Train Your Own Speech Recognition Model in 5 Simple Steps using python

Assuming you have a large amount of data for training the DeepSpeech model in audio and text files, you need to reform the data in a CSV file that is compatible with training it.(http://gyizer.com)

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Machine Learning is an exciting branch of computer science that enables solutions to a lot of problems, one of the gems of it is speech recognition. How fascinating it is when you say to your phone for playing some music and it does so just by listening to your voice.
Ever wondered how it is able to detect and recognize your voice so accurately? The answer is simple it runs some kind of neural network inside which is trained on a vast amount of data.
All the speech recognition services which we use in our daily life like Google assistant, Apple’s Siri, Amazon’s Alexa are owned by some tech giants which have a colossal amount of data to train their speech recognition model. To use the services provided by these tech giants we need to pay them for every hit we make to their services. So if you want to make your own speech recognition service and you have enough data why go with these services you can train your own model. Luckily there is one open-source model available which is based on Baidu’s Deep Speech research paper and referred to as Mozilla DeepSpeech.
You can train your own DeepSpeech model in five simple steps which I will explain but before that, there are some perquisites for training.

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