Now that we are done with the installation process, it is time to see how you can put it to use! With the virtual environment created and activated, and the Vosk API securely installed inside the virtualenv, the next step is to clone the Vosk Github repository in your root folder. If you got any error, make sure that the Python version is same as mentioned in the requirements. The Vosk API should be installed on your system now. Next, you can go on and install Vosk using the pip command: pip install vosk Once the virtual environment has been set up and activated, the next step is to check for Python inside the virtual environment whether it satisfies the following dependency requirements: Activating the virtual environment myenv\Scripts\activate //for windows myenv\bin\activate //for linux.Creating the virtual environment virtualenv myenv.Installing the virtualenv package pip install virtualenv.Virtual environment can be set up and activated in three steps: But before that, it is advisable to set up a virtual environment to work around the dependencies - it is definitely not necessary but a good practice when you are working with different versions of Python. The easiest way to install Vosk is through using the pip command. If not, you can modify the models to work better with your systems.Įnough about the toolkit, time to walk the talk. But you can still rely on Vosk to provide a fairly good level of accuracy in speech recognition. So, you can use it diversely for your video transcriptions as well as home technologies and chatbots.īefore we begin, it is important to note that the accuracy of speech recognition tools has not be perfected yet and hence the results can be unexpected at times. The models come with variety of features such as speaker identification, streaming APIs, and reconfigurable vocabulary.Īnother important feature of this toolkit is its availability for a variety of platforms including desktop servers with Python, Java, C#, Node bindings as well as lightweight devices such as Android, iOS, and Raspberry Pi. These are small sized models (50 MB each) but you can also find bigger server models as well. Since there are a number of languages supported by Vosk API, we have models for each of these languages. It supports speech recognition in 16 languages including English, Indian English, French, Spanish, Portuguese, Chinese, Russian, Turkish, Vietnamese, Italian, Dutch, Arabic, Greek, with German, Catalan, and Farsi being the recent additions. Vosk is an open-source and free Python toolkit used for offline speech recognition. But what if you wanted to use speech recognition in an offline environment? Luckily, we have a ready-made solution for that and one which doesn’t require much effort at your end! Let’s have an intro This is all well and good since the most prominent use of speech recognition is in household technologies and it will be hard to find a home without an internet connection in this “online era” (a little extra true for 2020). But, there’s a catch with them: their speech recognition APIs are only available over the internet and cannot be accessed without an internet connection. Google’s Assistant and Amazon’s Alexa top the list of the few most popular uses of speech recognition. Speech Recognition is one of the many applications of artificial intelligence and can be used in various everyday projects to enhance their accessibility. Image from Speech Recognition in Outlook by Berleburg, 19 dec.
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