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Oct 20, 2024 · On other POSIX-based systems, install the portaudio19-dev and python-all-dev (or python3-all-dev if using Python 3) packages (or their closest equivalents) using a package manager of your choice, and then install with PyAudio using Pip: pip install SpeechRecognition[audio] (replace pip with pip3 if using Python 3).
The accessibility improvements alone are worth considering. Speech recognition allows the elderly and the physically and visually impaired to interact with state-of-the-art products and services quickly and naturally—no GUI needed! Best of all, including speech recognition in a Python project is really simple. In this guide, you’ll find out ...
Mar 19, 2024 · Python, known for its simplicity and robust libraries, offers several modules to tackle speech recognition tasks effectively. In this article, we'll explore the essence of speech recognition in Python, including an overview of its key libraries, how they can be implemented, and their practical applications. Key Python Libraries for Speech ...
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Library for performing speech recognition, with support for several engines and APIs, online and offline.
UPDATE 2022-02-09: Hey everyone! This project started as a tech demo, but these days it needs more time than I have to keep up with all the PRs and issues. Therefore, I'd like to put out an open invite for collaborators - just reach out at me@anthonyz.ca if you're interested!
Speech recognition engine/API support:
•CMU Sphinx (works offline)
•Google Speech Recognition
•Google Cloud Speech API
The library reference documents every publicly accessible object in the library. This document is also included under reference/library-reference.rst.
See Notes on using PocketSphinx for information about installing languages, compiling PocketSphinx, and building language packs from online resources. This document is also included under reference/pocketsphinx.rst.
First, make sure you have all the requirements listed in the "Requirements" section.
The easiest way to install this is using pip install SpeechRecognition.
Otherwise, download the source distribution from PyPI, and extract the archive.
In the folder, run python setup.py install.
To use all of the functionality of the library, you should have:
•Python 3.8+ (required)
•PyAudio 0.2.11+ (required only if you need to use microphone input, Microphone)
•PocketSphinx (required only if you need to use the Sphinx recognizer, recognizer_instance.recognize_sphinx)
•Google API Client Library for Python (required only if you need to use the Google Cloud Speech API, recognizer_instance.recognize_google_cloud)
•FLAC encoder (required only if the system is not x86-based Windows/Linux/OS X)
The recognizer tries to recognize speech even when I'm not speaking, or after I'm done speaking.
Try increasing the recognizer_instance.energy_threshold property. This is basically how sensitive the recognizer is to when recognition should start. Higher values mean that it will be less sensitive, which is useful if you are in a loud room. This value depends entirely on your microphone or audio data. There is no one-size-fits-all value, but good values typically range from 50 to 4000. Also, check on your microphone volume settings. If it is too sensitive, the microphone may be picking up a lot of ambient noise. If it is too insensitive, the microphone may be rejecting speech as just noise.
The recognizer can't recognize speech right after it starts listening for the first time.
The recognizer_instance.energy_threshold property is probably set to a value that is too high to start off with, and then being adjusted lower automatically by dynamic energy threshold adjustment. Before it is at a good level, the energy threshold is so high that speech is just considered ambient noise. The solution is to decrease this threshold, or call recognizer_instance.adjust_for_ambient_noise beforehand, which will set the threshold to a good value automatically.
The recognizer doesn't understand my particular language/dialect.
Try setting the recognition language to your language/dialect. To do this, see the documentation for recognizer_instance.recognize_sphinx, recognizer_instance.recognize_google, recognizer_instance.recognize_wit, recognizer_instance.recognize_bing, recognizer_instance.recognize_api, recognizer_instance.recognize_houndify, and recognizer_instance.recognize_ibm. For example, if your language/dialect is British English, it is better to use "en-GB" as the language rather than "en-US".
To hack on this library, first make sure you have all the requirements listed in the "Requirements" section.
•Most of the library code lives in speech_recognition/__init__.py.
•Examples live under the examples/ directory, and the demo script lives in speech_recognition/__main__.py.
•The FLAC encoder binaries are in the speech_recognition/ directory.
•Documentation can be found in the reference/ directory.
•Third-party libraries, utilities, and reference material are in the third-party/ directory.
Please report bugs and suggestions at the issue tracker!
How to cite this library (APA style):
How to cite this library (Chicago style):
Also check out the Python Baidu Yuyin API, which is based on an older version of this project, and adds support for Baidu Yuyin. Note that Baidu Yuyin is only available inside China.
Copyright 2014-2017 Anthony Zhang (Uberi). The source code for this library is available online at GitHub.
SpeechRecognition is made available under the 3-clause BSD license. See LICENSE.txt in the project's root directory for more information.
For convenience, all the official distributions of SpeechRecognition already include a copy of the necessary copyright notices and licenses. In your project, you can simply say that licensing information for SpeechRecognition can be found within the SpeechRecognition README, and make sure SpeechRecognition is visible to users if they wish to see it.
SpeechRecognition distributes source code, binaries, and language files from CMU Sphinx. These files are BSD-licensed and redistributable as long as copyright notices are correctly retained. See speech_recognition/pocketsphinx-data/*/LICENSE*.txt and third-party/LICENSE-Sphinx.txt for license details for individual parts.
SpeechRecognition distributes source code and binaries from PyAudio. These files are MIT-licensed and redistributable as long as copyright notices are correctly retained. See third-party/LICENSE-PyAudio.txt for license details.
SpeechRecognition distributes binaries from FLAC - speech_recognition/flac-win32.exe, speech_recognition/flac-linux-x86, and speech_recognition/flac-mac. These files are GPLv2-licensed and redistributable, as long as the terms of the GPL are satisfied. The FLAC binaries are an aggregate of separate programs, so these GPL restrictions do not apply to the library or your programs that use the library, only to FLAC itself. See LICENSE-FLAC.txt for license details.
In the ever-evolving landscape of technology, voice recognition has become an integral part of various applications, from virtual assistants to transcription services. Python, a versatile and powerful programming language, offers a convenient module called SpeechRecognition that facilitates seamless integration of speech-to-text functionality into your projects. In this article, we will walk ...
This repository contains resources from The Ultimate Guide to Speech Recognition with Python tutorial on Real Python. Audio files for the examples in the Working With Audio Files section of the post can be found in the audio_files directory. To download them, use the green "Clone or download" button at the top right corner of this page.
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Sep 3, 2012 · If you really want to understand speech recognition from the ground up, look for a good signal processing package for python and then read up on speech recognition independently of the software. But speech recognition is an extremely complex problem (basically because sounds interact in all sorts of ways when we talk).
