Trigger Word Detection

Welcome to the final programming assignment of this specialization!

In this week's videos, you learned about applying deep learning to speech recognition. In this assignment, you will construct a speech dataset and implement an algorithm for trigger word detection (sometimes also called keyword detection, or wakeword detection). Trigger word detection is the technology that allows devices like Amazon Alexa, Google Home, Apple Siri, and Baidu DuerOS to wake up upon hearing a certain word.

For this exercise, our trigger word will be "Activate." Every time it hears you say "activate," it will make a "chiming" sound. By the end of this assignment, you will be able to record a clip of yourself talking, and have the algorithm trigger a chime when it detects you saying "activate."

After completing this assignment, perhaps you can also extend it to run on your laptop so that every time you say "activate" it starts up your favorite app, or turns on a network connected lamp in your house, or triggers some other event?

In this assignment you will learn to:

  • Structure a speech recognition project
  • Synthesize and process audio recordings to create train/dev datasets
  • Train a trigger word detection model and make predictions

Lets get started! Run the following cell to load the package you are going to use.

In [2]:
import numpy as np
from pydub import AudioSegment
import random
import sys
import io
import os
import glob
import IPython
from td_utils import *
%matplotlib inline
1 - Data synthesis: Creating a speech dataset

Let's start by building a dataset for your trigger word detection algorithm. A speech dataset should ideally be as close as possible to the application you will want to run it on. In this case, you'd like to detect the word "activate" in working environments (library, home, offices, open-spaces ...). You thus need to create recordings with a mix of positive words ("activate") and negative words (random words other than activate) on different background sounds. Let's see how you can create such a dataset.

1.1 - Listening to the data

One of your friends is helping you out on this project, and they've gone to libraries, cafes, restaurants, homes and offices all around the region to record background noises, as well as snippets of audio of people saying positive/negative words. This dataset includes people speaking in a variety of accents.

In the raw_data directory, you can find a subset of the raw audio files of the positive words, negative words, and background noise. You will use these audio files to synthesize a dataset to train the model. The "activate" directory contains positive examples of people saying the word "activate". The "negatives" directory contains negative examples of people saying random words other than "activate". There is one word per audio recording. The "backgrounds" directory contains 10 second clips of background noise in different environments.

Run the cells below to listen to some examples.

In [3]:
IPython.display.Audio("./raw_data/activates/1.wav")
 
In [4]:
IPython.display.Audio("./raw_data/negatives/4.wav")
 
In [5]:
IPython.display.Audio("./raw_data/backgrounds/1.wav")

You will use these three type of recordings (positives/negatives/backgrounds) to create a labelled dataset.

 

1.2 - From audio recordings to spectrograms

What really is an audio recording? A microphone records little variations in air pressure over time, and it is these little variations in air pressure that your ear also perceives as sound. You can think of an audio recording is a long list of numbers measuring the little air pressure changes detected by the microphone. We will use audio sampled at 44100 Hz (or 44100 Hertz). This means the microphone gives us 44100 numbers per second. Thus, a 10 second audio clip is represented by 441000 numbers (= 10×4410010×44100).

It is quite difficult to figure out from this "raw" representation of audio whether the word "activate" was said. In order to help your sequence model more easily learn to detect triggerwords, we will compute a spectrogram of the audio. The spectrogram tells us how much different frequencies are present in an audio clip at a moment in time.

(If you've ever taken an advanced class on signal processing or on Fourier transforms, a spectrogram is computed by sliding a window over the raw audio signal, and calculates the most active frequencies in each window using a Fourier transform. If you don't understand the previous sentence, don't worry about it.)

Lets see an example.

In [6]:
IPython.display.Audio("audio_examples/example_train.wav")
 
In [7]:
x = graph_spectrogram("audio_examples/example_train.wav")
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NpbfXTJ9dw+s+Lw6uVxOq/8JjDbbLPNNtsHZ/MmMNtss832IbZXfhNwIsQLBKUAVAcpJJjYActH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" alt="" />
 

The graph above represents how active each frequency is (y axis) over a number of time-steps (x axis).

Figure 1: Spectrogram of an audio recording, where the color shows the degree to which different frequencies are present (loud) in the audio at different points in time. Green squares means a certain frequency is more active or more present in the audio clip (louder); blue squares denote less active frequencies.

The dimension of the output spectrogram depends upon the hyperparameters of the spectrogram software and the length of the input. In this notebook, we will be working with 10 second audio clips as the "standard length" for our training examples. The number of timesteps of the spectrogram will be 5511. You'll see later that the spectrogram will be the input xx into the network, and so Tx=5511Tx=5511.

In [8]:
_, 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)
Time steps in audio recording before spectrogram (441000,)
Time steps in input after spectrogram (101, 5511)
 

Now, you can define:

In [9]:
Tx = 5511 # The number of time steps input to the model from the spectrogram
n_freq = 101 # Number of frequencies input to the model at each time step of the spectrogram

Note that even with 10 seconds being our default training example length, 10 seconds of time can be discretized to different numbers of value. You've seen 441000 (raw audio) and 5511 (spectrogram). In the former case, each step represents 10/441000≈0.00002310/441000≈0.000023 seconds. In the second case, each step represents 10/5511≈0.001810/5511≈0.0018 seconds.

For the 10sec of audio, the key values you will see in this assignment are:

  • 441000441000 (raw audio)
  • 5511=Tx5511=Tx (spectrogram output, and dimension of input to the neural network).
  • 1000010000 (used by the pydub module to synthesize audio)
  • 1375=Ty1375=Ty (the number of steps in the output of the GRU you'll build).

Note that each of these representations correspond to exactly 10 seconds of time. It's just that they are discretizing them to different degrees. All of these are hyperparameters and can be changed (except the 441000, which is a function of the microphone). We have chosen values that are within the standard ranges uses for speech systems.

Consider the Ty=1375Ty=1375 number above. This means that for the output of the model, we discretize the 10s into 1375 time-intervals (each one of length 10/1375≈0.007210/1375≈0.0072s) and try to predict for each of these intervals whether someone recently finished saying "activate."

Consider also the 10000 number above. This corresponds to discretizing the 10sec clip into 10/10000 = 0.001 second itervals. 0.001 seconds is also called 1 millisecond, or 1ms. So when we say we are discretizing according to 1ms intervals, it means we are using 10,000 steps.

In [10]:
Ty = 1375 # The number of time steps in the output of our model

1.3 - Generating a single training example

Because speech data is hard to acquire and label, you will synthesize your training data using the audio clips of activates, negatives, and backgrounds. It is quite slow to record lots of 10 second audio clips with random "activates" in it. Instead, it is easier to record lots of positives and negative words, and record background noise separately (or download background noise from free online sources).

To synthesize a single training example, you will:

  • Pick a random 10 second background audio clip
  • Randomly insert 0-4 audio clips of "activate" into this 10sec clip
  • Randomly insert 0-2 audio clips of negative words into this 10sec clip

Because you had synthesized the word "activate" into the background clip, you know exactly when in the 10sec clip the "activate" makes its appearance. You'll see later that this makes it easier to generate the labels y⟨t⟩y⟨t⟩ as well.

You will use the pydub package to manipulate audio. Pydub converts raw audio files into lists of Pydub data structures (it is not important to know the details here). Pydub uses 1ms as the discretization interval (1ms is 1 millisecond = 1/1000 seconds) which is why a 10sec clip is always represented using 10,000 steps.

In [11]:
# Load audio segments using pydub
activates, negatives, backgrounds = load_raw_audio()

print("background len: " + str(len(backgrounds[0]))) # Should be 10,000, since it is a 10 sec clip
print("activate[0] len: " + str(len(activates[0]))) # Maybe around 1000, since an "activate" audio clip is usually around 1 sec (but varies a lot)
print("activate[1] len: " + str(len(activates[1]))) # Different "activate" clips can have different lengths
background len: 10000
activate[0] len: 916
activate[1] len: 1579
 

Overlaying positive/negative words on the background:

Given a 10sec background clip and a short audio clip (positive or negative word), you need to be able to "add" or "insert" the word's short audio clip onto the background. To ensure audio segments inserted onto the background do not overlap, you will keep track of the times of previously inserted audio clips. You will be inserting multiple clips of positive/negative words onto the background, and you don't want to insert an "activate" or a random word somewhere that overlaps with another clip you had previously added.

For clarity, when you insert a 1sec "activate" onto a 10sec clip of cafe noise, you end up with a 10sec clip that sounds like someone saying "activate" in a cafe, with "activate" superimposed on the background cafe noise. You do not end up with an 11 sec clip. You'll see later how pydub allows you to do this.

Creating the labels at the same time you overlay:

Recall also that the labels y⟨t⟩y⟨t⟩ represent whether or not someone has just finished saying "activate." Given a background clip, we can initialize y⟨t⟩=0y⟨t⟩=0 for all tt, since the clip doesn't contain any "activates."

When you insert or overlay an "activate" clip, you will also update labels for y⟨t⟩y⟨t⟩, so that 50 steps of the output now have target label 1. You will train a GRU to detect when someone has finished saying "activate". For example, suppose the synthesized "activate" clip ends at the 5sec mark in the 10sec audio---exactly halfway into the clip. Recall that Ty=1375Ty=1375, so timestep 687=687= int(1375*0.5) corresponds to the moment at 5sec into the audio. So, you will set y⟨688⟩=1y⟨688⟩=1. Further, you would quite satisfied if the GRU detects "activate" anywhere within a short time-internal after this moment, so we actually set 50 consecutive values of the label y⟨t⟩y⟨t⟩ to 1. Specifically, we have y⟨688⟩=y⟨689⟩=⋯=y⟨737⟩=1y⟨688⟩=y⟨689⟩=⋯=y⟨737⟩=1.

This is another reason for synthesizing the training data: It's relatively straightforward to generate these labels y⟨t⟩y⟨t⟩ as described above. In contrast, if you have 10sec of audio recorded on a microphone, it's quite time consuming for a person to listen to it and mark manually exactly when "activate" finished.

Here's a figure illustrating the labels y⟨t⟩y⟨t⟩, for a clip which we have inserted "activate", "innocent", activate", "baby." Note that the positive labels "1" are associated only with the positive words.

Figure 2

To implement the training set synthesis process, you will use the following helper functions. All of these function will use a 1ms discretization interval, so the 10sec of audio is always discretized into 10,000 steps.

  1. get_random_time_segment(segment_ms) gets a random time segment in our background audio
  2. is_overlapping(segment_time, existing_segments) checks if a time segment overlaps with existing segments
  3. insert_audio_clip(background, audio_clip, existing_times) inserts an audio segment at a random time in our background audio using get_random_time_segment and is_overlapping
  4. insert_ones(y, segment_end_ms) inserts 1's into our label vector y after the word "activate"
 

The function get_random_time_segment(segment_ms) returns a random time segment onto which we can insert an audio clip of duration segment_ms. Read through the code to make sure you understand what it is doing.

In [12]:

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)

Next, suppose you have inserted audio clips at segments (1000,1800) and (3400,4500). I.e., the first segment starts at step 1000, and ends at step 1800. Now, if we are considering inserting a new audio clip at (3000,3600) does this overlap with one of the previously inserted segments? In this case, (3000,3600) and (3400,4500) overlap, so we should decide against inserting a clip here.

For the purpose of this function, define (100,200) and (200,250) to be overlapping, since they overlap at timestep 200. However, (100,199) and (200,250) are non-overlapping.

Exercise: Implement is_overlapping(segment_time, existing_segments) to check if a new time segment overlaps with any of the previous segments. You will need to carry out 2 steps:

  1. Create a "False" flag, that you will later set to "True" if you find that there is an overlap.
  2. Loop over the previous_segments' start and end times. Compare these times to the segment's start and end times. If there is an overlap, set the flag defined in (1) as True. You can use:
    for ....:
    if ... <= ... and ... >= ...:
    ...

    Hint: There is overlap if the segment starts before the previous segment ends, and the segment ends after the previous segment starts.

In [13]:
# GRADED FUNCTION: is_overlapping

def 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)
for previous_start, previous_end in previous_segments:
if previous_start <= segment_end and previous_end >= segment_start : #刚开始没想通觉得不同的overlap对应的条件不同
#画画图发现条件还真是都是这样的,previous_start <= segment_end and previous_end >= segment_start就是充要条件
overlap = True
### END CODE HERE ###

return overlap
In [14]:
overlap1 = is_overlapping((950, 1430), [(2000, 2550), (260, 949)])
overlap2 = is_overlapping((2305, 2950), [(824, 1532), (1900, 2305), (3424, 3656)])
print("Overlap 1 = ", overlap1)
print("Overlap 2 = ", overlap2)
Overlap 1 =  False
Overlap 2 = True
 

Expected Output:

Overlap 1 False
Overlap 2 True
 

Now, lets use the previous helper functions to insert a new audio clip onto the 10sec background at a random time, but making sure that any newly inserted segment doesn't overlap with the previous segments.

Exercise: Implement insert_audio_clip() to overlay an audio clip onto the background 10sec clip. You will need to carry out 4 steps:

  1. Get a random time segment of the right duration in ms.
  2. Make sure that the time segment does not overlap with any of the previous time segments. If it is overlapping, then go back to step 1 and pick a new time segment.
  3. Add the new time segment to the list of existing time segments, so as to keep track of all the segments you've inserted.
  4. Overlay the audio clip over the background using pydub. We have implemented this for you.
In [15]:# GRADED FUNCTION: insert_audio_clip

def 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
In [16]:
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")
Segment Time:  (2254, 3169)

Expected Output

Segment Time (2254, 3169)
In [18]:
# Expected audio
IPython.display.Audio("audio_examples/insert_reference.wav")

Finally, implement code to update the labels y⟨t⟩y⟨t⟩, assuming you just inserted an "activate." In the code below, y is a (1,1375) dimensional vector, since Ty=1375Ty=1375.

If the "activate" ended at time step tt, then set y⟨t+1⟩=1y⟨t+1⟩=1 as well as for up to 49 additional consecutive values. However, make sure you don't run off the end of the array and try to update y[0][1375], since the valid indices are y[0][0] through y[0][1374] because Ty=1375Ty=1375. So if "activate" ends at step 1370, you would get only y[0][1371] = y[0][1372] = y[0][1373] = y[0][1374] = 1

Exercise: Implement insert_ones(). You can use a for loop. (If you are an expert in python's slice operations, feel free also to use slicing to vectorize this.) If a segment ends at segment_end_ms (using a 10000 step discretization), to convert it to the indexing for the outputs yy (using a 13751375 step discretization), we will use this formula:

    segment_end_y = int(segment_end_ms * Ty / 10000.0)
In [19]:
for i in range(1, 10): #自己也忘了这个range的输出情况到底是咋样的,写个例子看看
print(i)
1
2
3
4
5
6
7
8
9
In [20]:
# GRADED FUNCTION: insert_ones

def 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 following 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): #因为segment_end_y是结束时间,所以之后开始标记为1,并延续50个time_step
if i < Ty: #Ty已经越界,所以是小于Ty
y[0, i] = 1
### END CODE HERE ### return y
In [21]:
arr1 = insert_ones(np.zeros((1, Ty)), 9700)
plt.plot(insert_ones(arr1, 4251)[0,:])
print("sanity checks:", arr1[0][1333], arr1[0][634], arr1[0][635])
sanity checks: 0.0 1.0 0.0
 
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oztHXNst+33V9tvs31e+6UCAOpaNNxtr5B0taStkjZLusz25pHdtkraVH1tl3RNy3UCj1fz9DfL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%0AMn8AAAAASUVORK5CYII=" alt="" />
 

Expected Output

sanity checks: 0.0 1.0 0.0

Finally, you can use insert_audio_clip and insert_ones to create a new training example.

Exercise: Implement create_training_example(). You will need to carry out the following steps:

  1. Initialize the label vector yy as a numpy array of zeros and shape (1,Ty)(1,Ty).
  2. Initialize the set of existing segments to an empty list.
  3. Randomly select 0 to 4 "activate" audio clips, and insert them onto the 10sec clip. Also insert labels at the correct position in the label vector yy.
  4. Randomly select 0 to 2 negative audio clips, and insert them into the 10sec clip.
In [22]:
# GRADED FUNCTION: create_training_example

def 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
 
In [23]:
x, y = create_training_example(backgrounds[0], activates, negatives)
File (train.wav) was saved in your directory.
 
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czLa2R0kGvgcNfmQsPIf1pxTJJF33HPuTOczlG1BmDzNdvOYFHihXnaCciGzmM9id/Ljdp9LxZF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%0ArkJggg==" alt="" />
 

Expected Output

Now you can listen to the training example you created and compare it to the spectrogram generated above.

In [25]:
IPython.display.Audio("train.wav")
In [26]:
IPython.display.Audio("audio_examples/train_reference.wav")

Finally, you can plot the associated labels for the generated training example.

In [27]:
plt.plot(y[0])
Out[27]:
[<matplotlib.lines.Line2D at 0x7f358223b4e0>]
 
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adCTq810n4q5GTrkGpiuEtShQz3ipXRnvaukNIkGO4Cymh5FFCiNBiGe+WKuCvktAuQKmS4V6zz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%0AQv8PcimiUyGDpLEAAAAASUVORK5CYII=" alt="" />
 

Expected Output

 

1.4 - Full training set

You've now implemented the code needed to generate a single training example. We used this process to generate a large training set. To save time, we've already generated a set of training examples.

In [28]:
# Load preprocessed training examples
X = np.load("./XY_train/X.npy")
Y = np.load("./XY_train/Y.npy")
1.5 - Development set

To test our model, we recorded a development set of 25 examples. While our training data is synthesized, we want to create a development set using the same distribution as the real inputs. Thus, we recorded 25 10-second audio clips of people saying "activate" and other random words, and labeled them by hand. This follows the principle described in Course 3 that we should create the dev set to be as similar as possible to the test set distribution; that's why our dev set uses real rather than synthesized audio.

In [29]:
# Load preprocessed dev set examples
X_dev = np.load("./XY_dev/X_dev.npy")
Y_dev = np.load("./XY_dev/Y_dev.npy")
2 - Model

Now that you've built a dataset, lets write and train a trigger word detection model!

The model will use 1-D convolutional layers, GRU layers, and dense layers. Let's load the packages that will allow you to use these layers in Keras. This might take a minute to load.

In [30]:
from keras.callbacks import ModelCheckpoint
from keras.models import Model, load_model, Sequential
from keras.layers import Dense, Activation, Dropout, Input, Masking, TimeDistributed, LSTM, Conv1D
from keras.layers import GRU, Bidirectional, BatchNormalization, Reshape
from keras.optimizers import Adam
Using TensorFlow backend.
 

2.1 - Build the model

Here is the architecture we will use. Take some time to look over the model and see if it makes sense.

 Figure 3

One key step of this model is the 1D convolutional step (near the bottom of Figure 3). It inputs the 5511 step spectrogram, and outputs a 1375 step output, which is then further processed by multiple layers to get the final Ty=1375Ty=1375 step output. This layer plays a role similar to the 2D convolutions you saw in Course 4, of extracting low-level features and then possibly generating an output of a smaller dimension.

Computationally, the 1-D conv layer also helps speed up the model because now the GRU has to process only 1375 timesteps rather than 5511 timesteps. The two GRU layers read the sequence of inputs from left to right, then ultimately uses a dense+sigmoid layer to make a prediction for y⟨t⟩y⟨t⟩. Because yy is binary valued (0 or 1), we use a sigmoid output at the last layer to estimate the chance of the output being 1, corresponding to the user having just said "activate."

Note that we use a uni-directional RNN rather than a bi-directional RNN. This is really important for trigger word detection, since we want to be able to detect the trigger word almost immediately after it is said. If we used a bi-directional RNN, we would have to wait for the whole 10sec of audio to be recorded before we could tell if "activate" was said in the first second of the audio clip.

 

Implementing the model can be done in four steps:

Step 1: CONV layer. Use Conv1D() to implement this, with 196 filters, a filter size of 15 (kernel_size=15), and stride of 4. [See documentation.]

Step 2: First GRU layer. To generate the GRU layer, use:

X = GRU(units = 128, return_sequences = True)(X)

Setting return_sequences=True ensures that all the GRU's hidden states are fed to the next layer. Remember to follow this with Dropout and BatchNorm layers.

Step 3: Second GRU layer. This is similar to the previous GRU layer (remember to use return_sequences=True), but has an extra dropout layer.

Step 4: Create a time-distributed dense layer as follows:

X = TimeDistributed(Dense(1, activation = "sigmoid"))(X)

This creates a dense layer followed by a sigmoid, so that the parameters used for the dense layer are the same for every time step. [See documentation.]

Exercise: Implement model(), the architecture is presented in Figure 3.

In [43]:
# GRADED FUNCTION: model

def 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, kernel_size=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
In [44]:
model = model(input_shape = (Tx, n_freq))

Let's print the model summary to keep track of the shapes.

In [45]:
model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_7 (InputLayer) (None, 5511, 101) 0
_________________________________________________________________
conv1d_7 (Conv1D) (None, 1375, 196) 297136
_________________________________________________________________
batch_normalization_3 (Batch (None, 1375, 196) 784
_________________________________________________________________
activation_3 (Activation) (None, 1375, 196) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 1375, 196) 0
_________________________________________________________________
gru_1 (GRU) (None, 1375, 128) 124800
_________________________________________________________________
dropout_2 (Dropout) (None, 1375, 128) 0
_________________________________________________________________
batch_normalization_4 (Batch (None, 1375, 128) 512
_________________________________________________________________
gru_2 (GRU) (None, 1375, 128) 98688
_________________________________________________________________
dropout_3 (Dropout) (None, 1375, 128) 0
_________________________________________________________________
batch_normalization_5 (Batch (None, 1375, 128) 512
_________________________________________________________________
dropout_4 (Dropout) (None, 1375, 128) 0
_________________________________________________________________
time_distributed_1 (TimeDist (None, 1375, 1) 129
=================================================================
Total params: 522,561
Trainable params: 521,657
Non-trainable params: 904
_________________________________________________________________
 

Expected Output:

Total params 522,561
Trainable params 521,657
Non-trainable params 904
 

The output of the network is of shape (None, 1375, 1) while the input is (None, 5511, 101). The Conv1D has reduced the number of steps from 5511 at spectrogram to 1375.

 

2.2 - Fit the model

 

Trigger word detection takes a long time to train. To save time, we've already trained a model for about 3 hours on a GPU using the architecture you built above, and a large training set of about 4000 examples. Let's load the model.

In [46]:
model = load_model('./models/tr_model.h5')

You can train the model further, using the Adam optimizer and binary cross entropy loss, as follows. This will run quickly because we are training just for one epoch and with a small training set of 26 examples.

In [47]:
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"])
In [48]:
model.fit(X, Y, batch_size = 5, epochs=1)
Epoch 1/1
26/26 [==============================] - 35s - loss: 0.0720 - acc: 0.9778
Out[48]:
<keras.callbacks.History at 0x7f350c1ffb70>
 

2.3 - Test the model

Finally, let's see how your model performs on the dev set.

In [49]:
loss, acc = model.evaluate(X_dev, Y_dev)
print("Dev set accuracy = ", acc)
25/25 [==============================] - 5s
Dev set accuracy = 0.94600725174
 

This looks pretty good! However, accuracy isn't a great metric for this task, since the labels are heavily skewed to 0's, so a neural network that just outputs 0's would get slightly over 90% accuracy. We could define more useful metrics such as F1 score or Precision/Recall. But let's not bother with that here, and instead just empirically see how the model does.

 

3 - Making Predictions

Now that you have built a working model for trigger word detection, let's use it to make predictions. This code snippet runs audio (saved in a wav file) through the network.

In [50]:
def detect_triggerword(filename):
plt.subplot(2, 1, 1)

x = graph_spectrogram(filename)
# the spectogram outputs (freqs, Tx) and we want (Tx, freqs) to input into the model
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

Once you've estimated the probability of having detected the word "activate" at each output step, you can trigger a "chiming" sound to play when the probability is above a certain threshold. Further, y⟨t⟩y⟨t⟩ might be near 1 for many values in a row after "activate" is said, yet we want to chime only once. So we will insert a chime sound at most once every 75 output steps. This will help prevent us from inserting two chimes for a single instance of "activate". (This plays a role similar to non-max suppression from computer vision.)

In [51]:
chime_file = "audio_examples/chime.wav"
def chime_on_activate(filename, predictions, threshold):
audio_clip = AudioSegment.from_wav(filename)
chime = AudioSegment.from_wav(chime_file)
Ty = predictions.shape[1]
# Step 1: Initialize the number of consecutive output steps to 0
consecutive_timesteps = 0
# Step 2: Loop over the output steps in the y
for i in range(Ty):
# Step 3: Increment consecutive output steps
consecutive_timesteps += 1
# Step 4: If prediction is higher than the threshold and more than 75 consecutive output steps have passed
if predictions[0,i,0] > threshold and consecutive_timesteps > 75:
# Step 5: Superpose audio and background using pydub
audio_clip = audio_clip.overlay(chime, position = ((i / Ty) * audio_clip.duration_seconds)*1000)
# Step 6: Reset consecutive output steps to 0
consecutive_timesteps = 0 audio_clip.export("chime_output.wav", format='wav')
3.3 - Test on dev examples
 

Let's explore how our model performs on two unseen audio clips from the development set. Lets first listen to the two dev set clips.

In [52]:
IPython.display.Audio("./raw_data/dev/1.wav")
In [53]:
IPython.display.Audio("./raw_data/dev/2.wav")

Now lets run the model on these audio clips and see if it adds a chime after "activate"!

In [54]:
filename = "./raw_data/dev/1.wav"
prediction = detect_triggerword(filename)
chime_on_activate(filename, prediction, 0.5)
IPython.display.Audio("./chime_output.wav")
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95PN9IOHaSe0e/uMc9l8VojsMhVCRY8U+yuSkouM9+r3gHNrWUEJ5ZJ2RpYvGZ25YZ1MohtWiuy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%0AXpYUjDHG9LKkYIwxppclBWOMMb3+P3JAAZ6GFvfVAAAAAElFTkSuQmCC" alt="" />
In [55]:
filename  = "./raw_data/dev/2.wav"
prediction = detect_triggerword(filename)
chime_on_activate(filename, prediction, 0.5)
IPython.display.Audio("./chime_output.wav")
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1lAWVXomuurnGcgC/SEMKH2hRBnrMAmMetfZwYvk2NgE20/7wSy43zmk8qAqAh08mhehyVbniPu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%0A8zqRAAAAAElFTkSuQmCC" alt="" />

Congratulations

You've come to the end of this assignment!

Here's what you should remember:

  • Data synthesis is an effective way to create a large training set for speech problems, specifically trigger word detection.
  • Using a spectrogram and optionally a 1D conv layer is a common pre-processing step prior to passing audio data to an RNN, GRU or LSTM.
  • An end-to-end deep learning approach can be used to built a very effective trigger word detection system.

Congratulations on finishing the final assignment!

Thank you for sticking with us through the end and for all the hard work you've put into learning deep learning. We hope you have enjoyed the course!

 

4 - Try your own example! (OPTIONAL/UNGRADED)

In this optional and ungraded portion of this notebook, you can try your model on your own audio clips!

Record a 10 second audio clip of you saying the word "activate" and other random words, and upload it to the Coursera hub as myaudio.wav. Be sure to upload the audio as a wav file. If your audio is recorded in a different format (such as mp3) there is free software that you can find online for converting it to wav. If your audio recording is not 10 seconds, the code below will either trim or pad it as needed to make it 10 seconds.

In [ ]:
# Preprocess the audio to the correct format
def preprocess_audio(filename):
# Trim or pad audio segment to 10000ms
padding = AudioSegment.silent(duration=10000)
segment = AudioSegment.from_wav(filename)[:10000]
segment = padding.overlay(segment)
# Set frame rate to 44100
segment = segment.set_frame_rate(44100)
# Export as wav
segment.export(filename, format='wav')
Once you've uploaded your audio file to Coursera, put the path to your file in the variable below.
In [ ]:
your_filename = "audio_examples/my_audio.wav"
In [ ]:
preprocess_audio(your_filename)
IPython.display.Audio(your_filename) # listen to the audio you uploaded

Finally, use the model to predict when you say activate in the 10 second audio clip, and trigger a chime. If beeps are not being added appropriately, try to adjust the chime_threshold.

In [ ]:
chime_threshold = 0.5
prediction = detect_triggerword(your_filename)
chime_on_activate(your_filename, prediction, chime_threshold)
IPython.display.Audio("./chime_output.wav")

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