Library for performing speech recognition, with support for several engines and APIs, online and offline.
Speech recognition engine/API support:
- CMU Sphinx (works offline)
- Google Speech Recognition
- Wit.ai
- Microsoft Bing Voice Recognition
- api.ai
- Houndify API
- IBM Speech to Text
Quickstart: pip install SpeechRecognition. See the "Installing" section for more details.
To quickly try it out, run python -m speech_recognition after installing.
Project links:
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.
See the examples/ directory in the repository root for usage examples:
- Recognize speech input from the microphone
- Transcribe an audio file
- Save audio data to an audio file
- Show extended recognition results
- Calibrate the recognizer energy threshold for ambient noise levels (see
recognizer_instance.energy_thresholdfor details) - Listening to a microphone in the background
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 2.6, 2.7, or 3.3+ (required)
- PyAudio 0.2.9+ (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) - FLAC encoder (required only if the system is not x86-based Windows/Linux/OS X)
The following requirements are optional, but can improve or extend functionality in some situations:
- On Python 2, and only on Python 2, some functions (like
recognizer_instance.recognize_bing) will run slower if you do not have Monotonic for Python 2 installed. - If using CMU Sphinx, you may want to install additional language packs to support languages like International French or Mandarin Chinese.
The following sections go over the details of each requirement.
The first software requirement is Python 2.6, 2.7, or Python 3.3+. This is required to use the library.
PyAudio is required if and only if you want to use microphone input (Microphone). PyAudio version 0.2.9+ is required, as earlier versions have overflow issues with recording on certain machines.
If not installed, everything in the library will still work, except attempting to instantiate a Microphone object will throw an AttributeError.
The installation instructions are quite good as of PyAudio v0.2.9. For convenience, they are summarized below:
- On Windows, install PyAudio using Pip: execute
pip install pyaudioin a terminal. - On Debian-derived Linux distributions (like Ubuntu and Mint), install PyAudio using APT: execute
sudo apt-get install python-pyaudio python3-pyaudioin a terminal. - If the version in the repositories is too old, install the latest release using Pip: execute
sudo apt-get install portaudio19-dev python-all-dev python3-all-dev && sudo pip install pyaudio(replacepipwithpip3if using Python 3).
- If the version in the repositories is too old, install the latest release using Pip: execute
- On Debian-derived Linux distributions (like Ubuntu and Mint), install PyAudio using APT: execute
- On OS X, install PortAudio using Homebrew:
brew install portaudio && sudo brew link portaudio. Then, install PyAudio using Pip:pip install pyaudio. - On other POSIX-based systems, install the
portaudio19-devandpython-all-dev(orpython3-all-devif using Python 3) packages (or their closest equivalents) using a package manager of your choice, and then install PyAudio using Pip:pip install pyaudio(replacepipwithpip3if using Python 3).
PyAudio wheel packages for 64-bit Python 2.7, 3.4, and 3.5 on Windows and Linux are included for convenience, under the third-party/ directory in the repository root. To install, simply run pip install wheel followed by pip install ./third-party/WHEEL_FILENAME (replace pip with pip3 if using Python 3) in the repository root directory.
PocketSphinx-Python is required if and only if you want to use the Sphinx recognizer (recognizer_instance.recognize_sphinx).
PocketSphinx-Python wheel packages for 64-bit Python 2.7, 3.4, and 3.5 on Windows are included for convenience, under the third-party/ directory. To install, simply run pip install wheel followed by pip install ./third-party/WHEEL_FILENAME (replace pip with pip3 if using Python 3) in the SpeechRecognition folder.
On Linux and other POSIX systems (such as OS X), follow the instructions under "Building PocketSphinx-Python from source" in Notes on using PocketSphinx for installation instructions.
Note that the versions available in most package repositories are outdated and will not work with the bundled language data. Using the bundled wheel packages or building from source is recommended.
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.
A FLAC encoder is required to encode the audio data to send to the API. If using Windows (x86 or x86-64), OS X (Intel Macs only, OS X 10.6 or higher), or Linux (x86 or x86-64), this is already bundled with this library - you do not need to install anything.
Otherwise, ensure that you have the flac command line tool, which is often available through the system package manager.
The included flac-win32 executable is the official FLAC 1.3.1 32-bit Windows binary.
The included flac-linux-x86 executable is built from the FLAC 1.3.1 source code with Manylinux to ensure that it's compatible with a wide variety of distributions. The exact commands used are:
# download and extract the FLAC source code
wget http://downloads.xiph.org/releases/flac/flac-1.3.1.tar.xz
tar xf flac-1.3.1.tar.xz
sudo apt-get install --yes docker.io
sudo docker run --tty --interactive --rm --volume "$(pwd):/root" quay.io/pypa/manylinux1_i686:latest bash # download and start a shell inside the Manylinux environment
# we're now in a Bash shell inside the Manylinux Docker image
cd /root/flac-1.3.1
./configure LDFLAGS=-static # compiler flags to make a static build
make
exit # return to the original shellThe resulting executable can then be found at ./flac-1.3.1/src/flac relative to the working directory. A copy of the source code can also be found at third-party/flac-1.3.1.tar.xz. The build should be bit-for-bit reproducible.
The included flac-mac executable is extracted from xACT 2.37, which is a frontend for FLAC that conveniently includes binaries for all of its encoders. Specifically, it is a copy of xACT 2.37/xACT.app/Contents/Resources/flac in xACT2.37.zip.
On Python 2, and only on Python 2, if you do not install the Monotonic for Python 2 library, some functions will run slower than they otherwise could (though everything will still work correctly).
On Python 3, that library's functionality is built into the Python standard library, which makes it unnecessary.
This is because monotonic time is necessary to handle cache expiry properly in the face of system time changes and other time-related issues. If monotonic time functionality is not available, then things like access token requests will not be cached.
To install, use Pip: execute pip install monotonic in a terminal.
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.
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.
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".
When you're using Python 2, and your language uses non-ASCII characters, and the terminal or file-like object you're printing to only supports ASCII, an error is thrown when trying to write non-ASCII characters.
This is because in Python 2, 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 return unicode strings (u"something") rather than byte strings ("something"). In Python 3, all strings are unicode strings.
To make printing of unicode strings work in Python 2 as well, replace all print statements in your code of the following form:
print SOME_UNICODE_STRING
With the following:
print SOME_UNICODE_STRING.encode("utf8")
This change, however, will prevent the code from working in Python 3.
The program doesn't run when compiled with PyInstaller.
As of PyInstaller version 3.0, SpeechRecognition is supported out of the box. If you're getting weird issues when compiling your program using PyInstaller, simply update PyInstaller.
You can easily do this by running pip install --upgrade pyinstaller.
On Ubuntu/Debian, I get errors like "jack server is not running or cannot be started" or "Cannot lock down [...] byte memory area (Cannot allocate memory)".
The Linux audio stack is pretty fickle. There are a few things that can cause these issues.
First, make sure JACK is installed - to install it, run sudo apt-get install multimedia-jack
You will then want to configure the JACK daemon correctly to avoid that "Cannot allocate memory" error. Run sudo dpkg-reconfigure -p high jackd2 and select "Yes" to do so.
Now, you will want to make sure your current user is in the audio group. You can add your current user to this group by running sudo adduser $(whoami) audio.
Unfortunately, these changes will require you to reboot before they take effect.
After rebooting, run pulseaudio --kill, followed by jack_control start, to fix the "jack server is not running or cannot be started" error.
On Ubuntu/Debian, I get annoying output in the terminal saying things like "bt_audio_service_open: [...] Connection refused" and various others.
The "bt_audio_service_open" error means that you have a Bluetooth audio device, but as a physical device is not currently connected, we can't actually use it - if you're not using a Bluetooth microphone, then this can be safely ignored. If you are, and audio isn't working, then double check to make sure your microphone is actually connected. There does not seem to be a simple way to disable these messages.
For errors of the form "ALSA lib [...] Unknown PCM", see this StackOverflow answer. Basically, to get rid of an error of the form "Unknown PCM cards.pcm.rear", simply comment out pcm.rear cards.pcm.rear in /usr/share/alsa/alsa.conf, ~/.asoundrc, and /etc/asound.conf.
On OS X, I get a ChildProcessError saying that it couldn't find the system FLAC converter, even though it's installed.
Installing FLAC for OS X directly from the source code will not work, since it doesn't correctly add the executables to the search path.
Installing FLAC using Homebrew ensures that the search path is correctly updated. First, ensure you have Homebrew, then run brew install flac to install the necessary files.
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 inspeech_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.
To install/reinstall the library locally, run python setup.py install in the project root directory.
Releases are done by running either build.sh or build.bat. These are bash and batch scripts, respectively, that automatically build Python source packages and Python Wheels, then upload them to PyPI.
Features and bugfixes should be tested, at minimum, on Python 2.7 and a recent version of Python 3. It is highly recommended to test new features on Python 2.6, 2.7, 3.3, and the latest version of Python 3.
Uberi <[email protected]> (Anthony Zhang) bobsayshilol arvindch <[email protected]> (Arvind Chembarpu) kevinismith <[email protected]> (Kevin Smith) haas85 DelightRun <[email protected]> maverickagm kamushadenes <[email protected]> (Kamus Hadenes) sbraden <[email protected]> (Sarah Braden) tb0hdan (Bohdan Turkynewych)
Please report bugs and suggestions at the issue tracker!
How to cite this library (APA style):
Zhang, A. (2016). Speech Recognition (Version 3.4) [Software]. Available from https://github.com/Uberi/speech_recognition#readme.
How to cite this library (Chicago style):
Zhang, Anthony. 2016. Speech Recognition (version 3.4).
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 2014-2016 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.