Library for performing speech recognition with the Google Speech Recognition API.
Links:
Quickstart: pip install SpeechRecognition. See the "Installing" section for more details.
Recognize speech input from the microphone:
# NOTE: this requires PyAudio because it uses the Microphone class
import speech_recognition as sr
r = sr.Recognizer()
with sr.Microphone() as source: # use the default microphone as the audio source
audio = r.listen(source) # listen for the first phrase and extract it into audio data
try:
print("You said " + r.recognize(audio)) # recognize speech using Google Speech Recognition
except LookupError: # speech is unintelligible
print("Could not understand audio")Transcribe a WAV audio file:
import speech_recognition as sr
r = sr.Recognizer()
with sr.WavFile("test.wav") as source: # use "test.wav" as the audio source
audio = r.record(source) # extract audio data from the file
try:
print("Transcription: " + r.recognize(audio)) # recognize speech using Google Speech Recognition
except LookupError: # speech is unintelligible
print("Could not understand audio")Transcribe a WAV audio file and show the confidence of each:
import speech_recognition as sr
r = sr.Recognizer()
with sr.WavFile("test.wav") as source: # use "test.wav" as the audio source
audio = r.record(source) # extract audio data from the file
try:
list = r.recognize(audio,True) # generate a list of possible transcriptions
print("Possible transcriptions:")
for prediction in list:
print(" " + prediction["text"] + " (" + str(prediction["confidence"]*100) + "%)")
except LookupError: # speech is unintelligible
print("Could not understand audio")Listening to a microphone in the background:
import speech_recognition as sr
def callback(recognizer, audio): # this is called from the background thread
try:
print("You said " + recognizer.recognize(audio)) # received audio data, now need to recognize it
except LookupError:
print("Oops! Didn't catch that")
r = sr.Recognizer()
r.listen_in_background(sr.Microphone(), callback)
import time
while True: time.sleep(0.1) # we're still listening even though the main thread is blockedFirst, make sure you have all the requirements, listed in the "Requirements" section.
The easiest way to install this is using pip install SpeechRecognition.
Otherwise, download the source distribution from PyPI, and extract the archive.
In the folder, run python setup.py install.
Google Speech Recognition API requires an API key. This library defaults to using one that was reverse engineered out of Chrome, but it is not recommended that you use this API key for anything other than personal or testing purposes.
Instead, it is best to obtain your own API key by following the steps on the API Keys page at the Chromium Developers site.
The first software requirement is Python 2.6, 2.7, or Python 3.3+. This is required to use the library.
If you want to use audio input from microphones, PyAudio is also necessary. If not installed, the library will still work, but Microphone will be undefined.
The official PyAudio builds seem to be broken on Windows. As a result, in the installers folder you will find unofficial PyAudio builds for Windows that actually work. Run the installer corresponding to your Python version to install PyAudio.
On Debain-based distributions such as Ubuntu, you can generally install PyAudio by running sudo apt-get install python-pyaudio python3-pyaudio, which will install it for both Python 2 and Python 3.
On other POSIX-based systems, simply use the packages provided on the downloads page linked above, or compile and install it from source.
A FLAC encoder is required to encode the audio data to send to the API. If using Windows or Linux on an i385-compatible architecture, the encoder is already bundled with this library.
Otherwise, ensure that you have the flac command line tool, which is often available through the system package manager.
In summary, this library requires:
- Python 2.6, 2.7, or 3.3+
- PyAudio (required only if you need to use microphone input)
- FLAC encoder (required only if the system is not x86-based Windows/Linux)
This class is not defined when PyAudio is not available.
Make sure you have PyAudio installed, and make sure you can import it correctly. Test this out by opening a Python console (make sure to use the same version you're running your program with!) and typing in import pyaudio. If you get an error, PyAudio is not installed or not configured correctly.
See the "Requirements" section for more information about installing PyAudio.
The recognizer tries to recognize speech even when I'm not speaking/the recognizer doesn't try to recognize when I'm speaking.
Try adjusting the recognizer_instance.energy_threshold property - a higher value if it tries to recognize when it shouldn't, and a lower value if it doesn't recognize when it should.
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.
Try setting the language code when creating a Recognizer instance. For example, for British English it is better to use Recognizer("en-GB") rather than the default US English.
See the "Reference" section for more information about language codes.
This is available if PyAudio is available, and is undefined otherwise.
Creates a new Microphone instance, which represents a physical microphone on the computer. Subclass of AudioSource.
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 for more details.
This class is to be used with with statements:
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 pointCreates a new WavFile instance, which represents a WAV audio file. Subclass of AudioSource.
If filename_or_fileobject is a string, then it is interpreted as a path to a WAV audio file on the filesystem. Otherwise, filename_or_fileobject should be a file-like object such as io.BytesIO or similar. In either case, the specified file is used as the audio source.
This class is to be used with with statements:
with WavFile("test.wav") as source: # open the WAV file for reading
pass # do things here - `source` is the WavFile instance created aboveCreates a new Recognizer instance, which represents a collection of speech recognition functionality.
The language is determined by language, a standard language code like "en-US" or "en-GB", and defaults to US English. A list of supported language codes can be found here. Basically, language codes can be just the language (en), or a language with a dialect (en-US).
The Google Speech Recognition API key is specified by key. If not specified, it uses a generic key that works out of the box.
WARNING: THE GENERIC KEY IS INTENDED FOR TESTING AND PERSONAL PURPOSES ONLY AND MAY BE REVOKED BY GOOGLE IN THE FUTURE.
If you need to use this module for purposes other than these, please obtain your own API key from Google. See the "Requirements" section for more information.
Represents the energy level threshold for sounds. Values below this threshold are considered silence. Can be changed.
This threshold is associated with the perceived loudness of the sound, but it is a nonlinear relationship. Typical values for a silent room are 0 to 1, and typical values for speaking are between 150 and 3500.
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. For example, a sensitive microphone or microphones in louder rooms might have a baseline energy level of up to 4000:
import speech_recognition as sr
r = sr.Recognizer()
r.energy_threshold = 4000
# rest of your code goes hereThe actual energy threshold you will need depends on your microphone or audio data.
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.
Records up to duration seconds of audio from source (an AudioSource instance) into an AudioData instance, which it returns.
If duration is not specified, then it will record until there is no more audio input.
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 silence or there is no more audio input. The ending silence is not included.
The timeout parameter is the maximum number of seconds that it will wait for a phrase to start before giving up and throwing a TimeoutException exception. If None, it will wait indefinitely.
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 the thread (a threading.Thread instance) immediately, while the background thread continues to run in parallel.
Phrase recognition uses the exact same mechanism as recognizer_instance.listen(source).
The callback parameter is a function that should accept two parameters - the recognizer_instance, and an AudioData instance representing the captured audio. Note that this function will be called from a non-main thread.
Performs speech recognition, using the Google Speech Recognition API, on audio_data (an AudioData instance).
Returns the most likely transcription if show_all is False, otherwise it returns a dict of all possible transcriptions and their confidence levels.
Note: confidence is set to 0 if it isn't given by Google
Also raises a LookupError exception if the speech is unintelligible, a KeyError if the key isn't valid or the quota for the key has been maxed out, and IndexError if there is no internet connection.
Note: KeyError and IndexError is a subclass of LookupError so a LookupError will catch all three types of errors. To catch subclasses you must place their handler clause before LookupError:
import speech_recognition as sr
r = sr.Recognizer()
with sr.WavFile("test.wav") as source: # use "test.wav" as the audio source
audio = r.record(source) # extract audio data from the file
try:
print("You said " + r.recognize(audio)) # recognize speech using Google Speech Recognition
except IndexError: # the API key didn't work
print("No internet connection")
except KeyError: # the API key didn't work
print("Invalid API key or quota maxed out")
except LookupError: # speech is unintelligible
print("Could not understand audio")Base class representing audio sources. Do not instantiate.
Instances of subclasses of this class, such as Microphone and WavFile, can be passed to things like recognizer_instance.record and recognizer_instance.listen.
Storage class for audio data.
Contains the fields rate and data, which represent the framerate and raw audio samples of the audio data, respectively.
Uberi <azhang9@gmail.com> (Anthony Zhang) bobsayshilol arvindch <achembarpu@gmail.com> (Arvind Chembarpu)
Please report bugs and suggestions at the issue tracker!
Copyright 2014-2015 Anthony Zhang (Uberi).
The source code is available online at GitHub.
This program is made available under the 3-clause BSD license. See LICENSE.txt for more information.