聲音匹配

每個人說話的內容雖然不一樣，但是頻率基本不會變，所以聲音匹配原理是根據頻率的分佈情況。

import librosa
import os
from collections import Counter
import numpy as np
from functools import reduce

dirpath = "/Users/birenjianmo/Desktop/learn/librosa/input/分類"

def initdata(dirpath):
    datainfo = {}
    for root, dirs, files in os.walk(dirpath):
        for file in files:
            if os.path.splitext(file)[-1] in [".mp3", ".wav"]:
                filepath = os.path.join(root, file)
                datainfo[file] = getfrequeciesdistribute(filepath, 40)
                # datainfo[file] = getfrequeciesdistribute2(filepath, 40)
    return datainfo

def clean2(y, sr):
    frequencies, D = librosa.ifgram(y, sr=sr)
    top = abs(D).max() / 30
    D[abs(D)<top] = 0
    y = librosa.istft(D)
    return y


def getfrequeciesdistribute(filepath, num=100):
    y, sr = librosa.load(filepath)
    y = clean2(y, sr)
    frequencies, D = librosa.ifgram(y, sr=sr)
    frequencies = frequencies.astype(int)
    frequencies = frequencies // 2 * 2
    c = Counter(frequencies.flatten().tolist())
    data = c.most_common(num)
    data = list(filter(lambda x:x[0]!=0,data))
    s = reduce(lambda x1, x2: (0, x1[1] + x2[1]), data)[1]
    frequeciesdistribute = np.array(list(map(lambda t: t[0] * t[1], data)))
    frequeciesdistribute = frequeciesdistribute / s
    print(frequeciesdistribute)
    return frequeciesdistribute

def getfrequeciesdistribute2(filepath, num=100):
    y, sr = librosa.load(filepath)
    y = clean2(y, sr)
    frequencies, D = librosa.ifgram(y, sr=sr)
    frequencies = frequencies.astype(int)
    frequencies = frequencies // 2 * 2
    c = Counter(frequencies.flatten().tolist())
    data = c.most_common(num)
    data = list(filter(lambda x:x[0]!=0,data))
    frequeciesdistribute = np.array(list(map(lambda t: t[0], data)))
    return frequeciesdistribute


def main():
    data = initdata(dirpath)
    for name1 in data:
        minlossname = name1
        minloss = -1
        for name2 in data:
            if name1 == name2:
                continue
            loss = (data[name1] - data[name2]) **2
            loss = loss.sum()

            if minloss == -1 or loss < minloss:
                minloss = loss
                minlossname = name2
            
        print(f"{name1} 相似 {minlossname} loss {minloss}")
if __name__ == '__main__':
    main()

輸出結果：

王小婷2.mp3 相似 王小婷1.mp3 loss 52119.23285928133
王小婷1.mp3 相似 王小婷2.mp3 loss 52119.23285928133
許森1.mp3 相似 許森2.mp3 loss 5928.603683607824
許森2.mp3 相似 許森1.mp3 loss 5928.603683607824
老男人2.mp3 相似 老男人1.mp3 loss 502.5641025641024
老男人3.mp3 相似 耿鎮源2.mp3 loss 6546.419460880999
老男人1.mp3 相似 老男人2.mp3 loss 502.5641025641024
小新1.wav 相似 小新2.wav loss 1091.3372936798748
小新2.wav 相似 小新1.wav loss 1091.3372936798748
杜蕾蕾1.mp3 相似 杜蕾蕾3.mp3 loss 13920.36756477113
杜蕾蕾3.mp3 相似 小新1.wav loss 4795.619575262194
杜蕾蕾2.mp3 相似 小新1.wav loss 6461.275476660093
耿鎮源1.mp3 相似 耿鎮源2.mp3 loss 7457.570019723867
耿鎮源2.mp3 相似 老男人3.mp3 loss 6546.419460880999
女人3.mp3 相似 許森2.mp3 loss 37871.214062747385
女人2.mp3 相似 女人1.mp3 loss 3239.918680026809
女人1.mp3 相似 女人2.mp3 loss 3239.918680026809

部分錄音內容太短只有1秒，導致誤差較大，在運行時應選擇時間長的錄音。