{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Trigger Word Detection\n", "\n", "Welcome to the final programming assignment of this specialization! \n", "\n", "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 wake word detection). \n", "\n", "* 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. \n", "* For this exercise, our trigger word will be \"Activate.\" Every time it hears you say \"activate,\" it will make a \"chiming\" sound. \n", "* 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.\" \n", "* 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? \n", "\n", "\n", "\n", "In this assignment you will learn to: \n", "- Structure a speech recognition project\n", "- Synthesize and process audio recordings to create train/dev datasets\n", "- Train a trigger word detection model and make predictions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Updates\n", "\n", "#### If you were working on the notebook before this update...\n", "* The current notebook is version \"1a\".\n", "* You can find your original work saved in the notebook with the previous version name (\"v1\") \n", "* To view the file directory, go to the menu \"File->Open\", and this will open a new tab that shows the file directory.\n", "\n", "#### List of updates\n", "* 2.1: build the model\n", " * Added sample code to show how to use the Keras layers.\n", " * Lets student to implement the `TimeDistributed` code.\n", "* Spelling, grammar and wording corrections." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get started! Run the following cell to load the package you are going to use. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from pydub import AudioSegment\n", "import random\n", "import sys\n", "import io\n", "import os\n", "import glob\n", "import IPython\n", "from td_utils import *\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1 - Data synthesis: Creating a speech dataset \n", "\n", "Let's start by building a dataset for your trigger word detection algorithm. \n", "* A speech dataset should ideally be as close as possible to the application you will want to run it on. \n", "* In this case, you'd like to detect the word \"activate\" in working environments (library, home, offices, open-spaces ...). \n", "* Therefore, you 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. \n", "\n", "## 1.1 - Listening to the data \n", "\n", "* 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. \n", "* 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. \n", " * The \"activate\" directory contains positive examples of people saying the word \"activate\". \n", " * The \"negatives\" directory contains negative examples of people saying random words other than \"activate\". \n", " * There is one word per audio recording. \n", " * The \"backgrounds\" directory contains 10 second clips of background noise in different environments.\n", "\n", "Run the cells below to listen to some examples." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IPython.display.Audio(\"./raw_data/activates/1.wav\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IPython.display.Audio(\"./raw_data/negatives/4.wav\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IPython.display.Audio(\"./raw_data/backgrounds/1.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You will use these three types of recordings (positives/negatives/backgrounds) to create a labeled dataset." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.2 - From audio recordings to spectrograms\n", "\n", "What really is an audio recording? \n", "* 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. \n", "* You can think of an audio recording is a long list of numbers measuring the little air pressure changes detected by the microphone. \n", "* We will use audio sampled at 44100 Hz (or 44100 Hertz). \n", " * This means the microphone gives us 44,100 numbers per second. \n", " * Thus, a 10 second audio clip is represented by 441,000 numbers (= $10 \\times 44,100$). \n", "\n", "#### Spectrogram\n", "* It is quite difficult to figure out from this \"raw\" representation of audio whether the word \"activate\" was said. \n", "* In order to help your sequence model more easily learn to detect trigger words, we will compute a *spectrogram* of the audio. \n", "* The spectrogram tells us how much different frequencies are present in an audio clip at any moment in time. \n", "* If you've ever taken an advanced class on signal processing or on Fourier transforms:\n", " * A spectrogram is computed by sliding a window over the raw audio signal, and calculating the most active frequencies in each window using a Fourier transform. \n", " * If you don't understand the previous sentence, don't worry about it.\n", "\n", "Let's look at an example. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IPython.display.Audio(\"audio_examples/example_train.wav\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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Ijx29/oMi8vv2t1+whvNf//g6yREIvMcnyo4rGYg7QX3Nf2LSyOPaaOvLSb7B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yAZvZ+xCjO6JlJALSTMVgXlBhWHXxiHxSbQRVQJbGdoDYAY8flcyXAtZiJmn5lbQ6UYvh5CM4zS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\nqZdVymBIvSFuCrTOzEqZe/ukRZVxOWPwljFIcMrYO+LOidohyvwalZnNe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KQpjQRmDNHy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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = graph_spectrogram(\"audio_examples/example_train.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph above represents how active each frequency is (y axis) over a number of time-steps (x axis). \n", "\n", "\n", "
**Figure 1**: Spectrogram of an audio recording
\n", "\n", "\n", "* The color in the spectrogram shows the degree to which different frequencies are present (loud) in the audio at different points in time. \n", "* Green means a certain frequency is more active or more present in the audio clip (louder).\n", "* Blue squares denote less active frequencies.\n", "* The dimension of the output spectrogram depends upon the hyperparameters of the spectrogram software and the length of the input. \n", "* In this notebook, we will be working with 10 second audio clips as the \"standard length\" for our training examples. \n", " * The number of timesteps of the spectrogram will be 5511. \n", " * You'll see later that the spectrogram will be the input $x$ into the network, and so $T_x = 5511$." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time steps in audio recording before spectrogram (441000,)\n", "Time steps in input after spectrogram (101, 5511)\n" ] } ], "source": [ "_, data = wavfile.read(\"audio_examples/example_train.wav\")\n", "print(\"Time steps in audio recording before spectrogram\", data[:,0].shape)\n", "print(\"Time steps in input after spectrogram\", x.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, you can define:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Tx = 5511 # The number of time steps input to the model from the spectrogram\n", "n_freq = 101 # Number of frequencies input to the model at each time step of the spectrogram" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Dividing into time-intervals\n", "Note that we may divide a 10 second interval of time with different units (steps).\n", "* Raw audio divides 10 seconds into 441,000 units.\n", "* A spectrogram divides 10 seconds into 5,511 units.\n", " * $T_x = 5511$\n", "* You will use a Python module `pydub` to synthesize audio, and it divides 10 seconds into 10,000 units.\n", "* The output of our model will divide 10 seconds into 1,375 units.\n", " * $T_y = 1375$\n", " * For each of the 1375 time steps, the model predicts whether someone recently finished saying the trigger word \"activate.\" \n", "* All of these are hyperparameters and can be changed (except the 441000, which is a function of the microphone). \n", "* We have chosen values that are within the standard range used for speech systems." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Ty = 1375 # The number of time steps in the output of our model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.3 - Generating a single training example\n", "\n", "#### Benefits of synthesizing data\n", "Because speech data is hard to acquire and label, you will synthesize your training data using the audio clips of activates, negatives, and backgrounds. \n", "* It is quite slow to record lots of 10 second audio clips with random \"activates\" in it. \n", "* 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). \n", "\n", "#### Process for Synthesizing an audio clip\n", "* To synthesize a single training example, you will:\n", " - Pick a random 10 second background audio clip\n", " - Randomly insert 0-4 audio clips of \"activate\" into this 10sec clip\n", " - Randomly insert 0-2 audio clips of negative words into this 10sec clip\n", "* Because you had synthesized the word \"activate\" into the background clip, you know exactly when in the 10 second clip the \"activate\" makes its appearance. \n", " * You'll see later that this makes it easier to generate the labels $y^{\\langle t \\rangle}$ as well. \n", "\n", "#### Pydub\n", "* You will use the pydub package to manipulate audio. \n", "* Pydub converts raw audio files into lists of Pydub data structures.\n", " * Don't worry about the details of the data structures.\n", "* Pydub uses 1ms as the discretization interval (1ms is 1 millisecond = 1/1000 seconds).\n", " * This is why a 10 second clip is always represented using 10,000 steps. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "background len should be 10,000, since it is a 10 sec clip\n", "10000 \n", "\n", "activate[0] len may be around 1000, since an `activate` audio clip is usually around 1 second (but varies a lot) \n", "916 \n", "\n", "activate[1] len: different `activate` clips can have different lengths\n", "1579 \n", "\n" ] } ], "source": [ "# Load audio segments using pydub \n", "activates, negatives, backgrounds = load_raw_audio()\n", "\n", "print(\"background len should be 10,000, since it is a 10 sec clip\\n\" + str(len(backgrounds[0])),\"\\n\")\n", "print(\"activate[0] len may be around 1000, since an `activate` audio clip is usually around 1 second (but varies a lot) \\n\" + str(len(activates[0])),\"\\n\")\n", "print(\"activate[1] len: different `activate` clips can have different lengths\\n\" + str(len(activates[1])),\"\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Overlaying positive/negative 'word' audio clips on top of the background audio\n", "\n", "* Given a 10 second background clip and a short audio clip containing a positive or negative word, you need to be able to \"add\" the word audio clip on top of the background audio.\n", "* You will be inserting multiple clips of positive/negative words into 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. \n", " * To ensure that the 'word' audio segments do not overlap when inserted, you will keep track of the times of previously inserted audio clips. \n", "* To be clear, when you insert a 1 second \"activate\" onto a 10 second clip of cafe noise, **you do not end up with an 11 sec clip.** \n", " * The resulting audio clip is still 10 seconds long.\n", " * You'll see later how pydub allows you to do this. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Label the positive/negative words\n", "* Recall that the labels $y^{\\langle t \\rangle}$ represent whether or not someone has just finished saying \"activate.\" \n", " * $y^{\\langle t \\rangle} = 1$ when that that clip has finished saying \"activate\".\n", " * Given a background clip, we can initialize $y^{\\langle t \\rangle}=0$ for all $t$, since the clip doesn't contain any \"activates.\" \n", "* When you insert or overlay an \"activate\" clip, you will also update labels for $y^{\\langle t \\rangle}$.\n", " * Rather than updating the label of a single time step, we will update 50 steps of the output to have target label 1. \n", " * Recall from the lecture on trigger word detection that updating several consecutive time steps can make the training data more balanced.\n", "* You will train a GRU (Gated Recurrent Unit) to detect when someone has **finished** saying \"activate\". \n", "\n", "##### Example\n", "* Suppose the synthesized \"activate\" clip ends at the 5 second mark in the 10 second audio - exactly halfway into the clip. \n", "* Recall that $T_y = 1375$, so timestep $687 = $ `int(1375*0.5)` corresponds to the moment 5 seconds into the audio clip. \n", "* Set $y^{\\langle 688 \\rangle} = 1$. \n", "* We will allow the GRU to detect \"activate\" anywhere within a short time-internal **after** this moment, so we actually **set 50 consecutive values** of the label $y^{\\langle t \\rangle}$ to 1. \n", " * Specifically, we have $y^{\\langle 688 \\rangle} = y^{\\langle 689 \\rangle} = \\cdots = y^{\\langle 737 \\rangle} = 1$. \n", "\n", "##### Synthesized data is easier to label\n", "* This is another reason for synthesizing the training data: It's relatively straightforward to generate these labels $y^{\\langle t \\rangle}$ as described above. \n", "* 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. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Visualizing the labels\n", "* Here's a figure illustrating the labels $y^{\\langle t \\rangle}$ in a clip.\n", " * We have inserted \"activate\", \"innocent\", activate\", \"baby.\" \n", " * Note that the positive labels \"1\" are associated only with the positive words. \n", "\n", "\n", "
**Figure 2**
\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Helper functions\n", "\n", "To implement the training set synthesis process, you will use the following helper functions. \n", "* All of these functions will use a 1ms discretization interval\n", "* The 10 seconds of audio is always discretized into 10,000 steps. \n", "\n", "\n", "1. `get_random_time_segment(segment_ms)`\n", " * Retrieves a random time segment from the background audio.\n", "2. `is_overlapping(segment_time, existing_segments)`\n", " * Checks if a time segment overlaps with existing segments\n", "3. `insert_audio_clip(background, audio_clip, existing_times)`\n", " * Inserts an audio segment at a random time in the background audio\n", " * Uses the functions `get_random_time_segment` and `is_overlapping`\n", "4. `insert_ones(y, segment_end_ms)`\n", " * Inserts additional 1's into the label vector y after the word \"activate\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Get a random time segment\n", "\n", "* 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`. \n", "* Please read through the code to make sure you understand what it is doing. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_random_time_segment(segment_ms):\n", " \"\"\"\n", " Gets a random time segment of duration segment_ms in a 10,000 ms audio clip.\n", " \n", " Arguments:\n", " segment_ms -- the duration of the audio clip in ms (\"ms\" stands for \"milliseconds\")\n", " \n", " Returns:\n", " segment_time -- a tuple of (segment_start, segment_end) in ms\n", " \"\"\"\n", " \n", " segment_start = np.random.randint(low=0, high=10000-segment_ms) # Make sure segment doesn't run past the 10sec background \n", " segment_end = segment_start + segment_ms - 1\n", " \n", " return (segment_start, segment_end)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Check if audio clips are overlapping\n", "\n", "* Suppose you have inserted audio clips at segments (1000,1800) and (3400,4500).\n", " * The first segment starts at step 1000 and ends at step 1800. \n", " * The second segment starts at 3400 and ends at 4500.\n", "* If we are considering whether to insert a new audio clip at (3000,3600) does this overlap with one of the previously inserted segments? \n", " * In this case, (3000,3600) and (3400,4500) overlap, so we should decide against inserting a clip here.\n", "* For the purpose of this function, define (100,200) and (200,250) to be overlapping, since they overlap at timestep 200. \n", "* (100,199) and (200,250) are non-overlapping. \n", "\n", "**Exercise**: \n", "* Implement `is_overlapping(segment_time, existing_segments)` to check if a new time segment overlaps with any of the previous segments. \n", "* You will need to carry out 2 steps:\n", "\n", "1. Create a \"False\" flag, that you will later set to \"True\" if you find that there is an overlap.\n", "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. \n", "\n", "You can use:\n", "```python\n", "for ....:\n", " if ... <= ... and ... >= ...:\n", " ...\n", "```\n", "Hint: There is overlap if:\n", "* The new segment starts before the previous segment ends **and**\n", "* The new segment ends after the previous segment starts." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: is_overlapping\n", "\n", "def is_overlapping(segment_time, previous_segments):\n", " \"\"\"\n", " Checks if the time of a segment overlaps with the times of existing segments.\n", " \n", " Arguments:\n", " segment_time -- a tuple of (segment_start, segment_end) for the new segment\n", " previous_segments -- a list of tuples of (segment_start, segment_end) for the existing segments\n", " \n", " Returns:\n", " True if the time segment overlaps with any of the existing segments, False otherwise\n", " \"\"\"\n", " \n", " segment_start, segment_end = segment_time\n", " \n", " ### START CODE HERE ### (≈ 4 lines)\n", " # Step 1: Initialize overlap as a \"False\" flag. (≈ 1 line)\n", " overlap = False\n", " \n", " # Step 2: loop over the previous_segments start and end times.\n", " # Compare start/end times and set the flag to True if there is an overlap (≈ 3 lines)\n", " for previous_start, previous_end in previous_segments:\n", " if segment_start <= previous_end and segment_end >= previous_start:\n", " overlap = True\n", " ### END CODE HERE ###\n", "\n", " return overlap" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overlap 1 = False\n", "Overlap 2 = True\n" ] } ], "source": [ "overlap1 = is_overlapping((950, 1430), [(2000, 2550), (260, 949)])\n", "overlap2 = is_overlapping((2305, 2950), [(824, 1532), (1900, 2305), (3424, 3656)])\n", "print(\"Overlap 1 = \", overlap1)\n", "print(\"Overlap 2 = \", overlap2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", " **Overlap 1**\n", " \n", " False\n", "
\n", " **Overlap 2**\n", " \n", " True\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Insert audio clip\n", "\n", "* Let's use the previous helper functions to insert a new audio clip onto the 10 second background at a random time.\n", "* We will ensure that any newly inserted segment doesn't overlap with previously inserted segments. \n", "\n", "**Exercise**:\n", "* Implement `insert_audio_clip()` to overlay an audio clip onto the background 10sec clip. \n", "* You implement 4 steps:\n", "\n", "1. Get the length of the audio clip that is to be inserted.\n", " * Get a random time segment whose duration equals the duration of the audio clip that is to be inserted.\n", "2. Make sure that the time segment does not overlap with any of the previous time segments. \n", " * If it is overlapping, then go back to step 1 and pick a new time segment.\n", "3. Append the new time segment to the list of existing time segments\n", " * This keeps track of all the segments you've inserted. \n", "4. Overlay the audio clip over the background using pydub. We have implemented this for you." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: insert_audio_clip\n", "\n", "def insert_audio_clip(background, audio_clip, previous_segments):\n", " \"\"\"\n", " Insert a new audio segment over the background noise at a random time step, ensuring that the \n", " audio segment does not overlap with existing segments.\n", " \n", " Arguments:\n", " background -- a 10 second background audio recording. \n", " audio_clip -- the audio clip to be inserted/overlaid. \n", " previous_segments -- times where audio segments have already been placed\n", " \n", " Returns:\n", " new_background -- the updated background audio\n", " \"\"\"\n", " \n", " # Get the duration of the audio clip in ms\n", " segment_ms = len(audio_clip)\n", " \n", " ### START CODE HERE ### \n", " # Step 1: Use one of the helper functions to pick a random time segment onto which to insert \n", " # the new audio clip. (≈ 1 line)\n", " segment_time = get_random_time_segment(segment_ms)\n", " \n", " # Step 2: Check if the new segment_time overlaps with one of the previous_segments. If so, keep \n", " # picking new segment_time at random until it doesn't overlap. (≈ 2 lines)\n", " while is_overlapping(segment_time, previous_segments):\n", " segment_time = get_random_time_segment(segment_ms)\n", "\n", " # Step 3: Add the new segment_time to the list of previous_segments (≈ 1 line)\n", " previous_segments.append(segment_time)\n", " ### END CODE HERE ###\n", " \n", " # Step 4: Superpose audio segment and background\n", " new_background = background.overlay(audio_clip, position = segment_time[0])\n", " \n", " return new_background, segment_time" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Segment Time: (2254, 3169)\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.seed(5)\n", "audio_clip, segment_time = insert_audio_clip(backgrounds[0], activates[0], [(3790, 4400)])\n", "audio_clip.export(\"insert_test.wav\", format=\"wav\")\n", "print(\"Segment Time: \", segment_time)\n", "IPython.display.Audio(\"insert_test.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**\n", "\n", "\n", " \n", " \n", " \n", " \n", "
\n", " **Segment Time**\n", " \n", " (2254, 3169)\n", "
" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Expected audio\n", "IPython.display.Audio(\"audio_examples/insert_reference.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Insert ones for the labels of the positive target\n", "\n", "* Implement code to update the labels $y^{\\langle t \\rangle}$, assuming you just inserted an \"activate\" audio clip.\n", "* In the code below, `y` is a `(1,1375)` dimensional vector, since $T_y = 1375$. \n", "* If the \"activate\" audio clip ends at time step $t$, then set $y^{\\langle t+1 \\rangle} = 1$ and also set the next 49 additional consecutive values to 1.\n", " * Notice that if the target word appears near the end of the entire audio clip, there may not be 50 additional time steps to set to 1.\n", " * 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 $T_y = 1375$. \n", " * So if \"activate\" ends at step 1370, you would get only set `y[0][1371] = y[0][1372] = y[0][1373] = y[0][1374] = 1`\n", "\n", "**Exercise**: \n", "Implement `insert_ones()`. \n", "* You can use a for loop. \n", "* If you want to use Python's array slicing operations, you can do so as well.\n", "* If a segment ends at `segment_end_ms` (using a 10000 step discretization),\n", " * To convert it to the indexing for the outputs $y$ (using a $1375$ step discretization), we will use this formula: \n", "```\n", " segment_end_y = int(segment_end_ms * Ty / 10000.0)\n", "```" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: insert_ones\n", "\n", "def insert_ones(y, segment_end_ms):\n", " \"\"\"\n", " Update the label vector y. The labels of the 50 output steps strictly after the end of the segment \n", " should be set to 1. By strictly we mean that the label of segment_end_y should be 0 while, the\n", " 50 following labels should be ones.\n", " \n", " \n", " Arguments:\n", " y -- numpy array of shape (1, Ty), the labels of the training example\n", " segment_end_ms -- the end time of the segment in ms\n", " \n", " Returns:\n", " y -- updated labels\n", " \"\"\"\n", " \n", " # duration of the background (in terms of spectrogram time-steps)\n", " segment_end_y = int(segment_end_ms * Ty / 10000.0)\n", " \n", " # Add 1 to the correct index in the background label (y)\n", " ### START CODE HERE ### (≈ 3 lines)\n", " for i in range(segment_end_y + 1, segment_end_y + 51):\n", " if i < Ty:\n", " y[0, i] = 1\n", " ### END CODE HERE ###\n", " \n", " return y" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sanity checks: 0.0 1.0 0.0\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "arr1 = insert_ones(np.zeros((1, Ty)), 9700)\n", "plt.plot(insert_ones(arr1, 4251)[0,:])\n", "print(\"sanity checks:\", arr1[0][1333], arr1[0][634], arr1[0][635])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**\n", "\n", " \n", " \n", " \n", " \n", "
\n", " **sanity checks**:\n", " \n", " 0.0 1.0 0.0\n", "
\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Creating a training example\n", "Finally, you can use `insert_audio_clip` and `insert_ones` to create a new training example.\n", "\n", "**Exercise**: Implement `create_training_example()`. You will need to carry out the following steps:\n", "\n", "1. Initialize the label vector $y$ as a numpy array of zeros and shape $(1, T_y)$.\n", "2. Initialize the set of existing segments to an empty list.\n", "3. Randomly select 0 to 4 \"activate\" audio clips, and insert them onto the 10 second clip. Also insert labels at the correct position in the label vector $y$.\n", "4. Randomly select 0 to 2 negative audio clips, and insert them into the 10 second clip. \n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: create_training_example\n", "\n", "def create_training_example(background, activates, negatives):\n", " \"\"\"\n", " Creates a training example with a given background, activates, and negatives.\n", " \n", " Arguments:\n", " background -- a 10 second background audio recording\n", " activates -- a list of audio segments of the word \"activate\"\n", " negatives -- a list of audio segments of random words that are not \"activate\"\n", " \n", " Returns:\n", " x -- the spectrogram of the training example\n", " y -- the label at each time step of the spectrogram\n", " \"\"\"\n", " \n", " # Set the random seed\n", " np.random.seed(18)\n", " \n", " # Make background quieter\n", " background = background - 20\n", "\n", " ### START CODE HERE ###\n", " # Step 1: Initialize y (label vector) of zeros (≈ 1 line)\n", " y = np.zeros((1, Ty))\n", "\n", " # Step 2: Initialize segment times as empty list (≈ 1 line)\n", " previous_segments = []\n", " ### END CODE HERE ###\n", " \n", " # Select 0-4 random \"activate\" audio clips from the entire list of \"activates\" recordings\n", " number_of_activates = np.random.randint(0, 5)\n", " random_indices = np.random.randint(len(activates), size=number_of_activates)\n", " random_activates = [activates[i] for i in random_indices]\n", " \n", " ### START CODE HERE ### (≈ 3 lines)\n", " # Step 3: Loop over randomly selected \"activate\" clips and insert in background\n", " for random_activate in random_activates:\n", " # Insert the audio clip on the background\n", " background, segment_time = insert_audio_clip(background, random_activate, previous_segments)\n", " # Retrieve segment_start and segment_end from segment_time\n", " segment_start, segment_end = segment_time\n", " # Insert labels in \"y\"\n", " y = insert_ones(y, segment_end_ms=segment_end)\n", " ### END CODE HERE ###\n", "\n", " # Select 0-2 random negatives audio recordings from the entire list of \"negatives\" recordings\n", " number_of_negatives = np.random.randint(0, 3)\n", " random_indices = np.random.randint(len(negatives), size=number_of_negatives)\n", " random_negatives = [negatives[i] for i in random_indices]\n", "\n", " ### START CODE HERE ### (≈ 2 lines)\n", " # Step 4: Loop over randomly selected negative clips and insert in background\n", " for random_negative in random_negatives:\n", " # Insert the audio clip on the background \n", " background, _ = insert_audio_clip(background, random_negative, previous_segments)\n", " ### END CODE HERE ###\n", " \n", " # Standardize the volume of the audio clip \n", " background = match_target_amplitude(background, -20.0)\n", "\n", " # Export new training example \n", " file_handle = background.export(\"train\" + \".wav\", format=\"wav\")\n", " print(\"File (train.wav) was saved in your directory.\")\n", " \n", " # Get and plot spectrogram of the new recording (background with superposition of positive and negatives)\n", " x = graph_spectrogram(\"train.wav\")\n", " \n", " return x, y" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "File (train.wav) was saved in your directory.\n" ] }, { "data": { "image/png": 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rn6L0BG6uZ4gnMKeHDveYspLEVExzI89BD71o6FBFgPuKB4dfauksxnlAOGlq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\nrkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x, y = create_training_example(backgrounds[0], activates, negatives)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you can listen to the training example you created and compare it to the spectrogram generated above." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IPython.display.Audio(\"train.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IPython.display.Audio(\"audio_examples/train_reference.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, you can plot the associated labels for the generated training example." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(y[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.4 - Full training set\n", "\n", "* You've now implemented the code needed to generate a single training example. \n", "* We used this process to generate a large training set. \n", "* To save time, we've already generated a set of training examples. " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load preprocessed training examples\n", "X = np.load(\"./XY_train/X.npy\")\n", "Y = np.load(\"./XY_train/Y.npy\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.5 - Development set\n", "\n", "* To test our model, we recorded a development set of 25 examples. \n", "* While our training data is synthesized, we want to create a development set using the same distribution as the real inputs. \n", "* Thus, we recorded 25 10-second audio clips of people saying \"activate\" and other random words, and labeled them by hand. \n", "* This follows the principle described in Course 3 \"Structuring Machine Learning Projects\" that we should create the dev set to be as similar as possible to the test set distribution\n", " * This is why our **dev set uses real audio** rather than synthesized audio. \n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load preprocessed dev set examples\n", "X_dev = np.load(\"./XY_dev/X_dev.npy\")\n", "Y_dev = np.load(\"./XY_dev/Y_dev.npy\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2 - Model\n", "\n", "* Now that you've built a dataset, let's write and train a trigger word detection model! \n", "* The model will use 1-D convolutional layers, GRU layers, and dense layers. \n", "* Let's load the packages that will allow you to use these layers in Keras. This might take a minute to load. " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "from keras.callbacks import ModelCheckpoint\n", "from keras.models import Model, load_model, Sequential\n", "from keras.layers import Dense, Activation, Dropout, Input, Masking, TimeDistributed, LSTM, Conv1D\n", "from keras.layers import GRU, Bidirectional, BatchNormalization, Reshape\n", "from keras.optimizers import Adam" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.1 - Build the model\n", "\n", "Our goal is to build a network that will ingest a spectrogram and output a signal when it detects the trigger word. This network will use 4 layers:\n", " * A convolutional layer\n", " * Two GRU layers\n", " * A dense layer. \n", "\n", "Here is the architecture we will use.\n", "\n", "\n", "
**Figure 3**
\n", "\n", "##### 1D convolutional layer\n", "One key layer of this model is the 1D convolutional step (near the bottom of Figure 3). \n", "* It inputs the 5511 step spectrogram. Each step is a vector of 101 units.\n", "* It outputs a 1375 step output\n", "* This output is further processed by multiple layers to get the final $T_y = 1375$ step output. \n", "* This 1D convolutional 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. \n", "* Computationally, the 1-D conv layer also helps speed up the model because now the GRU can process only 1375 timesteps rather than 5511 timesteps. \n", "\n", "##### GRU, dense and sigmoid\n", "* The two GRU layers read the sequence of inputs from left to right.\n", "* A dense plus sigmoid layer makes a prediction for $y^{\\langle t \\rangle}$. \n", "* Because $y$ is a binary value (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.\"\n", "\n", "#### Unidirectional RNN\n", "* Note that we use a **unidirectional RNN** rather than a bidirectional RNN. \n", "* 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. \n", "* If we used a bidirectional 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. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Implement the model\n", "\n", "Implementing the model can be done in four steps:\n", " \n", "**Step 1**: CONV layer. Use `Conv1D()` to implement this, with 196 filters, \n", "a filter size of 15 (`kernel_size=15`), and stride of 4. [conv1d](https://keras.io/layers/convolutional/#conv1d)\n", "\n", "```Python\n", "output_x = Conv1D(filters=...,kernel_size=...,strides=...)(input_x)\n", "```\n", "* Follow this with a ReLu activation. Note that we can pass in the name of the desired activation as a string, all in lowercase letters.\n", "\n", "```Python\n", "output_x = Activation(\"...\")(input_x)\n", "```\n", "\n", "* Follow this with dropout, using a keep rate of 0.8 \n", "\n", "```Python\n", "output_x = Dropout(rate=...)(input_x)\n", "```\n", "\n", "\n", "**Step 2**: First GRU layer. To generate the GRU layer, use 128 units.\n", "```Python\n", "output_x = GRU(units=..., return_sequences = ...)(input_x)\n", "```\n", "* Return sequences instead of just the last time step's prediction to ensures that all the GRU's hidden states are fed to the next layer. \n", "* Follow this with dropout, using a keep rate of 0.8.\n", "* Follow this with batch normalization. No parameters need to be set.\n", "```Python\n", "output_x = BatchNormalization()(input_x)\n", "```\n", "\n", "**Step 3**: Second GRU layer. This has the same specifications as the first GRU layer.\n", "* Follow this with a dropout, batch normalization, and then another dropout.\n", "\n", "**Step 4**: Create a time-distributed dense layer as follows: \n", "```Python\n", "X = TimeDistributed(Dense(1, activation = \"sigmoid\"))(X)\n", "```\n", "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. \n", "Documentation:\n", "* [Keras documentation on wrappers](https://keras.io/layers/wrappers/). \n", "* To learn more, you can read this blog post [How to Use the TimeDistributed Layer in Keras](https://machinelearningmastery.com/timedistributed-layer-for-long-short-term-memory-networks-in-python/).\n", "\n", "**Exercise**: Implement `model()`, the architecture is presented in Figure 3." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: model\n", "\n", "def model(input_shape):\n", " \"\"\"\n", " Function creating the model's graph in Keras.\n", " \n", " Argument:\n", " input_shape -- shape of the model's input data (using Keras conventions)\n", "\n", " Returns:\n", " model -- Keras model instance\n", " \"\"\"\n", " \n", " X_input = Input(shape = input_shape)\n", " \n", " ### START CODE HERE ###\n", " \n", " # Step 1: CONV layer (≈4 lines)\n", " X = Conv1D(196, 15, strides=4)(X_input) # CONV1D\n", " X = BatchNormalization()(X) # Batch normalization\n", " X = Activation('relu')(X) # ReLu activation\n", " X = Dropout(0.8)(X) # dropout (use 0.8)\n", "\n", " # Step 2: First GRU Layer (≈4 lines)\n", " X = GRU(units = 128, return_sequences=True)(X) # GRU (use 128 units and return the sequences)\n", " X = Dropout(0.8)(X) # dropout (use 0.8)\n", " X = BatchNormalization()(X) # Batch normalization\n", " \n", " # Step 3: Second GRU Layer (≈4 lines)\n", " X = GRU(units = 128, return_sequences=True)(X) # GRU (use 128 units and return the sequences)\n", " X = Dropout(0.8)(X) # dropout (use 0.8)\n", " X = BatchNormalization()(X) # Batch normalization\n", " X = Dropout(0.8)(X) # dropout (use 0.8)\n", " \n", " # Step 4: Time-distributed dense layer (≈1 line)\n", " X = TimeDistributed(Dense(1, activation = \"sigmoid\"))(X) # time distributed (sigmoid)\n", "\n", " ### END CODE HERE ###\n", "\n", " model = Model(inputs = X_input, outputs = X)\n", " \n", " return model " ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = model(input_shape = (Tx, n_freq))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's print the model summary to keep track of the shapes." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_1 (InputLayer) (None, 5511, 101) 0 \n", "_________________________________________________________________\n", "conv1d_1 (Conv1D) (None, 1375, 196) 297136 \n", "_________________________________________________________________\n", "batch_normalization_1 (Batch (None, 1375, 196) 784 \n", "_________________________________________________________________\n", "activation_1 (Activation) (None, 1375, 196) 0 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, 1375, 196) 0 \n", "_________________________________________________________________\n", "gru_1 (GRU) (None, 1375, 128) 124800 \n", "_________________________________________________________________\n", "dropout_2 (Dropout) (None, 1375, 128) 0 \n", "_________________________________________________________________\n", "batch_normalization_2 (Batch (None, 1375, 128) 512 \n", "_________________________________________________________________\n", "gru_2 (GRU) (None, 1375, 128) 98688 \n", "_________________________________________________________________\n", "dropout_3 (Dropout) (None, 1375, 128) 0 \n", "_________________________________________________________________\n", "batch_normalization_3 (Batch (None, 1375, 128) 512 \n", "_________________________________________________________________\n", "dropout_4 (Dropout) (None, 1375, 128) 0 \n", "_________________________________________________________________\n", "time_distributed_1 (TimeDist (None, 1375, 1) 129 \n", "=================================================================\n", "Total params: 522,561\n", "Trainable params: 521,657\n", "Non-trainable params: 904\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", " **Total params**\n", " \n", " 522,561\n", "
\n", " **Trainable params**\n", " \n", " 521,657\n", "
\n", " **Non-trainable params**\n", " \n", " 904\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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 to 1375. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.2 - Fit the model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Trigger word detection takes a long time to train. \n", "* 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. \n", "* Let's load the model. " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = load_model('./models/tr_model.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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. " ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01)\n", "model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[\"accuracy\"])" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1\n", "26/26 [==============================] - 36s - loss: 0.0727 - acc: 0.9806 \n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit(X, Y, batch_size = 5, epochs=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.3 - Test the model\n", "\n", "Finally, let's see how your model performs on the dev set." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "25/25 [==============================] - 5s\n", "Dev set accuracy = 0.946036338806\n" ] } ], "source": [ "loss, acc = model.evaluate(X_dev, Y_dev)\n", "print(\"Dev set accuracy = \", acc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This looks pretty good! \n", "* However, accuracy isn't a great metric for this task\n", " * Since the labels are heavily skewed to 0's, a neural network that just outputs 0's would get slightly over 90% accuracy. \n", "* We could define more useful metrics such as F1 score or Precision/Recall. \n", " * Let's not bother with that here, and instead just empirically see how the model does with some predictions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3 - Making Predictions\n", "\n", "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. \n", "\n", "" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def detect_triggerword(filename):\n", " plt.subplot(2, 1, 1)\n", "\n", " x = graph_spectrogram(filename)\n", " # the spectrogram outputs (freqs, Tx) and we want (Tx, freqs) to input into the model\n", " x = x.swapaxes(0,1)\n", " x = np.expand_dims(x, axis=0)\n", " predictions = model.predict(x)\n", " \n", " plt.subplot(2, 1, 2)\n", " plt.plot(predictions[0,:,0])\n", " plt.ylabel('probability')\n", " plt.show()\n", " return predictions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Insert a chime to acknowledge the \"activate\" trigger\n", "* 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. \n", "* $y^{\\langle t \\rangle}$ might be near 1 for many values in a row after \"activate\" is said, yet we want to chime only once. \n", " * So we will insert a chime sound at most once every 75 output steps. \n", " * This will help prevent us from inserting two chimes for a single instance of \"activate\". \n", " * This plays a role similar to non-max suppression from computer vision.\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "chime_file = \"audio_examples/chime.wav\"\n", "def chime_on_activate(filename, predictions, threshold):\n", " audio_clip = AudioSegment.from_wav(filename)\n", " chime = AudioSegment.from_wav(chime_file)\n", " Ty = predictions.shape[1]\n", " # Step 1: Initialize the number of consecutive output steps to 0\n", " consecutive_timesteps = 0\n", " # Step 2: Loop over the output steps in the y\n", " for i in range(Ty):\n", " # Step 3: Increment consecutive output steps\n", " consecutive_timesteps += 1\n", " # Step 4: If prediction is higher than the threshold and more than 75 consecutive output steps have passed\n", " if predictions[0,i,0] > threshold and consecutive_timesteps > 75:\n", " # Step 5: Superpose audio and background using pydub\n", " audio_clip = audio_clip.overlay(chime, position = ((i / Ty) * audio_clip.duration_seconds)*1000)\n", " # Step 6: Reset consecutive output steps to 0\n", " consecutive_timesteps = 0\n", " \n", " audio_clip.export(\"chime_output.wav\", format='wav')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3.3 - Test on dev examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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. " ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IPython.display.Audio(\"./raw_data/dev/1.wav\")" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IPython.display.Audio(\"./raw_data/dev/2.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets run the model on these audio clips and see if it adds a chime after \"activate\"!" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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ZWYHbamAyeWDyR5rVON49p2CbzPnj4xpv0LO8UOwyF4FLAsHucXip3iN0d7wj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\nFF+GeJo7urjpJ6vZcayZD809j5mVhWRmhIj9wanqxOnc65nvu+oZ26Nti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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filename = \"./raw_data/dev/1.wav\"\n", "prediction = detect_triggerword(filename)\n", "chime_on_activate(filename, prediction, 0.5)\n", "IPython.display.Audio(\"./chime_output.wav\")" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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Z1ES4bzqj8XcjEN86tC8D8qZSsDE68loHWWDmmXfjpPBfXdMOQEG0wGpPhqd5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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filename = \"./raw_data/dev/2.wav\"\n", "prediction = detect_triggerword(filename)\n", "chime_on_activate(filename, prediction, 0.5)\n", "IPython.display.Audio(\"./chime_output.wav\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Congratulations \n", "\n", "You've come to the end of this assignment! \n", "\n", "## Here's what you should remember:\n", "- Data synthesis is an effective way to create a large training set for speech problems, specifically trigger word detection. \n", "- 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.\n", "- An end-to-end deep learning approach can be used to build a very effective trigger word detection system. \n", "\n", "*Congratulations* on finishing the final assignment! \n", "\n", "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! " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4 - Try your own example! (OPTIONAL/UNGRADED)\n", "\n", "In this optional and ungraded portion of this notebook, you can try your model on your own audio clips! \n", "\n", "* 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`. \n", "* Be sure to upload the audio as a wav file. \n", "* 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. \n", "* If your audio recording is not 10 seconds, the code below will either trim or pad it as needed to make it 10 seconds. " ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Preprocess the audio to the correct format\n", "def preprocess_audio(filename):\n", " # Trim or pad audio segment to 10000ms\n", " padding = AudioSegment.silent(duration=10000)\n", " segment = AudioSegment.from_wav(filename)[:10000]\n", " segment = padding.overlay(segment)\n", " # Set frame rate to 44100\n", " segment = segment.set_frame_rate(44100)\n", " # Export as wav\n", " segment.export(filename, format='wav')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you've uploaded your audio file to Coursera, put the path to your file in the variable below." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "your_filename = \"audio_examples/my_audio.wav\"" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "preprocess_audio(your_filename)\n", "IPython.display.Audio(your_filename) # listen to the audio you uploaded " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": 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Bm/GvJPjD1xmYhTOw++5KpKvwmf4gvMJXzynYxPKTNBw5GFYIATavSPj6yTTC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\n0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RkHU8CkeDDjTZhguMgwslJO+WLEyWqG6SKUk+vgDX0rzJ7aiuO3tJ5fff2OqhhQRwOD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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chime_threshold = 0.5\n", "prediction = detect_triggerword(your_filename)\n", "chime_on_activate(your_filename, prediction, chime_threshold)\n", "IPython.display.Audio(\"./chime_output.wav\")" ] } ], "metadata": { "coursera": { "course_slug": "nlp-sequence-models", "graded_item_id": "rSupZ", "launcher_item_id": "cvGhe" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }