Speech Recognition in Python

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by ganesh dot icon Updated: Feb 1, 2023

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Real-time speech recognition in Python refers to the ability of a computer program to transcribe spoken words into written text in real-time. You can use a library like SpeechRecognition to recognize speech in real time in Python. It supports several various engines and APIs, such as Microsoft Bing Voice Recognition and Google Speech Recognition. 


Real-time voice recognition in Python has a wide range of uses, including: 

  • Voice-controlled assistants: These virtual assistants, like Siri or Alexa, can be operated via voice commands. 
  • Speech-to-text transcription: This tool turns audible words into written text and is useful in professions including journalism, law, and medicine. 
  • Voice biometrics: This application uses a person's distinctive voice patterns to authenticate and identify them. 
  • Real-time language translation: This program helps people who speak various languages communicate more easily by translating spoken words from one language to another. 
  • Speech-based accessibility: Applications that assist people with disabilities, such as text-to-speech or speech-to-text for the visually impaired. 


Here is how you can recognize speech in real-time in Python: 

Fig 1: Preview of the output that you will get on running this code from your IDE


Code


In this solution, we use the Recognizer function of the Speech Recognition library

  1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
  2. Run the file. You will be prompted to speak something through your microphone
  3. The speech in real-time gets processed and displayed on screen


I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.


I found this code snippet by searching for "speech recognition in python" in kandi. You can try any such use case!


Dependent Libraries

Python doticonstar image 7239 doticonVersion:3.10.0doticon
License: Permissive (BSD-3-Clause)

Speech recognition module for Python, supporting several engines and APIs, online and offline.

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            speech_recognitionby Uberi

            Python doticon star image 7239 doticonVersion:3.10.0doticon License: Permissive (BSD-3-Clause)

            Speech recognition module for Python, supporting several engines and APIs, online and offline.
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              Quality
                Security
                  License
                    Reuse

                      pyaudioby jleb

                      C doticonstar image 99 doticonVersion:Currentdoticon
                      no licences License: No License (null)

                      http://people.csail.mit.edu/hubert/pyaudio/

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                                pyaudioby jleb

                                C doticon star image 99 doticonVersion:Currentdoticonno licences License: No License

                                http://people.csail.mit.edu/hubert/pyaudio/
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                                          If you do not have Speech Recognition that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the Speech Recognition page in kandi.


                                          You can search for any dependent library on kandi like SpeechRecognition.

                                          Environment Tested


                                          I tested this solution in the following versions. Be mindful of changes when working with other versions.

                                          1. The solution is created in Python3.9.
                                          2. The solution is tested on SpeechRecognition 3.8.1 and PyAudio 0.2.12 versions.


                                          Using this solution, we are able to make blurred images using the OpenCV library in Python with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us to recognize speech in real-time in Python.

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                                          2. For further learning resources, visit the Open Weaver Community learning page.

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