Using AWS Transcribe to get IVR prompt verbiage

I’m a stickler for documentation and a bigger stickler for good documentation. Documentation allows the work you’ve produced to live on beyond you and help others get up to speed quickly. It feels that documentation is one of those things everyone says they do, but few really follow through. There’s nothing hard about it, but it’s something you need to work on as you’re going through your project. Do not leave documentation to the end, it will show. So DO IT!

I’ve recently started a new project to migrate an IVR over to CVP. To my pleasant surprise the customer had a call flow, prompts, a web service definition document, and test data. A dream come true! As I started the development I noticed that the verbiage in the prompts didn’t match the call flow and considering my “sticklerness” I wanted to update the call flow to ensure it matches 100% with the verbiage.

I’m always looking for excuses to play around with Python, so that’s what I used. I hacked together the script below which does the following:

  • Creates an AWS S3 bucket.
  • Uploads prompts from a specific directory to bucket.
  • Creates a job in AWS Transcribe to transcribe the prompts.
  • Waits for the job to be completed.
  • Creates a CSV names prompts.csv
  • Deletes transcriptions jobs
  • Deletes bucket

The only things you will need to change to match what you’re doing is the following:

local_directory = 'Spanish/'
file_extension = '.wav'
media_format = 'wav'
language_code = 'es-US'

The complete code is found below, be careful with the formatting it might be best to use copy it from this snippet:

</pre>
<pre>from __future__ import print_function
from botocore.exceptions import ClientError

import boto3
import uuid
import logging
import sys
import os
import time
import json
import urllib.request
import pandas

local_directory = 'French/'
file_extension = '.wav'
media_format = 'wav'
language_code = 'fr-CA'

def create_unique_bucket_name(bucket_prefix):
    # The generated bucket name must be between 3 and 63 chars long
    return ''.join([bucket_prefix, str(uuid.uuid4())])

def create_bucket(bucket_prefix, s3_connection):
    session = boto3.session.Session()
    current_region = session.region_name
    bucket_name = create_unique_bucket_name(bucket_prefix)
    bucket_response = s3_connection.create_bucket(
        Bucket=bucket_name,
    )
    # print(bucket_name, current_region)
    return bucket_name, bucket_response

def delete_all_objects(bucket_name):
    res = []
    bucket = s3Resource.Bucket(bucket_name)
    for obj_version in bucket.object_versions.all():
        res.append({'Key': obj_version.object_key,
                    'VersionId': obj_version.id})
    # print(res)
    bucket.delete_objects(Delete={'Objects': res})

s3Client = boto3.client('s3')
s3Resource = boto3.resource('s3')
transcribe = boto3.client('transcribe')
data_frame =  pandas.DataFrame()

# Create bucket
bucket_name, first_response = create_bucket(
    bucket_prefix = 'transcription-',
    s3_connection = s3Client)

print("Bucket created %s" % bucket_name)

print("Checking bucket.")
for bucket in s3Resource.buckets.all():
    if bucket.name == bucket_name:
        print("Bucket ready.")
        good_to_go = True

if not good_to_go:
    print("Error with bucket.")
    quit()

# enumerate local files recursively
for root, dirs, files in os.walk(local_directory):
    for filename in files:
        if filename.endswith(file_extension):
            # construct the full local path
            local_path = os.path.join(root, filename)
            print("Local path: %s" % local_path)
            # construct the full Dropbox path
            relative_path = os.path.relpath(local_path, local_directory)
            print("File name: %s" % relative_path)
            s3_path = local_path
            print("Searching for %s in bucket %s" % (s3_path, bucket_name))
            try:
                s3Client.head_object(Bucket=bucket_name, Key=s3_path)
                print("Path found on bucket. Skipping %s..." % s3_path)
            except:
                print("Uploading %s..." % s3_path)
                s3Client.upload_file(local_path, bucket_name, s3_path)
                job_name = relative_path
                job_uri = "https://%s.s3.amazonaws.com/%s" % (
                    bucket_name, s3_path)
                transcribe.start_transcription_job(
                    TranscriptionJobName=job_name,
                    Media={'MediaFileUri': job_uri},
                    MediaFormat=media_format,
                    LanguageCode=language_code
                )
                while True:
                    status = transcribe.get_transcription_job(TranscriptionJobName=job_name)
                    if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']:
                        break
                    print('Transcription ' + status['TranscriptionJob']['TranscriptionJobStatus'])
                    time.sleep(25)
                print('Transcription ' + status['TranscriptionJob']['TranscriptionJobStatus'])
                response = urllib.request.urlopen(status['TranscriptionJob']['Transcript']['TranscriptFileUri'])
                data = json.loads(response.read())
                text = data['results']['transcripts'][0]['transcript']
                print("%s, %s "%(job_name, text))
                data_frame = data_frame.append({"Prompt Name":job_name, "Verbiage":text}, ignore_index=True)
                print("Deleting transcription job.")
                status = transcribe.delete_transcription_job(TranscriptionJobName=job_name)

#Create csv
print("Writing CSV")
data_frame.to_csv('prompts.csv', index=False)

# Empty bucket
print("Emptying bucket.")
delete_all_objects(bucket_name)

# Delete empty bucket
s3Resource.Bucket(bucket_name).delete()
print("Bucket deleted.")</pre>
<pre>

I hope this helps someone out there create better documentation.

~david

AWS’s AI in the Contact Center Pitch: A Swing and a Miss.

Recently AWS released a “Knowledge Brief” illustrating how Fortune 1000 companies are taking a deeper interest in AI related products and services for their contact centers. While I think there are plenty of points which could be argued, for the sake of this post, I will focus on the intro graph as this is the springboard to the whole document created by the Aberdeen Group’s research. Let’s start with the graph:

Capture.PNG

First, I was surprised of the atribution for the spike in contact center solutions research to the Google Duplex presentation during I/O 2018. Second, the report goes on to state that the red line declining off to the right are the search results for PBX because “firms are not as active in researching best practices and trends in use of PBX.” These two points stuck to me as odd specially if you’re building a whole paper on those two premises so I took it upon myself to see if I could indepedently confirm their positions.

Considering the paper states that this is all about research I decided to go to world’s research webpage: Google; specifically Google Trends. Let’s tackle the spike in research due to the announcement of Google Duplex. You will see that Google registered the terms “google duplex” spiking in May which matches with their blog post linked above. The report’s graph has this spike happening in July which is not correct. But let’s give them the benefit of the doubt that the x-axis is mislabled since there certainly was a spike in research on these terms.

Capture.PNG

The papers second point is around the decline of research around the term PBX. The document states “..it’s reflected through the dark red line that’s particularly trending downwards between July and September 2018.” The main reason why this caught my eye is because of the term PBX. As those of you in the conctact center business know the term PBX really has gone out of use in the late 90s and even more today in the 2000s. Mainly because with VoIP the PBX term is not used as broadly. Make no mistake things like Cisco’s CommunicationManager and Asterisks are PBXes, but they are so much more thus why the term has fallen out of favor. Given this information let’s compare how the term PBX and ACD, a more broadly used term to almost mean the same thing, have trended for the time period this report covers.

Capture.PNG

Neither term has really seen a decline. Heck you could argue that PBX saw an increase between May and July while ACD saw an increase after July. Ultimately debunking the premise this whole document stands upon.

AI/ML is the hot new topic, but there’s a time and a place for everything. This paper’s whole premise for an AI future relies on faulty data which causes the whole article to fall apart. This, like may other pieces, are more hype than substance.

~david

 

 

 

 

 

 

 

Continuous deployment to Amazon Lambda using Bitbucket Pipeline

I’m not a developer (more of a hack) so I’m always looking for way to figure out efficiencies in my process when playing around with code as I’m a very slow coder. One of those efficiencies found is around deploying my code to Amazon Lambda.

First, let’s talk about your options when deploying code to Lambda. The easiest way is to just do your development using Amazon’s IDE. The benefit here is that you can manually run some tests to validate what you’re writing, however if you’re using any dependencies the IDE has a size restriction and at some point it’s no longer available to you.

image

The next method is doing local development and creating a zip file of all your code and dependencies. Then manually upload your code. You can then run the same manual tests as before on your code, but the process of zipping and uploading the file is tedious specially when working on large code bases.

image

Next process involves the very good Amazon CLI. Using the CLI you’ll be able to save the manual process of uploading the zip file. Below you’ll find the Windows scripts I use one for small code bases (without dependencies) and one for larger ones.


echo on

del index.zip

echo Deleted index.zip

"c:\Program Files\7-Zip\7z.exe" a index.zip index.js

aws lambda update-function-code --function-name mySmallLambdaFunction --zip-file fileb://index.zip

echo done

[/sourcode]</blockquote>
<blockquote>

echo on

del myZip.zip

echo Deleted myZip.zip

"c:\Program Files\7-Zip\7z.exe" a myZip.zip index.js node_modules

echo Zipped myZip.zip

aws lambda update-function-code --function-name myLargeLambdaFunction --zip-file fileb://myZip.zip

echo done

Finally, the process I’ve come to enjoy the most is deploying from git. The main reason being that it forces you have a bit of a process around using git which is pretty much the standard when collaborating with multiple developers. So if you’re dragging your feet around using git take the plunge it’s worth the learning. My favorite, mainly because they have a very generous free offering is Bitbucket. Besides having private repositories they also give you 50 free build minutes which is where our deployment to Lambda from Bitbucket comes in. To get started you first need to setup a few environmental variables. Go to your repository > settings > environment variables. You’ll need these named exactly this way.

image

The next step can be done in two ways. You can commit a bitbucket-pipelines.yml file to your repository or you can go to your repository > pipelines to have Bitbucket commit one for you. What the original yml file looks like doesn’t matter we’re going to change it specifically for Lambda deployment. Here’s what my file looks like with inline comments.


#I like to use the same version of Node as the Lambda function I’m using.

image: node:6.10

pipelines:

default:

- step:

script: # Modify the commands below to build your repository.

- apt-get update

- apt-get install -y zip

- python –version #From here to there is all to enable the AWS CLI installation

- apt-get install -y python-dev

- apt-get install -y python-pip

- pip install awscli #there

- zip index.zip index.js #this is for a Lambda with a small code base. For something large you can use “zip myZip.zip index.js privatekey.json -r node_modules” notice the –r parameter to zip up folders.

- aws lambda update-function-code --function-name botValidationScheduleMeeting --zip-file fileb://index.zip

Assuming you’ve done everything right you should see something like this under Pipelines.

imageThe last 3 commits were successfully built (sent to Lambda). You can click on the commit and see detailed information on the results of every command in your yml file. You’re done, you’ve developed some code locally, committed to git, and pushed it to Lambda all with a few clicks.

~david

Bringing Amazon Lex into your Amazon Connect flows

In this blog we’ll continue our discussion around Amazon Lex. Talk about a few things to keep in mind when integrating your Amazon Lex bot with your Amazon Connect flow. In my particular use case I wanted to use Amazon Lex to look at my Gmail calendar and book a meeting if I’m available. If you want to skip to the very end you can see the end result via video. You’ll see one video of the voice interaction and one of the Facebook Messenger interaction.

First, you might want to reference my previous post around Lex validation. Now let’s talk about our use case:

  • Lex easily allows you to build a bot which understand both voice and text, so our bot needs to handle calls into our call center as well as Facebook Messenger interactions.
  • Bot needs to to ask a few question in order to find out what time the user would like to meet.
  • Bot should only schedule calls between Monday-Friday and 10 AM – 4 PM Easter Time
  • Bot (using Lambda) should schedule a meeting and if slot already taken then suggest an alternate time to meet.

Second, let’s take a quick look at the Lex screen. The bot I created is very simple and it follows closely the Flowers example provided by Amazon. These are the slots I’m requiring my bot to confirm.

image

I used two different Lambda functions. One for validation and one for fulfillment. While most examples seem to focus on using the same function for both, for me it was easier to have different code bases for each with the added benefit of keeping the code manageable. As it is both validation and fulfillment both came in at around 250 lines of code, but fulfillment had around 9 megabytes of dependencies.

image

Finally, here are sample utterances I used for the main intent.

image

What this gets us is the following. The first video is the voice interaction. I went about it the long way to show some of the validation rules being set by the bot, such as no weekend meetings and no meetings too early in the day. At the end of the video you see I refresh the Gmail calendar to show the new appointment has been saved.

In the second video I go through the same Lex bot using Facebook Messenger and then show the calendar to prove that the appointment was saved.

Ultimately, Amazon makes it extremely easy to create a mutli channel bot, however the integration to back end systems is the tricky part. This bot needs a lot of tuning to make it more natural, but for just a few hours of work there’s very little out there that can get your call center to have some bot integration for self service.

~david

Creating a Lambda Function to Validate Lex Input

In this blog we’re going to step a bit away from Amazon Connect and focus on building a conversational interface using Amazon Lex. As you can probably guess down the line, this interface/bot is going to be connected to Amazon Connect for even more contact center goodness. Here we’re going to focus on creating a Lambda function strictly for validation, not for fulfillment.

First, let’s talk about what I’m building. I’m building a bot which can schedule a time to have a call with me. You tell your intention to the bot “schedule a meeting/call” and the bot will then ask you a few questions using directed language to figure out when you want to meet. Once Lex has all the information it needs it goes out to my calendar to figure out if I’m free or busy. Second, the validation code I have is mainly based on one of Amazon’s great blueprint for ordering flowers. I recommend you start with that before trying to write your own from scratch. Finally, read through the code and pay close attention to the comments marked in bold as these were the biggest gotchas as I went through.

A couple of things to keep in mind when building a conversation interface with Amazon Lex and you’re using validation.

– Have a clear scope of the conversation. I’m not a VUI designer by any means, but if you’re planning on going with an open-ended prompt “How may I help you?” you will be working on this for a long time. Instead try to focus on the smallest possible outcome. Ultimately, it is my opinion that no IVR is really NLU and they are all just directed speech apps with a lot more money sunk into them so they can be called NLU IVRs.

– If you’re going to use input validation, every user input will be ran through Lambda. This means that you must account for people saying random things which aren’t related to what your bot does and these random things will be processed through the validation function and might generate errors. Thus, you need to ignore this input and direct the customer to answer your question, so you can move on.

– Separating validation from fulfillment makes the most sense. Other than making your code easier to read and manage, you’re also able to separate responsibilities and permissions between your two Lambda functions.

– Play around with the examples Amazon provides. They are a great tool to get started and give you a ton of building blocks you can use in your own bot.

Here’s the validation code as well as some notes, hopefully this helps someone else along the way.

'use strict';

// --------------- Helpers to build responses which match the structure of the necessary dialog actions -----------------------

//elicitSlot is in charge of building the request back to Lex and tell Lex what slot needs to be re-filled.
function elicitSlot(sessionAttributes, intentName, slots, slotToElicit, message) {
return {
sessionAttributes,
dialogAction: {
type: 'ElicitSlot',
intentName,
slots,
slotToElicit,
message,
},
};
}

function close(sessionAttributes, fulfillmentState, message) {
return {
sessionAttributes,
dialogAction: {
type: 'Close',
fulfillmentState,
message,
},
};
}

function delegate(sessionAttributes, slots) {
return {
sessionAttributes,
dialogAction: {
type: 'Delegate',
slots,
},
};
}

function confirm(sessionAttributes, intentName, slots){
return{
sessionAttributes,
dialogAction:{
type: 'ConfirmIntent',
intentName,
slots,
message: {
contentType: 'PlainText',
content: 'We are set, do you want to schedule this meeting?'
}
},
};
}
// ---------------- Helper Functions --------------------------------------------------

function isDateWeekday(date) {
const myDate = parseLocalDate(date);
if (myDate.getDay() == 0 || myDate.getDay() == 6) {
console.log("Date is a weekend.");
return false;
} else {
console.log("Date is a weekday.");
return true;
}
}

function parseLocalDate(date) {
/**
* Construct a date object in the local timezone by parsing the input date string, assuming a YYYY-MM-DD format.
* Note that the Date(dateString) constructor is explicitly avoided as it may implicitly assume a UTC timezone.
*/

const dateComponents = date.split(/\-/);
return new Date(dateComponents[0], dateComponents[1] - 1, dateComponents[2]);
}

function isValidDate(date) {
try {
return !(isNaN(parseLocalDate(date).getTime()));
} catch (err) {
return false;
}
}
function buildValidationResult(isValid, violatedSlot, messageContent) {
if (messageContent == null) {
return {
isValid,
violatedSlot,
};
}

return {
isValid,
violatedSlot,
message: { contentType: 'PlainText', content: messageContent },
};
}

function validateMeeting(meetingDate, meetingTime, meetingLength) {

if (meetingDate) {
if (!isValidDate(meetingDate)) {
return buildValidationResult(false, 'MeetingDate', 'That date did not make sense. What date would you like to meet?');
}

if (parseLocalDate(meetingDate) < new Date()) {
return buildValidationResult(false, 'MeetingDate', 'You can only schedule meetings starting the next business day. What day would you like to meet?');
}

if (!isDateWeekday(meetingDate)) {
return buildValidationResult(false, 'MeetingDate', 'You can only schedule meetings during the normal weekday. What day would you like to meet?');
}

if (meetingTime) {
if (meetingTime.length !== 5) {
// Not a valid time; use a prompt defined on the build-time model.
return buildValidationResult(false, 'MeetingTime', null);
}
const hour = parseInt(meetingTime.substring(0, 2), 10);
const minute = parseInt(meetingTime.substring(3), 10);
if (isNaN(hour) || isNaN(minute)) {
//Not a valid time; use a prompt defined on the build-time model.
return buildValidationResult(false, 'MeetingTime', null);
}
if (hour < 10 || hour > 16) {
//Outside of business hours

return buildValidationResult(false, 'MeetingTime', 'Meetings can only be scheduled between 10 AM and 4 PM. Can you specify a time during this range?');

}
}

if(!meetingLength){
return buildValidationResult(false, 'MeetingLength', 'Will this be a short or long meeting?');
}
}
return buildValidationResult(true, null, null);
}

//--------------- Functions that control the bot's behavior -----------------------
function orderFlowers(intentRequest, callback) {
const source = intentRequest.invocationSource;
//get appointment slots
const meetingDate = intentRequest.currentIntent.slots.MeetingDate;
const meetingTime = intentRequest.currentIntent.slots.MeetingTime;
const meetingLength = intentRequest.currentIntent.slots.MeetingLength;

//For fullfilment source will NOT be DialogCodeHook
if (source === 'DialogCodeHook') {
//Perform basic validation on the supplied input slots. Use the elicitSlot dialog action to re-prompt for the first violation detected.
const slots = intentRequest.currentIntent.slots;
const validationResult = validateMeeting(meetingDate, meetingTime, meetingLength);

if (!validationResult.isValid) {
slots[`${validationResult.violatedSlot}`] = null;
callback(elicitSlot(intentRequest.sessionAttributes, intentRequest.currentIntent.name, slots, validationResult.violatedSlot, validationResult.message));
return;
}

//Pass the price of the flowers back through session attributes to be used in various prompts defined on the bot model.
const outputSessionAttributes = intentRequest.sessionAttributes || {};
callback(delegate(outputSessionAttributes, intentRequest.currentIntent.slots));
return;
}
}

// --------------- Intents -----------------------
function dispatch(intentRequest, callback) {
const intentName = intentRequest.currentIntent.name;

//Dispatch to your skill's intent handlers
if (intentName === 'MakeAppointment') {
return orderFlowers(intentRequest, callback);
}
throw new Error(`Intent with name ${intentName} not supported`);
}

//--------------- Main handler -----------------------
//Route the incoming request based on intent.
//The JSON body of the request is provided in the event slot.
//Execution starts here and moves up based on function exports.handler => dispatch =>orderFlowers=>validateMeeting=>buildValidationResult is the most typical path a request will take.

exports.handler = (event, context, callback) => {
try {
dispatch(event, (response) => callback(null, response));
} catch (err) {
callback(err);
}
};