![]() The main drawback about some cloud solutions is the cost. If accuracy is important in your project, you’re probably better off with a cloud solution.Ĭloud solutions for building a speech recognition project in Python have the big advantage of being easy to use, way more accurate than open source options, and don’t require you to host any models on your own hardware. Another important consideration is that open source python speech recognition options are usually a lot less accurate than cloud based API options. ![]() However, if you’re not made of money, this is a drawback. If you’re developing for a company or corporation that has a lot of cloud resources and money to use, this is not a deal breaker. Either locally, or on your own cloud resources. The biggest drawback of open source solutions is that the compute power required to do speech recognition will have to come from you. If you are a highly advanced engineer, another big advantage of open source python speech recognition solutions is that you can go in and change the code yourself. You can know exactly what’s being done, how it’s being done, and when it’s being done. Open source code means you can see the source code. One of the biggest advantages of Open Source Python Speech Recognition solutions is that they are open source. Open Source vs Cloud Python Speech Recognition Options Cloud solutions for Python speech recognition do the computation on cloud resources and are usually exposed via API endpoints you can send use. Open source solutions are libraries that are open source (usually on github) and that you import into your program and use programmatically, doing the computation on your own resources. ![]() We can separate our Python speech recognition and Speech-to-Text options into two main categories: open source and cloud. So you want to do Automatic Speech Recognition, or ASR, in Python and you’ve found that there are many different options available. ![]()
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