Hello there and welcome to the Moby Dev channel. In this short video, I would like to show you our short demo that explains the principle of work for automatic speech recognition models. ASR models are meant to extract speech information out of a plain audio. In this demo, we'll show you how ASR models work with a pre-recorded piece of audio to demonstrate how accurately they can extract the text. So here we'll take our podcast audio and drop it right into our model. We will also use a transcription text to visually validate how the model did its job. Let's listen to our sample for a few seconds. Hello there everyone and welcome back to another episode of Tech Insights for Visionaries. Now let's process this audio. In a recognized text field, you can see what our ASR model generated as a transcription text. Some words look corrupted here. So for the sake of our demo experiment, let's quickly fine tune it by manually picking those missing words. Here we apply corrections manually, but in the production environment, this process becomes automatic and the model will try to correct itself. After applying the corrections, you can see the output text now looks fine. In most cases, our audio will contain a certain amount of noise. This makes it harder for the model to recognize words, especially when there is a harsh background noise you can capture at the streets or indoors. So we need a noise suppression to extract the actual words. Let's see how it works. Here's our audio with added noise. Hello there everyone and welcome back to another episode of Tech Insights for Visionaries. Now let's add noise suppression. Noise suppression will clear the audio, but it will still require further fine tuning to get decent results. Hello there everyone and welcome back to another episode of Tech Insights for Visionaries. As you can see, the accuracy of word recognition is slightly lower this time. So we also need to manually correct those mistakes to improve the results of recognition. Such procedure for fine tuning in the combination of techniques can provide the best results for the ASR, but for more information, you can read our full article on audio processing models by the link in the description. Thank you for watching and don't forget to subscribe.