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ffmpeg for Google Speech API

ffmpeg -i call.mp3 -acodec pcm_s16le -ar 16000 call.wav  The following command will convert an audio file into a mono wav at a specified sample rate of 8000: ffmpeg -i /home/absin/Downloads/RT7345201f37e6ae014fcbbc353400bce8.mka -ar 8000 -ac 1 /home/absin/Downloads/audio.wav The following command will take a frame from a video ffmpeg -i input.flv -ss 00:00:14.435 -vframes 1 out.png
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Learning in 2018

So, this is a follow-up post from my earlier post on learning which you can read here . What has changed since January, well I started dissecting text and speech (or nlp and asr for the 3 letter proponents). What I realised in the process was that a lot of information seems quite foreign when you try to chew on something which doesn't fit your jaws. Learning how to understand language and speech was like that and I got bombarded by the sheer volume of ideas and concepts I had to absorb. As I reflected in the previous post, learning by doing is one of the best ways of learning new things is to just pull up your sleeves and dive into practising them. While this is very satisfying and indeed takes you forward in the journey of understanding things. It is equally important to have a depth-full understanding of the concepts you are practising. Why you may ask is it necessary to not just practice things but also understand them? Well couple of reasons, practising things gives you
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API Sandbox

Read More: https://smartbear.com/learn/api-testing/what-is-an-api-sandbox/ Today’s software architecture relies on APIs as core to the application in much the same way as a database or user interface are considered core components to the architecture. That reliance on APIs means that application testers have to test the application’s reaction to a variety of API responses. But, if those APIs are still in development or are developed by a third party, how can you fully test against them? This is where an API sandbox comes in. Fundamentally, an API sandbox is an environment that testers can use to mimic the characteristics of the production environment and create simulated responses from all APIs the application relies on. The API sandbox makes it possible to: reduce the cost and risk associated with calling 3rd party APIs during testing. allow for concurrent testing and development to fast-track app development cycles and reduce time-to-market. simulate error scenario
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SPARQL

Sparql can be used to query structured data sets like DBPedia. These queries can be run on SPAQL enpoints like http://lod.openlinksw.com/sparql or http://dbpedia.org/sparql/. The queries can be slightly different, but for an experienced SPARQLer (which I hope to be one day) this won't be a problem. Here are a few of my first queries, the structure will be purpose followed by the query: The urban and metro population of Patna PREFIX dbo: <http://dbpedia.org/ontology/> PREFIX dbr: <http://dbpedia.org/resource/> select ?populationUrban  ?populationMetro where {   dbpedia:Patna  dbo:populationUrban ?populationUrban;                  dbo:populationMetro ?populationMetro  . } Details of Hemmingway: prefix dbpedia: <http://dbpedia.org/resource/> prefix dbpedia-owl: <http://dbpedia.org/ontology/> select ?abstract ?thumbnail where {    dbpedia:Ernest_Hemingway dbpedia-owl:abstract ?abstract ;                            dbpedia-owl:thumbn
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Redis Supported Data Types and Commands

Redis support 5 types of data types. You need to know what type of value that key maps to, as for each data type, the command to retrieve it is different. Here are the commands to retrieve key value: if value is of type string -> GET  <key> if value is of type hash -> HGETALL  <key> if value is of type lists -> lrange  <key> <start> <end> if value is of type sets -> smembers  <key> if value is of type sorted sets -> ZRANGEBYSCORE  <key> <min> <max> command to check the type of value a key mapping to: type  <key>
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Wikipedia API

A collection of a number of wikipedia API links to perform a plethora of tasks: Searching : Search can be done using Opensearch or query on the api Open search method: Use url:  https://en.wikipedia.org/w/api.php?action=opensearch&search=concurrency+java&limit=10&namespace=0&format=json https : //en.wikipedia.org/w/api.php ? action = opensearch & search = zyz # search query & limit = 1 # return only the first result & namespace = 0 # search only articles, ignoring Talk, Mediawiki, etc. & format = json # jsonfm prints the JSON in HTML for debugging. Query method: Use url: https://en.wikipedia.org/w/api.php?action=query&list=search&srsearch=what+is+concurrency+java&utf8=&format=json   Titles : Once the search yields url (in case of opensearch) or titles in case of query search, each can be individually opened. In case of query search, plain text or w-test can be obtained using  https://e
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log4j experiments

2 loggers into 2 different files : log = C:/Apache24/htdocs/logs log4j.rootLogger=TRACE, stdout log4j.appender.stdout=org.apache.log4j.ConsoleAppender log4j.appender.stdout.Target=System.out   log4j.appender.stdout.layout=org.apache.log4j.PatternLayout log4j.appender.stdout.layout.ConversionPattern=%d [%24F:%t:%L] - %m%n log4j.appender.debugLog=org.apache.log4j.FileAppender log4j.appender.debugLog.File=${log}/debug.html log4j.appender.debugLog.layout=org.apache.log4j.HTMLLayout log4j.appender.debugLog.layout.Title=HTML Debug log4j.appender.debugLog.layout.LocationInfo=true log4j.appender.reportsLog=org.apache.log4j.FileAppender log4j.appender.reportsLog.File=${log}/reports.html log4j.appender.reportsLog.layout=org.apache.log4j.HTMLLayout log4j.appender.debugLog.layout.Title=HTML Reports log4j.appender.debugLog.layout.LocationInfo=true log4j.category.debugLogger=TRACE, debugLog log4j.additivity.debugLogger=false log4j.category.repor
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