from datetime import datetime
import librosa
from matplotlib import pyplot
from librosa.feature import mfcc
import numpy as np
from scipy.spatial.distance import euclidean


def extract_mfcc(filename, num_mfcc=13):
    # 取低频维度上的部分值输出，语音能量大多集中在低频域，数值一般取13。
    print("start extract_mfcc time:" + filename + str(datetime.now()))
    audio, sample_rate = librosa.load(filename)
    # pyplot.figure(figsize=(14, 5))
    # librosa.display.waveshow(y=audio, sr=sample_rate)
    # pyplot.title('Wave Form of ' + filename)
    # pyplot.show()
    mfcc_result = mfcc(y=audio, sr=sample_rate, n_mfcc=num_mfcc)
    mean_mfcc = np.mean(mfcc_result, axis=1)
    print("end extract_mfcc time:" + filename + str(datetime.now()))
    return mean_mfcc


def compute_similarity(feature_1, feature_2):
    print("start compute_similarity time:" + str(datetime.now()))
    distance = euclidean(feature_1, feature_2)
    result = (100-distance)/100
    print("end compute_similarity time:" + str(datetime.now()))
    return result