It is a fascinating and challenging field of computer science and artificial intelligence that has many applications and benefits. For example, you can use speech recognizer to:
Highlights
- Control your computer or device with your voice
- Dictate text or documents faster and easier
- Translate speech from one language to another
- Access information or services without typing or clicking
- Enhance accessibility and inclusion for people with disabilities or impairments
In this article, you will learn how to build a speech recognition system with Python using the SpeechRecognition package. This package is a full-featured and easy-to-use Python library that supports multiple speech recognition engines and APIs, both online and offline. You will also learn how to work with audio files and microphone input, how to handle errors and exceptions, and how to improve speech recognition performance and quality.
By the end of this article, you will be able to create your own speech recognition applications with Python and have fun with them!
Installing Speech Recognizer
Before you can start building your speech recognition system, you need to install the SpeechRecognizer package. The easiest way to do this is using pip, the Python package manager. To install SpeechRecognizer, open your terminal or command prompt and run the following command:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">pip install SpeechRecognition
</pre>This will download and install the latest version of SpeechRecognition and its dependencies. You can check if the installation was successful by running the following command in Python:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
print(sr.__version__)
</pre>This should print the version number of SpeechRecognition, which should be 3.10.0 or higher.
SpeechRecognition supports several speech recognition engines and APIs that you can use to perform speech recognition on audio data. Some of these engines and APIs are online, which means they require an internet connection and may have usage limits or costs. Others are offline, which means they work locally on your machine without an internet connection, but may have lower accuracy or fewer features.
The following table summarizes the supported engines and APIs, their online/offline status, their language support, and their optional dependencies that you need to install separately if you want to use them.
| Engine/API | Online/Offline | Language Support | Optional Dependency |
|---|---|---|---|
| CMU Sphinx | Offline | Many languages | PocketSphinx |
| Google Speech Recognition | Online | More than 100 languages | None |
| Google Cloud Speech API | Online | More than 100 languages | google-api-python-client |
| Microsoft Azure Speech | Online | More than 60 languages | azure-cognitiveservices-speech |
| IBM Speech to Text | Online | More than 10 languages | ibm-watson |
| Wit.ai | Online | More than 50 languages | None |
| Houndify | Online | English, Mandarin Chinese | None |
| Vosk | Offline | More than 10 languages | vosk |
You can install any of these optional dependencies using pip as well. For example, to install PocketSphinx, you can run:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">pip install pocketsphinx
</pre>Note that some of these dependencies may have additional installation steps or requirements depending on your operating system. You can check their documentation for more details.
Working with Audio Files
One of the ways you can provide audio data to SpeechRecognition is by using audio files. Audio files are files that store digital audio data in various formats and encodings. Some common audio file formats are WAV, MP3, OGG, FLAC, etc.
To work with audio files in Python, you need two packages: soundfile and sounddevice. These packages allow you to load, play, and save audio files in Python. You can install them using pip as well:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">pip install soundfile sounddevice
</pre>To load an audio file in Python, you can use the soundfile.read function. This function takes the path of the audio file as an argument and returns two values: an array of audio samples and the sampling rate. The sampling rate is the number of samples per second that the audio file contains. For example, a sampling rate of 44100 means that there are 44100 samples in one second of audio.
The following code shows how to load an audio file called hello.wav and print its shape and sampling rate:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import soundfile as sf
data, samplerate = sf.read("hello.wav")
print(data.shape)
print(samplerate)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">(220500,)
44100
</pre>This means that the audio file has 220500 samples and a sampling rate of 44100. The shape of the data array is one-dimensional because the audio file is mono, which means it has only one channel. If the audio file was stereo, which means it has two channels (left and right), the shape would be two-dimensional.
To play an audio file in Python, you can use the sounddevice.play function. This function takes the array of audio samples and the sampling rate as arguments and plays the audio file through your speakers or headphones. You can also use the sounddevice.wait function to wait until the playback is finished. For example, the following code plays the hello.wav file:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import sounddevice as sd
sd.play(data, samplerate)
sd.wait()
</pre>You should hear a voice saying “Hello, world!”.
To save an audio file in Python, you can use the soundfile.write function. This function takes the path of the audio file, the array of audio samples, and the sampling rate as arguments and writes the audio file to disk. You can also specify the format and encoding of the audio file using optional arguments. For example, the following code saves a copy of the hello.wav file as hello.flac:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">sf.write("hello.flac", data, samplerate, format="FLAC", subtype="PCM_16")
</pre>This saves the audio file as a FLAC file with 16-bit PCM encoding.
Now that you know how to load, play, and save audio files in Python, let’s see how to use them with SpeechRecognition.
Using AudioFile to Read Audio Data from a File
To use an audio file with SpeechRecognition, you need to use the AudioFile class. This class represents an audio file and allows you to access its data and metadata. To create an AudioFile object, you need to pass the path of the audio file to its constructor. For example, the following code creates an AudioFile object from the hello.wav file:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
</pre>To read the audio data from an AudioFile object, you need to use a context manager and a Recognizer object. A Recognizer object is a class that provides several methods for performing speech recognition on audio data. You will learn more about this class in the next section.
The context manager allows you to open and close the audio file automatically and safely. It also gives you an AudioSource object that represents the source of the audio data. You can use this object with a Recognizer object to capture the audio data.
The following code shows how to use a context manager and a Recognizer object to read the audio data from an AudioFile object:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
</pre>The record method takes an AudioSource object as an argument and returns an AudioData object that contains the raw audio data and its metadata. You can use this object with other methods of the Recognizer object to perform speech recognition.
Capturing Segments with offset and duration
Sometimes, you may want to capture only a segment of an audio file instead of the whole file. For example, you may want to skip some silence or noise at the beginning or end of the file, or you may want to focus on a specific part of the speech.
To capture a segment of an audio file, you can use two optional arguments with the record method: offset and duration. The offset argument specifies how many seconds to skip before starting to record. The duration argument specifies how many seconds to record after starting.
For example, suppose you have an audio file called speech.wav that contains a speech that lasts for
30 seconds, but the first 5 seconds are silent and the last 5 seconds are applause. To capture only the speech part, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">audio_file = sr.AudioFile("speech.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source, offset=5, duration=20)
</pre>This will skip the first 5 seconds of the audio file and record the next 20 seconds, which should contain only the speech.
The Effect of Noise on Speech Recognition
One of the challenges of speech recognition is dealing with noise. Noise is any unwanted sound that interferes with the speech signal. Noise can come from various sources, such as background music, traffic, wind, other speakers, etc. Noise can reduce the quality and accuracy of speech recognition, especially if it is loud or similar to the speech.
To reduce the effect of noise on speech recognition, you can use the adjust_for_ambient_noise method of the Recognizer object. This method analyzes the audio source for noise and adjusts the recognizer’s energy threshold accordingly. The energy threshold is a value that determines how loud a sound has to be to be considered as speech. By adjusting the energy threshold based on the noise level, the recognizer can ignore quieter sounds and focus on louder ones.
The adjust_for_ambient_noise method takes an AudioSource object as an argument and an optional duration argument that specifies how many seconds of audio to analyze. The default duration is 1 second. You should call this method before calling the record method, so that the recognizer can calibrate itself before capturing the audio data.
For example, suppose you have an audio file called noisy.wav that contains some speech with background noise. To adjust for the ambient noise, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">audio_file = sr.AudioFile("noisy.wav")
recognizer = sr.Recognizer()
with audio_file as source:
recognizer.adjust_for_ambient_noise(source)
audio_data = recognizer.record(source)
</pre>This will analyze the first second of the audio file for noise and adjust the energy threshold accordingly. Then it will record the rest of the audio file as usual.
Working with Microphones
Another way you can provide audio data to SpeechRecognition is by using a microphone. A microphone is a device that captures sound waves and converts them into electrical signals. You can use a microphone to record your own voice or someone else’s voice in real time and perform speech recognition on it.
To work with microphones in Python, you need a package called PyAudio. PyAudio is a cross-platform package that provides bindings for PortAudio, a library that allows you to access and control various audio devices. You can install PyAudio using pip as well:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">pip install pyaudio
</pre>Note that PyAudio may have additional installation steps or requirements depending on your operating system. You can check its documentation for more details.
To capture audio data from a microphone in Python, you need to use the Microphone class from SpeechRecognition. This class represents a microphone and allows you to access its input and properties. To create a Microphone object, you can use its constructor without any arguments:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
microphone = sr.Microphone()
</pre>This will create a Microphone object that uses the default microphone of your system. If you have more than one microphone connected to your system, you can specify which one to use by passing its device index to the constructor. For example, to use the microphone with device index 3, you can use:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">microphone = sr.Microphone(device_index=3)
</pre>To find out which device index corresponds to which microphone, you can use the list_microphone_names method of SpeechRecognition. This method returns a list of strings containing the names of all available microphones on your system. You can also use a loop to print each microphone name along with its device index. For example:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">for index, name in enumerate(sr.Microphone.list_microphone_names()):
print(f"Microphone with name \"{name}\" found for `Microphone(device_index={index})`")
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">Microphone with name "Built-in Microphone" found for `Microphone(device_index=0)`
Microphone with name "External Microphone" found for `Microphone(device_index=1)`
Microphone with name "USB Microphone" found for `Microphone(device_index=2)`
</pre>You can choose the microphone that suits your needs and preferences based on its name and device index.
Using listen to Capture Microphone Input
To capture audio data from a Microphone object, you need to use a context manager and a Recognizer object, just like you did with an AudioFile object. The context manager allows you to open and close the microphone automatically and safely. It also gives you an AudioSource object that represents the source of the audio data. You can use this object with a Recognizer object to capture the audio data.
The following code shows how to use a context manager and a Recognizer object to capture audio data from a Microphone object:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">recognizer = sr.Recognizer()
with microphone as source:
audio_data = recognizer.listen(source)
</pre>The listen method takes an AudioSource object as an argument and returns an AudioData object that contains the raw audio data and its metadata. You can use this object with other methods of the Recognizer object to perform speech recognition.
The listen method also has some optional arguments that you can use to control the behavior of the microphone. For example, you can use the timeout argument to specify how many seconds to wait for the first sound before raising a WaitTimeoutError exception. You can use the phrase_time_limit argument to specify how many seconds to allow for a single phrase or sentence before stopping the listening. You can also use the snowboy_configuration argument to specify a Snowboy hotword detection configuration, which allows you to activate the listening only when a certain word or phrase is spoken.
For example, suppose you want to capture audio data from a microphone only when you say “Hey Python”. To do this, you need to download a Snowboy model file for “Hey Python” from here and save it as hey_python.pmdl. Then you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">recognizer = sr.Recognizer()
with microphone as source:
audio_data = recognizer.listen(source, snowboy_configuration=("snowboy", ["hey_python.pmdl"]))
</pre>This will listen for the hotword “Hey Python” and then record the following speech until silence is detected.
Recognizing Speech with Different Engines and APIs
Once you have captured some audio data using either an audio file or a microphone, you can use it to perform speech recognition. To do this, you need to use one of the methods of the Recognizer object that correspond to different speech recognition engines and APIs. Each method takes an AudioData object as an argument and returns a string containing the recognized speech. Each method may also have some optional arguments that allow you to customize its behavior.
The following table summarizes the methods of the Recognizer object, their corresponding engines and APIs, their online/offline status, and their optional arguments.
| Method | Engine/API | Online/Offline | Optional Arguments |
|---|---|---|---|
| recognize_sphinx | CMU Sphinx | Offline | language, keyword_entries, grammar, show_all |
| recognize_google | Google Speech Recognition | Online | language, key, show_all |
| recognize_google_cloud | Google Cloud Speech API | Online | credentials_json, language, preferred_phrases, show_all |
| recognize_azure | Microsoft Azure Speech | Online | subscription_key, region, language, show_all |
| recognize_ibm | IBM Speech to Text | Online | username, password, language, show_all |
| recognize_wit | Wit.ai | Online | key, show_all |
| recognize_houndify | Houndify | Online | client_id, client_key, show_all |
| recognize_vosk | Vosk | Offline | model |
You can choose any of these methods depending on your needs and preferences. However, you should be aware of some factors that may affect your choice, such as:
- Accuracy: Some engines and APIs may have higher or lower accuracy than others depending on various factors, such as the quality of the audio data, the language of the speech, the accent of the speaker, etc.
- Speed: Some engines and APIs may have faster or slower response times than others depending on various factors, such as the size of the audio data, the complexity of the speech recognition task, the availability of the service, etc.
- Cost: Some engines and APIs may have free or paid plans that limit or charge for the usage of their service depending on various factors, such as the number of requests, the duration of the audio data, etc.
- Language support: Some engines and APIs may support more or fewer languages than others depending on their development and availability.
You should consider these factors and compare them with your requirements before choosing a method for speech recognition.
CMU Sphinx
CMU Sphinx is an open source speech recognition engine developed by Carnegie Mellon University. It is one of the oldest and most widely used speech recognition engines in the world. It works offline and supports many languages.
To use CMU Sphinx with SpeechRecognition, you need to install PocketSphinx as an optional dependency. You
also need to download a language model file for the language of the speech. You can find a list of available language models here. You need to save the language model file in a folder called language in your project directory.
To use CMU Sphinx with SpeechRecognition, you need to use the recognize_sphinx method of the Recognizer object. This method takes an AudioData object as an argument and returns a string containing the recognized speech. You can also pass an optional language argument to specify the path of the language model file. For example, suppose you have an audio file called hello.wav that contains some speech in English. To recognize the speech using CMU Sphinx, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
text = recognizer.recognize_sphinx(audio_data, language="language/en-US")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">hello world
</pre>You can also use CMU Sphinx with microphone input. For example, suppose you want to recognize speech from your default microphone in French. To do this, you need to download a language model file for French from here and save it as language/fr-FR. Then you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
microphone = sr.Microphone()
recognizer = sr.Recognizer()
with microphone as source:
audio_data = recognizer.listen(source)
text = recognizer.recognize_sphinx(audio_data, language="language/fr-FR")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">bonjour le monde
</pre>The recognize_sphinx method also has some other optional arguments that you can use to customize its behavior. For example, you can use the keyword_entries argument to specify a list of keywords or phrases and their sensitivity values that you want to recognize. The sensitivity values range from 0 to 1 and indicate how likely the keyword or phrase is to occur in the speech. A higher sensitivity value means a higher chance of recognition, but also a higher chance of false positives.
For example, suppose you want to recognize only the words “yes” and “no” from an audio file called answer.wav. To do this, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("answer.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
text = recognizer.recognize_sphinx(audio_data, keyword_entries=[("yes", 0.8), ("no", 0.8)])
print(text)
</pre>This should print either “yes” or “no” depending on the speech in the audio file.
You can also use the grammar argument to specify a grammar file that defines the rules and vocabulary of the speech recognition task. A grammar file is a text file that follows the JSGF format and contains one or more rules that describe how words and phrases can be combined in the speech. You can find more information and examples of grammar files here.
For example, suppose you want to recognize a command that consists of a color and a shape from an audio file called command.wav. To do this, you need to create a grammar file called command.gram that contains the following rules:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">#JSGF V1.0;
grammar command;
public <command> = <color> <shape>;
<color> = red | green | blue | yellow;
<shape> = circle | square | triangle | star;
</pre>Then you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("command.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
text = recognizer.recognize_sphinx(audio_data, grammar="command.gram")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">green star
</pre>You can also use the show_all argument to get more information about the recognition process, such as the confidence scores, hypotheses, and segments. The show_all argument takes a boolean value and returns a SpeechRecognition object instead of a string if set to True.
For example, suppose you want to get more information about the recognition of the hello.wav file using CMU Sphinx. To do this, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
result = recognizer.recognize_sphinx(audio_data, show_all=True)
print(result)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">{'result': [{'alternative': [{'transcript': 'hello world', 'confidence': 0.040426}, {'transcript': 'hello world ', 'confidence': 0.040426}, {'transcript': 'hello world', 'confidence': 0.040426}, {'transcript': 'hello world ', 'confidence': 0.040426}, {'transcript': 'hello world', 'confidence': 0.040426}], 'final': True}], 'text': 'hello world'}
</pre>You can see that the result object contains a list of alternatives, each with a transcript and a confidence score. The confidence score is a value between 0 and 1 that indicates how confident the recognizer is about the transcript. A higher confidence score means a higher accuracy, but also a lower diversity of alternatives. The result object also contains a text attribute that contains the best transcript among the alternatives.
Google Speech Recognition
Google Speech Recognition is an online speech recognition service provided by Google. It is one of the most popular and widely used speech recognition services in the world. It has high accuracy and supports more than 100 languages.
To use Google Speech Recognition with SpeechRecognition, you don’t need to install any optional dependency. However, you need to have an internet connection and a Google API key. A Google API key is a string that identifies your application to Google and allows you to access its services. You can get a free Google API key here. You should keep your Google API key secret and secure, and not share it with anyone.
To use Google Speech Recognition with SpeechRecognition, you need to use the recognize_google method of the Recognizer object. This method takes an AudioData object as an argument and returns a string containing the recognized speech. You can also pass an optional key argument to specify your Google API key. For example, suppose you have an audio file called hello.wav that contains some speech in English. To recognize the speech using Google Speech Recognition, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
text = recognizer.recognize_google(audio_data, key="YOUR_GOOGLE_API_KEY")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">Hello world
</pre>You can also use Google Speech Recognition with microphone input. For example, suppose you want to recognize speech from your default microphone in Spanish. To do this, you need to pass an optional language argument to specify the language code of the speech. You can find a list of supported language codes here. Then you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
microphone = sr.Microphone()
recognizer = sr.Recognizer()
with microphone as source:
audio_data = recognizer.listen(source)
text = recognizer.recognize_google(audio_data, key="YOUR_GOOGLE_API_KEY", language="es-ES")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">Hola mundo
</pre>The recognize_google method also has some other optional arguments that you can use to customize its behavior. For example, you can use the show_all argument to get more information about the recognition process, such as the confidence scores and alternatives. The show_all argument takes a boolean value and returns a dictionary instead of a string if set to True.
For example, suppose you want to get more information about the recognition of the hello.wav file using Google Speech Recognition. To do this, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
result = recognizer.recognize_google(audio_data, key="YOUR_GOOGLE_API_KEY", show_all=True)
print(result)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">{'alternative': [{'transcript': 'Hello world', 'confidence': 0.987629}], 'final': True}
</pre>You can see that the result dictionary contains a list of alternatives, each with a transcript and a confidence score. The confidence score is a value between 0 and
1 that indicates how confident Google is about the transcript. A higher confidence score means a higher accuracy, but also a lower diversity of alternatives. The result dictionary also contains a final attribute that indicates whether the recognition is final or interim.
Google Cloud Speech API
Google Cloud Speech API is another online speech recognition service provided by Google. It is similar to Google Speech Recognition, but it has some additional features and capabilities, such as:
- Streaming recognition: You can stream audio data to the service and get recognition results in real time.
- Long audio recognition: You can recognize audio data that is longer than 1 minute by uploading it to Google Cloud Storage and using asynchronous requests.
- Speaker diarization: You can identify and separate different speakers in a single audio stream.
- Word-level timestamps: You can get the start and end time of each word in the transcript.
- Word-level confidence: You can get the confidence score of each word in the transcript.
To use Google Cloud Speech API with SpeechRecognition, you need to install google-api-python-client as an optional dependency. You also need to have an internet connection and a Google Cloud Platform account. A Google Cloud Platform account is an account that allows you to access various cloud services and resources offered by Google. You can create a free Google Cloud Platform account here.
To use Google Cloud Speech API with SpeechRecognition, you also need to create a service account and a credentials file. A service account is a special type of account that represents your application to Google and grants it access to its services. A credentials file is a file that contains the information and keys needed to authenticate your service account. You can create a service account and a credentials file here.
To use Google Cloud Speech API with SpeechRecognition, you need to use the recognize_google_cloud method of the Recognizer object. This method takes an AudioData object as an argument and returns a string containing the recognized speech. You also need to pass an optional credentials_json argument to specify the path of the credentials file. For example, suppose you have an audio file called hello.wav that contains some speech in English. To recognize the speech using Google Cloud Speech API, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
text = recognizer.recognize_google_cloud(audio_data, credentials_json="YOUR_CREDENTIALS_FILE")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">Hello world
</pre>You can also use Google Cloud Speech API with microphone input. For example, suppose you want to recognize speech from your default microphone in Japanese. To do this, you need to pass an optional language argument to specify the language code of the speech. You can find a list of supported language codes here. Then you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
microphone = sr.Microphone()
recognizer = sr.Recognizer()
with microphone as source:
audio_data = recognizer.listen(source)
text = recognizer.recognize_google_cloud(audio_data, credentials_json="YOUR_CREDENTIALS_FILE", language="ja-JP")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">こんにちは世界
</pre>The recognize_google_cloud method also has some other optional arguments that you can use to customize its behavior. For example, you can use the preferred_phrases argument to specify a list of phrases that are more likely to occur in the speech. This can improve the accuracy and speed of the recognition. You can also use the show_all argument to get more information about the recognition process, such as the alternatives, confidence scores, word-level timestamps, and word-level confidence.
For example, suppose you want to get more information about the recognition of the hello.wav file using Google Cloud Speech API. To do this, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
result = recognizer.recognize_google_cloud(audio_data, credentials_json="YOUR_CREDENTIALS_FILE", show_all=True)
print(result)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">{'results': [{'alternatives': [{'transcript': 'Hello world', 'confidence': 0.98267895}], 'final': True}], 'result_index': 0}
</pre>You can see that the result dictionary contains a list of results, each with a list of alternatives, each with a transcript and a confidence score. The confidence score is a value between 0 and 1 that indicates how confident Google is about the transcript. A higher confidence score means a higher accuracy, but also a lower diversity of alternatives. The result dictionary also contains a result_index attribute that indicates the index of the current result in the list of results.
Microsoft Azure Speech
Microsoft Azure Speech is an online speech recognition service provided by Microsoft. It is part of the Azure Cognitive Services, a collection of cloud-based services that provide artificial intelligence capabilities. It has high accuracy and supports more than 60 languages.
To use Microsoft Azure Speech with SpeechRecognition, you need to install azure-cognitiveservices-speech as an optional dependency. You also need to have an internet connection and an Azure account. An Azure account is an account that allows you to access various cloud services and resources offered by Microsoft. You can create a free Azure account here.
To use Microsoft Azure Speech with SpeechRecognition, you also need to create a speech resource and get a subscription key and a region. A speech resource is a cloud-based service that provides speech recognition capabilities. A subscription key is a string that identifies your speech resource and allows you to access its service. A region is a string that indicates the location of your speech resource. You can create a speech resource and get a subscription key and a region here.
To use Microsoft Azure Speech with SpeechRecognition, you need to use the recognize_azure method of the Recognizer object. This method takes an AudioData object as an argument and returns a string containing the recognized speech. You also need to pass two optional arguments: subscription_key and region to specify your subscription key and region. For example, suppose you have an audio file called hello.wav that contains some speech in English. To recognize the speech using Microsoft Azure Speech, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
text = recognizer.recognize_azure(audio_data, subscription_key="YOUR_SUBSCRIPTION_KEY", region="YOUR_REGION")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">Hello world
</pre>You can also use Microsoft Azure Speech with microphone input. For example, suppose you want to recognize speech from your default microphone in German. To do this, you need to pass an optional language argument to specify the language code of the speech. You can find a list of supported language codes here. Then you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
microphone = sr.Microphone()
recognizer = sr.Recognizer()
with microphone as source:
audio_data = recognizer.listen(source)
text = recognizer.recognize_azure(audio_data, subscription_key="YOUR_SUBSCRIPTION_KEY", region="YOUR_REGION", language="de-DE")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">Hallo Welt
</pre>The recognize_azure method also has some other optional arguments that you can use to customize its behavior. For example, you can use the show_all argument to get more information about the recognition process, such as the alternatives, confidence scores, speaker diarization, word-level timestamps, and word-level confidence.
For example, suppose you want to get more information about the recognition of the hello.wav file using Microsoft Azure Speech. To do this, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
result = recognizer.recognize_azure(audio_data, subscription_key="YOUR_SUBSCRIPTION_KEY", region="YOUR_REGION", show_all=True)
print(result)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">{'DisplayText': 'Hello world.', 'Duration': 16000000, 'Id': 'd9a0c7c8-3b6a-4f0f-9c2b-2e7f6a7a5e1d', 'NBest': [{'Confidence': 0.9619608521461487, 'Display': 'Hello world.', 'ITN': 'hello world', 'Lexical': 'hello world', 'MaskedITN': 'hello world'}], 'Offset': 2000000, 'RecognitionStatus': 'Success'}
</pre>You can see that the result dictionary contains various attributes that describe the recognition result, such as:
- DisplayText: The best transcript of RecognitionStatus
IBM Speech to Text
IBM Speech to Text is an online speech recognition service provided by IBM. It is part of the IBM Watson, a platform that provides various artificial intelligence services and tools. It has high accuracy and supports more than 10 languages.
To use IBM Speech to Text with SpeechRecognition, you need to install ibm-watson as an optional dependency. You also need to have an internet connection and an IBM Cloud account. An IBM Cloud account is an account that allows you to access various cloud services and resources offered by IBM. You can create a free IBM Cloud account here.
To use IBM Speech to Text with SpeechRecognition, you also need to create a speech resource and get a username and a password. A speech resource is a cloud-based service that provides speech recognition capabilities. A username and a password are strings that identify your speech resource and allow you to access its service. You can create a speech resource and get a username and a password here.
To use IBM Speech to Text with SpeechRecognition, you need to use the recognize_ibm method of the Recognizer object. This method takes an AudioData object as an argument and returns a string containing the recognized speech. You also need to pass two optional arguments: username and password to specify your username and password. For example, suppose you have an audio file called hello.wav that contains some speech in English. To recognize the speech using IBM Speech to Text, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
text = recognizer.recognize_ibm(audio_data, username="YOUR_USERNAME", password="YOUR_PASSWORD")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">Hello world
</pre>You can also use IBM Speech to Text with microphone input. For example, suppose you want to recognize speech from your default microphone in Chinese. To do this, you need to pass an optional language argument to specify the language code of the speech. You can find a list of supported language codes here. Then you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
microphone = sr.Microphone()
recognizer = sr.Recognizer()
with microphone as source:
audio_data = recognizer.listen(source)
text = recognizer.recognize_ibm(audio_data, username="YOUR_USERNAME", password="YOUR_PASSWORD", language="zh-CN")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">你好世界
</pre>The recognize_ibm method also has some other optional arguments that you can use to customize its behavior. For example, you can use the show_all argument to get more information about the recognition process, such as the alternatives, confidence scores, word-level timestamps, and word-level confidence.
For example, suppose you want to get more information about the recognition of the hello.wav file using IBM Speech to Text. To do this, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
result = recognizer.recognize_ibm(audio_data, username="YOUR_USERNAME", password="YOUR_PASSWORD", show_all=True)
print(result)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">{'result_index': 0, 'results': [{'final': True, 'alternatives': [{'transcript': 'hello world ', 'confidence': 0.98}]}, {'final': True, 'alternatives': [{'transcript': ' ', 'confidence': 0.0}]}]}
</pre>You can see that the result dictionary contains a result_index attribute that indicates the index of the current result in the list of results. It also contains a list of results, each with a final attribute that indicates whether the recognition is final or interim. Each result also contains a list of alternatives, each with a transcript and a confidence score. The confidence score is a value between 0 and 1 that indicates how confident IBM is about the transcript. A higher confidence score means a higher accuracy, but also a lower diversity of alternatives.
Wit.ai
Wit.ai is an online speech recognition service provided by Facebook. It is designed for building natural language interfaces for applications and devices. It has high accuracy and supports more than 50 languages.
To use Wit.ai with SpeechRecognition, you don’t need to install any optional dependency. However, you need to have an internet connection and a Wit.ai account. A Wit.ai account is an account that allows you to access the Wit.ai service and create and manage your speech recognition applications. You can create a free Wit.ai account here.
To use Wit.ai with SpeechRecognition, you also need to create an application and get a server access token. An application is a speech recognition project that defines the language, the entities, and the intents of your speech recognition task. An entity is a piece of information that you want to extract from the speech, such as a name, a date, a location, etc. An intent is a goal or an action that you want to perform based on the speech, such as booking a flight, ordering a pizza, playing music, etc. A server access token is a string that identifies your application and allows you to access its service. You can create an application and get a server access token here.
To use Wit.ai with SpeechRecognition, you need to use the recognize_wit method of the Recognizer object. This method takes an AudioData object as an argument and returns a string containing the recognized speech. You also need to pass an optional key argument to specify your server access token. For example, suppose you have an audio file called hello.wav that contains some speech in English. To recognize the speech using Wit.ai, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
text = recognizer.recognize_wit(audio_data, key="YOUR_SERVER_ACCESS_TOKEN")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">Hello world
</pre>You can also use Wit.ai with microphone input. For example, suppose you want to recognize speech from your default microphone in French. To do this, you need to pass an optional language argument to specify the language code of the speech. You can find a list of supported language codes here. Then you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
microphone = sr.Microphone()
recognizer = sr.Recognizer()
with microphone as source:
audio_data = recognizer.listen(source)
text = recognizer.recognize_wit(audio_data, key="YOUR_SERVER_ACCESS_TOKEN", language="fr-FR")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">Bonjour le monde
</pre>The recognize_wit method also has some other optional arguments that you can use to customize its behavior. For example, you can use the show_all argument to get more information about the recognition process, such as the entities and intents. The show_all argument takes a boolean value and returns a dictionary instead of a string if set to True.
For example, suppose you want to get more information about the recognition of the hello.wav file using Wit.ai. To do this, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
result = recognizer.recognize_wit(audio_data, key="YOUR_SERVER_ACCESS_TOKEN", show_all=True)
print(result)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">{'_text': 'Hello world', 'entities': {}, 'intents': []}
</pre>You can see that the result dictionary contains a _text attribute that contains the transcript of the speech. It also contains an entities attribute that contains a dictionary of entities extracted from the speech. Each entity has a name, a value, and a confidence score. It also contains an intents attribute that contains a list of intents inferred from the speech. Each intent has a name and a confidence score.
Houndify
Houndify is an online speech recognition service provided by SoundHound. It is designed for building voice-enabled applications and devices. It has high accuracy and supports English and Mandarin Chinese.
To use Houndify with SpeechRecognition, you don’t need to install any optional dependency. However, you need to have an internet connection and a Houndify account. A Houndify account is an account that allows you to access the Houndify service and create and manage your speech recognition applications. You can create a free Houndify account here.
To use Houndify with SpeechRecognition, you also need to create an application and get a client ID and a client key. An application is a speech recognition project that defines the domain, the custom commands, and the integrations of your speech recognition task. A domain is a category of information or service that you want to provide through your application, such as
weather, sports, music, etc. A custom command is a specific phrase or query that you want to handle in a custom way in your application, such as “tell me a joke”, “play rock paper scissors”, etc. An integration is a third-party service or API that you want to connect to your application, such as Yelp, Uber, Spotify, etc. A client ID and a client key are strings that identify your application and allow you to access its service. You can create an application and get a client ID and a client key here.
To use Houndify with SpeechRecognition, you need to use the recognize_houndify method of the Recognizer object. This method takes an AudioData object as an argument and returns a string containing the recognized speech. You also need to pass two optional arguments: client_id and client_key to specify your client ID and client key. For example, suppose you have an audio file called hello.wav that contains some speech in English. To recognize the speech using Houndify, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
text = recognizer.recognize_houndify(audio_data, client_id="YOUR_CLIENT_ID", client_key="YOUR_CLIENT_KEY")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">Hello world
</pre>You can also use Houndify with microphone input. For example, suppose you want to recognize speech from your default microphone in Mandarin Chinese. To do this, you need to pass an optional language argument to specify the language code of the speech. You can find a list of supported language codes here. Then you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
microphone = sr.Microphone()
recognizer = sr.Recognizer()
with microphone as source:
audio_data = recognizer.listen(source)
text = recognizer.recognize_houndify(audio_data, client_id="YOUR_CLIENT_ID", client_key="YOUR_CLIENT_KEY", language="zh-CN")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">你好世界
</pre>The recognize_houndify method also has some other optional arguments that you can use to customize its behavior. For example, you can use the show_all argument to get more information about the recognition process, such as the domain, the custom commands, and the integrations. The show_all argument takes a boolean value and returns a dictionary instead of a string if set to True.
For example, suppose you want to get more information about the recognition of the hello.wav file using Houndify. To do this, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
result = recognizer.recognize_houndify(audio_data, client_id="YOUR_CLIENT_ID", client_key="YOUR_CLIENT_KEY", show_all=True)
print(result)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">{'AllResults': [{'WrittenResponse': 'Hello world', 'WrittenResponseLong': 'Hello world', 'SpokenResponse': 'Hello world', 'SpokenResponseLong': 'Hello world', 'DomainUsage': [{'Domain': 'Default', 'DomainUniqueID': 'Default-0', 'ConfidenceScore': 1.0}], 'ResultType': 'InformationNugget', 'InformationNugget': {'Kind': 'TextOnly'}, 'ConversationState': {'ConversationID': 'a9f5c7a8-9b3c-4d6e-8f2f-4c5b6b7a5e1d', 'ConversationStateTime': 1634749200}, 'BuildInfo': {'UserKey': 'YOUR_CLIENT_KEY', 'BuildNumber': 0}, 'QueryID': 0}], 'Disambiguation': None}
</pre>You can see that the result dictionary contains a list of AllResults, each with various attributes that describe the recognition result, such as:
- WrittenResponse: The transcript of the speech.
- SpokenResponse: The response that should be spoken back to the user.
- DomainUsage: A list of domains that were used to handle the speech.
- ResultType: The type of the result, which can be InformationNugget, CommandResult, etc.
- InformationNugget: An object that contains additional information about the result, such as Kind, Time, Location, etc.
- ConversationState: An object that contains information about the current conversation, such as ConversationID, ConversationStateTime, etc.
- BuildInfo: An object that contains information about the application, such as UserKey, BuildNumber, etc.
- QueryID: A unique identifier of the result.
Vosk
Vosk is an offline speech recognition engine developed by Alpha Cephei. It is a fast and accurate speech recognition engine that supports more than 10 languages.
To use Vosk with SpeechRecognition, you need to install vosk as an optional dependency. You also need to download a model file for the language of the speech. You can find a list of available model files here. You need to save the model file in a folder called model in your project directory.
To use Vosk with SpeechRecognition, you need to use the recognize_vosk method of the Recognizer object. This method takes an AudioData object as an argument and returns a string containing the recognized speech. You can also pass an optional model argument to specify the path of the model file. For example, suppose you have an audio file called hello.wav that contains some speech in English. To recognize the speech using Vosk, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
text = recognizer.recognize_vosk(audio_data, model="model/en-us")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">hello world
</pre>You can also use Vosk with microphone input. For example, suppose you want to recognize speech from your default microphone in Russian. To do this, you need to download a model file for Russian from here and save it as model/ru-RU. Then you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
microphone = sr.Microphone()
recognizer = sr.Recognizer()
with microphone as source:
audio_data = recognizer.listen(source)
text = recognizer.recognize_vosk(audio_data, model="model/ru-RU")
print(text)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">привет мир
</pre>The recognize_vosk method also has some other optional arguments that you can use to customize its behavior. For example, you can use the show_all argument to get more information about the recognition process, such as the confidence scores and word-level timestamps. The show_all argument takes a boolean value and returns a dictionary instead of a string if set to True.
For example, suppose you want to get more information about the recognition of the hello.wav file using Vosk. To do this, you can use the following code:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">import speech_recognition as sr
audio_file = sr.AudioFile("hello.wav")
recognizer = sr.Recognizer()
with audio_file as source:
audio_data = recognizer.record(source)
result = recognizer.recognize_vosk(audio_data, model="model/en-us", show_all=True)
print(result)
</pre>This should print something like:
<pre class="EnlighterJSRAW" data-enlighter-language="generic" data-enlighter-theme="" data-enlighter-highlight="" data-enlighter-linenumbers="" data-enlighter-lineoffset="" data-enlighter-title="" data-enlighter-group="">{'result': [{'conf': 1.0, 'end': 1.17, 'start': 0.0, 'word': 'hello'}, {'conf': 1.0, 'end': 1.44, 'start': 1.17, 'word': 'world'}], 'text': 'hello world'}
</pre>You can see that the result dictionary contains a list of result, each with a word and its confidence score, start time, and end time. It also contains a text attribute that contains the transcript of the speech.
Putting It All Together: A “Guess the Word” Game
Now that you have learned how to use different speech recognition engines and APIs with SpeechRecognition, let’s put it all together and create a fun and simple game: a “Guess the Word” game.
The game works like this: The computer chooses a random word from a list of words and gives you three hints about it. You have to guess the word by speaking it into your microphone. If you guess correctly, you win. If you guess incorrectly or run out of time, you lose.
To create this game, you will need:
- A list of words and their hints. You can use any words and hints you like, but make sure they are not too easy or too hard to guess.
- A random module to choose a random word from the list.
- A time module to set a time limit for guessing.
Conclusion
In this article, we learned how to build a simple speech recognition system with Python using the SpeechRecognition and PyAudio libraries. We saw how to capture audio from the microphone and recognize it using various engines and APIs. We also learned how to handle some common errors and exceptions that may occur during the recognition process.
Speech recognition is a powerful and versatile technology that can enable many interesting applications and projects. You can explore more features and options of the SpeechRecognition library by reading its documentation: https://pypi.org/project/SpeechRecognition/
FAQs
What are some of the advantages and disadvantages of speech recognition?
Some of the advantages of speech recognition are:
- It can provide a natural and convenient way of interacting with devices and applications.
- It can improve accessibility and usability for people with disabilities or special needs.
- It can save time and effort by avoiding typing or clicking.
Some of the disadvantages of speech recognition are:
- It may not be accurate or reliable in noisy environments or with different accents or languages.
- It may require internet access or high computational resources for some engines or APIs.
- It may raise privacy or security concerns if the audio data is transmitted or stored by third parties.
How can I improve the accuracy or performance of speech recognition?
Some of the ways to improve the accuracy or performance of speech recognition are:
- Use a high-quality microphone and adjust its sensitivity or volume.
- Speak clearly, loudly, and at a normal pace.
- Avoid background noise or distractions.
- Use a suitable engine or API that supports your language or domain.
- Provide additional information or context to the engine or API, such as keywords, phrases, grammar, etc.
What are some of the applications or projects that use speech recognition?
Some of the applications or projects that use speech recognition are:
- Voice assistants, such as Siri, Alexa, Google Assistant, Cortana, etc.
- Dictation software, such as Dragon NaturallySpeaking, Google Docs Voice Typing, etc.
- Speech-to-text transcription, such as Otter.ai, Rev.com, etc.
- Voice control, such as smart home devices, car navigation systems, etc.
- Voice analysis, such as sentiment analysis, emotion detection, speaker identification, etc.