【吴恩达深度学习编程作业】5.3序列模型和注意力机制——机器翻译与触发词检测

╰半夏微凉° 2022-12-27 11:14 184阅读 0赞

参考博客：[机器翻译与触发词机制][Link 1]

最后一个编程作业，做的云里雾里，好多不懂的地方，非术业专攻等想起来的时候再弄明白吧。

# 机器翻译 #

## main.py ##

from keras.layers import Bidirectional, Concatenate, Permute, Dot, Input, LSTM, Multiply
    from keras.layers import RepeatVector, Dense, Activation, Lambda
    from keras.optimizers import Adam
    from keras.models import Model
    from Deep_Learning.test5_3.nmt_utils import *
    
    
    # 1.1.1 数据集，运行单元数值数据集并打印一些样例
    m = 10000
    dataset, human_vocab, machine_vocab, inv_machine_vocab = load_dataset(m)
    print(dataset[:10])
    
    """ dataset: 一个元组列表 (人类可读日期, 机器可读日期)。 human_vocab: 一个python字典，将人类可读日期中使用的所有字符映射到整数值索引。 machine_vocab: 一个python字典，将机器可读日期中使用的所有字符映射到整数值索引。这些索引不一定与 human_vocab 的索引一致。 inv_machine_vocab: machine_vocab的逆字典，从索引到字符的映射。 """
    
    Tx = 30     # 假设人类可读日期的最大长度，如果得到更长的输入，就截断它
    Ty = 10
    X, Y, Xoh, Yoh = preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty)
    print("X.shape:", X.shape)      # X.shape: (10000, 30)
    print("Y.shape:", Y.shape)      # Y.shape: (10000, 10)
    print("Xoh.shape:", Xoh.shape)  # Xoh.shape: (10000, 30, 37)
    print("Yoh.shape:", Yoh.shape)  # Yoh.shape: (10000, 10, 11)
    
    # 查看预处理训练样本的示例
    index = 0
    print("Source date:", dataset[index][0])    # Source date: 9 may 1998
    print("Target date:", dataset[index][1])    # Target date: 1998-05-09
    print()
    print("Source after preprocessing (indices):", X[index])
    print("Target after preprocessing (indices):", Y[index])
    print()
    print("Source after preprocessing (one-hot):", Xoh[index])
    print("Target after preprocessing (one-hot):", Yoh[index])
    
    # 1.2带注意力的神经机器翻译
    # 将共享层定义为全局变量
    repeator = RepeatVector(Tx)
    concatenator = Concatenate(axis=-1)
    densor1 = Dense(10, activation="tanh")
    densor2 = Dense(1, activation="relu")
    activator = Activation(softmax, name='attention_weights')   # 在这个notebook我们正使用自定义的softmax(axis=1)
    dotor = Dot(axes=1)
    
    # Graded function
    def one_step_attention(a, s_prev):
        """ 执行一步attention,输出一个上下文向量，输出作为注意力权重的点积计算上下文向量 "alphas" Bi-LSTM的隐藏状态"a" :param a: -Bi-LSTM的输出隐藏状态，numpy-array，维度为(m,Tx,2*n_a) :param s_prev: -(post-attention)LSTM的前一个隐藏状态，numpy-array，维度为(m,n_s) :return:context -上下文向量，下一个(post-attention)LSTM单元的输入 """
    
        # 使用repeator重复s_prev维度(m,Tx,n_s)这样就可以将它与所有隐藏状态a连接起来
        s_prev = repeator(s_prev)
    
        # 使用concatenator在最后一个轴上连接a和s_prev
        concat = concatenator([a, s_prev])
    
        # 使用densor1传入参数concat,通过一个小的全连接神经网络来计算中间能量变量e
        e = densor1(concat)
    
        # 使用densor2传入参数e,通过一个小的全连接神经网络来计算中间能量变量energies
        energies = densor2(e)
    
        # 使用activator传入参数energies计算注意力权重alphas
        alphas = activator(energies)
    
        # 使用dotor传入参数alphas和a计算下一个(post-attention)LSTM单元的上下文向量
        context = dotor([alphas, a])
    
        return context
    
    # 定义全局图层用于在model()中共享权重
    n_a = 32
    n_s = 64
    post_activation_LSTM_cell = LSTM(n_s, return_state=True)
    output_layer = Dense(len(machine_vocab), activation=softmax)
    
    # Graded function
    def model(Tx, Ty, n_a, n_s, human_vocab_size, machine_vocab_size):
        """ :param Tx: -输入序列的长度 :param Ty: -输出序列的长度 :param n_a: -Bi-LSTM的隐藏状态大小 :param n_s: -post-attention LSTM的隐藏状态大小 :param human_vocab_size: -字典human_vocab的大小 :param machine_vocab_size: -字典machine_vocab的大小 :return: model -keras模型实例 """
    
        # 定义模型的输入，维度(Tx,)
        # 定义s0和c0，初始化解码器LSTM的隐藏状态，维度(n_s,)
        X = Input(shape=(Tx, human_vocab_size))
        s0 = Input(shape=(n_s,), name='s0')
        c0 = Input(shape=(n_s,), name='c0')
        s = s0
        c = c0
    
        # 初始化一个空的输出列表
        outputs = []
    
        # 1.定义pre-attention Bi-LSTM
        a = Bidirectional(LSTM(n_a, return_sequences=True), input_shape=(m, Tx, n_a*2))(X)
    
        # 2.迭代Ty步
        for t in range(Ty):
            # 2.1执行一步注意机制，得到在t步的上下文向量
            context = one_step_attention(a, s)
    
            # 2.2使用post-attention LSTM单元的到的新的context
            s, _, c = post_activation_LSTM_cell(context, initial_state=[s, c])
    
            # 2.3使用全连接层处理post-attention LSTM的隐藏状态输出
            out = output_layer(s)
    
            # 2.4追加out到outputs
            outputs.append(out)
    
        # 3.创建模型实例，获取三个输入并返回输出列表
        model = Model(inputs=[X, s0, c0], outputs=outputs)
    
        return model
    
    model = model(Tx, Ty, n_a, n_s, len(human_vocab), len(machine_vocab))
    
    model.summary()
    
    opt = Adam(lr=0.005, beta_1=0.9, beta_2=0.999, decay=0.01)
    model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
    
    s0 = np.zeros((m, n_s))
    c0 = np.zeros((m, n_s))
    # ?????????????
    outputs = list(Yoh.swapaxes(0, 1))
    model.fit([Xoh, s0, c0], outputs, epochs=1, batch_size=100)
    
    # 加载训练好了的模型
    model.load_weights('models1/model.h5')
    
    EXAMPLES = ['3 May 1979', '5 April 09', '21th of August 2016', 'Tue 10 Jul 2007', 'Saturday May 9 2018', 'March 3 2001', 'March 3rd 2001', '1 March 2001']
    for example in EXAMPLES:
        s0 = np.zeros((1, n_s))
        c0 = np.zeros((1, n_s))
        source = string_to_int(example, Tx, human_vocab)
        source = np.array(list(map(lambda x: to_categorical(x, num_classes=len(human_vocab)), source)))
        source = np.expand_dims(source, axis=0)
        prediction = model.predict([source, s0, c0])
        prediction = np.argmax(prediction, axis=-1)
        output = [inv_machine_vocab[int(i)] for i in prediction]
    
        print("source:", example)
        print("output:".join(output))

## 运行结果 ##

Source after preprocessing (indices): [12  0 24 13 34  0  4 12 12 11 36 36 36 36 36 36 36 36 36 36 36 36 36 36
     36 36 36 36 36 36]
    Target after preprocessing (indices): [ 2 10 10  9  0  1  6  0  1 10]
    
    Source after preprocessing (one-hot): [[0. 0. 0. ... 0. 0. 0.]
     [1. 0. 0. ... 0. 0. 0.]
     [0. 0. 0. ... 0. 0. 0.]
     ...
     [0. 0. 0. ... 0. 0. 1.]
     [0. 0. 0. ... 0. 0. 1.]
     [0. 0. 0. ... 0. 0. 1.]]
    Target after preprocessing (one-hot): [[0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
     [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
     [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
     [0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
     [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
     [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
     [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
     [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
     [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
     [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]]
    Model: "functional_1"
    ...
    Total params: 52,960
    Trainable params: 52,960
    Non-trainable params: 0
    __________________________________________________________________________________________________
    100/100 [==============================] - 5s 45ms/step - loss: 16.7616 - dense_2_loss: 1.2131 - dense_2_1_loss: 1.0301 - dense_2_2_loss: 1.7838 - dense_2_3_loss: 2.6970 - dense_2_4_loss: 0.7895 - dense_2_5_loss: 1.2747 - dense_2_6_loss: 2.6956 - dense_2_7_loss: 0.9524 - dense_2_8_loss: 1.7066 - dense_2_9_loss: 2.6189 - dense_2_accuracy: 0.5136 - dense_2_1_accuracy: 0.6962 - dense_2_2_accuracy: 0.3020 - dense_2_3_accuracy: 0.0872 - dense_2_4_accuracy: 0.9473 - dense_2_5_accuracy: 0.3513 - dense_2_6_accuracy: 0.0537 - dense_2_7_accuracy: 0.9264 - dense_2_8_accuracy: 0.2435 - dense_2_9_accuracy: 0.1067
    source: 3 May 1979
    1output:9output:7output:9output:-output:0output:5output:-output:3output:3
    source: 5 April 09
    2output:0output:0output:9output:-output:0output:4output:-output:0output:5
    source: 21th of August 2016
    2output:0output:1output:6output:-output:0output:8output:-output:2output:0
    source: Tue 10 Jul 2007
    2output:0output:0output:7output:-output:0output:7output:-output:1output:0
    source: Saturday May 9 2018
    2output:0output:1output:8output:-output:0output:5output:-output:0output:9
    source: March 3 2001
    2output:0output:0output:1output:-output:0output:3output:-output:0output:3
    source: March 3rd 2001
    2output:0output:0output:1output:-output:0output:3output:-output:0output:3
    source: 1 March 2001
    2output:0output:0output:1output:-output:0output:3output:-output:0output:1

# 触发词检测 #

## main.py ##

""" 代码实现： 1.构建语音识别项目 2.合成和处理录音，创建训练/开发数据集 3.训练触发词检测模型并进行预测 """
    import numpy as np
    import IPython
    from Deep_Learning.test5_3.td_utils import *
    import tensorflow as tf
    tf.keras.layers.GRU.reset_after = False
    
    # 2.1数据合成：创建语音数据集
    # 听取数据
    IPython.display.Audio("./raw_data/activates/1.wav")
    IPython.display.Audio("./raw_data/negatives/1.wav")
    IPython.display.Audio("./raw_data/backgrounds/1.wav")
    
    IPython.display.Audio("./audio_examples/example_train.wav")
    
    x = graph_spectrogram("audio_examples/example_train.wav")
    plt.show()
    
    _, data = wavfile.read("audio_examples/example_train.wav")
    print("Time steps in audio recording before spectrogram", data[:, 0].shape)
    print("Time steps in input after spectrogram", x.shape)
    
    Tx = 5511   # 从频谱图输入到模型的时间步数
    n_freq = 101    # 在频谱图的每个时间步输入模型的频率数
    Ty = 1375   # 模型输出中的时间步数
    
    # 生成单个训练样例
    # 使用pydub加载音频片段
    activates, negatives, backgrounds = load_raw_audio()
    
    
    print("background len: " + str(len(backgrounds[0])))    # 应该是10,000，因为它是一个10秒的剪辑
    print("activate[0] len: " + str(len(activates[0])))     # 也许大约1000，因为 "activate" 音频剪辑通常大约1秒（但变化很大）
    print("activate[1] len: " + str(len(activates[1])))     # 不同的 "activate" 剪辑可以具有不同的长度
    
    def get_random_time_segment(segment_ms):
        """ 获取10000ms音频剪辑中时间长为segment_ms的随机时间段 :param segment_ms: -音频片段的持续时间，以毫秒为单位 :return: segment_time -以毫秒为单位的元组(segment_start, segment_end) """
        segment_start = np.random.randint(low=0, high=10000-segment_ms)     # 确保段不会超过10s背景
        segment_end = segment_start + segment_ms - 1
    
        return (segment_start, segment_end)
    
    
    def is_overlapping(segment_time, previous_segments):
        """ 检查段的时间是否与现有段的时间重叠 :param segment_time: -新段的元组(segment_start, segment_end) :param previous_segments: -现有段的元组列表(segment_start, segment_end) :return: -如果时间段与任何现有段重叠，则为True，否则为False """
    
        segment_start, segment_end = segment_time
    
        # 1.将重叠标识overlap初始化为False标志
        overlap = False
    
        # 2.循环遍历previous_segments的开始和结束时间
        # 比较开始/结束时间，如果存在重叠，则将标志overlap设置为True
        for previous_start, previous_end in previous_segments:
            if segment_start <= previous_end and segment_end >= previous_start:
                overlap = True
    
        return overlap
    
    overlap1 = is_overlapping((950, 1430), [(2000, 2550), (260, 949)])
    overlap2 = is_overlapping((2305, 2950), [(824, 1532), (1900, 2305), (3424, 3656)])
    print("Overlap 1 = ", overlap1)     # False
    print("Overlap 2 = ", overlap2)     # True
    
    
    def insert_audio_clip(background, audio_clip, previous_segments):
        """ 在随机时间步骤中在背景噪声上插入新的音频片段，确保音频片段与现有片段不重叠 :param background: -10s背景录音 :param audio_clip: -要插入/叠加的音频剪辑 :param previous_segments: -已放置的音频片段的时间 :return: new_background -更新的背景音频 """
    
        # 以ms为单位获取音频片段的持续时间
        segment_ms = len(audio_clip)
    
        # 1.使用其中一个辅助函数来选择要插入的随机时间段
        # 新的音频剪辑
        segment_time = get_random_time_segment(segment_ms)
    
        # 2.检查新的segment_time是否与previous_segments之一重叠
        # 如果重叠则继续选择新的直到不重叠
        while is_overlapping(segment_time, previous_segments):
            segment_time = get_random_time_segment(segment_ms)
    
        # 3.将新的segment_time添加到previous_segments列表中
        previous_segments.append(segment_time)
    
        # 4.叠加音频片段和背景
        new_background = background.overlay(audio_clip, position=segment_time[0])
    
        return new_background, segment_time
    
    np.random.seed(5)
    audio_clip, segment_time = insert_audio_clip(backgrounds[0], activates[0], [(3790, 4400)])
    audio_clip.export("insert_test.wav", format="wav")
    print("Segment Time:", segment_time)
    IPython.display.Audio("insert_test.wav")
    
    # 预测的音频
    IPython.display.Audio("audio_examples/insert_reference.wav")
    
    
    def insert_ones(y, segment_end_ms):
        """ 更新标签向量y，段结尾的后面50个输出的标签应设为1 严格的说，segment_end_y的标签是0，随后的50个标签是1 :param y: -numpy数组， 维度为(1,Ty),训练样例的标签 :param segment_end_ms: -以ms为单位的段的结束时间 :return: y -更新标签 """
    
        # 背景持续时间(以频谱图时间步长表示)
        segment_end_y = int(segment_end_ms * Ty / 10000.0)
    
        # 将1添加到背景标签(y)中的正确索引
        for i in range(segment_end_y + 1, segment_end_y + 51):
            if i < Ty:
                y[0, i] = 1
    
        return y
    
    arr1 = insert_ones(np.zeros((1, Ty)), 9700)
    plt.plot(insert_ones(arr1, 4251)[0, :])
    plt.show()
    print("sanity checks:", arr1[0][1333], arr1[0][634], arr1[0][635])
    
    
    def create_trainging_example(background, activates, negatives):
        """ 创建具有给定背景，正例和负例的训练示例 :param background: -10s背景录音 :param activates: -activate一词的音频片段列表 :param negatives: -不是activate一词的音频片段列表 :return: x -训练样例的频谱图 y -频谱图每个时间步的标签 """
    
        # 设置随机种子
        np.random.seed(18)
    
        # 让背景更安静
        background = background - 20
    
        # 1.初始化y(标签向量)为0
        y = np.zeros((1, Ty))
    
        # 2.将段时间初始化为空列表
        previous_segments = []
    
        # 从整个activate录音列表中选择0-4随机activate音频片段
        number_of_activates = np.random.randint(0, 5)
        random_indices = np.random.randint(len(activates), size=number_of_activates)
        random_activates = [activates[i] for i in random_indices]
    
        # 3.循环选择activate剪辑插入背景
        for random_activate in random_activates:
            # 插入音频剪辑到背景
            background, segment_time = insert_audio_clip(background, random_activate, previous_segments)
            # 从segment_time中取segment_start和segment_end
            segment_start, segment_end = segment_time
            # 在y中插入标签
            y = insert_ones(y, segment_end_ms=segment_end)
    
        # 从整个负例录音列表中随机选择0-2个负例录音
        number_of_negatives = np.random.randint(0, 3)
        random_indices = np.random.randint(len(negatives), size=number_of_negatives)
        random_negatives = [negatives[i] for i in random_indices]
    
        # 4.循环随机选择负例片段插入背景中
        for random_negative in random_negatives:
            # 插入音频剪辑到背景
            background, _ = insert_audio_clip(background, random_negative, previous_segments)
    
        # 标准化音频剪辑的音量
        background = match_target_amplitude(background, -20.0)
    
        # 导出新的训练样例
        file_handle = background.export("train" + ".wav", format="wav")
        print("文件(train.wav)已保存在您的目录中")
    
        # 获取并绘制录音的频谱图(正例和负例叠加的背景)
        x = graph_spectrogram("train.wav")
        plt.show()
    
        return x, y
    
    x, y = create_trainging_example(backgrounds[0], activates, negatives)
    
    IPython.display.Audio("train.wav")
    
    # 绘制生成的训练示例的关联标签
    plt.plot(y[0])
    
    # 加载预处理的训练样例
    X = np.load("./XY_train/X.npy")
    Y = np.load("./XY_train/Y.npy")
    
    # 加载预处理开发集示例
    X_dev = np.load("./XY_dev/X_dev.npy")
    Y_dev = np.load("./XY_dev/Y_dev.npy")
    
    from keras.models import Model, load_model
    from keras.layers import Dense, Activation, Dropout, Input, TimeDistributed, Conv1D
    from keras.layers import GRU, BatchNormalization
    from keras.optimizers import Adam
    
    
    def model(input_shape):
        """ 用keras创建模型的图 Function creating the model's graph in Keras :param input_shape: -模型输入数据的维度(使用keras约定) :return: model -keras模型实例 """
    
        X_input = Input(shape=input_shape)
    
        # 1.卷积层
        X = Conv1D(196, 15, strides=4)(X_input)
        X = BatchNormalization()(X)
        X = Activation('relu')(X)
        X = Dropout(0.8)(X)
    
        # 2.第一个GRU
        X = GRU(units=128, return_sequences=True)(X)
        X = Dropout(0.8)(X)
        X = BatchNormalization()(X)
    
        # 3.第二个GRU
        X = GRU(units=128, return_sequences=True)(X)
        X = Dropout(0.8)(X)
        X = BatchNormalization()(X)
        X = Dropout(0.8)(X)
    
        # 4.时间分布全连接层
        X = TimeDistributed(Dense(1, activation="sigmoid"))(X)
    
        model = Model(inputs=X_input, outputs=X)
    
        return model
    
    model = model(input_shape=(Tx, n_freq))
    model.summary()
    
    # 训练模型,加载模型时会报错就注释掉了
    # model = load_model('./models2/tr_model.h5')
    opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01)
    model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
    model.fit(X, Y, batch_size=5, epochs=1)
    
    # 测试模型
    loss, acc = model.evaluate(X_dev, Y_dev)
    print("Dev set accuracy = ", acc)
    
    # 作出预测
    def detect_triggerword(filename):
        plt.subplot(2, 1, 1)
    
        x = graph_spectrogram(filename)
        # 频谱图输出(freqs,Tx)，我们想要(Tx,freqs)输入到模型中
        x = x.swapaxes(0, 1)
        x = np.expand_dims(x, axis=0)
        predictions = model.predict(x)
    
        plt.subplot(2, 1, 2)
        plt.plot(predictions[0, :, 0])
        plt.ylabel('probability')
        plt.show()
    
        return predictions
    
    clime_file = "audio_examples/chime.wav"
    def chime_on_activate(filename, predictions, threshold):
        audio_clip = AudioSegment.from_wav(filename)
        chime = AudioSegment.from_wav(clime_file)
        Ty = predictions.shape[1]
    
        # 1.将连续输出步初始化为0
        consecutive_timesteps = 0
        # 2.循环y中的输出步
        for i in range(Ty):
            # 3.增加连续输出步
            consecutive_timesteps += 1
            # 4.如果预测高于阈值并且已经过了75个连续输出步
            if predictions[0, i, 0] > threshold and consecutive_timesteps > 75:
                # 5.使用pydub叠加音频和背景
                audio_clip = audio_clip.overlay(chime, position=((i / Ty) * audio_clip.duration_seconds) * 1000)
                # 6.将连续输出步重置为0
                consecutive_timesteps = 0
    
        audio_clip.export("chime_output.wav", format='wav')
    
    
    # 测试开发样例
    IPython.display.Audio("./raw_data/dev/1.wav")
    IPython.display.Audio("./raw_data/dev/2.wav")
    
    filename = "./raw_data/dev/1.wav"
    prediction = detect_triggerword(filename)
    chime_on_activate(filename, prediction, 0.5)
    IPython.display.Audio("./chime_output.wav")
    
    filename = "./raw_data/dev/2.wav"
    prediction = detect_triggerword(filename)
    chime_on_activate(filename, prediction, 0.5)
    IPython.display.Audio("./chime_output.wav")

## 运行结果 ##

Time steps in audio recording before spectrogram (441000,)
    Time steps in input after spectrogram (101, 5511)
    background len: 10000
    activate[0] len: 721
    activate[1] len: 731
    Overlap 1 =  False
    Overlap 2 =  True
    Segment Time: (2915, 3635)
    sanity checks: 0.0 1.0 0.0
    文件(train.wav)已保存在您的目录中
    Model: "functional_1"
    _________________________________________________________________
    Layer (type)                 Output Shape              Param # 
    =================================================================
    input_1 (InputLayer)         [(None, 5511, 101)]       0         
    _________________________________________________________________
    conv1d (Conv1D)              (None, 1375, 196)         297136    
    _________________________________________________________________
    batch_normalization (BatchNo (None, 1375, 196)         784       
    _________________________________________________________________
    activation (Activation)      (None, 1375, 196)         0         
    _________________________________________________________________
    dropout (Dropout)            (None, 1375, 196)         0         
    _________________________________________________________________
    gru (GRU)                    (None, 1375, 128)         125184    
    _________________________________________________________________
    dropout_1 (Dropout)          (None, 1375, 128)         0         
    _________________________________________________________________
    batch_normalization_1 (Batch (None, 1375, 128)         512       
    _________________________________________________________________
    gru_1 (GRU)                  (None, 1375, 128)         99072     
    _________________________________________________________________
    dropout_2 (Dropout)          (None, 1375, 128)         0         
    _________________________________________________________________
    batch_normalization_2 (Batch (None, 1375, 128)         512       
    _________________________________________________________________
    dropout_3 (Dropout)          (None, 1375, 128)         0         
    _________________________________________________________________
    time_distributed (TimeDistri (None, 1375, 1)           129       
    =================================================================
    Total params: 523,329
    Trainable params: 522,425
    Non-trainable params: 904
    _________________________________________________________________
    6/6 [==============================] - 4s 703ms/step - loss: 1.3067 - accuracy: 0.4988
    1/1 [==============================] - 0s 501us/step - loss: 0.7014 - accuracy: 0.1298
    Dev set accuracy =  0.1298036426305771

**audio\_examples/example\_train.wav的频谱图**  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70]

**insert\_ones()功能显示**  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70 1]

**train.wav频谱图**![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70 2]

**raw\_data/dev/1.wav模型运行结果**  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70 3]

**raw\_data/dev/2.wav模型运行结果**  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70 4]

[Link 1]: https://blog.csdn.net/u013733326/article/details/97619187
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70]: /images/20221120/4c86e6771310403991d6f0bf2d94a8ad.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70 1]: /images/20221120/43085431362d4cddbf021bf1026591c5.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70 2]: https://img-blog.csdnimg.cn/20201210161905914.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8=,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70 3]: https://img-blog.csdnimg.cn/2020121016191070.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8=,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70 4]: https://img-blog.csdnimg.cn/20201210161914508.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8=,size_16,color_FFFFFF,t_70