forked from Open-CT/openbrain
56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
import os
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from random import shuffle
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from train import getFeature
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from drawRadar import draw
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import joblib
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import numpy as np
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import pyaudio
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import wave
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path = r'wave'
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wav_paths = []
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person_dirs = os.listdir(path)
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for person in person_dirs:
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if person.endswith('txt'):
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continue
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emotion_dir_path = os.path.join(path, person)
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emotion_dirs = os.listdir(emotion_dir_path)
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for emotion_dir in emotion_dirs:
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if emotion_dir.endswith('.ini'):
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continue
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emotion_file_path = os.path.join(emotion_dir_path, emotion_dir)
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emotion_files = os.listdir(emotion_file_path)
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for file in emotion_files:
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if not file.endswith('wav'):
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continue
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wav_path = os.path.join(emotion_file_path, file)
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wav_paths.append(wav_path)
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# 将语音文件随机排列
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#shuffle(wav_paths)
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model = joblib.load("classfier.m")
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p = pyaudio.PyAudio()
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for wav_path in wav_paths:
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f = wave.open(wav_path, 'rb')
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stream = p.open(
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format=p.get_format_from_width(f.getsampwidth()),
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channels=f.getnchannels(),
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rate=f.getframerate(),
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output=True)
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data = f.readframes(f.getparams()[3])
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stream.write(data)
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stream.stop_stream()
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stream.close()
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f.close()
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data_feature = getFeature(wav_path, 48)
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print(model.predict([data_feature]))
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print(model.predict_proba([data_feature]))
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labels = np.array(['angry', 'Delate', 'disgust', 'fear', 'happy','neutral','sad','surprised','TS'])
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draw(model.predict_proba([data_feature])[0], labels, 6)
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p.terminate()
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