Your last job is to prepare running scripts to create ASR system of your choice. I put some comments in prepared scripts for ease of understanding.
I decided to use two different training methods:. These two methods are enough to show noticable differences in decoding results using only digits lexicon and small training dataset. Now all you have to do is to run run. If I have made any mistakes in this tutorial, logs from the terminal should guide you how to deal with it. You may notice there folders with mono and tri1 results as well - directories structure are the same. Logs for decoding process may be found in log folder same directory. This is just an example.
The point of this short tutorial is to show you how to create 'anything' in Kaldi and to get a better understanding of how to think while using this toolkit. There are two very useful sections for beginners inside: a. Kaldi tutorial - almost 'step by step' tutorial on how to set up an ASR system; up to some point this can be done without RM dataset. It is good to read it, b.
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Data preparation - very detailed explanation of how to use your own data in Kaldi. Introduction This is a step by step tutorial for absolute beginners on how to create a simple ASR Automatic Speech Recognition system in Kaldi toolkit using your own set of data. Operating system and all the necessary Linux tools are ready to go. Download Kaldi Just follow the instruction carefully: Downloading and installing Kaldi. Your exemplary project For the purpose of this tutorial, imagine that you have the same simple set of data as me described below, in Audio data section.
Your precondition You have some amount of audio data that contain only spoken digits by at least several different speakers.
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Your purpose You want to divide your data into train and test sets, set up an ASR system, train it, test it and get some decoding results. Data preparation Audio data I assume that you want to set up an ASR system, basing on your own audio data.
Acoustic data Now you have to create some text files that will allow Kaldi to communicate with your audio data. Create in each subfolder following files so you have files named in the same way in test and train subfolders but they relate to two different datasets that you created before : a. Language data This section relates to language modeling files that also need to be considered as 'must be done'. Your project structure will become complete. Tools attachment You need to add necessary Kaldi tools that are widely used in exemplary scripts.
Scoring script This script will help you to get decoding results. Configuration files It is not necessary to create configuration files but it can be a good habit for future. You can read more about Composer on its official webpage.
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First of all, in Prophecy every word has a logical meaning, even the name of the library itself Prophecy. When you start feeling that, you'll become very fluid with this tool. For example, Prophecy has been named that way because it concentrates on describing the future behavior of objects with very limited knowledge about them. But as with any other prophecy, those object prophecies can't create themselves - there should be a Prophet:. The result of the prophesize method call is a new object of class ObjectProphecy. Yes, that's your specific object prophecy, which describes how your object would behave in the near future.
But first, you need to specify which object you're talking about, right? There are 2 interesting calls - willExtend and willImplement.
The first one tells object prophecy that our object should extend specific class, the second one says that it should implement some interface. Obviously, objects in PHP can implement multiple interfaces, but extend only one parent class. Ok, now we have our object prophecy. What can we do with it? First of all, we can get our object dummy by revealing its prophecy:. The key point about dummies is that they do not hold any logic - they just do nothing.
Any method of the dummy will always return null and the dummy will never throw any exceptions.
Dummy is your friend if you don't care about the actual behavior of this double and just need a token object to satisfy a method typehint. You need to understand one thing - a dummy is not a prophecy. Ok, now we know how to create basic prophecies and reveal dummies from them. That's awesome if we don't care about our doubles objects that reflect originals interactions. If we do, we need to use stubs or mocks. A stub is an object double, which doesn't have any expectations about the object behavior, but when put in specific environment, behaves in specific way.
Ok, I know, it's cryptic, but bear with me for a minute. Simply put, a stub is a dummy, which depending on the called method signature does different things has logic. To create stubs in Prophecy:. Oh wow. We've just made an arbitrary call on the object prophecy? Yes, we did. And this call returned us a new object instance of class MethodProphecy. Yep, that's a specific method with arguments prophecy. Method prophecies give you the ability to create method promises or predictions. We'll talk about method predictions later in the Mocks section.
As a matter of fact, the call that we made earlier willReturn 'value' is a simple shortcut to:.
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This promise will cause any call to our double's read method with exactly one argument - '' to always return 'value'. But that's only for this promise, there's plenty others you can use:. Prophecy enforces same method prophecies and, as a consequence, same promises and predictions for the same method calls with the same arguments. This means:. That's interesting, right? Now you might ask me how would you define more complex behaviors where some method call changes behavior of others.
Stitiching the pipeline. Once the images have been pushed we are ready to stitch the pipeline once again. This zip file produced after the compilation can either be uploaded to create a kubeflow pipeline through the Kubeflow UI route or can be created using the following script. The above article describes the most basic explanation for beginners on how to create Kubeflow pipeline to be able to deliver Machine Learning at scale. NOTE : Pipelines can be built using a combination of heavy-weight and light-weight components.
Try mixing the above explained two process to do so. Sign in. Get started. Prafful Mishra Follow. Towards Data Science Sharing concepts, ideas, and codes. See more stories from Towards Data Science. Write the first response. Discover Medium.