if predict_proba is True:
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prediction = self._clf.predict_proba(features.reshape(1, -1))
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else:
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prediction = self._clf.predict(features.reshape(1, -1))
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# Decode label emotion
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if decode is True:
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prediction = (self._encoder.inverse_transform((prediction.astype(int).flatten())))
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# Remove gender recognition
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prediction = prediction[0][2:]
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return prediction
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'''
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Function to predict speech emotion over time from video
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'''
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def predict_emotion_from_file(self, filename, sample_rate, chunk_size=0, chunk_step=0, predict_proba=False,
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decode=True):
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# Initialize Audio Basic object
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audio_signal = AudioSignal(sample_rate, filename=filename)
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# Split audio signals into chunks
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if chunk_size > 0:
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chunks = audio_signal.framing(chunk_size, chunk_step)
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# Initialize time stamp
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timestamp = []
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# Emotion prediction for each chunks
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prediction = []
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for signal in chunks:
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if len(timestamp) == 0:
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timestamp.append(chunk_size)
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else:
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timestamp.append(timestamp[-1] + chunk_step)
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prediction.append(self.predict_emotion(signal, predict_proba=predict_proba, decode=decode))
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# Return emotion prediction and related timestamp
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return prediction, timestamp
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else:
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# Emotion prediction
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prediction = self.predict_emotion(audio_signal, predict_proba=predict_proba, decode=decode)
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# Return emotion prediction
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return prediction
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