a little background: I'm faced with converting 3000 IVR scripts for a new PBX & IVR.
Currently the voice scripts exist in .VOX format--AND..they're not 'written-out'/documented.
I humbly ask if anyone knows of a program, such that, I can dump the .vox file in the program and have it produce a text document. If the .vox format is a problem I could probably convert all of them to .wav or whatever.
Yes there are tons of apps/programs which do speech to text in real time, but I want to be able to "upload/dump" the recording into a program, and obtain text.
Can someone point me in the right direction?
Thank you in advance for any sort of comment/help.
SF
The problem is that you are hoping to perform generic natural language processing on low quality audio files. Low quality audio data significantly reduces the reliability of natural language processing software. Upsampling your audio files will not improve their content which means poor results even if you did have access to a natural language engine.
Your best bet is to work with a company that performs hybrid machine/human transcription and pay them for each transcriptions. Alternatively you could consider working with Amazon Mechanical Turk and buy some general purpose human effort to get these transcribed. In both of those cases it is likely that VOX files would not work, you would first need to convert them to WAV or MP3 files to make it easier for the third party to use off the shelf software to listen to the prompts.
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I know this has already been posted more than 10 years ago but I want to believe that some progress has been made on this side. (we have Deepfake nowadays, so much progress on the AI side).
I tried some tutorials with audacity but was highly disappointed with the result (to be fair the resulting output is not that bad, but not good enough for prod).
What reputable algorithm could I use to process myself a mp3 file and remove the vocals while preserving the drums and centered instruments, and removing vocal echo?
This task is known in the community as "Vocal Source Separation" or "Vocal Signal Separation" or "Singing Voice Source Separation", which are specialized "Music Source Separation" tasks, again an example of the more general "Source Separtion" task.
Here are some papers: Music Source Separation.
One of the most actively developed open source solutions is Spleeter, which has been used commercially in various audio products. There is an online tool based on it, you can try it out at Splitter.ai. The "2 stem" version will give you one track with vocals, and one track with everything else.
Lets say I have the audio file for Happy Birthday. I want to convert that audio file into an audio file that sounds like this : happy birthday.
First, I'd like to know if I have the ability to program this? Can a highschooler who's almost finished with APCS program this?
If I can:
How would I change the bpm of the song? I've searched through a bunch of websites, but they weren't very helpful.
I know that audio files can be represented in waveforms. How would I scan for each individual wave in an audio file (I need this to isolate the notes)?
This is a very ambitious project, actually. One reason is that it involves using digital signal processing tools like FFT (Fast fourier transforms) to analyze the sound to pick out the pitches. You might be able to find a library that can do this, but as far as coding such a tool, that would involve a steep learning curve.
If you would like to look further into this, there is a good online resource called "The Scientists and Engineers Guide to Digital Signal Processing". I was able to work through and understand the discrete fourier transform with only high school math (lots of trig) and a bit of calculus. It was a lift, though.
Trying to analyze rhythm is also no easy task. Even with advanced tools provided in professional notation system such as Finale, people have trouble playing rhythms in time well enough for the best transcription tools. Algorithms that "quantize" the beats help but also limit the amount of detail that can be included in the playback.
My guess is that as interesting and worthwhile as this project would be, to bring it to completion before the semester ends would require putting together prebuilt pieces. A lot of programming is done that way, these days.
If you scale the project back to something like just getting your code to analyze a short sample of a single note and give its pitch, that would be both impressive and doable with a lot of work. It could be done with a DFT algorithm instead of requiring FFT, reducing the amount of info you'd have to acquire first. That way, you'd only have to work your way up to understanding and implementing the material on this link which is about calculating the DFT. Notice that there is example code in BASIC. The code examples throughout this book are a big help.
I'm a beginner with tensorflow and Python and I'm trying to build an app that automatically detects, in a football (soccer) match some key moments (yellow/red cards, goals, etc).
I'm starting to understand how to do a video analysis training the program on a dataset built by me, downloading images from the web and tagging them. In order to obtain some better results for the analysis, I was wondering if someone had some suggestions on tutorials to follow in order to understand how to train my app also on audio files, to make the program able to understand when there is a pitch variation in the audio of the video and combine both video and audio analysis in order to get better results.
Thank you in advance
Since you are new to Python and to tensorflow, I recommend you focus on just audio for now, especially since its a strong indicator of events of importance in a football match (red/yellow cards, nasty fouls, goals, strong chances, good plays, etc).
Very simply, without using much ML at all, you can use the average volume of a time period to infer significance. If you want to get a little more sophisticated, you can consider speech-to-text libraries to look for keywords in commentator speech.
Using video to try to determine when something important is happening is much, much more challenging.
This page can help you get started with audio signal processing in Python.
https://bastibe.de/2012-11-02-real-time-signal-processing-in-python.html
I'm looking for a program that is able to recognize individual audio samples from my computer and reroute them to trigger WAV files from a library. In my project, it would need to be realtime as the latency would not be a desired result. I tried using dictation software that would recognize words to trigger opening a file and that's the direction where I want to go, but instead of words I want it to be sounds and it would happen in realtime. I'm not sure where to go and am just looking for some guidance. Does anyone have any suggestions of what I should do?
That's a fairly broad question, but I can tell you how I would do it. (Hardly the only way, but where I would start.)
If you're looking for real time input, the Java Sound library (excellent tutorial here) allows for that. (Just note that microphone input from a web page is difficult on anything, due to major security concerns, so this would be a desktop application.)
If it needs to be real time, the first thing I would suggest is stream and multithread the hell out of it. I would suggest the Java 8 Stream API, but since you're looking for subsamples that match a specific pattern, then each data point will have to be aware of the state of its neighbors, and that isn't easy with streams.
You will probably want to know if a sound roughly resembles an audio profile, so for that, I would pick a tolerance on just how close you want it to be for a match (remembering that samples may not line up 100% anyway, so "exact" is not an option), and then look up Hidden Markov Models. I suggest these because they're what voice recognition software typically uses, and while your sounds may not be voices, it will give you an idea of what has already been done.
You'll also want to maintain a limited list of audio samples in memory. Specifically, you will likely need the most recent data, because an audio signal is a time-variant signal, and you can't get a match from just one point. I wouldn't make it much longer than the longest sample you're looking to recognize, as audio takes up a boatload of memory.
Lastly (for audio), I would recommend picking a standard format for comparison. Make it as good as gets you decent results, and start high. You will want to convert everything to that format before you compare it.
Once you recognize a specific sound, it's basically a Command Pattern. Specific sounds can be mapped, even with a java.util.HashMap, to specific files, which (if there are few enough) you might even have pre-loaded.
Lastly, it's worth looking at the Java Speech API. It's not part of the JDK and it's quite dated, but you might get some good advice from its implementation.
This is of course the advice of a Java-preferring programmer, but I imagine that there might be some decent libraries in Python and Ruby to help you as well; and of course there's something in C somewhere. This may sound like a lot, but most of the material is already implemented and ready-to-go.
Hopefully this helps, let's look forward to other answers.
I am working on a project where biometric system is used to secure the system. We are planning to use human voice to secure the system.
Idea is to allow the person to say some words or sentences and system will store that voice in digital format. Next time person wants to enter the system, he/she has to speak some words which may or may not be different from the words used earlier.
We don't want to match words but want to match voice frequency.
I have read some research papers regarding this system but those papers don't have any implementation details.
So just want to know whether there is any software/API which can convert analog voice into digital format and will also tell us the frequency of voice.
Until now I was working on normal web based applications so I know normal APIs and platforms like Java EE, C#, etc but I don't have any experience about this kind of application.
Please enlighten !!!
http://www.loquendo.com/en/products/speaker-verification/
http://www.nuance.com/for-business/by-solution/contact-center-customer-care/cccc-solutions-services/verifier/index.htm
(two links removed due to reported virus content)
http://www.persay.com/products.asp
This is as good a starting point as any : http://marsyas.info/
It's a open source software framework for audio processing. They've listed a bunch of projects that have used their framework in various ways so you could probably draw inspiration from it.
http://marsyas.info/about/projects. The Telligence project in particular seems the closest to your needs as it it was used to gender classify audio : http://marsyas.info/about/projects#5Teligence
There are two steps on a project like this one I believe:
First step would be to record the voice from an analog input into digital format (let's assume wav-pcm). For this you can use DirectShow API in C#, or standard Wav-In as in this project: http://www.codeproject.com/KB/audio-video/cswavrec.aspx. You may consider compressing your audio files later on, there are many options for this, in Windows you may consider Windows Media Format SDK to avoid licensing issues with other formats.
Second step is to build or use a voice recognition framework, if you want to build a recognition framework you will probably need to define a set of "features" for your sound fragments and select+implement a recognition algorithm. There are many aproaches available for this, IEEE amd ACM.org websties are usually good sources. If you want to use an existing framework you may want to consider Nuance Recognizer (commercial) or http://cmusphinx.sourceforge.net (open source).
Hope this helps.