这一部分的实验我觉得挺跳跃的，突然就到语音识别了

时间重叠判定

# 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 segmentprevious_segments -- a list of tuples of (segment_start, segment_end) for the existing segmentsReturns: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)for previous_start, previous_end in previous_segments:if segment_start >= previous_start and segment_start <= previous_end or segment_end >= previous_start and segment_end <= previous_end :overlap = True### END CODE HERE ###return overlap

补齐

# 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, the50 followinf labels should be ones.Arguments:y -- numpy array of shape (1, Ty), the labels of the training examplesegment_end_ms -- the end time of the segment in msReturns: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### END CODE HERE ###return y

创建训练样例

# 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 recordingactivates -- 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 exampley -- the label at each time step of the spectrogram"""# Set the random seednp.random.seed(18)# Make background quieterbackground = 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" recordingsnumber_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 backgroundfor random_activate in random_activates:# Insert the audio clip on the backgroundbackground, segment_time = insert_audio_clip(background, random_activate, previous_segments)# Retrieve segment_start and segment_end from segment_timesegment_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" recordingsnumber_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 backgroundfor 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

keras模型搭建

# 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)                    # CONV1DX = BatchNormalization()(X)                                # Batch normalizationX = Activation('relu')(X)                                  # ReLu activationX = 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 normalizationX = 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