1D-CNNでSpeaker Recognition
1D-CNNを使って、音声データから発話者の識別を行います。
下のデータセットを使います。
Speaker Recognition Dataset|Kaggle Dataset
作成のきっかけKerasのSpeaker Recognitionです。こちらは、FFT(高速フーリエ変換)をしていますが、ここでは変換なしで行います。(Normalizeすらしてなかったので、後でします。)
PyTorchのコード
import time import os import glob from pathlib import Path import numpy as np import matplotlib.pyplot as plt import librosa import librosa.display %matplotlib inline import torch import torch.nn as nn import torch.optim as optim from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score
ラベル
speaker_label = {'Jens_Stoltenberg' : 0, 'Benjamin_Netanyau': 1, 'Julia_Gillard' : 2, 'Magaret_Tarcher' : 3, 'Nelson_Mandela' : 4, }
データの読み込み
def Read_file_label(path_name, speaker_label): file_label_list = [] file_list = [] label_list = [] for label in speaker_label.keys(): n_file = len(glob.glob(os.path.join(path_name, list(speaker_label.keys())[0])+"/*.wav")) for i in range(n_file): file_label_list.append((os.path.join(path_name, label, f"{i}.wav"), speaker_label[label])) file_list.append(os.path.join(path_name, label, f"{i}.wav")) label_list.append(speaker_label[label]) return file_label_list, file_list, label_list class Audio_Datasets(torch.utils.data.Dataset): def __init__(self, file_label_list, sr): self.path = file_label_list self.sr = sr def __len__(self): return len(self.path) def __getitem__(self, idx): data, _ = librosa.load(self.path[idx][0], sr=self.sr) #data = data[:self.sr] data = data.reshape(1, self.sr) label = np.array(self.path[idx][1]) return data, label file_label_list, file_list, label_list = Read_file_label(path_name, speaker_label) X_train, X_valid, y_train, y_valid = train_test_split(file_list, label_list, test_size=0.2, random_state=31) train = [(X_train[idx], y_train[idx]) for idx in range(len(X_train))] valid = [(X_valid[idx], y_valid[idx]) for idx in range(len(X_valid))] train_loader = torch.utils.data.DataLoader(Audio_Datasets(train, sr), batch_size=128, shuffle = True) valid_loader = torch.utils.data.DataLoader(Audio_Datasets(valid, sr), batch_size=128, shuffle = True)
ネットワーク
ネットワークの構成は適当なので、参考程度に…
後で、しっかりしたものにしたい…
class Net1D(nn.Module): def __init__(self): super(Net1D,self).__init__() self.conv1 = nn.Sequential(nn.Conv1d(1, 8, kernel_size=3, stride=1), nn.BatchNorm1d(8), nn.ReLU(inplace=True), nn.MaxPool1d(kernel_size=3, stride=2), ) self.conv2 = nn.Sequential(nn.Conv1d(8, 32, kernel_size=5, stride=1), nn.BatchNorm1d(32), nn.ReLU(inplace=True), nn.MaxPool1d(kernel_size=5, stride=2), ) self.conv3 = nn.Sequential(nn.Conv1d(32, 64, kernel_size=7, stride=1), nn.BatchNorm1d(64), nn.ReLU(inplace=True), nn.MaxPool1d(kernel_size=7, stride=2), ) self.conv4 = nn.Sequential(nn.Conv1d(64, 128, kernel_size=9, stride=1), nn.BatchNorm1d(128), nn.ReLU(inplace=True), nn.MaxPool1d(kernel_size=9, stride=2), ) self.dense = nn.Sequential(nn.Linear(126464, 512), nn.ReLU(inplace=True), nn.Dropout(0.5), nn.Linear(512,128), nn.ReLU(inplace=True), nn.Dropout(0.5), nn.Linear(128, 5), ) def forward(self,x): x = self.conv1(x) x = self.conv2(x) x = self.conv3(x) x = self.conv4(x) x = x.view(x.size(0),-1) x = self.dense(x) return x def check_size(self, x): x = self.conv1(x) x = self.conv2(x) x = self.conv3(x) x = self.conv4(x) x = x.view(x.size(0),-1) return x
LossとAccuracy
コード全体
GitHub - betashort/Speaker_Recognition
...
参考
![](https://keras.io/img/logo-k-keras-wb.png)
Keras documentation: Speaker Recognition
...
![](https://miro.medium.com/v2/resize:fit:1017/1*Yi4LbmlBTc8JiTdjsNtfXA.png)
Deep Learning for Time Series Classification (InceptionTime)
...