音频数据集 .wav 文件的形状相同答案

【问题标题】：same shape for audio dataset .wav files音频数据集 .wav 文件的形状相同
【发布时间】：2022-01-08 15:00:17
【问题描述】：

因此，我正在创建一种 ANN 神经网络类型，它可以对说话的人是否是我进行分类，问题是我可以根据数据的形状对其进行训练。

X 数据是

(262144,)

y 数据是

(261768,)

如何使我的 .wav 音频文件数据具有相同的形状？

这是我的完整代码

    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    import tensorflow as tf
    import numpy as np
    from scipy.io import wavfile
    from pathlib import Path
    import os

    ### DATASET 
    pathlist = Path(os.path.abspath('Voiceclassification/Data/me/')).rglob('*.wav')

    # My voice data
    for path in pathlist:
        filename = str(path)

        # convert audio to numpy array and then 2D to 1D np Array
        samplerate, data = wavfile.read(filename)
        #print(f"sample rate: {samplerate}")
        data = data.flatten()
        #print(f"data: {data}")

    pathlist2 = Path(os.path.abspath('Voiceclassification/Data/other/')).rglob('*.wav')

    # other voice data
    for path2 in pathlist2:
        filename2 = str(path2)

        samplerate2, data2 = wavfile.read(filename2)
        data2 = data2.flatten()
        #print(data2)


    ### ADAPTING THE DATA FOR THE MODEL
    X = data # My voice
    y = data2 # Other data
    #print(X.shape)
    #print(y.shape)

    ### Trainig the model
    x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)

    # Performing future scaling
    sc = StandardScaler()

    x_train = sc.fit_transform(x_train)
    x_test = sc.transform(x_test)

    ### Creating the ANN
    ann = tf.keras.models.Sequential()

    # First hidden layer of the ann
    ann.add(tf.keras.layers.Dense(units=6, activation="relu"))
    # Second one
    ann.add(tf.keras.layers.Dense(units=6, activation="relu"))
    # Output layer
    ann.add(tf.keras.layers.Dense(units=6, activation="sigmoid"))

    # Compile our neural network
    ann.compile(optimizer="adam",
                loss="binary_crossentropy",
                metrics=['accuracy'])

    # Fit ANN
    ann.fit(x_train, y_train, batch_size=32, epochs=100)
    ann.save('train_model.model')

任何想法，对于每个 X 或 y，我总共有 18 个 .wav 文件

【问题讨论】：

标签： python machine-learning neural-network dataset

【解决方案1】：

您可以将 scipy.io 用于 wav 文件，只需 5 秒就可以重写文件，我创建了这个小代码来帮助您

def trim_wav( originalWavPath, newWavPath , start, new ):
   sampleRate, waveData = wavfile.read( originalWavPath )
   startSample = int( start * sampleRate )
   endSample = int( new * sampleRate )
   wavfile.write( newWavPath, sampleRate, waveData[startSample:endSample])

wp = "path of the wav file"
trim_wav(wp, wp.replace(".wav", ".wav"), 0,5)

这将裁剪您的音频文件并删除不会改变数据形状的毫秒

【讨论】：