title: Ð Ð°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ ÑÐµÑÐ¸ Ð² Ð°ÑÐ´Ð¸Ð¾ÑÐ°Ð¹Ð»Ðµ Ñ Ð¿Ð¾ÑÐ»ÐµÐ´ÑÑÑÐ¸Ð¼ Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð¸ÐµÐ¼ Ð¾ÑÐ½Ð¾Ð²Ð½Ð¾Ð³Ð¾ ÑÐ¾Ð´ÐµÑÐ¶Ð°Ð½Ð¸Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Speech recognition in an audio file with subsequent allocation of the main content using neural network
Creators: name: Ð­ÑÑÐ¸Ð±Ð°ÑÐ¾Ð², ÐÑÑÐ°Ð½
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÐºÐ¾Ð´ÐµÑ
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ÑÑÐ½ÐºÑÐ¸Ñ Ð°ÐºÑÐ¸Ð²Ð°ÑÐ¸Ð¸
encoder
decoder
mutual entropy
transformer
spectrogram
data set
optimised
loss function
data increase
activation function

description: Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ â ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐµÑÐ¸ Ð¸ Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¾ÑÐ½Ð¾Ð²Ð½Ð¾Ð³Ð¾ ÑÐ¾Ð´ÐµÑÐ¶Ð°Ð½Ð¸Ñ Ð¸Ð· Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐµÐºÑÑÐ°. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð° Ð²ÑÐ±ÑÐ°Ð½Ð° Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐµÑÐ¸ Ð¿Ð¾ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ°Ð¼ ÐºÐ°ÑÐµÑÑÐ²Ð° ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¸ ÑÐµÑÑÑÑÐ¾Ð·Ð°ÑÑÐ°ÑÐ½Ð¾ÑÑÑÑ. Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ ÑÐ°ÐºÐ¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð±ÑÐ»Ð° Ð²ÑÐ±ÑÐ°Ð½Ð° Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° Conformer. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° Ð±ÑÐ»Ð° Ð¼Ð¾Ð´Ð¸ÑÐ¸ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð° Ð¿Ð¾Ð´ Ð¸Ð¼ÐµÑÑÐ¸ÐµÑÑ Ð´Ð°Ð½Ð½ÑÐµ. Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¸ÑÑÐ¾Ð´Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð±ÑÐ»Ð¸ Ð²Ð·ÑÑÑ ÑÐ¾ÑÐ½Ð¸ ÑÐ°ÑÐ¾Ð² ÑÑÑÑÐºÐ¾Ð¹ ÑÐµÑÐ¸ Ñ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð½Ð¾Ð¹ ÑÑÐ°Ð½ÑÐºÑÐ¸Ð¿ÑÐ¸ÐµÐ¹. Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð´Ð»Ñ Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¾ÑÐ½Ð¾Ð²Ð½Ð¾Ð³Ð¾ ÑÐ¾Ð´ÐµÑÐ¶Ð°Ð½Ð¸Ñ Ð¸Ð· ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð½Ð½Ð¾Ð³Ð¾ ÑÐµÐºÑÑÐ° Ð±ÑÐ»Ð° Ð²ÑÐ±ÑÐ°Ð½Ð° Ð¿ÑÐµÐ´Ð¾Ð±ÑÑÐµÐ½Ð½Ð°Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° Ð¢5. ÐÐ¾Ð´ÐµÐ»Ñ Ð±ÑÐ»Ð° ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð° Ð½Ð° ÑÐ·ÑÐºÐµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð¼ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¿Ð¾Ð»Ð½Ð¾ÑÑÑÑ Ð³Ð¾ÑÐ¾Ð²Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ, Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð½Ð°Ñ Ð½Ð° Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½ÑÑ Ð¿Ð¾ ÑÐµÑÑÑÑÐ¾Ð·Ð°ÑÑÐ°ÑÐ½Ð¾ÑÑÐ¸ Ð¸ ÐºÐ°ÑÐµÑÑÐ²Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ°Ñ, Ð¿ÑÐ¸Ð½Ð¸Ð¼Ð°ÑÑÐ°Ñ Ð½Ð° Ð²ÑÐ¾Ð´ Ð°ÑÐ´Ð¸Ð¾ÑÐ¸Ð³Ð½Ð°Ð» Ñ ÑÑÑÑÐºÐ¾Ð¹ ÑÐµÑÑÑ, Ð¸, Ð²Ð¾Ð·Ð²ÑÐ°ÑÐ°ÑÑÐ°Ñ Ð½Ð° Ð²ÑÑÐ¾Ð´Ðµ Ð¾ÑÐ½Ð¾Ð²Ð½Ð¾Ðµ ÑÐ¾Ð´ÐµÑÐ¶Ð°Ð½Ð¸Ðµ Ð´Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð°ÑÐ´Ð¸Ð¾ÑÐ¸Ð³Ð½Ð°Ð»Ð°.
description: The given work is devoted to developing a model for speech recognition and extraction of the main content from received text using neural networks. As a result of the work, the optimal model for speech recognition was selected based on the characteristics of recognition quality and resource consumption. Conformer architecture was chosen as such a model. In the course of the work, the architecture was modified to fit the available data. Hundreds of hours of Russian speech with transcription were taken as initial data. The pre-trained T5 architecture was chosen as a model for highlighting the main content from the recognized text. The model was implemented in the Python programming language. The result of the work is a completely ready-made model built on architectures that are optimal in terms of resource consumption and quality, accepting an audio signal with Russian speech as input, and returning the main content of this audio signal at the output. Despite the fact that the problem has existed for a long time, only recently has the solution of these problems with the help of neural networks achieved a good result, which is due both to the emergence of new neural network architectures and to the fact that the computing power available now is much greater than a few decades ago. The scope of application is social networks, video portals, voice assistants.