The target use case, then, is searching for words that occur in lecture transcripts with the search results conveniently allowing a user to see and hear the lecture at the point that the word occurs.  We can make this happen by integrating a number of independent processes.

• Obtain or generate a transcript for the lecture video.
• Format the transcript so that it is an appropriate target for GSA search.
• Modify the default Google XSL transformation so that it returns our custom results page.
• Modify a video player so that it plays the lecture from near the point that the search term occurs.

Generate a Transcript for a Lecture Video

Generating transcripts from lecture videos is described here.

Format a Transcript as a GSA Search Target

The speech recognition process produces a variety of transcript files.  Some are just text, some are text and time codes.  None of the generated files include metadata describing the transcript they contain.  For the transcripts to serve as search targets they must include additional data that the XSL can use to produce the search results page.  This metadata is the minimum that the search target must include:

• path to lecture video file
• speaker
• order of lecture within its course
• video title

Customize Google Search XSL

Google returns its search results as a structured document that contains no presentation markup.  Each page that contains a search input control also specifies an XSL transformation to present the search results.  This is an example of the markup that appears in the page:

<input name="proxystylesheet" type="hidden" value="/oeit/OcwWeb/search/google-ocw.xsl" />

The default transformation creates the familiar Google search results page, but this transformation may be customized.  This is our strategy; our custom transformation will use GSA to search the transcripts and return the hits in a page that will allow viewing the lectures.

Edit Video Lecture Pages

GSA indexes pages by following links.  Since we will add a page to the site for each lecture transcript

Global Changes to Google search textbox

Google provides a XSL stylesheet for customizing the search results page.  Substitute your own custom xsl page for Google’s default stylesheet and change the reference to the stylesheet on every page that contains a search box to cause your customization take effect.

These command line commands makes this change.  (A Unix-like OS is assumed.)  Each command line follows a similar pattern.  They first use the find utility to create a list of the files to change.  This list is passed to the grep utility to filter the file list down to those files that contain the text to change.  This filtered list is passed to the perl utility to perform the change by performing a global substitution.

The first changes the XSL file from the default to your customized XSL.  Note that the argument to grep simply identifies those files that contain a search box.  (This argument could as easily have been ‘google-ocw.xsl’ which would been simpler and as effective.)

find .  -name '*.htm*' | xargs grep -l '<input type="hidden" value="ocw" name="site">' | xargs perl -p -i.bak -w -e 's/google-ocw.xsl/google-greenfield.xsl/g'

[update: the replacement above is incorrect. It fails to update the enough of the URL. Here’s a fix if you ran that replace script.]
find . -name 'index.htm' | xargs grep -l 'http://ocw.mit.edu/search/google-greenfield.xsl' | xargs perl -p -i.bak -w -e 's|http://ocw.mit.edu/search/google-greenfield.xsl|http://greenfield.mit.edu/oeit/OcwWeb/search/google-greenfield.xsl|g'

The next changes the name of the site to search.  You may not have to change this value.  In our case, we created our own Google search “collection” that contained our copy of the OCW site.

find . -name '*.htm*' | xargs grep -l '<input type="hidden" value="ocw" name="site">' | xargs perl -p -i.bak -w -e 's/input type="hidden" value="ocw" name="site"/input type="hidden" value="oeit-ocw" name="site"/g'

The last specifies the URL of our Google search engine.  The reason for this change is that the courses in our OCW instance were collected by using the “download course materials” zip that many OCW courses provide.  Since these are intended for download, their search feature is disabled.

find . -name '*.htm*' | xargs grep -l 'form method="get" action="(../){1,3}common/search/AdvancedSearch.htm"' | xargs  perl -p -i.bak -w -e 's|form method="get" action="(../){1,3}common/search/AdvancedSearch.htm"|form method="get" action="http://search.mit.edu/search"|g'


find . -name '*.htm' | xargs perl -p -i.bak -w -e 's|<input type="hidden" name="proxystylesheet" value="/oeit/OcwWeb/search/google-ocw.xsl" />|<input type="hidden" name="proxystylesheet" value="http://greenfield.mit.edu/oeit/OcwWeb/search/google-greenfield.xsl" />|g'


There are two steps to produce the transcript.  The first creates an audio file in the correct format for speech recognition.  The second processes that audio file into the transcript.

Setup

The scripts require a specific folder hierarchy.  All paths are rooted here:

/usr/users/mckinney/sm

unless a fully qualified path is specified, assume this is “home” for paths that occur below.  For example, when I refer to the lectures folder, since I don’t explicitly specify its full path, I am referring to this folder:

/usr/users/mckinney/sm/lectures

The scripts require that each video is found in a child folder named for its lecture ordinal.  The parent is a folder named for the course of these videos.  The child folder is referred to as the “lecture” folder, the parent folder is the “course” folder.

For example, in the path

lectures/OCW-18.01-f07/L01

The course is OCW-18.01-f07 and the lecture is L01. The video file itself is in the L01 folder.

So, In the lectures folder, create a folder for the course and a folder for the lecture.  Drop the video file there in the lecture folder.

Create the Audio file

The audio file that is the input for speech recognition must be a .WAV file (16 bit PCM, bit rate of 256 kbits/sec).  The script that creates this file is:

create_wavefile.cmd

and is located in the scripts folder

The script takes three parameters:

• course folder name
• lecture folder name
• format of the video file, expressed as its file extension

For example, to run the script from the lectures folder, execute this command:

../create_wavefile.cmd OCW-18.01-f07 L01 mp4

Note that the command as well as two of the parameters are given relative to the folder where the command is executed.  The following is the terminal output of a successful run of the audio file creation script.

pwilkins@sls:/usr/users/mckinney/sm/lectures$ ../scripts/create_wavefile.cmd OCW-18.01-f07 L01 mp4
*** Creating wave file /usr/users/mckinney/sm/lectures/OCW-18.01-f07/L01/OCW-18.01-f07-L01.wav OCW-18.01-f07-L01
FFmpeg version r11872+debian_0.svn20080206-18+lenny1, Copyright (c) 2000-2008 Fabrice Bellard, et al.
configuration: --enable-gpl --enable-libfaad --enable-pp --enable-swscaler --enable-x11grab --prefix=/usr --enable-libgsm --enable-libtheora --enable-libvorbis --enable-pthreads --disable-strip --enable-libdc1394 --enable-shared --disable-static
libavutil version: 49.6.0
libavcodec version: 51.50.0
libavformat version: 52.7.0
libavdevice version: 52.0.0
built on Jan 25 2010 18:27:39, gcc: 4.3.2

Seems stream 1 codec frame rate differs from container frame rate: 30000.00 (30000/1) -> 14.99 (15000/1001)
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '/usr/users/mckinney/sm/lectures/OCW-18.01-f07/L01/OCW-18.01-f07-L01.mp4':
Duration: 00:51:32.2, start: 0.000000, bitrate: 303 kb/s
Stream #0.0(und): Audio: mpeg4aac, 44100 Hz, stereo
Stream #0.1(und): Video: mpeg4, yuv420p, 480x360 [PAR 1:1 DAR 4:3], 14.99 tb(r)
Stream #0.2(und): Data: mp4s / 0x7334706D
Stream #0.3(und): Data: mp4s / 0x7334706D
Output #0, wav, to '/usr/users/mckinney/sm/lectures/OCW-18.01-f07/L01/OCW-18.01-f07-L01.wav':
Stream #0.0(und): Audio: pcm_s16le, 16000 Hz, mono, 256 kb/s
Stream mapping:
Stream #0.0 -> #0.0
Press [q] to stop encoding
size= 96633kB time=3092.2 bitrate= 256.0kbits/s
video:0kB audio:96633kB global headers:0kB muxing overhead 0.000044%


Process the Audio file into a Transcript

In any but a trivial implementation, searching lecture transcripts presents challenges not found in other search targets.  Major among them is that each transcript word requires its own metadata (start and stop times).  Solr, a web application that derives its search muscle from Apache Lucene, has a query interface that is both rich and flexible.  It doesn’t hurt that it’s also very fast.  Properly configured, it provides an able platform to support lecture transcript searching.  Although Solr is the server, the search itself is performed by Lucene so much of the discussion will address Lucene specifically.  The integration with the server will be discussed in a subsequent posting.

Objective

We want to implement an automated work flow that can take a file that contains all the words spoken in the lecture, along with their start and stop times and persist them into a repository that will allow us to:

• search all transcripts for a word, phrase, or keyword with factored searches, word-stemming, result ranking, and spelling correction.
• Have the query result include metadata that will allow us to show a video clip mapping the word to the place in the video where it is uttered.
• Allow a transcript editing application to modify the content of the word file, as well as the time codes, in real-time.
• Dependably maintain mapping between words and their time codes.

Technique

The WRD file contains the transcript for a single lecture, one word to a line.  Preceding each word on its line is its start and stop time in milliseconds.  We call this format a WRD file[1] and you can see a snippit of it below.

To use Lucene/Solr, the first task is loading the data.  Lucene reads the data and creates an index.

The first transformation of the workflow is to convert the WRD file into a format that may be easily POST’ed into Solr.  (Solr allows uploading documents by passing a URL to the Solr server with an appended query string specifying the upload document.)  I’m currently performing the transformation from WRD to the required XML format manually in an editor.  I’m taking a series of lines like this:

...
6183 6288 in
6288 6868 physics
7186 7342 we
7342 8013 explore
9091 9181 the
9181 9461 very
9461 9956 small
10741 10862 to
10862 10946 the
10946 11226 very
11226 11686 large
...

to this:

...
<field name="trans_word">In</field>
<field name="trans_word">physics</field>
<field name="trans_word">we</field>
<field name="trans_word">explore</field>
<field name="trans_word">the</field>
<field name="trans_word">very</field>
<field name="trans_word">small</field>
<field name="trans_word">to</field>
<field name="trans_word">the</field>
<field name="trans_word">very</field>
<field name="trans_word">large</field>
...

Automating the transformation as shown above will be trivial, especially since there are libraries available for a variety of programming language.  What you will have noticed is that the uploaded content doesn’t include the time codes.  Snap!

So it seems that it is easy to use Lucene/Solr to perform a full featured search of transcripts, and it’s easy to use a database to search for single transcript words and retrieve their timecodes, but there isn’t a tool that allow me to integrate these two requirements out-of-the-box.

Ideally, we want to store the word together with its time codes while still permitting Solr to work its search magic.

There are a couple of ways to perform this integration as an additional step, which isn’t my preference.[2]  Once uploaded, I can use a test tool to return character offsets for words in transcripts.  That’s the raw data I need to work with individual words and still have useful full-text searching, now I’ve got to figure out what to do with it.
Here’s what the data looks like for the word “yes” in Lewin’s first lecture:
...
<lst name="yes">
<int name="tf">3</int>
<lst name="offsets">
<int name="start">6402</int>
<int name="end">6405</int>
<int name="start">20045</int>
<int name="end">20048</int>
<int name="start">22858</int>
<int name="end">22861</int>
</lst>
<int name="df">1</int>
</lst>
...

“tf” is term frequency
“offsets” are the start and end position in characters.

Lucene Payloads

Lucene has an advanced feature that stores additional metadata with each indexed token (in our case, that is a transcript word).  The payload feature is usually used to influence the ranking of search results, but it can be used for other purposes as well.[3]  What recommends it for our use is that it stores the data in the index, so it is readily available as part of the search result.  The preparation of the transcript would change also.  Using the WRD file example from the Technique section above, we would produce this file:

" in|6183 physics|6288 we|7186 explore|7342 the|9091 very|9181 small|9461 to|10741 the|10862 very|10946 large|11226 "

[1] The .WRD extension is an artifact of previous development and has no relationship to desktop software applications that may use this same file extension.

[2] The reason I don’t favor integrating these two as an extra step is that I would need to maintain referential integrity between two independent datastores.  (Remember, that we also have the requirement of editing both transcript words timecodes.)

[3] Update: Lucene has a feature called payloads which will allow me to store the timecodes with the words.  Here is a link to a blog post that explains the technique.

The stand-alone player allows users to view and search video transcripts without network access. Due to the technologies that the player uses, the stand-alone player requires a small web server to work.  These instructions describe how to package a video, its associated transcripts, and its supporting files into a stand-alone sm-player.  The package can be zipped into a single file, downloaded, unzipped, and run locally.

This package is what is downloaded when we publish a contributed video and its transcript.

Audience

This document is intended for those who will create these packages.  A separate README describes how to deploy and run the package.

Folder Structure


+ css
+ img
index.html
jetty.xml
+ lib
+ js
run
run.bat
+ swf
+ transcripts

css folder – cascading style sheets
ceebox-min.css – pop-up window that displays when the video is clicked.  It contains the player and transcript.
page.css – styles index.html [don’t change]
reset.css – Eric Meyer’s styling reset script. [don’t change]
style.css – styles the pop-up window driven by ceebox script. [don’t change]

img folder – images
cee-*-btn.png – button images  [don’t change]
<video-frame>.png – one or more thumbnail images to display for videos [change for each video in package]
logo-mit-oeit.png – the MIT and OEIT logos  [don’t change]
spokenmedia-website-header.png – the SpokenMedia logo for page header  [don’t change]

index.html – the page containing the video(s) in package [change for each package]

jetty.xml – configuration file for the Jetty web server

js – javascripts
data.js – [change for each package]
jquery-1.4.2.min.js –  [don’t change]
jquery.ceebox-min.js –   [don’t change]
jquery.scrollTo.js –   [don’t change]
player.js –   [don’t change]
swfobject2.2.js –   [don’t change]

lib –

run.* – start-up script for Jetty web server.  Versions for MS Windows and Linux are included.  [don’t change]

swf –

transcripts – the video transcripts
.t1.html

SpokenMedia Editing Protocol (PDF)

The protocol conforms to WC3’s proposed Timed Text Markup Language (TTML) 1.0 specification.[1]  We selected this specification because our primary data is time-aligned text and this specification is a standard used by our collaborators.

[1] http://www.w3.org/TR/ttaf1-dfxp/