Uberi/speech_recognition

SpeechRecognition

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Library for performing speech recognition, with support for several engines and APIs, online and offline.

Recall.ai - Meeting Transcription API

If you’re working with speech detection or transcription for meetings, consider checking out Recall.ai <https://www.recall.ai/product/meeting-transcription-api?utm_source=github&utm_medium=sponsorship&utm_campaign=uberi-speech_recognition>__, an API that works with Zoom, Google Meet, Microsoft Teams, and more. Recall.ai diarizes by pulling the speaker data and separate audio streams from the meeting platforms, which means 100% accurate speaker diarization with actual speaker names and speaker emails.

Getting Started

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/>__
• 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)
• OpenAI Transcription API <https://platform.openai.com/docs/guides/speech-to-text>__
  • OpenAI compatible self-hosted endpoints (e.g. vLLM, Ollama)
• Groq Whisper API <https://console.groq.com/docs/speech-to-text>__
• Cohere Transcribe API <https://docs.cohere.com/docs/transcribe>__

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.

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 -m pip install ..

Requirements

To use all of the functionality of the library, you should have:

• Python 3.10+ (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)
• Faster Whisper (required only if you need to use Faster Whisper recognizer_instance.recognize_faster_whisper)
• openai (required only if you need to use OpenAI Transcription API speech recognition recognizer_instance.recognize_openai)
  • includes OpenAI compatible self-hosted endpoints (e.g. vLLM, Ollama)
• groq (required only if you need to use Groq Whisper API speech recognition recognizer_instance.recognize_groq)
• cohere (required only if you need to use Cohere Transcribe API speech recognition recognizer_instance.recognize_cohere_api; install with pip install SpeechRecognition[cohere-api]. Set CO_API_KEY as documented by the Cohere SDK.)

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 3.10+ <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 with PyAudio using Pip <https://pip.readthedocs.org/>__: execute pip install SpeechRecognition[audio] 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 SpeechRecognition[audio] (replace pip with pip3 if using Python 3).
• On OS X, install PortAudio using Homebrew <http://brew.sh/>: brew install portaudio. Then, install with PyAudio using Pip <https://pip.readthedocs.org/>: pip install SpeechRecognition[audio].
• On other POSIX-based systems, install the portaudio19-dev and python-all-dev (or python3-all-dev if using Python 3) packages (or their closest equivalents) using a package manager of your choice, and then install with PyAudio using Pip <https://pip.readthedocs.org/>__: pip install SpeechRecognition[audio] (replace pip with pip3 if using Python 3).

PocketSphinx (for Sphinx users)

`PocketSphinx <https://github.com/cmusphinx/pocketsphinx>`__ is **required if and only if you want to use the Sphinx recognizer** (``recognizer_instance.recognize_sphinx``).

On Linux and other POSIX systems (such as OS X), run ``pip install SpeechRecognition[pocketsphinx]``. 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 SpeechRecognition[vosk]``.

You also have to install Vosk Models:

`Here <https://alphacephei.com/vosk/models>`__ are models available for download. You have to place them in the ``model`` directory of your project, like "your-project-folder/model".
You can also run ``sprc download vosk`` to download the default model.

Google Cloud Speech Library for Python (for Google Cloud Speech-to-Text API users)

The library google-cloud-speech <https://pypi.org/project/google-cloud-speech/>__ is required if and only if you want to use Google Cloud Speech-to-Text API (recognizer_instance.recognize_google_cloud). You can install it with python3 -m pip install SpeechRecognition[google-cloud]. (ref: official installation instructions <https://cloud.google.com/speech-to-text/docs/transcribe-client-libraries#client-libraries-install-python>__)

Prerequisite: Create local authentication credentials for your Google account

• Digest: Before you begin (Transcribe speech to text by using client libraries) <https://cloud.google.com/speech-to-text/docs/transcribe-client-libraries#before-you-begin>__
• Set up Speech-to-Text <https://cloud.google.com/speech-to-text/docs/before-you-begin>__
• User credentials (Set up ADC for a local development environment) <https://cloud.google.com/docs/authentication/set-up-adc-local-dev-environment#local-user-cred>__

Currently only V1 <https://cloud.google.com/speech-to-text/docs/quickstart>__ is supported. (V2 <https://cloud.google.com/speech-to-text/v2/docs/quickstart>__ is not supported)

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 SpeechRecognition[whisper-local].

Faster Whisper (for Faster Whisper users)

The library `faster-whisper <https://pypi.org/project/faster-whisper/>`__ is **required if and only if you want to use Faster Whisper** (``recognizer_instance.recognize_faster_whisper``).

You can install it with ``python3 -m pip install SpeechRecognition[faster-whisper]``.

OpenAI Transcription API (for OpenAI Transcription API users)

The library openai <https://pypi.org/project/openai/>__ is required if and only if you want to use OpenAI Transcription API (recognizer_instance.recognize_openai).

You can install it with python3 -m pip install SpeechRecognition[openai].

Please set the environment variable OPENAI_API_KEY before calling recognizer_instance.recognize_openai.

OpenAI-compatible self-hosted Whisper endpoints (for users of vLLM, Ollama, etc.) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

recognizer_instance.recognize_openai also supports OpenAI-compatible endpoints.

Set OPENAI_BASE_URL to point to your custom endpoint with dummy OPENAI_API_KEY.

Groq Whisper API (for Groq Whisper API users)

The library `groq <https://pypi.org/project/groq/>`__ is **required if and only if you want to use Groq Whisper API** (``recognizer_instance.recognize_groq``).

You can install it with ``python3 -m pip install SpeechRecognition[groq]``.

Please set the environment variable ``GROQ_API_KEY`` before calling ``recognizer_instance.recognize_groq``.

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_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.

Contributing
------------

See `CONTRIBUTING.rst <https://github.com/Uberi/speech_recognition/blob/master/CONTRIBUTING.rst>`__ for information on setting up a development environment, running tests, and contribution guidelines.

Authors
-------

::

    Uberi <[email protected]> (Anthony Zhang)
    bobsayshilol
    arvindch <[email protected]> (Arvind Chembarpu)
    kevinismith <[email protected]> (Kevin Smith)
    haas85
    DelightRun <[email protected]>
    maverickagm
    kamushadenes <[email protected]> (Kamus Hadenes)
    sbraden <[email protected]> (Sarah Braden)
    tb0hdan (Bohdan Turkynewych)
    Thynix <[email protected]> (Steve Dougherty)
    beeedy <[email protected]> (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 3.11) [Software]. Available from https://github.com/Uberi/speech_recognition#readme.

How to cite this library (Chicago style):

    Zhang, Anthony. 2017. *Speech Recognition* (version 3.11).

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- `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 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`` for license details for individual parts.

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.