I would like to be able to extract utternaces of a person from a stream of telephone audio. The phone audio is routed to my server which then creates a streaming recognition request. How can I tell when a word exists as part of a complete utterance or is part of an utterance currently being transcribed? Should I compare timestamps between words? Will the API continue to return interim results even if there is no speech for a certain amount of time in the streaming phone audio? How can I exceed the 1-minute of streaming audio limit?
About your first 3 questions:
You don’t need to compare timestamps between words, you can tell if a word is part of a complete utterance (final result) by looking at the is_final flag in the Streaming Recognition Result. If the flag is set to true, the response corresponds to a completed transcription, otherwise, it is an interim result. More on this here.
Once you get the final results, no interim results should be generated until new utterances are streamed.
Regarding your last question, you can’t exceed the 1 minute limit, you need to send multiple requests instead.
Related
I'm trying to use application insights to keep track of a counter of number of active streams in my application. I have 2 goals to achieve:
Show the current (or at least recent) number of active streams in a dashboard
Activate a kind of warning if the number exceeds a certain limit.
These streams can be quite long lived, and sometimes brief. So the number can sometimes change say 100 times a second, and sometimes remain unchanged for many hours.
I have been trying to track this active streams count as an application insights metric.
I'm incrementing a counter in my application when a new stream opens, and decrementing when one closes. On each change I use the telemetry client something like this
var myMetric = myTelemetryClient.GetMetric("Metricname");
myMetric.TrackValue(myCount);
When I query my metric values with Kusto, I see that because of these clusters of activity within a 10 sec period, my metric values get aggregated. For the purposes of my alarm, I can live with that, as I can look at the max value of the aggregate. But I can't present a dashboard of the number of active streams, as I have no way of knowing the number of active streams between my measurement points. I know the min value, max and average, but I don't know the last value of the aggregate period, and since it can be somewhere between 0 and 1000, its no help.
So the solution I have doesn't serve my needs, I thought of a couple of changes:
Adding a scheduled pump to my counter component, which will send the current counter value, once every say 5 minutes. But I don't like that I then have to add a thread for each of these counters.
Adding a timer to send the current value once, 5 minutes after the last change. Countdown gets reset each time the counter changes. This has the same problem as above, and does an excessive amount of work to reset the counter when it could be changing thousands of times a second.
In the end, I don't think my needs are all that exotic, so I wonder if I'm using app insights incorrectly.
Is there some way I can change the metric's behavior to suit my purposes? I appreciate that it's pre-aggregating before sending data in order to reduce ingest costs, but it's preventing me from solving a simple problem.
Is a metric even the right way to do this? Are there alternative approaches within app insights?
You can use TrackMetric instead of the GetMetric ceremony to track individual values withouth aggregation. From the docs:
Microsoft.ApplicationInsights.TelemetryClient.TrackMetric is not the preferred method for sending metrics. Metrics should always be pre-aggregated across a time period before being sent. Use one of the GetMetric(..) overloads to get a metric object for accessing SDK pre-aggregation capabilities. If you are implementing your own pre-aggregation logic, you can use the TrackMetric() method to send the resulting aggregates.
But you can also use events as described next:
If your application requires sending a separate telemetry item at every occasion without aggregation across time, you likely have a use case for event telemetry; see TelemetryClient.TrackEvent (Microsoft.ApplicationInsights.DataContracts.EventTelemetry).
In Mturk, we want to show a set of tweets, e.g., 20 tweets, in boxes in one page. Then, Workers click JUST on the tweets (boxes) that are relevant to a specific concept like "entrepreneurship". For example for 3 tweets:
Tweet 1: Money and supporting customers are essential for a business
Tweet 2: I like tennis
Tweet 3: I spend my invest for buying my home.
Tweets should be shown in boxes and Workers should just click on Tweet 1 (instead of clicking on Yes or No buttons) and MTurk returns the results in a file (like csv format) in this way:
Yes (or 1)
No (or 0)
No (or 0)
We want to show multiple tweets (multiple hits) in one page.
How can we create a code so that for a batch of tweets, MTurk reads 20 tweets from the batch and puts them in their place for the Workers?
Is there such design? If yes, would you please guide me how I can do it? With many thanks. Jafar
All 20 tweets would be required to be part of the same HIT. There's no way to create one HIT per tweet, then have Mechanical Turk display 20 of them as one task for a worker to complete.
I have a question regarding the Python API of Interactive Brokers.
Can multiple asset and stock contracts be passed into reqMktData() function and obtain the last prices? (I can set the snapshots = TRUE in reqMktData to get the last price. You can assume that I have subscribed to the appropriate data services.)
To put things in perspective, this is what I am trying to do:
1) Call reqMktData, get last prices for multiple assets.
2) Feed the data into my prediction engine, and do something
3) Go to step 1.
When I contacted Interactive Brokers, they said:
"Only one contract can be passed to reqMktData() at one time, so there is no bulk request feature in requesting real time data."
Obviously one way to get around this is to do a loop but this is too slow. Another way to do this is through multithreading but this is a lot of work plus I can't afford the extra expense of a new computer. I am not interested in either one.
Any suggestions?
You can only specify 1 contract in each reqMktData call. There is no choice but to use a loop of some type. The speed shouldn't be an issue as you can make up to 50 requests per second, maybe even more for snapshots.
The speed issue could be that you want too much data (> 50/s) or you're using an old version of the IB python api, check in connection.py for lock.acquire, I've deleted all of them. Also, if there has been no trade for >10 seconds, IB will wait for a trade before sending a snapshot. Test with active symbols.
However, what you should do is request live streaming data by setting snapshot to false and just keep track of the last price in the stream. You can stream up to 100 tickers with the default minimums. You keep them separate by using unique ticker ids.
Consider a Java application that receives financial trading transactions to determine their vality by applying several checks, such as if the transaction is allowed under contractual and legal constraints. The application implements a JMS message handler to receive messages on one queue, and a second queue to send back the message to the consumer.
In order to measure response times and enable post-processing performance analysis, the application logs the start and end time of several steps, e.g. reception of message, processing, prepare and send answer back to the client. There are approx. 3 million messages received by the application per day, and hence a multiple of this number of time measurements (around 18 million logged measurements a day). Each measurement consists of the following data: ID of measurement (e.g. RECEIVE_START/END, PROCESS_START/END, SEND_START/END), time stamp as given by java.lang.system.nanoTime(), a unique message id. The time measurements are sent to a log file.
To find the processing times, the log file is transformed and stored in a MySQL database on a daily basis. This is done by a sequence of Python scripts that take the raw log data, transform and store it into a MySQL table, whereby each record corresponds to one processed message, with each measurement in one column (i.e. the table groups records by the unique message id).
My question is this: what are the best tactics and tools to analyse this relatively large data set (consider a month or several month worth of log data)? In particular I would like to calculate and graph:
a) the distribution of measurements in terms of response times (e.g. SEND_END - RECEIVE_START), for a selected time frame (e.g. monthly, daily, hourly).
b) the frequencies of messages per time unit (second, hour, day, week, month), over a selected time period (e.g. day, week, month, year)
Any hints or reports on your own experience is appreciated.
We've had a lot of success with splunk for processing/reporting on large log files. It's a tool that's built specifically for that purpose. You can run SQL-like queries on your data files to get the kind of reports/graphs you are looking for. I believe it can be pretty expensive though, IIRC they charge you based on the amount of data that you process.
http://www.splunk.com/?r=header
I need to calculate the number of video and audio file downloads from our media server. Our media server only hosts audio/video files (mp3 and mp4) and we parse our IIS log files monthly using Stone Steps Webalizer.
When I look at the Webalizer stats most of the ‘hits’ are ‘code 206 partial content’ and most of the remainder are ‘code 200 ok’. So for instance our most recent monthly Webalizer stats look something like this -
Total hits: 1,600,000
Code 200 - ok: 300,000
Code 206 - Partial Content: 1,300,000
The total hits figure is much larger than I would expect it to be in relation to the amount of data being served (Total Kbytes).
When I analyse the log files it looks as though media players (iTunes, Quicktime etc) create multiple 206's for a single download/play and I suspect that Webalizer does not group these multiple 206's from the same IP/visit and instead records each 206 as a ‘hit’ - and because of this the total hits figure is vastly inflated. There is a criticism of Weblizer on the Wiki page which appears to confirm this - http://en.wikipedia.org/wiki/Webalizer
Am I correct about the 206's and Webalizer, and if I am correct how would I calculate the number of downloads? Is there an industry standard methodology and/or are there alternative web analytics applications that would be better suited to the task?
Any help or advice would be much appreciated.
Didn't receive any response to my question but thought I would give an update.
We have analysed a one hour sample of our log files and we have done some testing of different browsers / media players on an mp3 and mp4 file.
Here are our findings -
Some media players, particularly iTunes/Quicktime, produce a series
of 206 requests but do not produce a 200 request.
Most but not all web broswers (Chrome is the exception), produce a
200 request and no 206 requests when downloading a media file i.e.
download to desktop as opposed to playing in a desktop media player
or media player plug-in
If the file is cached by the browser/media player it may produce 304
request and no 200 and no 206 request.
Given the above we think it's impossible to count 'downloads' of media files from log file analysis unless the software has an intelligent algorithm designed specifically for that purpose. For example, it would need to group all requests for a specific media file from the same IP within a set time period (say 30 minutes) and count that as one download. As far as I'm aware there isn't any log file analysis software on the market which can offer that functionality.
I did a quick Google search to find out more about podcast/video metrics / log file analysis and it does seem to be a very real, albeit niche problem. Google Analytics and other web metrics tools that use web beacons e.g. SiteStat, are not an option unless your media files are only available for download from your website i.e. no RSS or iTunes syndication etc. Even then I'm not sure if they could do the job.
I think this is why companies such as podtrac and blubrry offer specialised podcast/video measurement tools using redirects as opposed to log file analysis.
Podtrac
http://podtrac.com/publisher/measurement
Blubrry
http://www.blubrry.com/podcast_statistics/
If anyone has experience or expertise in this area feel free to chime in and offer advice or correct me if I'm wrong.
Try my software. I encountered the same issue with mp3's being split into multiple streams for IPods and Iphones. It is really easy to implement and works a treat.
Github
This is probably WAY too late to help you specifically but if you have parsed your server logs and stored them somewhere sensible like a DBMS a quick bit of SQL will give you the combined results you're after. Given a very simple log table where each 206 is recorded with a 'hit time' the ip address of the endpoint and an id/foreign key of the item fetched you could run this query:
select min(hit_time) as hit_time, ip_address, episode_id
from podcast_hit
group by DATE(hit_time), ip_address, episode_id
This will group up all the 206 records and make them unique by day and user giving you more accurate stats. Hope this helps someone!