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new speech_recognition

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SevaSk 2023-05-11 22:20:49 -04:00
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176
speech_recognition/.gitignore vendored
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# ----- Python.gitignore -----
# from: https://github.com/github/gitignore/blob/main/Python.gitignore
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
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celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
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env/
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ENV/
env.bak/
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# Spyder project settings
.spyderproject
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# Rope project settings
.ropeproject
# mkdocs documentation
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# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
# ----- End of Python.gitignore -----
# SpeechRecognition specific .gitignore
speech_recognition/pocketsphinx-data/fr-FR/
speech_recognition/pocketsphinx-data/zh-CN/
fr-FR.zip
zh-CN.zip
it-IT.zip
pocketsphinx-python/
examples/TEST.py
*.geany
*.out

35
speech_recognition/.travis.yml
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language: python
python:
- "2.7"
- "3.3"
- "3.4"
- "3.5"
- "3.6"
- "3.6-dev"
- "3.7-dev"
- "nightly"
addons:
apt:
packages:
- swig
- libpulse-dev
- libasound2-dev
install:
- trap 'sleep 3' ERR
- pip install pocketsphinx monotonic
- pip install flake8 rstcheck
- pip install -e .
script:
- flake8 --ignore=E501,E701,W504 speech_recognition tests examples setup.py # ignore errors for long lines and multi-statement lines
- rstcheck README.rst reference/*.rst # ensure RST is well-formed
- python -m unittest discover --verbose # run unit tests
sudo: false # this allows TravisCI to use the fast Docker build environment rather than the slower VMs
env:
global:
- secure: "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" # encrypted version of "WIT_AI_KEY=(my key)"
- secure: "ZKs+ywhJett8CpA24wR8js3C5B0uzaXMFIaiWBgkQfVhwbwkecCjG2HbLaJ1ncXP5VZnrXF6Ym4pZm87q0mIp/S0dMS7ZC5Jikowc3Bdyph9L49MDubZL0SO98+YR9j0QeKw8wxiVP6kv9cw12uVWn4VNgGcuW6AYZ0AqzdvUfW4+zby+Ua9U8LC0RcDKY3GR4Svq6dUjNFtJmI5uJ129UFO4oujCzuHNZL3KSSUJVt1KelVX+1eUNJ67sN3AvoMfx86jXNtN0kS12lZ+dP4YDo+lCtViG/W1dHCCdBmnUZsPE4Bc+Uyvg/BeKZaL1hgrNb6QHCNWmZC7jGxzkP2akwX5PxmKW7ClXn/79c7e84YUiRHlYQgL0qP+kZ7WDG6nJyKqLNFAtTHAw5F++5cpomNThYoCJeQOmkhi+KLEs9KMmn4d/bOLzW1RCeuf0bhjOOCI89+761aqJ1ky8UHJUZCKjYegHLM/bZ9LkKnUi+d+KYNQB8qpluZSLqObknCczh6ekKt/1FdrC+FBbFmpkTCuru1K9APdz01+ipVV8Av6NB+ax0+KPlKp49TA9uzANKWyLRkW9j6LD67MGF6SH/h8t5OeNZXdmf4DGjqv1erbKZeW+y25Hw7lVbqEo1m4T9wn1lmA1nse0kBrqGF+kQ4mNdfNSmWGWKxj+gFuxA=" # encrypted version of "BING_KEY=(my key)"
- secure: "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" # encrypted version of "HOUNDIFY_CLIENT_ID=(my client ID)"
- secure: "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" # encrypted version of "HOUNDIFY_CLIENT_KEY=(my client key)"
- secure: "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" # encrypted version of "IBM_USERNAME=(my username)"
- secure: "fqWkYnsx5xrYjDosEkHramkzuuRjAu6NUkSx/yJf78WTDgJ0XAvu7BP9vdfO9g+KvwVZ9uBSClBXiNM7c1i/CpZCJcZJQtQS9PqL3YB9+76J3hPwOsQx0t3oRiYKPDmHX3WFUFuGhI2k90iw4n6nWHUUiU2WxWk/8sibXxyCf99CRMGwpfycd+w8mhsi/MkzbyxWBjzgRKIFu6tg28bs6GcjrMyoq6avD3jpwghGAu1CA3UnuxdOqY9WI2+d9MwmtK6cUQ88o/5MX7GjPZzfkiohru03yn1sBBBivf1V7Vwvd7xsnUZ+6/WiJnzRkaqoGaYSOnI5bhJ/qR21zNMwNEaYrbdyCWau+YLuOheTJzihyeTN9f5zQg/PiBQMLDyKWBw7+v2rQMzTmKoif7fz+SAN5GMXvqgcoMlZ7se9sk0QH6z+GLYbnZNtu0Qpf01gNaJaveQRuurdLtihF8EBTET+hBouiRTUWHvJMgd6PI2pp9BRdnvwwHlhCQLwUjqprLUHX6OdbhFc2ixHwao+Qbg+oCEv+IhCrW1HoTCFIBy/SllRx0l7MfroEiRDRkaZeKA6bOr+3yirVmUOQVLH5rmVUuoNCmI0BZG5GPt5+AhZ36Wlw3/CXkcJAf7VNcya+u4ls+Hdxb9SyFNsZ5IF0ZWNRPfZlG8uEGDy/o05fbY=" # encrypted version of "IBM_PASSWORD=(my password)"

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speech_recognition/LICENSE-FLAC.txt
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PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
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WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
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YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
POSSIBILITY OF SUCH DAMAGES.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
convey the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
Also add information on how to contact you by electronic and paper mail.
If the program is interactive, make it output a short notice like this
when it starts in an interactive mode:
Gnomovision version 69, Copyright (C) year name of author
Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, the commands you use may
be called something other than `show w' and `show c'; they could even be
mouse-clicks or menu items--whatever suits your program.
You should also get your employer (if you work as a programmer) or your
school, if any, to sign a "copyright disclaimer" for the program, if
necessary. Here is a sample; alter the names:
Yoyodyne, Inc., hereby disclaims all copyright interest in the program
`Gnomovision' (which makes passes at compilers) written by James Hacker.
<signature of Ty Coon>, 1 April 1989
Ty Coon, President of Vice
This General Public License does not permit incorporating your program into
proprietary programs. If your program is a subroutine library, you may
consider it more useful to permit linking proprietary applications with the
library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License.

12
speech_recognition/LICENSE.txt
View File

@ -1,12 +0,0 @@
Copyright (c) 2014-2017, Anthony Zhang <azhang9@gmail.com>
All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

6
speech_recognition/MANIFEST.in
View File

@ -1,6 +0,0 @@
graft speech_recognition
graft reference
recursive-exclude speech_recognition *.pyc
include README.rst
include LICENSE.txt
include LICENSE-FLAC.txt

384
speech_recognition/README.rst
View File

@ -1,384 +0,0 @@
SpeechRecognition
=================
.. image:: https://img.shields.io/pypi/v/SpeechRecognition.svg
:target: https://pypi.python.org/pypi/SpeechRecognition/
:alt: Latest Version
.. image:: https://img.shields.io/pypi/status/SpeechRecognition.svg
:target: https://pypi.python.org/pypi/SpeechRecognition/
:alt: Development Status
.. image:: https://img.shields.io/pypi/pyversions/SpeechRecognition.svg
:target: https://pypi.python.org/pypi/SpeechRecognition/
:alt: Supported Python Versions
.. image:: https://img.shields.io/pypi/l/SpeechRecognition.svg
:target: https://pypi.python.org/pypi/SpeechRecognition/
:alt: License
.. image:: https://api.travis-ci.org/Uberi/speech_recognition.svg?branch=master
:target: https://travis-ci.org/Uberi/speech_recognition
:alt: Continuous Integration Test Results
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 <http://cmusphinx.sourceforge.net/wiki/>`__ (works offline)
* Google Speech Recognition
* `Google Cloud Speech API <https://cloud.google.com/speech/>`__
* `Wit.ai <https://wit.ai/>`__
* `Microsoft Azure Speech <https://azure.microsoft.com/en-us/services/cognitive-services/speech/>`__
* `Microsoft Bing Voice Recognition (Deprecated) <https://www.microsoft.com/cognitive-services/en-us/speech-api>`__
* `Houndify API <https://houndify.com/>`__
* `IBM Speech to Text <http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/speech-to-text.html>`__
* `Snowboy Hotword Detection <https://snowboy.kitt.ai/>`__ (works offline)
* `Tensorflow <https://www.tensorflow.org/>`__
* `Vosk API <https://github.com/alphacep/vosk-api/>`__ (works offline)
* `OpenAI whisper <https://github.com/openai/whisper>`__ (works offline)
* `Whisper API <https://platform.openai.com/docs/guides/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:
- `PyPI <https://pypi.python.org/pypi/SpeechRecognition/>`__
- `Source code <https://github.com/Uberi/speech_recognition>`__
- `Issue tracker <https://github.com/Uberi/speech_recognition/issues>`__
Library Reference
-----------------
The `library reference <https://github.com/Uberi/speech_recognition/blob/master/reference/library-reference.rst>`__ documents every publicly accessible object in the library. This document is also included under ``reference/library-reference.rst``.
See `Notes on using PocketSphinx <https://github.com/Uberi/speech_recognition/blob/master/reference/pocketsphinx.rst>`__ for information about installing languages, compiling PocketSphinx, and building language packs from online resources. This document is also included under ``reference/pocketsphinx.rst``.
You have to install Vosk models for using Vosk. `Here <https://alphacephei.com/vosk/models>`__ are models avaiable. You have to place them in models folder of your project, like "your-project-folder/models/your-vosk-model"
Examples
--------
See the ``examples/`` `directory <https://github.com/Uberi/speech_recognition/tree/master/examples>`__ in the repository root for usage examples:
- `Recognize speech input from the microphone <https://github.com/Uberi/speech_recognition/blob/master/examples/microphone_recognition.py>`__
- `Transcribe an audio file <https://github.com/Uberi/speech_recognition/blob/master/examples/audio_transcribe.py>`__
- `Save audio data to an audio file <https://github.com/Uberi/speech_recognition/blob/master/examples/write_audio.py>`__
- `Show extended recognition results <https://github.com/Uberi/speech_recognition/blob/master/examples/extended_results.py>`__
- `Calibrate the recognizer energy threshold for ambient noise levels <https://github.com/Uberi/speech_recognition/blob/master/examples/calibrate_energy_threshold.py>`__ (see ``recognizer_instance.energy_threshold`` for details)
- `Listening to a microphone in the background <https://github.com/Uberi/speech_recognition/blob/master/examples/background_listening.py>`__
- `Various other useful recognizer features <https://github.com/Uberi/speech_recognition/blob/master/examples/special_recognizer_features.py>`__
Installing
----------
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 <https://pypi.python.org/pypi/SpeechRecognition/>`__, and extract the archive.
In the folder, run ``python setup.py install``.
Requirements
------------
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)
* **Vosk** (required only if you need to use Vosk API speech recognition ``recognizer_instance.recognize_vosk``)
* **Whisper** (required only if you need to use Whisper ``recognizer_instance.recognize_whisper``)
* **openai** (required only if you need to use Whisper API speech recognition ``recognizer_instance.recognize_whisper_api``)
The following requirements are optional, but can improve or extend functionality in some situations:
* If using CMU Sphinx, you may want to `install additional language packs <https://github.com/Uberi/speech_recognition/blob/master/reference/pocketsphinx.rst#installing-other-languages>`__ to support languages like International French or Mandarin Chinese.
The following sections go over the details of each requirement.
Python
~~~~~~
The first software requirement is `Python 3.8+ <https://www.python.org/downloads/>`__. This is required to use the library.
PyAudio (for microphone users)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
`PyAudio <http://people.csail.mit.edu/hubert/pyaudio/#downloads>`__ is required if and only if you want to use microphone input (``Microphone``). PyAudio version 0.2.11+ is required, as earlier versions have known memory management bugs when recording from microphones in certain situations.
If not installed, everything in the library will still work, except attempting to instantiate a ``Microphone`` object will raise an ``AttributeError``.
The installation instructions on the PyAudio website are quite good - for convenience, they are summarized below:
* On Windows, install PyAudio using `Pip <https://pip.readthedocs.org/>`__: execute ``pip install pyaudio`` in a terminal.
* On Debian-derived Linux distributions (like Ubuntu and Mint), install PyAudio using `APT <https://wiki.debian.org/Apt>`__: execute ``sudo apt-get install python-pyaudio python3-pyaudio`` in 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`` (replace ``pip`` with ``pip3`` if using Python 3).
* On OS X, install PortAudio using `Homebrew <http://brew.sh/>`__: ``brew install portaudio``. Then, install PyAudio using `Pip <https://pip.readthedocs.org/>`__: ``pip install pyaudio``.
* 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 PyAudio using `Pip <https://pip.readthedocs.org/>`__: ``pip install pyaudio`` (replace ``pip`` with ``pip3`` if using Python 3).
PyAudio `wheel packages <https://pypi.python.org/pypi/wheel>`__ for common 64-bit Python versions on Windows and Linux are included for convenience, under the ``third-party/`` `directory <https://github.com/Uberi/speech_recognition/tree/master/third-party>`__ 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 <https://github.com/Uberi/speech_recognition>`__.
PocketSphinx-Python (for Sphinx users)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
`PocketSphinx-Python <https://github.com/bambocher/pocketsphinx-python>`__ is **required if and only if you want to use the Sphinx recognizer** (``recognizer_instance.recognize_sphinx``).
PocketSphinx-Python `wheel packages <https://pypi.python.org/pypi/wheel>`__ for 64-bit Python 3.4, and 3.5 on Windows are included for convenience, under the ``third-party/`` `directory <https://github.com/Uberi/speech_recognition/tree/master/third-party>`__. 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 <https://github.com/Uberi/speech_recognition/blob/master/reference/pocketsphinx.rst>`__ 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 <https://github.com/Uberi/speech_recognition/blob/master/reference/pocketsphinx.rst>`__ for information about installing languages, compiling PocketSphinx, and building language packs from online resources. This document is also included under ``reference/pocketsphinx.rst``.
Vosk (for Vosk users)
~~~~~~~~~~~~~~~~~~~~~
Vosk API is **required if and only if you want to use Vosk recognizer** (``recognizer_instance.recognize_vosk``).
You can install it with ``python3 -m pip install vosk``.
You also have to install Vosk Models:
`Here <https://alphacephei.com/vosk/models>`__ are models avaiable for download. You have to place them in models folder of your project, like "your-project-folder/models/your-vosk-model"
Google Cloud Speech Library for Python (for Google Cloud Speech API users)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
`Google Cloud Speech library for Python <https://cloud.google.com/speech-to-text/docs/quickstart>`__ is required if and only if you want to use the Google Cloud Speech API (``recognizer_instance.recognize_google_cloud``).
If not installed, everything in the library will still work, except calling ``recognizer_instance.recognize_google_cloud`` will raise an ``RequestError``.
According to the `official installation instructions <https://cloud.google.com/speech-to-text/docs/quickstart>`__, the recommended way to install this is using `Pip <https://pip.readthedocs.org/>`__: execute ``pip install google-cloud-speech`` (replace ``pip`` with ``pip3`` if using Python 3).
FLAC (for some systems)
~~~~~~~~~~~~~~~~~~~~~~~
A `FLAC encoder <https://xiph.org/flac/>`__ 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. For example, this would usually be ``sudo apt-get install flac`` on Debian-derivatives, or ``brew install flac`` on OS X with Homebrew.
Whisper (for Whisper users)
~~~~~~~~~~~~~~~~~~~~~~~~~~~
Whisper is **required if and only if you want to use whisper** (``recognizer_instance.recognize_whisper``).
You can install it with ``python3 -m pip install git+https://github.com/openai/whisper.git soundfile``.
Whisper API (for Whisper API users)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The library `openai <https://pypi.org/project/openai/>`__ is **required if and only if you want to use Whisper API** (``recognizer_instance.recognize_whisper_api``).
If not installed, everything in the library will still work, except calling ``recognizer_instance.recognize_whisper_api`` will raise an ``RequestError``.
You can install it with ``python3 -m pip install openai``.
Troubleshooting
---------------
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"``.
The recognizer hangs on ``recognizer_instance.listen``; specifically, when it's calling ``Microphone.MicrophoneStream.read``.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This usually happens when you're using a Raspberry Pi board, which doesn't have audio input capabilities by itself. This causes the default microphone used by PyAudio to simply block when we try to read it. If you happen to be using a Raspberry Pi, you'll need a USB sound card (or USB microphone).
Once you do this, change all instances of ``Microphone()`` to ``Microphone(device_index=MICROPHONE_INDEX)``, where ``MICROPHONE_INDEX`` is the hardware-specific index of the microphone.
To figure out what the value of ``MICROPHONE_INDEX`` should be, run the following code:
.. code:: python
import speech_recognition as sr
for index, name in enumerate(sr.Microphone.list_microphone_names()):
print("Microphone with name \"{1}\" found for `Microphone(device_index={0})`".format(index, name))
This will print out something like the following:
::
Microphone with name "HDA Intel HDMI: 0 (hw:0,3)" found for `Microphone(device_index=0)`
Microphone with name "HDA Intel HDMI: 1 (hw:0,7)" found for `Microphone(device_index=1)`
Microphone with name "HDA Intel HDMI: 2 (hw:0,8)" found for `Microphone(device_index=2)`
Microphone with name "Blue Snowball: USB Audio (hw:1,0)" found for `Microphone(device_index=3)`
Microphone with name "hdmi" found for `Microphone(device_index=4)`
Microphone with name "pulse" found for `Microphone(device_index=5)`
Microphone with name "default" found for `Microphone(device_index=6)`
Now, to use the Snowball microphone, you would change ``Microphone()`` to ``Microphone(device_index=3)``.
Calling ``Microphone()`` gives the error ``IOError: No Default Input Device Available``.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
As the error says, the program doesn't know which microphone to use.
To proceed, either use ``Microphone(device_index=MICROPHONE_INDEX, ...)`` instead of ``Microphone(...)``, or set a default microphone in your OS. You can obtain possible values of ``MICROPHONE_INDEX`` using the code in the troubleshooting entry right above this one.
The program doesn't run when compiled with `PyInstaller <https://github.com/pyinstaller/pyinstaller/wiki>`__.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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 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 <http://stackoverflow.com/questions/7088672/pyaudio-working-but-spits-out-error-messages-each-time>`__. 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``.
For "jack server is not running or cannot be started" or "connect(2) call to /dev/shm/jack-1000/default/jack_0 failed (err=No such file or directory)" or "attempt to connect to server failed", these are caused by ALSA trying to connect to JACK, and can be safely ignored. I'm not aware of any simple way to turn those messages off at this time, besides `entirely disabling printing while starting the microphone <https://github.com/Uberi/speech_recognition/issues/182#issuecomment-266256337>`__.
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 <https://xiph.org/flac/download.html>`__ directly from the source code will not work, since it doesn't correctly add the executables to the search path.
Installing FLAC using `Homebrew <http://brew.sh/>`__ ensures that the search path is correctly updated. First, ensure you have Homebrew, then run ``brew install flac`` to install the necessary files.
Developing
----------
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 <https://github.com/Uberi/speech_recognition/tree/master/examples>`__, and the demo script lives in ``speech_recognition/__main__.py``.
- The FLAC encoder binaries are in the ``speech_recognition/`` `directory <https://github.com/Uberi/speech_recognition/tree/master/speech_recognition>`__.
- Documentation can be found in the ``reference/`` `directory <https://github.com/Uberi/speech_recognition/tree/master/reference>`__.
- Third-party libraries, utilities, and reference material are in the ``third-party/`` `directory <https://github.com/Uberi/speech_recognition/tree/master/third-party>`__.
To install/reinstall the library locally, run ``python setup.py install`` in the project `root directory <https://github.com/Uberi/speech_recognition>`__.
Before a release, the version number is bumped in ``README.rst`` and ``speech_recognition/__init__.py``. Version tags are then created using ``git config gpg.program gpg2 && git config user.signingkey DB45F6C431DE7C2DCD99FF7904882258A4063489 && git tag -s VERSION_GOES_HERE -m "Version VERSION_GOES_HERE"``.
Releases are done by running ``make-release.sh VERSION_GOES_HERE`` to build the Python source packages, sign them, and upload them to PyPI.
Testing
~~~~~~~
To run all the tests:
.. code:: bash
python -m unittest discover --verbose
Testing is also done automatically by TravisCI, upon every push. To set up the environment for offline/local Travis-like testing on a Debian-like system:
.. code:: bash
sudo docker run --volume "$(pwd):/speech_recognition" --interactive --tty quay.io/travisci/travis-python:latest /bin/bash
su - travis && cd /speech_recognition
sudo apt-get update && sudo apt-get install swig libpulse-dev
pip install --user pocketsphinx && pip install --user flake8 rstcheck && pip install --user -e .
python -m unittest discover --verbose # run unit tests
python -m flake8 --ignore=E501,E701 speech_recognition tests examples setup.py # ignore errors for long lines and multi-statement lines
python -m rstcheck README.rst reference/*.rst # ensure RST is well-formed
FLAC Executables
~~~~~~~~~~~~~~~~
The included ``flac-win32`` executable is the `official FLAC 1.3.2 32-bit Windows binary <http://downloads.xiph.org/releases/flac/flac-1.3.2-win.zip>`__.
The included ``flac-linux-x86`` and ``flac-linux-x86_64`` executables are built from the `FLAC 1.3.2 source code <http://downloads.xiph.org/releases/flac/flac-1.3.2.tar.xz>`__ with `Manylinux <https://github.com/pypa/manylinux>`__ to ensure that it's compatible with a wide variety of distributions.
The built FLAC executables should be bit-for-bit reproducible. To rebuild them, run the following inside the project directory on a Debian-like system:
.. code:: bash
# download and extract the FLAC source code
cd third-party
sudo apt-get install --yes docker.io
# build FLAC inside the Manylinux i686 Docker image
tar xf flac-1.3.2.tar.xz
sudo docker run --tty --interactive --rm --volume "$(pwd):/root" quay.io/pypa/manylinux1_i686:latest bash
cd /root/flac-1.3.2
./configure LDFLAGS=-static # compiler flags to make a static build
make
exit
cp flac-1.3.2/src/flac/flac ../speech_recognition/flac-linux-x86 && sudo rm -rf flac-1.3.2/
# build FLAC inside the Manylinux x86_64 Docker image
tar xf flac-1.3.2.tar.xz
sudo docker run --tty --interactive --rm --volume "$(pwd):/root" quay.io/pypa/manylinux1_x86_64:latest bash
cd /root/flac-1.3.2
./configure LDFLAGS=-static # compiler flags to make a static build
make
exit
cp flac-1.3.2/src/flac/flac ../speech_recognition/flac-linux-x86_64 && sudo rm -r flac-1.3.2/
The included ``flac-mac`` executable is extracted from `xACT 2.39 <http://xact.scottcbrown.org/>`__, which is a frontend for FLAC 1.3.2 that conveniently includes binaries for all of its encoders. Specifically, it is a copy of ``xACT 2.39/xACT.app/Contents/Resources/flac`` in ``xACT2.39.zip``.
Authors
-------
::
Uberi <me@anthonyz.ca> (Anthony Zhang)
bobsayshilol
arvindch <achembarpu@gmail.com> (Arvind Chembarpu)
kevinismith <kevin_i_smith@yahoo.com> (Kevin Smith)
haas85
DelightRun <changxu.mail@gmail.com>
maverickagm
kamushadenes <kamushadenes@hyadesinc.com> (Kamus Hadenes)
sbraden <braden.sarah@gmail.com> (Sarah Braden)
tb0hdan (Bohdan Turkynewych)
Thynix <steve@asksteved.com> (Steve Dougherty)
beeedy <broderick.carlin@gmail.com> (Broderick Carlin)
Please report bugs and suggestions at the `issue tracker <https://github.com/Uberi/speech_recognition/issues>`__!
How to cite this library (APA style):
Zhang, A. (2017). Speech Recognition (Version 3.8) [Software]. Available from https://github.com/Uberi/speech_recognition#readme.
How to cite this library (Chicago style):
Zhang, Anthony. 2017. *Speech Recognition* (version 3.8).
Also check out the `Python Baidu Yuyin API <https://github.com/DelightRun/PyBaiduYuyin>`__, which is based on an older version of this project, and adds support for `Baidu Yuyin <http://yuyin.baidu.com/>`__. Note that Baidu Yuyin is only available inside China.
License
-------
Copyright 2014-2017 `Anthony Zhang (Uberi) <http://anthonyz.ca/>`__. The source code for this library is available online at `GitHub <https://github.com/Uberi/speech_recognition>`__.
SpeechRecognition is made available under the 3-clause BSD license. See ``LICENSE.txt`` in the project's `root directory <https://github.com/Uberi/speech_recognition>`__ 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 <http://cmusphinx.sourceforge.net/>`__. 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 <http://people.csail.mit.edu/hubert/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 <https://xiph.org/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 <https://www.gnu.org/licenses/gpl-faq.html#MereAggregation>`__ of `separate programs <https://www.gnu.org/licenses/gpl-faq.html#NFUseGPLPlugins>`__, 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.

36
speech_recognition/speech_recognition/__init__.py → speech_recognition/__init__.py
View File

@ -71,12 +71,13 @@ class Microphone(AudioSource):
Higher ``chunk_size`` values help avoid triggering on rapidly changing ambient noise, but also makes detection less sensitive. This value, generally, should be left at its default.
"""
def __init__(self, device_index=None, sample_rate=None, chunk_size=1024):
def __init__(self, device_index=None, sample_rate=None, chunk_size=1024, speaker=False):
assert device_index is None or isinstance(device_index, int), "Device index must be None or an integer"
assert sample_rate is None or (isinstance(sample_rate, int) and sample_rate > 0), "Sample rate must be None or a positive integer"
assert isinstance(chunk_size, int) and chunk_size > 0, "Chunk size must be a positive integer"
# set up PyAudio
self.speaker=speaker
self.pyaudio_module = self.get_pyaudio()
audio = self.pyaudio_module.PyAudio()
try:
@ -173,7 +174,40 @@ class Microphone(AudioSource):
def __enter__(self):
assert self.stream is None, "This audio source is already inside a context manager"
self.audio = self.pyaudio_module.PyAudio()
try:
if self.speaker:
p = self.audio
pyaudio = self.pyaudio_module
try:
wasapi_info = p.get_host_api_info_by_type(pyaudio.paWASAPI)
except:
pass
default_speakers = p.get_device_info_by_index(wasapi_info["defaultOutputDevice"])
if not default_speakers["isLoopbackDevice"]:
for loopback in p.get_loopback_device_info_generator():
"""
Try to find loopback device with same name(and [Loopback suffix]).
Unfortunately, this is the most adequate way at the moment.
"""
if default_speakers["name"] in loopback["name"]:
default_speakers = loopback
break
else:
exit()
self.stream = Microphone.MicrophoneStream(
p.open(
input_device_index=default_speakers["index"],
channels=default_speakers["maxInputChannels"],
format=self.format,
rate=int(default_speakers["defaultSampleRate"]),
frames_per_buffer=pyaudio.get_sample_size(pyaudio.paInt16),
input=True,
)
)
else:
self.stream = Microphone.MicrophoneStream(
self.audio.open(
input_device_index=self.device_index, channels=1, format=self.format,

0
speech_recognition/speech_recognition/__main__.py → speech_recognition/__main__.py
View File

4
speech_recognition/speech_recognition/audio.py → speech_recognition/audio.py
View File

@ -133,7 +133,7 @@ class AudioData(object):
return raw_data
def get_wav_data(self, convert_rate=None, convert_width=None):
def get_wav_data(self, convert_rate=None, convert_width=None, nchannels = 1):
"""
Returns a byte string representing the contents of a WAV file containing the audio represented by the ``AudioData`` instance.
@ -157,7 +157,7 @@ class AudioData(object):
try: # note that we can't use context manager, since that was only added in Python 3.4
wav_writer.setframerate(sample_rate)
wav_writer.setsampwidth(sample_width)
wav_writer.setnchannels(1)
wav_writer.setnchannels(nchannels)
wav_writer.writeframes(raw_data)
wav_data = wav_file.getvalue()
finally: # make sure resources are cleaned up

89
speech_recognition/examples/audio_transcribe.py
View File

@ -1,89 +0,0 @@
#!/usr/bin/env python3
import speech_recognition as sr
# obtain path to "english.wav" in the same folder as this script
from os import path
AUDIO_FILE = path.join(path.dirname(path.realpath(__file__)), "english.wav")
# AUDIO_FILE = path.join(path.dirname(path.realpath(__file__)), "french.aiff")
# AUDIO_FILE = path.join(path.dirname(path.realpath(__file__)), "chinese.flac")
# use the audio file as the audio source
r = sr.Recognizer()
with sr.AudioFile(AUDIO_FILE) as source:
audio = r.record(source) # read the entire audio file
# recognize speech using Sphinx
try:
print("Sphinx thinks you said " + r.recognize_sphinx(audio))
except sr.UnknownValueError:
print("Sphinx could not understand audio")
except sr.RequestError as e:
print("Sphinx error; {0}".format(e))
# recognize speech using Google Speech Recognition
try:
# for testing purposes, we're just using the default API key
# to use another API key, use `r.recognize_google(audio, key="GOOGLE_SPEECH_RECOGNITION_API_KEY")`
# instead of `r.recognize_google(audio)`
print("Google Speech Recognition thinks you said " + r.recognize_google(audio))
except sr.UnknownValueError:
print("Google Speech Recognition could not understand audio")
except sr.RequestError as e:
print("Could not request results from Google Speech Recognition service; {0}".format(e))
# recognize speech using Google Cloud Speech
GOOGLE_CLOUD_SPEECH_CREDENTIALS = r"""INSERT THE CONTENTS OF THE GOOGLE CLOUD SPEECH JSON CREDENTIALS FILE HERE"""
try:
print("Google Cloud Speech thinks you said " + r.recognize_google_cloud(audio, credentials_json=GOOGLE_CLOUD_SPEECH_CREDENTIALS))
except sr.UnknownValueError:
print("Google Cloud Speech could not understand audio")
except sr.RequestError as e:
print("Could not request results from Google Cloud Speech service; {0}".format(e))
# recognize speech using Wit.ai
WIT_AI_KEY = "INSERT WIT.AI API KEY HERE" # Wit.ai keys are 32-character uppercase alphanumeric strings
try:
print("Wit.ai thinks you said " + r.recognize_wit(audio, key=WIT_AI_KEY))
except sr.UnknownValueError:
print("Wit.ai could not understand audio")
except sr.RequestError as e:
print("Could not request results from Wit.ai service; {0}".format(e))
# recognize speech using Microsoft Azure Speech
AZURE_SPEECH_KEY = "INSERT AZURE SPEECH API KEY HERE" # Microsoft Speech API keys 32-character lowercase hexadecimal strings
try:
print("Microsoft Azure Speech thinks you said " + r.recognize_azure(audio, key=AZURE_SPEECH_KEY))
except sr.UnknownValueError:
print("Microsoft Azure Speech could not understand audio")
except sr.RequestError as e:
print("Could not request results from Microsoft Azure Speech service; {0}".format(e))
# recognize speech using Microsoft Bing Voice Recognition
BING_KEY = "INSERT BING API KEY HERE" # Microsoft Bing Voice Recognition API keys 32-character lowercase hexadecimal strings
try:
print("Microsoft Bing Voice Recognition thinks you said " + r.recognize_bing(audio, key=BING_KEY))
except sr.UnknownValueError:
print("Microsoft Bing Voice Recognition could not understand audio")
except sr.RequestError as e:
print("Could not request results from Microsoft Bing Voice Recognition service; {0}".format(e))
# recognize speech using Houndify
HOUNDIFY_CLIENT_ID = "INSERT HOUNDIFY CLIENT ID HERE" # Houndify client IDs are Base64-encoded strings
HOUNDIFY_CLIENT_KEY = "INSERT HOUNDIFY CLIENT KEY HERE" # Houndify client keys are Base64-encoded strings
try:
print("Houndify thinks you said " + r.recognize_houndify(audio, client_id=HOUNDIFY_CLIENT_ID, client_key=HOUNDIFY_CLIENT_KEY))
except sr.UnknownValueError:
print("Houndify could not understand audio")
except sr.RequestError as e:
print("Could not request results from Houndify service; {0}".format(e))
# recognize speech using IBM Speech to Text
IBM_USERNAME = "INSERT IBM SPEECH TO TEXT USERNAME HERE" # IBM Speech to Text usernames are strings of the form XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX
IBM_PASSWORD = "INSERT IBM SPEECH TO TEXT PASSWORD HERE" # IBM Speech to Text passwords are mixed-case alphanumeric strings
try:
print("IBM Speech to Text thinks you said " + r.recognize_ibm(audio, username=IBM_USERNAME, password=IBM_PASSWORD))
except sr.UnknownValueError:
print("IBM Speech to Text could not understand audio")
except sr.RequestError as e:
print("Could not request results from IBM Speech to Text service; {0}".format(e))

40
speech_recognition/examples/background_listening.py
View File

@ -1,40 +0,0 @@
#!/usr/bin/env python3
# NOTE: this example requires PyAudio because it uses the Microphone class
import time
import speech_recognition as sr
# this is called from the background thread
def callback(recognizer, audio):
# received audio data, now we'll recognize it using Google Speech Recognition
try:
# for testing purposes, we're just using the default API key
# to use another API key, use `r.recognize_google(audio, key="GOOGLE_SPEECH_RECOGNITION_API_KEY")`
# instead of `r.recognize_google(audio)`
print("Google Speech Recognition thinks you said " + recognizer.recognize_google(audio))
except sr.UnknownValueError:
print("Google Speech Recognition could not understand audio")
except sr.RequestError as e:
print("Could not request results from Google Speech Recognition service; {0}".format(e))
r = sr.Recognizer()
m = sr.Microphone()
with m as source:
r.adjust_for_ambient_noise(source) # we only need to calibrate once, before we start listening
# start listening in the background (note that we don't have to do this inside a `with` statement)
stop_listening = r.listen_in_background(m, callback)
# `stop_listening` is now a function that, when called, stops background listening
# do some unrelated computations for 5 seconds
for _ in range(50): time.sleep(0.1) # we're still listening even though the main thread is doing other things
# calling this function requests that the background listener stop listening
stop_listening(wait_for_stop=False)
# do some more unrelated things
while True: time.sleep(0.1) # we're not listening anymore, even though the background thread might still be running for a second or two while cleaning up and stopping

23
speech_recognition/examples/calibrate_energy_threshold.py
View File

@ -1,23 +0,0 @@
#!/usr/bin/env python3
# NOTE: this example requires PyAudio because it uses the Microphone class
import speech_recognition as sr
# obtain audio from the microphone
r = sr.Recognizer()
with sr.Microphone() as source:
r.adjust_for_ambient_noise(source) # listen for 1 second to calibrate the energy threshold for ambient noise levels
print("Say something!")
audio = r.listen(source)
# recognize speech using Google Speech Recognition
try:
# for testing purposes, we're just using the default API key
# to use another API key, use `r.recognize_google(audio, key="GOOGLE_SPEECH_RECOGNITION_API_KEY")`
# instead of `r.recognize_google(audio)`
print("Google Speech Recognition thinks you said " + r.recognize_google(audio))
except sr.UnknownValueError:
print("Google Speech Recognition could not understand audio")
except sr.RequestError as e:
print("Could not request results from Google Speech Recognition service; {0}".format(e))

BIN
speech_recognition/examples/chinese.flac
View File

Binary file not shown.

11
speech_recognition/examples/counting.gram
View File

@ -1,11 +0,0 @@
#JSGF V1.0;
/**
* JSGF Grammar for English counting example
*/
grammar counting;
public <counting> = ( <digit> ) +;
<digit> = one | two | three | four | five | six | seven ;

89
speech_recognition/examples/extended_results.py
View File

@ -1,89 +0,0 @@
#!/usr/bin/env python3
from pprint import pprint
import speech_recognition as sr
# obtain path to "english.wav" in the same folder as this script
from os import path
AUDIO_FILE = path.join(path.dirname(path.realpath(__file__)), "english.wav")
# AUDIO_FILE = path.join(path.dirname(path.realpath(__file__)), "french.aiff")
# AUDIO_FILE = path.join(path.dirname(path.realpath(__file__)), "chinese.flac")
# use the audio file as the audio source
r = sr.Recognizer()
with sr.AudioFile(AUDIO_FILE) as source:
audio = r.record(source) # read the entire audio file
# recognize speech using Sphinx
try:
print("Sphinx thinks you said " + r.recognize_sphinx(audio))
except sr.UnknownValueError:
print("Sphinx could not understand audio")
except sr.RequestError as e:
print("Sphinx error; {0}".format(e))
# recognize speech using Google Speech Recognition
try:
# for testing purposes, we're just using the default API key
# to use another API key, use `r.recognize_google(audio, key="GOOGLE_SPEECH_RECOGNITION_API_KEY", show_all=True)`
# instead of `r.recognize_google(audio, show_all=True)`
print("Google Speech Recognition results:")
pprint(r.recognize_google(audio, show_all=True)) # pretty-print the recognition result
except sr.UnknownValueError:
print("Google Speech Recognition could not understand audio")
except sr.RequestError as e:
print("Could not request results from Google Speech Recognition service; {0}".format(e))
# recognize speech using Google Cloud Speech
GOOGLE_CLOUD_SPEECH_CREDENTIALS = r"""INSERT THE CONTENTS OF THE GOOGLE CLOUD SPEECH JSON CREDENTIALS FILE HERE"""
try:
print("Google Cloud Speech recognition results:")
pprint(r.recognize_google_cloud(audio, credentials_json=GOOGLE_CLOUD_SPEECH_CREDENTIALS, show_all=True)) # pretty-print the recognition result
except sr.UnknownValueError:
print("Google Cloud Speech could not understand audio")
except sr.RequestError as e:
print("Could not request results from Google Cloud Speech service; {0}".format(e))
# recognize speech using Wit.ai
WIT_AI_KEY = "INSERT WIT.AI API KEY HERE" # Wit.ai keys are 32-character uppercase alphanumeric strings
try:
print("Wit.ai recognition results:")
pprint(r.recognize_wit(audio, key=WIT_AI_KEY, show_all=True)) # pretty-print the recognition result
except sr.UnknownValueError:
print("Wit.ai could not understand audio")
except sr.RequestError as e:
print("Could not request results from Wit.ai service; {0}".format(e))
# recognize speech using Microsoft Bing Voice Recognition
BING_KEY = "INSERT BING API KEY HERE" # Microsoft Bing Voice Recognition API keys 32-character lowercase hexadecimal strings
try:
print("Bing recognition results:")
pprint(r.recognize_bing(audio, key=BING_KEY, show_all=True))
except sr.UnknownValueError:
print("Microsoft Bing Voice Recognition could not understand audio")
except sr.RequestError as e:
print("Could not request results from Microsoft Bing Voice Recognition service; {0}".format(e))
# recognize speech using Houndify
HOUNDIFY_CLIENT_ID = "INSERT HOUNDIFY CLIENT ID HERE" # Houndify client IDs are Base64-encoded strings
HOUNDIFY_CLIENT_KEY = "INSERT HOUNDIFY CLIENT KEY HERE" # Houndify client keys are Base64-encoded strings
try:
print("Houndify recognition results:")
pprint(r.recognize_houndify(audio, client_id=HOUNDIFY_CLIENT_ID, client_key=HOUNDIFY_CLIENT_KEY, show_all=True))
except sr.UnknownValueError:
print("Houndify could not understand audio")
except sr.RequestError as e:
print("Could not request results from Houndify service; {0}".format(e))
# recognize speech using IBM Speech to Text
IBM_USERNAME = "INSERT IBM SPEECH TO TEXT USERNAME HERE" # IBM Speech to Text usernames are strings of the form XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX
IBM_PASSWORD = "INSERT IBM SPEECH TO TEXT PASSWORD HERE" # IBM Speech to Text passwords are mixed-case alphanumeric strings
try:
print("IBM Speech to Text results:")
pprint(r.recognize_ibm(audio, username=IBM_USERNAME, password=IBM_PASSWORD, show_all=True)) # pretty-print the recognition result
except sr.UnknownValueError:
print("IBM Speech to Text could not understand audio")
except sr.RequestError as e:
print("Could not request results from IBM Speech to Text service; {0}".format(e))

BIN
speech_recognition/examples/french.aiff
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Binary file not shown.

101
speech_recognition/examples/microphone_recognition.py
View File

@ -1,101 +0,0 @@
#!/usr/bin/env python3
# NOTE: this example requires PyAudio because it uses the Microphone class
import speech_recognition as sr
# obtain audio from the microphone
r = sr.Recognizer()
with sr.Microphone() as source:
print("Say something!")
audio = r.listen(source)
# recognize speech using Sphinx
try:
print("Sphinx thinks you said " + r.recognize_sphinx(audio))
except sr.UnknownValueError:
print("Sphinx could not understand audio")
except sr.RequestError as e:
print("Sphinx error; {0}".format(e))
# recognize speech using Google Speech Recognition
try:
# for testing purposes, we're just using the default API key
# to use another API key, use `r.recognize_google(audio, key="GOOGLE_SPEECH_RECOGNITION_API_KEY")`
# instead of `r.recognize_google(audio)`
print("Google Speech Recognition thinks you said " + r.recognize_google(audio))
except sr.UnknownValueError:
print("Google Speech Recognition could not understand audio")
except sr.RequestError as e:
print("Could not request results from Google Speech Recognition service; {0}".format(e))
# recognize speech using Google Cloud Speech
GOOGLE_CLOUD_SPEECH_CREDENTIALS = r"""INSERT THE CONTENTS OF THE GOOGLE CLOUD SPEECH JSON CREDENTIALS FILE HERE"""
try:
print("Google Cloud Speech thinks you said " + r.recognize_google_cloud(audio, credentials_json=GOOGLE_CLOUD_SPEECH_CREDENTIALS))
except sr.UnknownValueError:
print("Google Cloud Speech could not understand audio")
except sr.RequestError as e:
print("Could not request results from Google Cloud Speech service; {0}".format(e))
# recognize speech using Wit.ai
WIT_AI_KEY = "INSERT WIT.AI API KEY HERE" # Wit.ai keys are 32-character uppercase alphanumeric strings
try:
print("Wit.ai thinks you said " + r.recognize_wit(audio, key=WIT_AI_KEY))
except sr.UnknownValueError:
print("Wit.ai could not understand audio")
except sr.RequestError as e:
print("Could not request results from Wit.ai service; {0}".format(e))
# recognize speech using Microsoft Bing Voice Recognition
BING_KEY = "INSERT BING API KEY HERE" # Microsoft Bing Voice Recognition API keys 32-character lowercase hexadecimal strings
try:
print("Microsoft Bing Voice Recognition thinks you said " + r.recognize_bing(audio, key=BING_KEY))
except sr.UnknownValueError:
print("Microsoft Bing Voice Recognition could not understand audio")
except sr.RequestError as e:
print("Could not request results from Microsoft Bing Voice Recognition service; {0}".format(e))
# recognize speech using Microsoft Azure Speech
AZURE_SPEECH_KEY = "INSERT AZURE SPEECH API KEY HERE" # Microsoft Speech API keys 32-character lowercase hexadecimal strings
try:
print("Microsoft Azure Speech thinks you said " + r.recognize_azure(audio, key=AZURE_SPEECH_KEY))
except sr.UnknownValueError:
print("Microsoft Azure Speech could not understand audio")
except sr.RequestError as e:
print("Could not request results from Microsoft Azure Speech service; {0}".format(e))
# recognize speech using Houndify
HOUNDIFY_CLIENT_ID = "INSERT HOUNDIFY CLIENT ID HERE" # Houndify client IDs are Base64-encoded strings
HOUNDIFY_CLIENT_KEY = "INSERT HOUNDIFY CLIENT KEY HERE" # Houndify client keys are Base64-encoded strings
try:
print("Houndify thinks you said " + r.recognize_houndify(audio, client_id=HOUNDIFY_CLIENT_ID, client_key=HOUNDIFY_CLIENT_KEY))
except sr.UnknownValueError:
print("Houndify could not understand audio")
except sr.RequestError as e:
print("Could not request results from Houndify service; {0}".format(e))
# recognize speech using IBM Speech to Text
IBM_USERNAME = "INSERT IBM SPEECH TO TEXT USERNAME HERE" # IBM Speech to Text usernames are strings of the form XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX
IBM_PASSWORD = "INSERT IBM SPEECH TO TEXT PASSWORD HERE" # IBM Speech to Text passwords are mixed-case alphanumeric strings
try:
print("IBM Speech to Text thinks you said " + r.recognize_ibm(audio, username=IBM_USERNAME, password=IBM_PASSWORD))
except sr.UnknownValueError:
print("IBM Speech to Text could not understand audio")
except sr.RequestError as e:
print("Could not request results from IBM Speech to Text service; {0}".format(e))
# recognize speech using whisper
try:
print("Whisper thinks you said " + r.recognize_whisper(audio, language="english"))
except sr.UnknownValueError:
print("Whisper could not understand audio")
except sr.RequestError as e:
print("Could not request results from Whisper")
# recognize speech using Whisper API
OPENAI_API_KEY = "INSERT OPENAI API KEY HERE"
try:
print(f"Whisper API thinks you said {r.recognize_whisper_api(audio, api_key=OPENAI_API_KEY)}")
except sr.RequestError as e:
print("Could not request results from Whisper API")

46
speech_recognition/examples/special_recognizer_features.py
View File

@ -1,46 +0,0 @@
#!/usr/bin/env python3
import speech_recognition as sr
from os import path
AUDIO_FILE_EN = path.join(path.dirname(path.realpath(__file__)), "english.wav")
AUDIO_FILE_FR = path.join(path.dirname(path.realpath(__file__)), "french.aiff")
# use the audio file as the audio source
r = sr.Recognizer()
with sr.AudioFile(AUDIO_FILE_EN) as source:
audio_en = r.record(source) # read the entire audio file
with sr.AudioFile(AUDIO_FILE_FR) as source:
audio_fr = r.record(source) # read the entire audio file
# recognize keywords using Sphinx
try:
print("Sphinx recognition for \"one two three\" with different sets of keywords:")
print(r.recognize_sphinx(audio_en, keyword_entries=[("one", 1.0), ("two", 1.0), ("three", 1.0)]))
print(r.recognize_sphinx(audio_en, keyword_entries=[("wan", 0.95), ("too", 1.0), ("tree", 1.0)]))
print(r.recognize_sphinx(audio_en, keyword_entries=[("un", 0.95), ("to", 1.0), ("tee", 1.0)]))
except sr.UnknownValueError:
print("Sphinx could not understand audio")
except sr.RequestError as e:
print("Sphinx error; {0}".format(e))
# grammar example using Sphinx
try:
print("Sphinx recognition for \"one two three\" for counting grammar:")
print(r.recognize_sphinx(audio_en, grammar='counting.gram'))
except sr.UnknownValueError:
print("Sphinx could not understand audio")
except sr.RequestError as e:
print("Sphinx error; {0}".format(e))
# recognize preferred phrases using Google Cloud Speech
GOOGLE_CLOUD_SPEECH_CREDENTIALS = r"""INSERT THE CONTENTS OF THE GOOGLE CLOUD SPEECH JSON CREDENTIALS FILE HERE"""
try:
print("Google Cloud Speech recognition for \"numero\" with different sets of preferred phrases:")
print(r.recognize_google_cloud(audio_fr, credentials_json=GOOGLE_CLOUD_SPEECH_CREDENTIALS, preferred_phrases=["noomarow"]))
print(r.recognize_google_cloud(audio_fr, credentials_json=GOOGLE_CLOUD_SPEECH_CREDENTIALS, preferred_phrases=["newmarrow"]))
except sr.UnknownValueError:
print("Google Cloud Speech could not understand audio")
except sr.RequestError as e:
print("Could not request results from Google Cloud Speech service; {0}".format(e))

26
speech_recognition/examples/tensorflow_commands.py
View File

@ -1,26 +0,0 @@
#!/usr/bin/env python3
import time
import speech_recognition as sr
from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio # noqa
# obtain audio from the microphone
r = sr.Recognizer()
m = sr.Microphone()
with m as source:
r.adjust_for_ambient_noise(source)
def callback(recognizer, audio):
try:
# You can download the data here: http://download.tensorflow.org/models/speech_commands_v0.01.zip
spoken = recognizer.recognize_tensorflow(audio, tensor_graph='speech_recognition/tensorflow-data/conv_actions_frozen.pb', tensor_label='speech_recognition/tensorflow-data/conv_actions_labels.txt')
print(spoken)
except sr.UnknownValueError:
print("Tensorflow could not understand audio")
except sr.RequestError as e:
print("Could not request results from Tensorflow service; {0}".format(e))
stop_listening = r.listen_in_background(m, callback, phrase_time_limit=0.6)
time.sleep(100)

48
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#!/usr/bin/env python3
# NOTE: this example requires PyAudio because it uses the Microphone class
from threading import Thread
from queue import Queue
import speech_recognition as sr
r = sr.Recognizer()
audio_queue = Queue()
def recognize_worker():
# this runs in a background thread
while True:
audio = audio_queue.get() # retrieve the next audio processing job from the main thread
if audio is None: break # stop processing if the main thread is done
# received audio data, now we'll recognize it using Google Speech Recognition
try:
# for testing purposes, we're just using the default API key
# to use another API key, use `r.recognize_google(audio, key="GOOGLE_SPEECH_RECOGNITION_API_KEY")`
# instead of `r.recognize_google(audio)`
print("Google Speech Recognition thinks you said " + r.recognize_google(audio))
except sr.UnknownValueError:
print("Google Speech Recognition could not understand audio")
except sr.RequestError as e:
print("Could not request results from Google Speech Recognition service; {0}".format(e))
audio_queue.task_done() # mark the audio processing job as completed in the queue
# start a new thread to recognize audio, while this thread focuses on listening
recognize_thread = Thread(target=recognize_worker)
recognize_thread.daemon = True
recognize_thread.start()
with sr.Microphone() as source:
try:
while True: # repeatedly listen for phrases and put the resulting audio on the audio processing job queue
audio_queue.put(r.listen(source))
except KeyboardInterrupt: # allow Ctrl + C to shut down the program
pass
audio_queue.join() # block until all current audio processing jobs are done
audio_queue.put(None) # tell the recognize_thread to stop
recognize_thread.join() # wait for the recognize_thread to actually stop

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#!/usr/bin/env python3
# NOTE: this example requires PyAudio because it uses the Microphone class
import speech_recognition as sr
# obtain audio from the microphone
r = sr.Recognizer()
with sr.Microphone() as source:
print("Say something!")
audio = r.listen(source)
# write audio to a RAW file
with open("microphone-results.raw", "wb") as f:
f.write(audio.get_raw_data())
# write audio to a WAV file
with open("microphone-results.wav", "wb") as f:
f.write(audio.get_wav_data())
# write audio to an AIFF file
with open("microphone-results.aiff", "wb") as f:
f.write(audio.get_aiff_data())
# write audio to a FLAC file
with open("microphone-results.flac", "wb") as f:
f.write(audio.get_flac_data())

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#!/usr/bin/env bash
# set up bash to handle errors more aggressively - a "strict mode" of sorts
set -e # give an error if any command finishes with a non-zero exit code
set -u # give an error if we reference unset variables
set -o pipefail # for a pipeline, if any of the commands fail with a non-zero exit code, fail the entire pipeline with that exit code
echo "Making release for SpeechRecognition-$1"
python setup.py bdist_wheel
gpg --detach-sign -a dist/SpeechRecognition-$1-*.whl
twine upload dist/SpeechRecognition-$1-*.whl dist/SpeechRecognition-$1-*.whl.asc

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Speech Recognition Library Reference
====================================
``Microphone(device_index: Union[int, None] = None, sample_rate: int = 16000, chunk_size: int = 1024) -> Microphone``
---------------------------------------------------------------------------------------------------------------------
Creates a new ``Microphone`` instance, which represents a physical microphone on the computer. Subclass of ``AudioSource``.
This will throw an ``AttributeError`` if you don't have PyAudio 0.2.11 or later installed.
If ``device_index`` is unspecified or ``None``, the default microphone is used as the audio source. Otherwise, ``device_index`` should be the index of the device to use for audio input.
A device index is an integer between 0 and ``pyaudio.get_device_count() - 1`` (assume we have used ``import pyaudio`` beforehand) inclusive. It represents an audio device such as a microphone or speaker. See the `PyAudio documentation <http://people.csail.mit.edu/hubert/pyaudio/docs/>`__ for more details.
The microphone audio is recorded in chunks of ``chunk_size`` samples, at a rate of ``sample_rate`` samples per second (Hertz).
Higher ``sample_rate`` values result in better audio quality, but also more bandwidth (and therefore, slower recognition). Additionally, some machines, such as some Raspberry Pi models, can't keep up if this value is too high.
Higher ``chunk_size`` values help avoid triggering on rapidly changing ambient noise, but also makes detection less sensitive. This value, generally, should be left at its default.
Instances of this class are context managers, and are designed to be used with ``with`` statements:
.. code:: python
with Microphone() as source: # open the microphone and start recording
pass # do things here - ``source`` is the Microphone instance created above
# the microphone is automatically released at this point
``Microphone.list_microphone_names() -> List[str]``
---------------------------------------------------
Returns a list of the names of all available microphones. For microphones where the name can't be retrieved, the list entry contains ``None`` instead.
The index of each microphone's name in the returned list is the same as its device index when creating a ``Microphone`` instance - if you want to use the microphone at index 3 in the returned list, use ``Microphone(device_index=3)``.
To create a ``Microphone`` instance by name:
.. code:: python
m = None
for i, microphone_name in enumerate(Microphone.list_microphone_names()):
if microphone_name == "HDA Intel HDMI: 0 (hw:0,3)":
m = Microphone(device_index=i)
``Microphone.list_working_microphones() -> Dict[int, str]``
-----------------------------------------------------------
Returns a dictionary mapping device indices to microphone names, for microphones that are currently hearing sounds. When using this function, ensure that your microphone is unmuted and make some noise at it to ensure it will be detected as working.
Each key in the returned dictionary can be passed to the ``Microphone`` constructor to use that microphone. For example, if the return value is ``{3: "HDA Intel PCH: ALC3232 Analog (hw:1,0)"}``, you can do ``Microphone(device_index=3)`` to use that microphone.
To create a ``Microphone`` instance for the first working microphone:
.. code:: python
for device_index in Microphone.list_working_microphones():
m = Microphone(device_index=device_index)
break
else:
print("No working microphones found!")
``AudioFile(filename_or_fileobject: Union[str, io.IOBase]) -> AudioFile``
-------------------------------------------------------------------------
Creates a new ``AudioFile`` instance given a WAV/AIFF/FLAC audio file ``filename_or_fileobject``. Subclass of ``AudioSource``.
If ``filename_or_fileobject`` is a string, then it is interpreted as a path to an audio file on the filesystem. Otherwise, ``filename_or_fileobject`` should be a file-like object such as ``io.BytesIO`` or similar.
Note that functions that read from the audio (such as ``recognizer_instance.record`` or ``recognizer_instance.listen``) will move ahead in the stream. For example, if you execute ``recognizer_instance.record(audiofile_instance, duration=10)`` twice, the first time it will return the first 10 seconds of audio, and the second time it will return the 10 seconds of audio right after that. This is always reset when entering the context with a context manager.
WAV files must be in PCM/LPCM format; WAVE_FORMAT_EXTENSIBLE and compressed WAV are not supported and may result in undefined behaviour.
Both AIFF and AIFF-C (compressed AIFF) formats are supported.
FLAC files must be in native FLAC format; OGG-FLAC is not supported and may result in undefined behaviour.
Instances of this class are context managers, and are designed to be used with ``with`` statements:
.. code:: python
import speech_recognition as sr
with sr.AudioFile("SOME_AUDIO_FILE") as source: # open the audio file for reading
pass # do things here - ``source`` is the AudioFile instance created above
``audiofile_instance.DURATION # type: float``
----------------------------------------------
Represents the length of the audio stored in the audio file in seconds. This property is only available when inside a context - essentially, that means it should only be accessed inside the body of a ``with audiofile_instance ...`` statement. Outside of contexts, this property is ``None``.
This is useful when combined with the ``offset`` parameter of ``recognizer_instance.record``, since when together it is possible to perform speech recognition in chunks.
However, note that recognizing speech in multiple chunks is not the same as recognizing the whole thing at once. If spoken words appear on the boundaries that we split the audio into chunks on, each chunk only gets part of the word, which may result in inaccurate results.
``Recognizer() -> Recognizer``
------------------------------
Creates a new ``Recognizer`` instance, which represents a collection of speech recognition settings and functionality.
``recognizer_instance.energy_threshold = 300 # type: float``
-------------------------------------------------------------
Represents the energy level threshold for sounds. Values below this threshold are considered silence, and values above this threshold are considered speech. Can be changed.
This is adjusted automatically if dynamic thresholds are enabled (see ``recognizer_instance.dynamic_energy_threshold``). A good starting value will generally allow the automatic adjustment to reach a good value faster.
This threshold is associated with the perceived loudness of the sound, but it is a nonlinear relationship. The actual energy threshold you will need depends on your microphone sensitivity or audio data. Typical values for a silent room are 0 to 100, and typical values for speaking are between 150 and 3500. Ambient (non-speaking) noise has a significant impact on what values will work best.
If you're having trouble with the recognizer trying to recognize words even when you're not speaking, try tweaking this to a higher value. If you're having trouble with the recognizer not recognizing your words when you are speaking, try tweaking this to a lower value. For example, a sensitive microphone or microphones in louder rooms might have a ambient energy level of up to 4000:
.. code:: python
import speech_recognition as sr
r = sr.Recognizer()
r.energy_threshold = 4000
# rest of your code goes here
The dynamic energy threshold setting can mitigate this by increasing or decreasing this automatically to account for ambient noise. However, this takes time to adjust, so it is still possible to get the false positive detections before the threshold settles into a good value.
To avoid this, use ``recognizer_instance.adjust_for_ambient_noise(source, duration = 1)`` to calibrate the level to a good value. Alternatively, simply set this property to a high value initially (4000 works well), so the threshold is always above ambient noise levels: over time, it will be automatically decreased to account for ambient noise levels.
``recognizer_instance.dynamic_energy_threshold = True # type: bool``
---------------------------------------------------------------------
Represents whether the energy level threshold (see ``recognizer_instance.energy_threshold``) for sounds should be automatically adjusted based on the currently ambient noise level while listening. Can be changed.
Recommended for situations where the ambient noise level is unpredictable, which seems to be the majority of use cases. If the ambient noise level is strictly controlled, better results might be achieved by setting this to ``False`` to turn it off.
``recognizer_instance.dynamic_energy_adjustment_damping = 0.15 # type: float``
-------------------------------------------------------------------------------
If the dynamic energy threshold setting is enabled (see ``recognizer_instance.dynamic_energy_threshold``), represents approximately the fraction of the current energy threshold that is retained after one second of dynamic threshold adjustment. Can be changed (not recommended).
Lower values allow for faster adjustment, but also make it more likely to miss certain phrases (especially those with slowly changing volume). This value should be between 0 and 1. As this value approaches 1, dynamic adjustment has less of an effect over time. When this value is 1, dynamic adjustment has no effect.
``recognizer_instance.dynamic_energy_adjustment_ratio = 1.5 # type: float``
----------------------------------------------------------------------------
If the dynamic energy threshold setting is enabled (see ``recognizer_instance.dynamic_energy_threshold``), represents the minimum factor by which speech is louder than ambient noise. Can be changed (not recommended).
For example, the default value of 1.5 means that speech is at least 1.5 times louder than ambient noise. Smaller values result in more false positives (but fewer false negatives) when ambient noise is loud compared to speech.
``recognizer_instance.pause_threshold = 0.8 # type: float``
------------------------------------------------------------
Represents the minimum length of silence (in seconds) that will register as the end of a phrase. Can be changed.
Smaller values result in the recognition completing more quickly, but might result in slower speakers being cut off.
``recognizer_instance.operation_timeout = None # type: Union[float, None]``
----------------------------------------------------------------------------
Represents the timeout (in seconds) for internal operations, such as API requests. Can be changed.
Setting this to a reasonable value ensures that these operations will never block indefinitely, though good values depend on your network speed and the expected length of the audio to recognize.
``recognizer_instance.record(source: AudioSource, duration: Union[float, None] = None, offset: Union[float, None] = None) -> AudioData``
----------------------------------------------------------------------------------------------------------------------------------------
Records up to ``duration`` seconds of audio from ``source`` (an ``AudioSource`` instance) starting at ``offset`` (or at the beginning if not specified) into an ``AudioData`` instance, which it returns.
If ``duration`` is not specified, then it will record until there is no more audio input.
``recognizer_instance.adjust_for_ambient_noise(source: AudioSource, duration: float = 1) -> None``
--------------------------------------------------------------------------------------------------
Adjusts the energy threshold dynamically using audio from ``source`` (an ``AudioSource`` instance) to account for ambient noise.
Intended to calibrate the energy threshold with the ambient energy level. Should be used on periods of audio without speech - will stop early if any speech is detected.
The ``duration`` parameter is the maximum number of seconds that it will dynamically adjust the threshold for before returning. This value should be at least 0.5 in order to get a representative sample of the ambient noise.
``recognizer_instance.listen(source: AudioSource, timeout: Union[float, None] = None, phrase_time_limit: Union[float, None] = None, snowboy_configuration: Union[Tuple[str, Iterable[str]], None] = None) -> AudioData``
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Records a single phrase from ``source`` (an ``AudioSource`` instance) into an ``AudioData`` instance, which it returns.
This is done by waiting until the audio has an energy above ``recognizer_instance.energy_threshold`` (the user has started speaking), and then recording until it encounters ``recognizer_instance.pause_threshold`` seconds of non-speaking or there is no more audio input. The ending silence is not included.
The ``timeout`` parameter is the maximum number of seconds that this will wait for a phrase to start before giving up and throwing an ``speech_recognition.WaitTimeoutError`` exception. If ``timeout`` is ``None``, there will be no wait timeout.
The ``phrase_time_limit`` parameter is the maximum number of seconds that this will allow a phrase to continue before stopping and returning the part of the phrase processed before the time limit was reached. The resulting audio will be the phrase cut off at the time limit. If ``phrase_timeout`` is ``None``, there will be no phrase time limit.
The ``snowboy_configuration`` parameter allows integration with `Snowboy <https://snowboy.kitt.ai/>`__, an offline, high-accuracy, power-efficient hotword recognition engine. When used, this function will pause until Snowboy detects a hotword, after which it will unpause. This parameter should either be ``None`` to turn off Snowboy support, or a tuple of the form ``(SNOWBOY_LOCATION, LIST_OF_HOT_WORD_FILES)``, where ``SNOWBOY_LOCATION`` is the path to the Snowboy root directory, and ``LIST_OF_HOT_WORD_FILES`` is a list of paths to Snowboy hotword configuration files (`*.pmdl` or `*.umdl` format).
This operation will always complete within ``timeout + phrase_timeout`` seconds if both are numbers, either by returning the audio data, or by raising a ``speech_recognition.WaitTimeoutError`` exception.
``recognizer_instance.listen_in_background(source: AudioSource, callback: Callable[[Recognizer, AudioData], Any]) -> Callable[bool, None]``
-------------------------------------------------------------------------------------------------------------------------------------------
Spawns a thread to repeatedly record phrases from ``source`` (an ``AudioSource`` instance) into an ``AudioData`` instance and call ``callback`` with that ``AudioData`` instance as soon as each phrase are detected.
Returns a function object that, when called, requests that the background listener thread stop. The background thread is a daemon and will not stop the program from exiting if there are no other non-daemon threads. The function accepts one parameter, ``wait_for_stop``: if truthy, the function will wait for the background listener to stop before returning, otherwise it will return immediately and the background listener thread might still be running for a second or two afterwards. Additionally, if you are using a truthy value for ``wait_for_stop``, you must call the function from the same thread you originally called ``listen_in_background`` from.
Phrase recognition uses the exact same mechanism as ``recognizer_instance.listen(source)``. The ``phrase_time_limit`` parameter works in the same way as the ``phrase_time_limit`` parameter for ``recognizer_instance.listen(source)``, as well.
The ``callback`` parameter is a function that should accept two parameters - the ``recognizer_instance``, and an ``AudioData`` instance representing the captured audio. Note that ``callback`` function will be called from a non-main thread.
``recognizer_instance.recognize_sphinx(audio_data: AudioData, language: str = "en-US", keyword_entries: Union[Iterable[Tuple[str, float]], None] = None, grammar: Union[str, None] = None, show_all: bool = False) -> Union[str, pocketsphinx.pocketsphinx.Decoder]``
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using CMU Sphinx.
The recognition language is determined by ``language``, an RFC5646 language tag like ``"en-US"`` or ``"en-GB"``, defaulting to US English. Out of the box, only ``en-US`` is supported. See `Notes on using `PocketSphinx <https://github.com/Uberi/speech_recognition/blob/master/reference/pocketsphinx.rst>`__ for information about installing other languages. This document is also included under ``reference/pocketsphinx.rst``. The ``language`` parameter can also be a tuple of filesystem paths, of the form ``(acoustic_parameters_directory, language_model_file, phoneme_dictionary_file)`` - this allows you to load arbitrary Sphinx models.
If specified, the keywords to search for are determined by ``keyword_entries``, an iterable of tuples of the form ``(keyword, sensitivity)``, where ``keyword`` is a phrase, and ``sensitivity`` is how sensitive to this phrase the recognizer should be, on a scale of 0 (very insensitive, more false negatives) to 1 (very sensitive, more false positives) inclusive. If not specified or ``None``, no keywords are used and Sphinx will simply transcribe whatever words it recognizes. Specifying ``keyword_entries`` is more accurate than just looking for those same keywords in non-keyword-based transcriptions, because Sphinx knows specifically what sounds to look for.
Sphinx can also handle FSG or JSGF grammars. The parameter ``grammar`` expects a path to the grammar file. Note that if a JSGF grammar is passed, an FSG grammar will be created at the same location to speed up execution in the next run. If ``keyword_entries`` are passed, content of ``grammar`` will be ignored.
Returns the most likely transcription if ``show_all`` is false (the default). Otherwise, returns the Sphinx ``pocketsphinx.pocketsphinx.Decoder`` object resulting from the recognition.
Raises a ``speech_recognition.UnknownValueError`` exception if the speech is unintelligible. Raises a ``speech_recognition.RequestError`` exception if there are any issues with the Sphinx installation.
``recognizer_instance.recognize_google(audio_data: AudioData, key: Union[str, None] = None, language: str = "en-US", , pfilter: Union[0, 1], show_all: bool = False) -> Union[str, Dict[str, Any]]``
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using the Google Speech Recognition API.
The Google Speech Recognition API key is specified by ``key``. If not specified, it uses a generic key that works out of the box. This should generally be used for personal or testing purposes only, as it **may be revoked by Google at any time**.
To obtain your own API key, simply follow the steps on the `API Keys <http://www.chromium.org/developers/how-tos/api-keys>`__ page at the Chromium Developers site. In the Google Developers Console, Google Speech Recognition is listed as "Speech API". Note that **the API quota for your own keys is 50 requests per day**, and there is currently no way to raise this limit.
The recognition language is determined by ``language``, an IETF language tag like ``"en-US"`` or ``"en-GB"``, defaulting to US English. A list of supported language tags can be found `here <http://stackoverflow.com/questions/14257598/what-are-language-codes-for-voice-recognition-languages-in-chromes-implementati>`__. Basically, language codes can be just the language (``en``), or a language with a dialect (``en-US``).
The profanity filter level can be adjusted with ``pfilter``: 0 - No filter, 1 - Only shows the first character and replaces the rest with asterisks. The default is level 0.
Returns the most likely transcription if ``show_all`` is false (the default). Otherwise, returns the raw API response as a JSON dictionary.
Raises a ``speech_recognition.UnknownValueError`` exception if the speech is unintelligible. Raises a ``speech_recognition.RequestError`` exception if the speech recognition operation failed, if the key isn't valid, or if there is no internet connection.
``recognizer_instance.recognize_google_cloud(audio_data: AudioData, credentials_json: Union[str, None] = None, language: str = "en-US", preferred_phrases: Union[Iterable[str], None] = None, show_all: bool = False) -> Union[str, Dict[str, Any]]``
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using the Google Cloud Speech API.
This function requires a Google Cloud Platform account; see the `Google Cloud Speech API Quickstart <https://cloud.google.com/speech/docs/getting-started>`__ for details and instructions. Basically, create a project, enable billing for the project, enable the Google Cloud Speech API for the project, and set up Service Account Key credentials for the project. The result is a JSON file containing the API credentials. The text content of this JSON file is specified by ``credentials_json``. If not specified, the library will try to automatically `find the default API credentials JSON file <https://developers.google.com/identity/protocols/application-default-credentials>`__.
The recognition language is determined by ``language``, which is a BCP-47 language tag like ``"en-US"`` (US English). A list of supported language tags can be found in the `Google Cloud Speech API documentation <https://cloud.google.com/speech/docs/languages>`__.
If ``preferred_phrases`` is an iterable of phrase strings, those given phrases will be more likely to be recognized over similar-sounding alternatives. This is useful for things like keyword/command recognition or adding new phrases that aren't in Google's vocabulary. Note that the API imposes certain `restrictions on the list of phrase strings <https://cloud.google.com/speech/limits#content>`__.
Returns the most likely transcription if ``show_all`` is False (the default). Otherwise, returns the raw API response as a JSON dictionary.
Raises a ``speech_recognition.UnknownValueError`` exception if the speech is unintelligible. Raises a ``speech_recognition.RequestError`` exception if the speech recognition operation failed, if the credentials aren't valid, or if there is no Internet connection.
``recognizer_instance.recognize_wit(audio_data: AudioData, key: str, show_all: bool = False) -> Union[str, Dict[str, Any]]``
----------------------------------------------------------------------------------------------------------------------------
Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using the Wit.ai API.
The Wit.ai API key is specified by ``key``. Unfortunately, these are not available without `signing up for an account <https://wit.ai/>`__ and creating an app. You will need to add at least one intent to the app before you can see the API key, though the actual intent settings don't matter.
To get the API key for a Wit.ai app, go to the app's overview page, go to the section titled "Make an API request", and look for something along the lines of ``Authorization: Bearer XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX``; ``XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX`` is the API key. Wit.ai API keys are 32-character uppercase alphanumeric strings.
The recognition language is configured in the Wit.ai app settings.
Returns the most likely transcription if ``show_all`` is false (the default). Otherwise, returns the `raw API response <https://wit.ai/docs/http/20141022#get-intent-via-text-link>`__ as a JSON dictionary.
Raises a ``speech_recognition.UnknownValueError`` exception if the speech is unintelligible. Raises a ``speech_recognition.RequestError`` exception if the speech recognition operation failed, if the key isn't valid, or if there is no internet connection.
``recognizer_instance.recognize_bing(audio_data: AudioData, key: str, language: str = "en-US", show_all: bool = False) -> Union[str, Dict[str, Any]]``
------------------------------------------------------------------------------------------------------------------------------------------------------
Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using the Microsoft Bing Speech API.
The Microsoft Bing Speech API key is specified by ``key``. Unfortunately, these are not available without `signing up for an account <https://azure.microsoft.com/en-ca/pricing/details/cognitive-services/speech-api/>`__ with Microsoft Azure.
To get the API key, go to the `Microsoft Azure Portal Resources <https://portal.azure.com/>`__ page, go to "All Resources" > "Add" > "See All" > Search "Bing Speech API > "Create", and fill in the form to make a "Bing Speech API" resource. On the resulting page (which is also accessible from the "All Resources" page in the Azure Portal), go to the "Show Access Keys" page, which will have two API keys, either of which can be used for the `key` parameter. Microsoft Bing Speech API keys are 32-character lowercase hexadecimal strings.
The recognition language is determined by ``language``, a BCP-47 language tag like ``"en-US"`` (US English) or ``"fr-FR"`` (International French), defaulting to US English. A list of supported language values can be found in the `API documentation <https://docs.microsoft.com/en-us/azure/cognitive-services/speech/api-reference-rest/bingvoicerecognition#recognition-language>`__ under "Interactive and dictation mode".
Returns the most likely transcription if ``show_all`` is false (the default). Otherwise, returns the `raw API response <https://docs.microsoft.com/en-us/azure/cognitive-services/speech/api-reference-rest/bingvoicerecognition#sample-responses>`__ as a JSON dictionary.
Raises a ``speech_recognition.UnknownValueError`` exception if the speech is unintelligible. Raises a ``speech_recognition.RequestError`` exception if the speech recognition operation failed, if the key isn't valid, or if there is no internet connection.
``recognizer_instance.recognize_houndify(audio_data: AudioData, client_id: str, client_key: str, show_all: bool = False) -> Union[str, Dict[str, Any]]``
--------------------------------------------------------------------------------------------------------------------------------------------------------
Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using the Houndify API.
The Houndify client ID and client key are specified by ``client_id`` and ``client_key``, respectively. Unfortunately, these are not available without `signing up for an account <https://www.houndify.com/signup>`__. Once logged into the `dashboard <https://www.houndify.com/dashboard>`__, you will want to select "Register a new client", and fill in the form as necessary. When at the "Enable Domains" page, enable the "Speech To Text Only" domain, and then select "Save & Continue".
To get the client ID and client key for a Houndify client, go to the `dashboard <https://www.houndify.com/dashboard>`__ and select the client's "View Details" link. On the resulting page, the client ID and client key will be visible. Client IDs and client keys are both Base64-encoded strings.
Currently, only English is supported as a recognition language.
Returns the most likely transcription if ``show_all`` is false (the default). Otherwise, returns the raw API response as a JSON dictionary.
Raises a ``speech_recognition.UnknownValueError`` exception if the speech is unintelligible. Raises a ``speech_recognition.RequestError`` exception if the speech recognition operation failed, if the key isn't valid, or if there is no internet connection.
``recognizer_instance.recognize_ibm(audio_data: AudioData, username: str, password: str, language: str = "en-US", show_all: bool = False) -> Union[str, Dict[str, Any]]``
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using the IBM Speech to Text API.
The IBM Speech to Text username and password are specified by ``username`` and ``password``, respectively. Unfortunately, these are not available without `signing up for an account <https://console.ng.bluemix.net/registration/>`__. Once logged into the Bluemix console, follow the instructions for `creating an IBM Watson service instance <https://www.ibm.com/watson/developercloud/doc/getting_started/gs-credentials.shtml>`__, where the Watson service is "Speech To Text". IBM Speech to Text usernames are strings of the form XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX, while passwords are mixed-case alphanumeric strings.
The recognition language is determined by ``language``, an RFC5646 language tag with a dialect like ``"en-US"`` (US English) or ``"zh-CN"`` (Mandarin Chinese), defaulting to US English. The supported language values are listed under the ``model`` parameter of the `audio recognition API documentation <https://www.ibm.com/watson/developercloud/speech-to-text/api/v1/#sessionless_methods>`__, in the form ``LANGUAGE_BroadbandModel``, where ``LANGUAGE`` is the language value.
Returns the most likely transcription if ``show_all`` is false (the default). Otherwise, returns the `raw API response <https://www.ibm.com/watson/developercloud/speech-to-text/api/v1/#sessionless_methods>`__ as a JSON dictionary.
Raises a ``speech_recognition.UnknownValueError`` exception if the speech is unintelligible. Raises a ``speech_recognition.RequestError`` exception if the speech recognition operation failed, if the key isn't valid, or if there is no internet connection.
``recognizer_instance.recognize_whisper(audio_data: AudioData, model: str="base", show_dict: bool=False, load_options: Dict[Any, Any]=None, language:Optional[str]=None, translate:bool=False, **transcribe_options):``
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using Whisper.
The recognition language is determined by ``language``, an uncapitalized full language name like "english" or "chinese". See the full language list at https://github.com/openai/whisper/blob/main/whisper/tokenizer.py
model can be any of tiny, base, small, medium, large, tiny.en, base.en, small.en, medium.en. See https://github.com/openai/whisper for more details.
If show_dict is true, returns the full dict response from Whisper, including the detected language. Otherwise returns only the transcription.
You can translate the result to english with Whisper by passing translate=True
Other values are passed directly to whisper. See https://github.com/openai/whisper/blob/main/whisper/transcribe.py for all options
``recognizer_instance.recognize_whisper_api(audio_data: AudioData, model: str = "whisper-1", api_key: str | None = None)``
--------------------------------------------------------------------------------------------------------------------------
Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using the OpenAI Whisper API.
This function requires an OpenAI account; visit https://platform.openai.com/signup, then generate API Key in `User settings <https://platform.openai.com/account/api-keys>`__.
Detail: https://platform.openai.com/docs/guides/speech-to-text
Raises a ``speech_recognition.exceptions.SetupError`` exception if there are any issues with the openai installation, or the environment variable is missing.
``AudioSource``
---------------
Base class representing audio sources. Do not instantiate.
Instances of subclasses of this class, such as ``Microphone`` and ``AudioFile``, can be passed to things like ``recognizer_instance.record`` and ``recognizer_instance.listen``. Those instances act like context managers, and are designed to be used with ``with`` statements.
For more information, see the documentation for the individual subclasses.
``AudioData(frame_data: bytes, sample_rate: int, sample_width: int) -> AudioData``
----------------------------------------------------------------------------------
Creates a new ``AudioData`` instance, which represents mono audio data.
The raw audio data is specified by ``frame_data``, which is a sequence of bytes representing audio samples. This is the frame data structure used by the PCM WAV format.
The width of each sample, in bytes, is specified by ``sample_width``. Each group of ``sample_width`` bytes represents a single audio sample.
The audio data is assumed to have a sample rate of ``sample_rate`` samples per second (Hertz).
Usually, instances of this class are obtained from ``recognizer_instance.record`` or ``recognizer_instance.listen``, or in the callback for ``recognizer_instance.listen_in_background``, rather than instantiating them directly.
``audiodata_instance.get_segment(start_ms: Union[float, None] = None, end_ms: Union[float, None] = None) -> AudioData``
-----------------------------------------------------------------------------------------------------------------------
Returns a new ``AudioData`` instance, trimmed to a given time interval. In other words, an ``AudioData`` instance with the same audio data except starting at ``start_ms`` milliseconds in and ending ``end_ms`` milliseconds in.
If not specified, ``start_ms`` defaults to the beginning of the audio, and ``end_ms`` defaults to the end.
``audiodata_instance.get_raw_data(convert_rate: Union[int, None] = None, convert_width: Union[int, None] = None) -> bytes``
---------------------------------------------------------------------------------------------------------------------------
Returns a byte string representing the raw frame data for the audio represented by the ``AudioData`` instance.
If ``convert_rate`` is specified and the audio sample rate is not ``convert_rate`` Hz, the resulting audio is resampled to match.
If ``convert_width`` is specified and the audio samples are not ``convert_width`` bytes each, the resulting audio is converted to match.
Writing these bytes directly to a file results in a valid `RAW/PCM audio file <https://en.wikipedia.org/wiki/Raw_audio_format>`__.
``audiodata_instance.get_wav_data(convert_rate: Union[int, None] = None, convert_width: Union[int, None] = None) -> bytes``
---------------------------------------------------------------------------------------------------------------------------
Returns a byte string representing the contents of a WAV file containing the audio represented by the ``AudioData`` instance.
If ``convert_width`` is specified and the audio samples are not ``convert_width`` bytes each, the resulting audio is converted to match.
If ``convert_rate`` is specified and the audio sample rate is not ``convert_rate`` Hz, the resulting audio is resampled to match.
Writing these bytes directly to a file results in a valid `WAV file <https://en.wikipedia.org/wiki/WAV>`__.
``audiodata_instance.get_aiff_data(convert_rate: Union[int, None] = None, convert_width: Union[int, None] = None) -> bytes``
----------------------------------------------------------------------------------------------------------------------------
Returns a byte string representing the contents of an AIFF-C file containing the audio represented by the ``AudioData`` instance.
If ``convert_width`` is specified and the audio samples are not ``convert_width`` bytes each, the resulting audio is converted to match.
If ``convert_rate`` is specified and the audio sample rate is not ``convert_rate`` Hz, the resulting audio is resampled to match.
Writing these bytes directly to a file results in a valid `AIFF-C file <https://en.wikipedia.org/wiki/Audio_Interchange_File_Format>`__.
``audiodata_instance.get_flac_data(convert_rate: Union[int, None] = None, convert_width: Union[int, None] = None) -> bytes``
----------------------------------------------------------------------------------------------------------------------------
Returns a byte string representing the contents of a FLAC file containing the audio represented by the ``AudioData`` instance.
Note that 32-bit FLAC is not supported. If the audio data is 32-bit and ``convert_width`` is not specified, then the resulting FLAC will be a 24-bit FLAC.
If ``convert_rate`` is specified and the audio sample rate is not ``convert_rate`` Hz, the resulting audio is resampled to match.
If ``convert_width`` is specified and the audio samples are not ``convert_width`` bytes each, the resulting audio is converted to match.
Writing these bytes directly to a file results in a valid `FLAC file <https://en.wikipedia.org/wiki/FLAC>`__.

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Notes on using PocketSphinx
===========================
Installing other languages
--------------------------
By default, SpeechRecognition's Sphinx functionality supports only US English. Additional language packs are also available, but not included due to the files being too large:
* `International French <https://drive.google.com/file/d/0Bw_EqP-hnaFNN2FlQ21RdnVZSVE/view?usp=sharing&resourcekey=0-CEkuW10BcLuDdDnKDbzO4w>`__
* `Mandarin Chinese <https://drive.google.com/file/d/0Bw_EqP-hnaFNSWdqdm5maWZtTGc/view?usp=sharing&resourcekey=0-AYS4yrQJO-ieZqyo0g6h3g>`__
* `Italian <https://drive.google.com/file/d/0Bw_EqP-hnaFNSXUtMm8tRkdUejg/view?usp=sharing&resourcekey=0-9IOo0qEMHOAR3z6rzIqgBg>`__
To install a language pack, download the ZIP archives and extract them directly into the module install directory (you can find the module install directory by running ``python -c "import speech_recognition as sr, os.path as p; print(p.dirname(sr.__file__))"``).
Here is a simple Bash script to install all of them, assuming you've downloaded all three ZIP files into your current directory:
.. code:: bash
#!/usr/bin/env bash
SR_LIB=$(python -c "import speech_recognition as sr, os.path as p; print(p.dirname(sr.__file__))")
sudo apt-get install --yes unzip
sudo unzip -o fr-FR.zip -d "$SR_LIB"
sudo chmod --recursive a+r "$SR_LIB/pocketsphinx-data/fr-FR/"
sudo unzip -o zh-CN.zip -d "$SR_LIB"
sudo chmod --recursive a+r "$SR_LIB/pocketsphinx-data/zh-CN/"
sudo unzip -o it-IT.zip -d "$SR_LIB"
sudo chmod --recursive a+r "$SR_LIB/pocketsphinx-data/it-IT/"
Once installed, you can simply specify the language using the ``language`` parameter of ``recognizer_instance.recognize_sphinx``. For example, French would be specified with ``"fr-FR"`` and Mandarin with ``"zh-CN"``.
Building PocketSphinx-Python from source
----------------------------------------
For Windows, it is recommended to install the precompiled Wheel packages in the ``third-party`` directory. These are provided because building Pocketsphinx on Windows requires a lot of work, and can take hours to download and install all the surrounding software.
For Linux and other POSIX systems (like OS X), you'll want to build from source. It should take less than two minutes on a fast machine.
* On any Debian-derived Linux distributions (like Ubuntu and Mint):
1. Run ``sudo apt-get install python3 python3-all-dev python3-pip build-essential swig git libpulse-dev libasound2-dev`` for Python 3.
2. Run ``pip3 install pocketsphinx`` for Python 3.
* On OS X:
1. Run ``brew install swig git python3`` for Python 3.
2. Install PocketSphinx-Python using Pip: ``pip install pocketsphinx``.
* If this gives errors when importing the library in your program, try running ``brew link --overwrite python``.
* On other POSIX-based systems:
1. Install `Python <https://www.python.org/downloads/>`__, `Pip <https://pip.pypa.io/en/stable/installing/>`__, `SWIG <http://www.swig.org/download.html>`__, and `Git <https://git-scm.com/downloads>`__, preferably using a package manager.
2. Install PocketSphinx-Python using Pip: ``pip install pocketsphinx``.
* On Windows:
1. Install `Python <https://www.python.org/downloads/>`__, `Pip <https://pip.pypa.io/en/stable/installing/>`__, `SWIG <http://www.swig.org/download.html>`__, and `Git <https://git-scm.com/downloads>`__, preferably using a package manager.
2. Install the necessary `compilers suite <http://blog.ionelmc.ro/2014/12/21/compiling-python-extensions-on-windows/>`__ (`here's a PDF version <https://github.com/Uberi/speech_recognition/blob/master/third-party/Compiling%20Python%20extensions%20on%20Windows.pdf>`__ in case the link goes down) for compiling modules for your particular Python version:
* `Visual Studio 2015 Community Edition <https://www.visualstudio.com/downloads/download-visual-studio-vs>`__ for Python 3.5.
* The installation process for Python 3.4 is outlined in the article above.
3. Add the folders containing the Python, SWIG, and Git binaries to your ``PATH`` environment variable.
* My ``PATH`` environment variable looks something like: ``C:\Users\Anthony\Desktop\swigwin-3.0.8;C:\Program Files\Git\cmd;(A BUNCH OF OTHER PATHS)``.
4. Reboot to apply changes.
5. Download the full PocketSphinx-Python source code by running ``git clone --recursive --depth 1 https://github.com/cmusphinx/pocketsphinx-python`` (downloading the ZIP archive from GitHub will not work).
6. Run ``python setup.py install`` in the PocketSphinx-Python source code folder to compile and install PocketSphinx.
7. Side note: when I build the precompiled Wheel packages, I skip steps 5 and 6 and do the following instead:
* For Python 3.4: ``C:\Python34\python.exe setup.py bdist_wheel``.
* For Python 3.5: ``C:\Users\Anthony\AppData\Local\Programs\Python\Python35\python.exe setup.py bdist_wheel``.
* The resulting packages are located in the ``dist`` folder of the PocketSphinx-Python project directory.
Notes on the structure of the language data
-------------------------------------------
* Every language has its own folder under ``/speech_recognition/pocketsphinx-data/LANGUAGE_NAME/``, where ``LANGUAGE_NAME`` is the IETF language tag, like ``"en-US"`` (US English) or ``"en-GB"`` (UK English).
* For example, the US English data is stored in ``/speech_recognition/pocketsphinx-data/en-US/``.
* The ``language`` parameter of ``recognizer_instance.recognize_sphinx`` simply chooses the folder with the given name.
* Languages are composed of 3 parts:
* An acoustic model ``/speech_recognition/pocketsphinx-data/LANGUAGE_NAME/acoustic-model/``, which describes how to interpret audio data.
* Acoustic models can be downloaded from the `CMU Sphinx files <http://sourceforge.net/projects/cmusphinx/files/Acoustic%20and%20Language%20Models/>`__. These are pretty disorganized, but instructions for cleaning up specific versions are listed below.
* All of these should be 16 kHz (broadband) models, since that's what the library will assume is being used.
* A language model ``/speech_recognition/pocketsphinx-data/LANGUAGE_NAME/language-model.lm.bin`` (in `CMU binary format <http://cmusphinx.sourceforge.net/wiki/tutoriallm#language_models>`__).
* A pronounciation dictionary ``/speech_recognition/pocketsphinx-data/LANGUAGE_NAME/pronounciation-dictionary.dict``, which describes how words in the language are pronounced.
Notes on building the language data from source
-----------------------------------------------
* All of the following points assume a Debian-derived Linux Distibution (like Ubuntu or Mint).
* To work with any complete, real-world languages, you will need quite a bit of RAM (16 GB recommended) and a fair bit of disk space (20 GB recommended).
* `SphinxBase <https://github.com/cmusphinx/sphinxbase>`__ is needed for all language model file format conversions. We use it to convert between ``*.dmp`` DMP files (an obselete Sphinx binary format), ``*.lm`` ARPA files, and Sphinx binary ``*.lm.bin`` files:
* Install all the SphinxBase build dependencies with ``sudo apt-get install build-essential automake autotools-dev autoconf libtool``.
* Download and extract the `SphinxBase source code <https://github.com/cmusphinx/sphinxbase/archive/master.zip>`__.
* Follow the instructions in the README to install SphinxBase. Basically, run ``sh autogen.sh --force && ./configure && make && sudo make install`` in the SphinxBase folder.
* Pruning (getting rid of less important information) is useful if language model files are too large. We can do this using `IRSTLM <https://github.com/irstlm-team/irstlm>`__:
* Install all the IRSTLM build dependencies with ``sudo apt-get install build-essential automake autotools-dev autoconf libtool``
* Download and extract the `IRSTLM source code <https://github.com/irstlm-team/irstlm/archive/master.zip>`__.
* Follow the instructions in the README to install IRSTLM. Basically, run ``sh regenerate-makefiles.sh --force && ./configure && make && sudo make install`` in the IRSTLM folder.
* If the language model is not in ARPA format, convert it to the ARPA format. To do this, ensure that SphinxBase is installed and run ``sphinx_lm_convert -i LANGUAGE_MODEL_FILE_GOES_HERE -o language-model.lm -ofmt arpa``.
* Prune the model using IRSTLM: run ``prune-lm --threshold=1e-8 t.lm pruned.lm`` to prune with a threshold of 0.00000001. The higher the threshold, the smaller the resulting file.
* Convert the model back into binary format if it was originally not in ARPA format. To do this, ensure that SphinxBase is installed and run ``sphinx_lm_convert -i language-model.lm -o LANGUAGE_MODEL_FILE_GOES_HERE``.
* US English: ``/speech_recognition/pocketsphinx-data/en-US/`` is taken directly from the contents of `PocketSphinx's US English model <https://github.com/cmusphinx/pocketsphinx/tree/master/model/en-us>`__.
* International French: ``/speech_recognition/pocketsphinx-data/fr-FR/``:
* ``/speech_recognition/pocketsphinx-data/fr-FR/language-model.lm.bin`` is ``fr-small.lm.bin`` from the `Sphinx French language model <http://sourceforge.net/projects/cmusphinx/files/Acoustic%20and%20Language%20Models/French%20Language%20Model/>`__.
* ``/speech_recognition/pocketsphinx-data/fr-FR/pronounciation-dictionary.dict`` is ``fr.dict`` from the `Sphinx French language model <http://sourceforge.net/projects/cmusphinx/files/Acoustic%20and%20Language%20Models/French%20Language%20Model/>`__.
* ``/speech_recognition/pocketsphinx-data/fr-FR/acoustic-model/`` contains all of the files extracted from ``cmusphinx-fr-5.2.tar.gz`` in the `Sphinx French acoustic model <http://sourceforge.net/projects/cmusphinx/files/Acoustic%20and%20Language%20Models/French/>`__.
* To get better French recognition accuracy at the expense of higher disk space and RAM usage:
1. Download ``fr.lm.gmp`` from the `Sphinx French language model <http://sourceforge.net/projects/cmusphinx/files/Acoustic%20and%20Language%20Models/French%20Language%20Model/>`__.
2. Convert from DMP (an obselete Sphinx binary format) to ARPA format: ``sphinx_lm_convert -i fr.lm.gmp -o french.lm.bin``.
3. Replace ``/speech_recognition/pocketsphinx-data/fr-FR/language-model.lm.bin`` with ``french.lm.bin`` created in the previous step.
* Mandarin Chinese: ``/speech_recognition/pocketsphinx-data/zh-CN/``:
* ``/speech_recognition/pocketsphinx-data/zh-CN/language-model.lm.bin`` is generated as follows:
1. Download ``zh_broadcastnews_64000_utf8.DMP`` from the `Sphinx Mandarin language model <http://sourceforge.net/projects/cmusphinx/files/Acoustic%20and%20Language%20Models/Mandarin%20Language%20Model/>`__.
2. Convert from DMP (an obselete Sphinx binary format) to ARPA format: ``sphinx_lm_convert -i zh_broadcastnews_64000_utf8.DMP -o chinese.lm -ofmt arpa``.
3. Prune with a threshold of 0.00000004 using ``prune-lm --threshold=4e-8 chinese.lm chinese.lm``.
4. Convert from ARPA format to Sphinx binary format: ``sphinx_lm_convert -i chinese.lm -o chinese.lm.bin``.
5. Replace ``/speech_recognition/pocketsphinx-data/zh-CN/language-model.lm.bin`` with ``chinese.lm.bin`` created in the previous step.
* ``/speech_recognition/pocketsphinx-data/zh-CN/pronounciation-dictionary.dict`` is ``zh_broadcastnews_utf8.dic`` from the `Sphinx Mandarin language model <http://sourceforge.net/projects/cmusphinx/files/Acoustic%20and%20Language%20Models/Mandarin%20Language%20Model/>`__.
* ``/speech_recognition/pocketsphinx-data/zh-CN/acoustic-model/`` contains all of the files extracted from ``zh_broadcastnews_16k_ptm256_8000.tar.bz2`` in the `Sphinx Mandarin acoustic model <http://sourceforge.net/projects/cmusphinx/files/Acoustic%20and%20Language%20Models/Mandarin%20Broadcast%20News%20acoustic%20models/>`__.
* To get better Chinese recognition accuracy at the expense of higher disk space and RAM usage, simply skip step 3 when preparing ``zh_broadcastnews_64000_utf8.DMP``.
* Italian: ``/speech_recognition/pocketsphinx-data/it-IT/``:
* ``/speech_recognition/pocketsphinx-data/it-IT/language-model.lm.bin`` is generated as follows:
1. Download ``cmusphinx-it-5.2.tar.gz`` from the `Sphinx Italian language model <https://sourceforge.net/projects/cmusphinx/files/Acoustic%20and%20Language%20Models/Italian/>`__.
2. Extract ``/etc/voxforge_it_sphinx.lm`` from ``cmusphinx-it-5.2.tar.gz`` as ``italian.lm``.
3. Convert from ARPA format to Sphinx binary format: ``sphinx_lm_convert -i italian.lm -o italian.lm.bin``.
4. Replace ``/speech_recognition/pocketsphinx-data/it-IT/language-model.lm.bin`` with ``italian.lm.bin`` created in the previous step.
* ``/speech_recognition/pocketsphinx-data/it-IT/pronounciation-dictionary.dict`` is ``/etc/voxforge_it_sphinx.dic`` from ``cmusphinx-it-5.2.tar.gz`` (from the `Sphinx Italian language model <https://sourceforge.net/projects/cmusphinx/files/Acoustic%20and%20Language%20Models/Italian/>`__).
* ``/speech_recognition/pocketsphinx-data/it-IT/acoustic-model/`` contains all of the files in ``/model_parameters`` extracted from ``cmusphinx-it-5.2.tar.gz`` (from the `Sphinx Italian language model <https://sourceforge.net/projects/cmusphinx/files/Acoustic%20and%20Language%20Models/Italian/>`__).

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@ -1,8 +0,0 @@
[bdist_wheel]
# the `universal` setting means that the project runs unmodified on both Python 2 and 3,
# and doesn't use any C extensions to Python
universal=1
[options.extras_require]
whisper-api =
openai

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speech_recognition/setup.py
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@ -1,69 +0,0 @@
#!/usr/bin/env python3
import sys
import os
import stat
from setuptools import setup
from setuptools.command.install import install
from distutils import log
import speech_recognition
FILES_TO_MARK_EXECUTABLE = ["flac-linux-x86", "flac-linux-x86_64", "flac-mac", "flac-win32.exe"]
class InstallWithExtraSteps(install):
def run(self):
install.run(self) # do the original install steps
# mark the FLAC executables as executable by all users (this fixes occasional issues when file permissions get messed up)
for output_path in self.get_outputs():
if os.path.basename(output_path) in FILES_TO_MARK_EXECUTABLE:
log.info("setting executable permissions on {}".format(output_path))
stat_info = os.stat(output_path)
os.chmod(
output_path,
stat_info.st_mode |
stat.S_IRUSR | stat.S_IXUSR | # owner can read/execute
stat.S_IRGRP | stat.S_IXGRP | # group can read/execute
stat.S_IROTH | stat.S_IXOTH # everyone else can read/execute
)
setup(
name="SpeechRecognition",
version=speech_recognition.__version__,
packages=["speech_recognition"],
include_package_data=True,
cmdclass={"install": InstallWithExtraSteps},
# PyPI metadata
author=speech_recognition.__author__,
author_email="azhang9@gmail.com",
description=speech_recognition.__doc__,
long_description=open("README.rst").read(),
long_description_content_type="text/x-rst",
license=speech_recognition.__license__,
keywords="speech recognition voice sphinx google wit bing api houndify ibm snowboy",
url="https://github.com/Uberi/speech_recognition#readme",
classifiers=[
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Developers",
"Natural Language :: English",
"License :: OSI Approved :: BSD License",
"Operating System :: Microsoft :: Windows",
"Operating System :: POSIX :: Linux",
"Operating System :: MacOS :: MacOS X",
"Operating System :: Other OS",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Topic :: Software Development :: Libraries :: Python Modules",
"Topic :: Multimedia :: Sound/Audio :: Speech",
],
python_requires=">=3.8",
install_requires=['requests>=2.26.0'],
)

1
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@ -1 +0,0 @@
# placeholder file to make this folder a module - this allows tests in this folder to be discovered by `python -m unittest discover`

BIN
speech_recognition/tests/audio-mono-16-bit-44100Hz.aiff
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#!/usr/bin/env python3
import unittest
from os import path
import speech_recognition as sr
class TestAudioFile(unittest.TestCase):
def assertSimilar(self, bytes_1, bytes_2):
for i, (byte_1, byte_2) in enumerate(zip(bytes_1, bytes_2)):
if abs(byte_1 - byte_2) > 2:
raise AssertionError("{} is really different from {} at index {}".format(bytes_1, bytes_2, i))
def test_get_segment(self):
r = sr.Recognizer()
with sr.AudioFile(path.join(path.dirname(path.realpath(__file__)), "audio-mono-32-bit-44100Hz.wav")) as source: audio = r.record(source)
self.assertEqual(audio.get_raw_data(), audio.get_segment().get_raw_data())
self.assertEqual(audio.get_raw_data()[8:], audio.get_segment(0.022675738 * 2).get_raw_data())
self.assertEqual(audio.get_raw_data()[:16], audio.get_segment(None, 0.022675738 * 4).get_raw_data())
self.assertEqual(audio.get_raw_data()[8:16], audio.get_segment(0.022675738 * 2, 0.022675738 * 4).get_raw_data())
def test_wav_mono_8_bit(self):
r = sr.Recognizer()
with sr.AudioFile(path.join(path.dirname(path.realpath(__file__)), "audio-mono-8-bit-44100Hz.wav")) as source: audio = r.record(source)
self.assertIsInstance(audio, sr.AudioData)
self.assertEqual(audio.sample_rate, 44100)
self.assertEqual(audio.sample_width, 1)
self.assertSimilar(audio.get_raw_data()[:32], b"\x00\xff\x00\xff\x00\xff\xff\x00\xff\x00\xff\x00\xff\x00\x00\xff\x00\x00\xff\x00\xff\x00\xff\x00\xff\x00\xff\x00\xff\x00\xff\xff")
def test_wav_mono_16_bit(self):
r = sr.Recognizer()
with sr.AudioFile(path.join(path.dirname(path.realpath(__file__)), "audio-mono-16-bit-44100Hz.wav")) as source: audio = r.record(source)
self.assertIsInstance(audio, sr.AudioData)
self.assertEqual(audio.sample_rate, 44100)
self.assertEqual(audio.sample_width, 2)
self.assertSimilar(audio.get_raw_data()[:32], b"\x00\x00\xff\xff\x01\x00\xff\xff\x00\x00\x01\x00\xfe\xff\x01\x00\xfe\xff\x04\x00\xfc\xff\x04\x00\xfe\xff\xff\xff\x03\x00\xfe\xff")
def test_wav_mono_24_bit(self):
r = sr.Recognizer()
with sr.AudioFile(path.join(path.dirname(path.realpath(__file__)), "audio-mono-24-bit-44100Hz.wav")) as source: audio = r.record(source)
self.assertIsInstance(audio, sr.AudioData)
self.assertEqual(audio.sample_rate, 44100)
if audio.sample_width == 3:
self.assertSimilar(audio.get_raw_data()[:32], b"\x00\x00\x00\x00\xff\xff\x00\x01\x00\x00\xff\xff\x00\x00\x00\x00\x01\x00\x00\xfe\xff\x00\x01\x00\x00\xfe\xff\x00\x04\x00\x00\xfb")
else:
self.assertSimilar(audio.get_raw_data()[:32], b"\x00\x00\x00\x00\x00\x00\xff\xff\x00\x00\x01\x00\x00\x00\xff\xff\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\xfe\xff\x00\x00\x01\x00")
def test_wav_mono_32_bit(self):
r = sr.Recognizer()
audio_file_path = path.join(path.dirname(path.realpath(__file__)), "audio-mono-32-bit-44100Hz.wav")
with sr.AudioFile(audio_file_path) as source: audio = r.record(source)
self.assertIsInstance(audio, sr.AudioData)
self.assertEqual(audio.sample_rate, 44100)
self.assertEqual(audio.sample_width, 4)
self.assertSimilar(audio.get_raw_data()[:32], b"\x00\x00\x00\x00\x00\x00\xff\xff\x00\x00\x01\x00\x00\x00\xff\xff\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\xfe\xff\x00\x00\x01\x00")
def test_wav_stereo_8_bit(self):
r = sr.Recognizer()
with sr.AudioFile(path.join(path.dirname(path.realpath(__file__)), "audio-stereo-8-bit-44100Hz.wav")) as source: audio = r.record(source)
self.assertIsInstance(audio, sr.AudioData)
self.assertEqual(audio.sample_rate, 44100)
self.assertEqual(audio.sample_width, 1)
self.assertSimilar(audio.get_raw_data()[:32], b"\x00\xff\x00\xff\x00\x00\xff\x7f\x7f\x00\xff\x00\xff\x00\x00\xff\x00\x7f\x7f\x7f\x00\x00\xff\x00\xff\x00\xff\x00\x7f\x7f\x7f\x7f")
def test_wav_stereo_16_bit(self):
r = sr.Recognizer()
with sr.AudioFile(path.join(path.dirname(path.realpath(__file__)), "audio-stereo-16-bit-44100Hz.wav")) as source: audio = r.record(source)
self.assertIsInstance(audio, sr.AudioData)
self.assertEqual(audio.sample_rate, 44100)
self.assertEqual(audio.sample_width, 2)
self.assertSimilar(audio.get_raw_data()[:32], b"\x02\x00\xfb\xff\x04\x00\xfe\xff\xfe\xff\x07\x00\xf6\xff\x07\x00\xf9\xff\t\x00\xf5\xff\x0c\x00\xf8\xff\x02\x00\x04\x00\xfa\xff")
def test_wav_stereo_24_bit(self):
r = sr.Recognizer()
with sr.AudioFile(path.join(path.dirname(path.realpath(__file__)), "audio-stereo-24-bit-44100Hz.wav")) as source: audio = r.record(source)
self.assertIsInstance(audio, sr.AudioData)
self.assertEqual(audio.sample_rate, 44100)
if audio.sample_width == 3:
self.assertSimilar(audio.get_raw_data()[:32], b"\x00\x00\x00\x00\xfe\xff\x00\x02\x00\x00\xfe\xff\x00\x00\x00\x00\x02\x00\x00\xfc\xff\x00\x02\x00\x00\xfc\xff\x00\x08\x00\x00\xf6")
else:
self.assertSimilar(audio.get_raw_data()[:32], b"\x00\x00\x00\x00\x00\x00\xfe\xff\x00\x00\x02\x00\x00\x00\xfe\xff\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\xfc\xff\x00\x00\x02\x00")
def test_wav_stereo_32_bit(self):
r = sr.Recognizer()
with sr.AudioFile(path.join(path.dirname(path.realpath(__file__)), "audio-stereo-32-bit-44100Hz.wav")) as source: audio = r.record(source)
self.assertIsInstance(audio, sr.AudioData)
self.assertEqual(audio.sample_rate, 44100)
self.assertEqual(audio.sample_width, 4)
self.assertSimilar(audio.get_raw_data()[:32], b"\x00\x00\x00\x00\x00\x00\xfe\xff\x00\x00\x02\x00\x00\x00\xfe\xff\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\xfc\xff\x00\x00\x02\x00")
def test_aiff_mono_16_bit(self):
r = sr.Recognizer()
with sr.AudioFile(path.join(path.dirname(path.realpath(__file__)), "audio-mono-16-bit-44100Hz.aiff")) as source: audio = r.record(source)
self.assertIsInstance(audio, sr.AudioData)
self.assertEqual(audio.sample_rate, 44100)
self.assertEqual(audio.sample_width, 2)
self.assertSimilar(audio.get_raw_data()[:32], b"\x00\x00\x00\x00\xff\xff\x01\x00\xff\xff\x01\x00\xfe\xff\x02\x00\xfd\xff\x04\x00\xfc\xff\x03\x00\x00\x00\xfe\xff\x03\x00\xfd\xff")
def test_aiff_stereo_16_bit(self):
r = sr.Recognizer()
with sr.AudioFile(path.join(path.dirname(path.realpath(__file__)), "audio-stereo-16-bit-44100Hz.aiff")) as source: audio = r.record(source)
self.assertIsInstance(audio, sr.AudioData)
self.assertEqual(audio.sample_rate, 44100)
self.assertEqual(audio.sample_width, 2)
self.assertSimilar(audio.get_raw_data()[:32], b"\x00\x00\xfe\xff\x02\x00\xfe\xff\xff\xff\x04\x00\xfa\xff\x04\x00\xfa\xff\t\x00\xf6\xff\n\x00\xfa\xff\xff\xff\x08\x00\xf5\xff")
def test_flac_mono_16_bit(self):
r = sr.Recognizer()
with sr.AudioFile(path.join(path.dirname(path.realpath(__file__)), "audio-mono-16-bit-44100Hz.flac")) as source: audio = r.record(source)
self.assertIsInstance(audio, sr.AudioData)
self.assertEqual(audio.sample_rate, 44100)
self.assertEqual(audio.sample_width, 2)
self.assertSimilar(audio.get_raw_data()[:32], b"\x00\x00\xff\xff\x01\x00\xff\xff\x00\x00\x01\x00\xfe\xff\x02\x00\xfc\xff\x06\x00\xf9\xff\x06\x00\xfe\xff\xfe\xff\x05\x00\xfa\xff")
def test_flac_mono_24_bit(self):
r = sr.Recognizer()
with sr.AudioFile(path.join(path.dirname(path.realpath(__file__)), "audio-mono-24-bit-44100Hz.flac")) as source: audio = r.record(source)
self.assertIsInstance(audio, sr.AudioData)
self.assertEqual(audio.sample_rate, 44100)
if audio.sample_width == 3:
self.assertSimilar(audio.get_raw_data()[:32], b"\x00\x00\x00\xff\xfe\xff\x02\x01\x00\xfd\xfe\xff\x04\x00\x00\xfc\x00\x00\x04\xfe\xff\xfb\x00\x00\x05\xfe\xff\xfc\x03\x00\x04\xfb")
else:
self.assertSimilar(audio.get_raw_data()[:32], b"\x00\x00\x00\x00\x00\xff\xfe\xff\x00\x02\x01\x00\x00\xfd\xfe\xff\x00\x04\x00\x00\x00\xfc\x00\x00\x00\x04\xfe\xff\x00\xfb\x00\x00")
def test_flac_stereo_16_bit(self):
r = sr.Recognizer()
with sr.AudioFile(path.join(path.dirname(path.realpath(__file__)), "audio-stereo-16-bit-44100Hz.flac")) as source: audio = r.record(source)
self.assertIsInstance(audio, sr.AudioData)
self.assertEqual(audio.sample_rate, 44100)
self.assertEqual(audio.sample_width, 2)
self.assertSimilar(audio.get_raw_data()[:32], b"\xff\xff\xff\xff\x02\x00\xfe\xff\x00\x00\x01\x00\xfd\xff\x01\x00\xff\xff\x04\x00\xfa\xff\x05\x00\xff\xff\xfd\xff\x08\x00\xf6\xff")
def test_flac_stereo_24_bit(self):
r = sr.Recognizer()
with sr.AudioFile(path.join(path.dirname(path.realpath(__file__)), "audio-stereo-24-bit-44100Hz.flac")) as source: audio = r.record(source)
self.assertIsInstance(audio, sr.AudioData)
self.assertEqual(audio.sample_rate, 44100)
if audio.sample_width == 3:
self.assertSimilar(audio.get_raw_data()[:32], b"\x00\x00\x00\x00\xfe\xff\x00\x02\x00\x00\xfe\xff\x00\x00\x00\xff\x01\x00\x02\xfc\xff\xfe\x01\x00\x02\xfc\xff\xfe\x07\x00\x01\xf6")
else:
self.assertSimilar(audio.get_raw_data()[:32], b"\x00\x00\x00\x00\x00\x00\xfe\xff\x00\x00\x02\x00\x00\x00\xfe\xff\x00\x00\x00\x00\x00\xff\x01\x00\x00\x02\xfc\xff\x00\xfe\x01\x00")
if __name__ == "__main__":
unittest.main()

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import unittest
import speech_recognition as sr
class TestRecognition(unittest.TestCase):
def setUp(self):
self.AUDIO_FILE_EN = os.path.join(os.path.dirname(os.path.realpath(__file__)), "english.wav")
self.AUDIO_FILE_FR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "french.aiff")
self.AUDIO_FILE_ZH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "chinese.flac")
self.WHISPER_CONFIG = {"temperature": 0}
def test_sphinx_english(self):
r = sr.Recognizer()
with sr.AudioFile(self.AUDIO_FILE_EN) as source: audio = r.record(source)
self.assertEqual(r.recognize_sphinx(audio), "one two three")
def test_google_english(self):
r = sr.Recognizer()
with sr.AudioFile(self.AUDIO_FILE_EN) as source: audio = r.record(source)
self.assertIn(r.recognize_google(audio), ["123", "1 2 3", "one two three"])
def test_google_french(self):
r = sr.Recognizer()
with sr.AudioFile(self.AUDIO_FILE_FR) as source: audio = r.record(source)
self.assertEqual(r.recognize_google(audio, language="fr-FR"), u"et c'est la dictée numéro 1")
def test_google_chinese(self):
r = sr.Recognizer()
with sr.AudioFile(self.AUDIO_FILE_ZH) as source: audio = r.record(source)
self.assertEqual(r.recognize_google(audio, language="zh-CN"), u"砸自己的脚")
@unittest.skipUnless("WIT_AI_KEY" in os.environ, "requires Wit.ai key to be specified in WIT_AI_KEY environment variable")
def test_wit_english(self):
r = sr.Recognizer()
with sr.AudioFile(self.AUDIO_FILE_EN) as source: audio = r.record(source)
self.assertEqual(r.recognize_wit(audio, key=os.environ["WIT_AI_KEY"]), "one two three")
@unittest.skipUnless("BING_KEY" in os.environ, "requires Microsoft Bing Voice Recognition key to be specified in BING_KEY environment variable")
def test_bing_english(self):
r = sr.Recognizer()
with sr.AudioFile(self.AUDIO_FILE_EN) as source: audio = r.record(source)
self.assertEqual(r.recognize_bing(audio, key=os.environ["BING_KEY"]), "123.")
@unittest.skipUnless("BING_KEY" in os.environ, "requires Microsoft Bing Voice Recognition key to be specified in BING_KEY environment variable")
def test_bing_french(self):
r = sr.Recognizer()
with sr.AudioFile(self.AUDIO_FILE_FR) as source: audio = r.record(source)
self.assertEqual(r.recognize_bing(audio, key=os.environ["BING_KEY"], language="fr-FR"), u"Essaye la dictée numéro un.")
@unittest.skipUnless("BING_KEY" in os.environ, "requires Microsoft Bing Voice Recognition key to be specified in BING_KEY environment variable")
def test_bing_chinese(self):
r = sr.Recognizer()
with sr.AudioFile(self.AUDIO_FILE_ZH) as source: audio = r.record(source)
self.assertEqual(r.recognize_bing(audio, key=os.environ["BING_KEY"], language="zh-CN"), u"砸自己的脚。")
@unittest.skipUnless("HOUNDIFY_CLIENT_ID" in os.environ and "HOUNDIFY_CLIENT_KEY" in os.environ, "requires Houndify client ID and client key to be specified in HOUNDIFY_CLIENT_ID and HOUNDIFY_CLIENT_KEY environment variables")
def test_houndify_english(self):
r = sr.Recognizer()
with sr.AudioFile(self.AUDIO_FILE_EN) as source: audio = r.record(source)
self.assertEqual(r.recognize_houndify(audio, client_id=os.environ["HOUNDIFY_CLIENT_ID"], client_key=os.environ["HOUNDIFY_CLIENT_KEY"]), "one two three")
@unittest.skipUnless("IBM_USERNAME" in os.environ and "IBM_PASSWORD" in os.environ, "requires IBM Speech to Text username and password to be specified in IBM_USERNAME and IBM_PASSWORD environment variables")
def test_ibm_english(self):
r = sr.Recognizer()
with sr.AudioFile(self.AUDIO_FILE_EN) as source: audio = r.record(source)
self.assertEqual(r.recognize_ibm(audio, username=os.environ["IBM_USERNAME"], password=os.environ["IBM_PASSWORD"]), "one two three ")
@unittest.skipUnless("IBM_USERNAME" in os.environ and "IBM_PASSWORD" in os.environ, "requires IBM Speech to Text username and password to be specified in IBM_USERNAME and IBM_PASSWORD environment variables")
def test_ibm_french(self):
r = sr.Recognizer()
with sr.AudioFile(self.AUDIO_FILE_FR) as source: audio = r.record(source)
self.assertEqual(r.recognize_ibm(audio, username=os.environ["IBM_USERNAME"], password=os.environ["IBM_PASSWORD"], language="fr-FR"), u"si la dictée numéro un ")
@unittest.skipUnless("IBM_USERNAME" in os.environ and "IBM_PASSWORD" in os.environ, "requires IBM Speech to Text username and password to be specified in IBM_USERNAME and IBM_PASSWORD environment variables")
def test_ibm_chinese(self):
r = sr.Recognizer()
with sr.AudioFile(self.AUDIO_FILE_ZH) as source: audio = r.record(source)
self.assertEqual(r.recognize_ibm(audio, username=os.environ["IBM_USERNAME"], password=os.environ["IBM_PASSWORD"], language="zh-CN"), u"砸 自己 的 脚 ")
def test_whisper_english(self):
r = sr.Recognizer()
with sr.AudioFile(self.AUDIO_FILE_EN) as source: audio = r.record(source)
self.assertEqual(r.recognize_whisper(audio, language="english", **self.WHISPER_CONFIG), " 1, 2, 3.")
def test_whisper_french(self):
r = sr.Recognizer()
with sr.AudioFile(self.AUDIO_FILE_FR) as source: audio = r.record(source)
self.assertEqual(r.recognize_whisper(audio, language="french", **self.WHISPER_CONFIG), " et c'est la dictée numéro 1.")
def test_whisper_chinese(self):
r = sr.Recognizer()
with sr.AudioFile(self.AUDIO_FILE_ZH) as source: audio = r.record(source)
self.assertEqual(r.recognize_whisper(audio, model="small", language="chinese", **self.WHISPER_CONFIG), u"砸自己的腳")
if __name__ == "__main__":
unittest.main()

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import unittest
import speech_recognition as sr
class TestSpecialFeatures(unittest.TestCase):
def setUp(self):
self.AUDIO_FILE_EN = os.path.join(os.path.dirname(os.path.realpath(__file__)), "english.wav")
self.addTypeEqualityFunc(str, self.assertSameWords)
def test_sphinx_keywords(self):
r = sr.Recognizer()
with sr.AudioFile(self.AUDIO_FILE_EN) as source: audio = r.record(source)
self.assertEqual(r.recognize_sphinx(audio, keyword_entries=[("one", 1.0), ("two", 1.0), ("three", 1.0)]), "three two one")
self.assertEqual(r.recognize_sphinx(audio, keyword_entries=[("wan", 0.95), ("too", 1.0), ("tree", 1.0)]), "tree too wan")
self.assertEqual(r.recognize_sphinx(audio, keyword_entries=[("un", 0.95), ("to", 1.0), ("tee", 1.0)]), "tee to un")
def assertSameWords(self, tested, reference, msg=None):
set_tested = set(tested.split())
set_reference = set(reference.split())
if set_tested != set_reference:
raise self.failureException(msg if msg is not None else "%r doesn't consist of the same words as %r" % (tested, reference))
if __name__ == "__main__":
unittest.main()

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Copyright (c) 2006 Hubert Pham
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Copyright (c) 1999-2015 Carnegie Mellon University. All rights
reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in
the documentation and/or other materials provided with the
distribution.
This work was supported in part by funding from the Defense Advanced
Research Projects Agency and the National Science Foundation of the
United States of America, and the CMU Sphinx Speech Consortium.
THIS SOFTWARE IS PROVIDED BY CARNEGIE MELLON UNIVERSITY ``AS IS'' AND
ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CARNEGIE MELLON UNIVERSITY
NOR ITS EMPLOYEES BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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speech_recognition/third-party/flac-1.3.2.tar.xz vendored
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speech_recognition/third-party/pocketsphinx-0.1.3-cp27-cp27m-win_amd64.whl vendored
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speech_recognition/third-party/pocketsphinx-0.1.3.zip vendored
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