访问官网
Github
文档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>__ (seerecognizer_instance.energy_thresholdfor 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/>__: executepip install pyaudioin a terminal. - On Debian-derived Linux distributions (like Ubuntu and Mint), install PyAudio using
APT <https://wiki.debian.org/Apt>__: executesudo apt-get install python-pyaudio python3-pyaudioin a terminal.- If the version in the repositories is too old, install the latest release using Pip: execute
sudo apt-get install portaudio19-dev python-all-dev python3-all-dev && sudo pip install pyaudio(replacepipwithpip3if using Python 3).
- If the version in the repositories is too old, install the latest release using Pip: execute
- On OS X, install PortAudio using
Homebrew <http://brew.sh/>:brew install portaudio. Then, install PyAudio usingPip <https://pip.readthedocs.org/>:pip install pyaudio. - On other POSIX-based systems, install the
portaudio19-devandpython-all-dev(orpython3-all-devif using Python 3) packages (or their closest equivalents) using a package manager of your choice, and then install PyAudio usingPip <https://pip.readthedocs.org/>__:pip install pyaudio(replacepipwithpip3if 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 SpeechRecognition[whisper-local].
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 SpeechRecognition[whisper-api]``.
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
