1 - Data synthesis: Creating a speech dataset

import numpy as npfrom pydub import AudioSegment      # pip install, 处理音频的函数库import randomimport sysimport ioimport osimport globimport IPython                      # pip installfrom td_utils import *%matplotlib inline

def get_random_time_segment(segment_ms):    """    Gets a random time segment of duration segment_ms in a 10,000 ms audio clip.    Arguments:    segment_ms -- the duration of the audio clip in ms ("ms" stands for "milliseconds")    Returns:    segment_time -- a tuple of (segment_start, segment_end) in ms    """    segment_start = np.random.randint(low=0,                                      high=10000 - segment_ms)  # Make sure segment doesn't run past the 10sec background    segment_end = segment_start + segment_ms - 1    return (segment_start, segment_end)

# GRADED FUNCTION: is_overlappingdef is_overlapping(segment_time, previous_segments):    """    Checks if the time of a segment overlaps with the times of existing segments.    Arguments:    segment_time -- a tuple of (segment_start, segment_end) for the new segment    previous_segments -- a list of tuples of (segment_start, segment_end) for the existing segments    Returns:    True if the time segment overlaps with any of the existing segments, False otherwise    """    segment_start, segment_end = segment_time    ### START CODE HERE ### (≈ 4 line)    # Step 1: Initialize overlap as a "False" flag. (≈ 1 line)    overlap = False    # Step 2: loop over the previous_segments start and end times.    # Compare start/end times and set the flag to True if there is an overlap (≈ 3 lines)    print(previous_segments)    for previous_start, previous_end in previous_segments:        if segment_start <= previous_end and segment_end >= previous_start:            overlap = True    ### END CODE HERE ###    return overlap

# GRADED FUNCTION: insert_audio_clipdef insert_audio_clip(background, audio_clip, previous_segments):    """    Insert a new audio segment over the background noise at a random time step, ensuring that the    audio segment does not overlap with existing segments.    Arguments:    background -- a 10 second background audio recording.    audio_clip -- the audio clip to be inserted/overlaid.    previous_segments -- times where audio segments have already been placed    Returns:    new_background -- the updated background audio    """    # Get the duration of the audio clip in ms    segment_ms = len(audio_clip)    ### START CODE HERE ###    # Step 1: Use one of the helper functions to pick a random time segment onto which to insert    # the new audio clip. (≈ 1 line)    segment_time = get_random_time_segment(segment_ms)    # Step 2: Check if the new segment_time overlaps with one of the previous_segments. If so, keep    # picking new segment_time at random until it doesn't overlap. (≈ 2 lines)    while is_overlapping(segment_time, previous_segments):        segment_time = get_random_time_segment(segment_ms)    # Step 3: Add the new segment_time to the list of previous_segments (≈ 1 line)    previous_segments.append(segment_time)    ### END CODE HERE ###    # Step 4: Superpose audio segment and background    new_background = background.overlay(audio_clip, position=segment_time[0])    return new_background, segment_time

# GRADED FUNCTION: insert_onesdef insert_ones(y, segment_end_ms):    """    Update the label vector y. The labels of the 50 output steps strictly after the end of the segment    should be set to 1. By strictly we mean that the label of segment_end_y should be 0 while, the    50 followinf labels should be ones.    Arguments:    y -- numpy array of shape (1, Ty), the labels of the training example    segment_end_ms -- the end time of the segment in ms    Returns:    y -- updated labels    """    # duration of the background (in terms of spectrogram time-steps)    segment_end_y = int(segment_end_ms * Ty / 10000.0)    # Add 1 to the correct index in the background label (y)    ### START CODE HERE ### (≈ 3 lines)    for i in range(segment_end_y + 1, segment_end_y + 51):        if i < Ty:            y[0, i] = 1.0    ### END CODE HERE ###    return y

# GRADED FUNCTION: create_training_exampledef create_training_example(background, activates, negatives):    """    Creates a training example with a given background, activates, and negatives.    Arguments:    background -- a 10 second background audio recording    activates -- a list of audio segments of the word "activate"    negatives -- a list of audio segments of random words that are not "activate"    Returns:    x -- the spectrogram of the training example    y -- the label at each time step of the spectrogram    """    # Set the random seed    np.random.seed(18)    # Make background quieter    background = background - 20    ### START CODE HERE ###    # Step 1: Initialize y (label vector) of zeros (≈ 1 line)    y = np.zeros((1, Ty))    # Step 2: Initialize segment times as empty list (≈ 1 line)    previous_segments = []    ### END CODE HERE ###    # Select 0-4 random "activate" audio clips from the entire list of "activates" recordings    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]    ### START CODE HERE ### (≈ 3 lines)    # Step 3: Loop over randomly selected "activate" clips and insert in background    for random_activate in random_activates:        # Insert the audio clip on the background        background, segment_time = insert_audio_clip(background, random_activate, previous_segments)        # Retrieve segment_start and segment_end from segment_time        segment_start, segment_end = segment_time        # Insert labels in "y"        y = insert_ones(y, segment_end)    ### END CODE HERE ###    # Select 0-2 random negatives audio recordings from the entire list of "negatives" recordings    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]    ### START CODE HERE ### (≈ 2 lines)    # Step 4: Loop over randomly selected negative clips and insert in background    for random_negative in random_negatives:        # Insert the audio clip on the background        background, _ = insert_audio_clip(background, random_negative, previous_segments)    ### END CODE HERE ###    # Standardize the volume of the audio clip    background = match_target_amplitude(background, -20.0)    # Export new training example    file_handle = background.export("train" + ".wav", format="wav")    print("File (train.wav) was saved in your directory.")    # Get and plot spectrogram of the new recording (background with superposition of positive and negatives)    x = graph_spectrogram("train.wav")    return x, y

2 - Model

# GRADED FUNCTION: modeldef model(input_shape):    """    Function creating the model's graph in Keras.    Argument:    input_shape -- shape of the model's input data (using Keras conventions)    Returns:    model -- Keras model instance    """    X_input = Input(shape = input_shape)    ### START CODE HERE ###    # Step 1: CONV layer (≈4 lines)    X = Conv1D(196, 15, strides=4)(X_input)             # CONV1D    X = BatchNormalization()(X)                         # Batch normalization    X = Activation('relu')(X)                           # ReLu activation    X = Dropout(0.8)(X)                                 # dropout (use 0.8)    # Step 2: First GRU Layer (≈4 lines)    X = GRU(units = 128, return_sequences = True)(X)    # GRU (use 128 units and return the sequences)    X = Dropout(0.8)(X)                                 # dropout (use 0.8)    X = BatchNormalization()(X)                         # Batch normalization    # Step 3: Second GRU Layer (≈4 lines)    X = GRU(units = 128, return_sequences = True)(X)    # GRU (use 128 units and return the sequences)    X = Dropout(0.8)(X)                                 # dropout (use 0.8)    X = BatchNormalization()(X)                         # Batch normalization    X = Dropout(0.8)(X)                                 # dropout (use 0.8)    # Step 4: Time-distributed dense layer (≈1 line)    X = TimeDistributed(Dense(1, activation = "sigmoid"))(X) # time distributed  (sigmoid)    ### END CODE HERE ###    model = Model(inputs = X_input, outputs = X)    return model

3 - Making Predictions

filename = "./raw_data/dev/1.wav"prediction = detect_triggerword(filename)chime_on_activate(filename, prediction, 0.5)IPython.display.Audio("./chime_output.wav")## 多次训练可以找到比较好的结果

这里写图片描述

filename  = "./raw_data/dev/2.wav"prediction = detect_triggerword(filename)chime_on_activate(filename, prediction, 0.5)IPython.display.Audio("./chime_output.wav")

这里写图片描述