I'm looking to create an IFO file for a VOB so that I can produce a video DVD.
I have the specifications for the contents of the VTS_01_1.IFO file however I'm not sure on how to generate the time map data for the VTS_TMAP section.
I think the entries are based on frame rate but this is not obvious from the example DVD's I've looked at.
Any help or pointers to the appropriate source would be greatly appreciated.
http://forum.videolan.org/viewtopic.php?f=32&t=76308&p=315301#p315301 is a patch describing its use.
http://dvd.sourceforge.net/dvdinfo/ifo_vts.html#tmap also lists the spec. http://www.mail-archive.com/dvdnav-discuss#mplayerhq.hu/msg01052.html also has some information, and http://forum.videolan.org/viewtopic.php?f=32&t=76308
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Could anybody give me pointers on how to process Switchboard dataset for training with RETURNN? I did see BlissDataset class that seems to be designed for switchboard, but it's not clear to me what I should include in the paths given in the example:
Example:
./tools/dump-dataset.py "
{'class':'BlissDataset',
'path': '/u/tuske/work/ASR/switchboard/corpus/xml/train.corpus.gz',
'bpe_file': '/u/zeyer/setups/switchboard/subwords/swb-bpe-codes',
'vocab_file': '/u/zeyer/setups/switchboard/subwords/swb-vocab'}"
The switchboard dataset has several folders with audios, i.e. swb1_d2/data/*.sph and transcripts swb1_LDC97S62/swb_ms98_transcriptions/**/*
I'm not quite sure how to proceed with this to get a dataset that can be used to train RETURNN.
At our group (RWTH Aachen University), we use the config as it was published on GitHub. As you see, this one uses ExternSprintDataset. That dataset uses
The implementation uses Sprint (publicly called RWTH ASR (RASR), see here) as an external tool (ran in a subprocess) to handle the data (feature extraction, etc). Sprint gets a Bliss XML file which describes all the segments with path to audio and audio offsets and transcriptions, and also it gets further configs for the feature extraction and maybe other things. There is an open source version of RASR which should work but it might be a bit involved to get this to work.
The BlissDataset was planned to be a simpler replacement for that. However, the implementation is incomplete. Also, you still would need to generate the Bliss XML by yourself in some way (we have used some own internal scripts to prepare that based on the official LDC data).
So, unfortunately, there is no simple way yet. Actually, I think the easiest way would be to come up with yet another custom format, which might be similar to the LibriSpeechDataset implementation, or maybe just the same, and then you could just reuse LibriSpeechDataset, or at least parts of that. That dataset implementation takes the data in some zip format which contains the transcripts in txt files and the audio in ogg or wav files. It uses librosa to do MFCC feature extraction (or also other feature types). I planned to implement that for Switchboard, and then reproduce the results, however I did not have time yet and not sure when I will get to that. But if you want to try that on your own, I will be happy to help you however I can. The starting point would be to look at LibriSpeechDataset and understand how the format of that looks like.
I have a large collection of video files, which have missing atributes like length, resolution, bitrate etc. I would like to fill those out somehow but im not sure what solution to use. It would take ages by hand but i'd rather see if a program can do it first. I tried searching for a solution but didnt find anything substantial.
The reason why, is becuase im trying to filter out the videos by atributes. If a vid is shorter than 30 minutes, i delete it. Can sort with the windows explorer, which cant do that if the atributes are missing.
Since you asked this on StackOverflow and not on SuperUser I'm going to answer this as a programming question.
Those properties are provided by the Windows Property System. It is possible to write your own property provider and overwrite the system default provider registration but this is a fair amount of work if all you want to do is sort some files.
I'm starting to work with the Google Tango Tablet, hopefully to create (basic) 2D / 3D maps from scanned areas. But first I would like to read as much about the Tango (sensors / API) as I can, in order to create a plan to be as time efficient as possible.
I instantly noticed the ability to learn areas, which is a very interesting concept, nevertheless I couldn't find anything about these so called Area Description Files (ADF).
I know the ADF files can be geographically referenced, that they contain metadata and an unique UUID. Furthermore I know their basic functionalities, but that's about it.
In some parts of the modules ADF files are referred to as 'maps', in other parts they are just called 'descriptions'.
So what do these files look like? Are they already basic (GRID) (2D) maps, or are they just descriptions?
I know there are people who already extracted the ADF files, so any help would be greatly appreciated!
From Tango ADF Doco
Important: Saved area descriptions do not directly record images or
video of the location, but rather contain descriptions of images of
the environment in a very compressed form. While those descriptions
can’t be directly viewed as images, it is in principle possible to
write an algorithm that can reconstruct a viewable image. Therefore,
you must ask the user for permission before saving any of their
learned areas to the cloud or sharing areas between users to protect
the user's privacy, just as you would treat images and video.
Other than that there doesn't seem to be much info about the file internals - I use a lot of them, but I've never been compelled to look inside - curious yes, but not compelled
Without any direct info from the project Tango folks anything we provide would be merely speculation. I'm with Mark, not much compelling reason to get details. My speculation: probably contains a set of image descriptors, like SIFT, and whatever other known device settings are available, like GPS location, orientation (gravity), time(?), etc.
I got the ADF file, basically coded binaries and seems difficult to decode.
I will be happy to share the file if anyone is still interested.
My dearest stackoverflowers,
I want to access the serialized data contained in files with strange, to me, extensions. The bulk of the data seems to be in a .st and an .idt file.
The program is meant to be run on Windows, and the unix file command gives me only false positives. Any ideas on either what these extensions mean or on how to investigate and extract their contents?
Below I provide the entirety of the extensions in a long list in hope somebody recognizes them. Googling also gives me false positives. For example: .st is commonly used for ATARI emulation files.
Thanks in advance!
.cix
.cmp
.cnt
.dam
.das
.drf
.idt
.irc
.lxp
.mp
.mbr
.str
.vlf
.rpf
.st
.st
Some general advice on how to approach this:
One way to approach this is to use a site like http://filext.com/ to try to figure out where the files came from. This can be tough, because it's not like there's a file extension standard anywhere - anyone can use any extension, so you're going to have a lot of conflicts/disambiguation issues to solve.
Sometimes you can get lucky, and if you open up the files in a plain text editor you can occasionally see plain string data that is readable, which can help identify the general sort of data contained in a file, and therefore help cut down on the possible number of sources for a file. For example, I have often helped people who received a file as an email attachment with no extension, figure out what file type it was using this technique, adding the file extension, and then opening it in the appropriate program.
There are also sites like http://www.oldversion.com/ that keep old versions of programs that you (typcially) can download for free. This is especially helpful if the data you're working with was created 5+ years ago, in a proprietary program, and that program is no longer available/purchasable from the vendor who created it.
Once you have a good idea of what files belong to what programs, then you're probably going to spend a lot of time trying to find online resources for what the structure of the files are. If that isn't available, you can get a copy of the original program, but either the program won't open the files you're interested in or you still want raw access to the data, then try generating some sample output files with data that you input, and go Rosetta Stone on it, comparing your known file to the original file.
From there, the additional knowledge you'll probably want, is to try to find out what language/compiler the software was written in, which can give you a lead on what code libraries were used to serialize the data in the first place. Once you know all that, then it's matter of reading through any available documentation on the serialization process, and then writing a deserializer.
The one thing this technique won't solve is, if you're dealing with corrupt/truncated data files, it may be very difficult to tell the difference between that and whether or not you have the file structure correct. The "Rosetta Stone" technique might be helpful in that case.
Depending on how many different pieces of source software you're talking about, sounds like a pretty big project. Good luck!
I hope this is not a stupid question. So, I simply want to duplicate a file from the Isolated Storage to be used as a backup. However, speed is really important in this case and I wondered what's the fastest way to do that. Should I open the file from the IS, read it to a stream, then create a backup file and write to it, well from what I've seen so far this will take at least half a second which is a lot.
There's no API for copy/duplicate so yes, your answer is the best way.
If you want to avoid the half a second delay then you'll need to do that via your application design - e.g. writing new data to a new file, or perhaps using smaller files.
If you're interested in the details of IsolatedStorage performance, then this blog has done a superb analysis:
http://appangles.com/blogs/mickn/wp7/?p=6