python speech to text

• How to Convert Speech to Text in Python

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Speech recognition is the ability of computer software to identify words and phrases in spoken language and convert them to human-readable text. In this tutorial, you will learn how you can convert speech to text in Python using the SpeechRecognition library .

As a result, we do not need to build any machine learning model from scratch, this library provides us with convenient wrappers for various well-known public speech recognition APIs (such as Google Cloud Speech API, IBM Speech To Text, etc.).

Note that if you do not want to use APIs, and directly perform inference on machine learning models instead, then definitely check this tutorial , in which I'll show you how you can use the current state-of-the-art machine learning model to perform speech recognition in Python.

Also, if you want other methods to do ASR, then check this speech recognition comprehensive tutorial .

Learn also:   How to Translate Text in Python .

Getting Started

Alright, let's get started, installing the library using pip :

Okay, open up a new Python file and import it:

The nice thing about this library is it supports several recognition engines:

• CMU Sphinx (offline)
• Google Speech Recognition
• Google Cloud Speech API
• Microsoft Bing Voice Recognition
• Houndify API
• IBM Speech To Text
• Snowboy Hotword Detection (offline)

We gonna use Google Speech Recognition here, as it's straightforward and doesn't require any API key.

Transcribing an Audio File

Make sure you have an audio file in the current directory that contains English speech (if you want to follow along with me, get the audio file here ):

This file was grabbed from the LibriSpeech dataset, but you can use any audio WAV file you want, just change the name of the file, let's initialize our speech recognizer:

The below code is responsible for loading the audio file, and converting the speech into text using Google Speech Recognition:

This will take a few seconds to finish, as it uploads the file to Google and grabs the output, here is my result:

The above code works well for small or medium size audio files. In the next section, we gonna write code for large files.

Transcribing Large Audio Files

If you want to perform speech recognition of a long audio file, then the below function handles that quite well:

Note: You need to install Pydub using pip for the above code to work.

The above function uses split_on_silence() function from pydub.silence module to split audio data into chunks on silence. The min_silence_len parameter is the minimum length of silence in milliseconds to be used for a split.

silence_thresh is the threshold in which anything quieter than this will be considered silence, I have set it to the average dBFS minus 14 , keep_silence argument is the amount of silence to leave at the beginning and the end of each chunk detected in milliseconds.

These parameters won't be perfect for all sound files, try to experiment with these parameters with your large audio needs.

After that, we iterate over all chunks and convert each speech audio into text, and then add them up altogether, here is an example run:

Note : You can get 7601-291468-0006.wav file here .

So, this function automatically creates a folder for us and puts the chunks of the original audio file we specified, and then it runs speech recognition on all of them.

In case you want to split the audio file into fixed intervals, we can use the below function instead:

The above function splits the large audio file into chunks of 5 minutes. You can change the minutes parameter to fit your needs. Since my audio file isn't that large, I'm trying to split it into chunks of 10 seconds:

Reading from the Microphone

This requires PyAudio to be installed on your machine, here is the installation process depending on your operating system:

You can just pip install it:

You need to first install the dependencies:

You need to first install portaudio , then you can just pip install it:

Now let's use our microphone to convert our speech:

This will hear from your microphone for 5 seconds and then try to convert that speech into text!

It is pretty similar to the previous code, but we are using the Microphone() object here to read the audio from the default microphone, and then we used the duration parameter in the record() function to stop reading after 5 seconds and then upload the audio data to Google to get the output text.

You can also use the offset parameter in the record() function to start recording after offset seconds.

Also, you can recognize different languages by passing the language parameter to the recognize_google() function. For instance, if you want to recognize Spanish speech, you would use:

Check out supported languages in this StackOverflow answer .

As you can see, it is pretty easy and simple to use this library for converting speech to text. This library is widely used out there in the wild. Check the official documentation .

If you want to convert text to speech in Python as well, check this tutorial .

Read Also: How to Recognize Optical Characters in Images in Python .

Happy Coding ♥

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The Ultimate Guide To Speech Recognition With Python

Table of Contents

How Speech Recognition Works – An Overview

Picking a python speech recognition package, installing speechrecognition, the recognizer class, supported file types, using record() to capture data from a file, capturing segments with offset and duration, the effect of noise on speech recognition, installing pyaudio, the microphone class, using listen() to capture microphone input, handling unrecognizable speech, putting it all together: a “guess the word” game, recap and additional resources, appendix: recognizing speech in languages other than english.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Speech Recognition With Python

Have you ever wondered how to add speech recognition to your Python project? If so, then keep reading! It’s easier than you might think.

Far from a being a fad, the overwhelming success of speech-enabled products like Amazon Alexa has proven that some degree of speech support will be an essential aspect of household tech for the foreseeable future. If you think about it, the reasons why are pretty obvious. Incorporating speech recognition into your Python application offers a level of interactivity and accessibility that few technologies can match.

The accessibility improvements alone are worth considering. Speech recognition allows the elderly and the physically and visually impaired to interact with state-of-the-art products and services quickly and naturally—no GUI needed!

Best of all, including speech recognition in a Python project is really simple. In this guide, you’ll find out how. You’ll learn:

• How speech recognition works,
• What packages are available on PyPI; and
• How to install and use the SpeechRecognition package—a full-featured and easy-to-use Python speech recognition library.

In the end, you’ll apply what you’ve learned to a simple “Guess the Word” game and see how it all comes together.

Free Bonus: Click here to download a Python speech recognition sample project with full source code that you can use as a basis for your own speech recognition apps.

Before we get to the nitty-gritty of doing speech recognition in Python, let’s take a moment to talk about how speech recognition works. A full discussion would fill a book, so I won’t bore you with all of the technical details here. In fact, this section is not pre-requisite to the rest of the tutorial. If you’d like to get straight to the point, then feel free to skip ahead.

Speech recognition has its roots in research done at Bell Labs in the early 1950s. Early systems were limited to a single speaker and had limited vocabularies of about a dozen words. Modern speech recognition systems have come a long way since their ancient counterparts. They can recognize speech from multiple speakers and have enormous vocabularies in numerous languages.

The first component of speech recognition is, of course, speech. Speech must be converted from physical sound to an electrical signal with a microphone, and then to digital data with an analog-to-digital converter. Once digitized, several models can be used to transcribe the audio to text.

Most modern speech recognition systems rely on what is known as a Hidden Markov Model (HMM). This approach works on the assumption that a speech signal, when viewed on a short enough timescale (say, ten milliseconds), can be reasonably approximated as a stationary process—that is, a process in which statistical properties do not change over time.

In a typical HMM, the speech signal is divided into 10-millisecond fragments. The power spectrum of each fragment, which is essentially a plot of the signal’s power as a function of frequency, is mapped to a vector of real numbers known as cepstral coefficients. The dimension of this vector is usually small—sometimes as low as 10, although more accurate systems may have dimension 32 or more. The final output of the HMM is a sequence of these vectors.

To decode the speech into text, groups of vectors are matched to one or more phonemes —a fundamental unit of speech. This calculation requires training, since the sound of a phoneme varies from speaker to speaker, and even varies from one utterance to another by the same speaker. A special algorithm is then applied to determine the most likely word (or words) that produce the given sequence of phonemes.

One can imagine that this whole process may be computationally expensive. In many modern speech recognition systems, neural networks are used to simplify the speech signal using techniques for feature transformation and dimensionality reduction before HMM recognition. Voice activity detectors (VADs) are also used to reduce an audio signal to only the portions that are likely to contain speech. This prevents the recognizer from wasting time analyzing unnecessary parts of the signal.

Fortunately, as a Python programmer, you don’t have to worry about any of this. A number of speech recognition services are available for use online through an API, and many of these services offer Python SDKs .

A handful of packages for speech recognition exist on PyPI. A few of them include:

• google-cloud-speech
• pocketsphinx
• SpeechRecognition
• watson-developer-cloud

Some of these packages—such as wit and apiai—offer built-in features, like natural language processing for identifying a speaker’s intent, which go beyond basic speech recognition. Others, like google-cloud-speech, focus solely on speech-to-text conversion.

There is one package that stands out in terms of ease-of-use: SpeechRecognition.

Recognizing speech requires audio input, and SpeechRecognition makes retrieving this input really easy. Instead of having to build scripts for accessing microphones and processing audio files from scratch, SpeechRecognition will have you up and running in just a few minutes.

The SpeechRecognition library acts as a wrapper for several popular speech APIs and is thus extremely flexible. One of these—the Google Web Speech API—supports a default API key that is hard-coded into the SpeechRecognition library. That means you can get off your feet without having to sign up for a service.

The flexibility and ease-of-use of the SpeechRecognition package make it an excellent choice for any Python project. However, support for every feature of each API it wraps is not guaranteed. You will need to spend some time researching the available options to find out if SpeechRecognition will work in your particular case.

So, now that you’re convinced you should try out SpeechRecognition, the next step is getting it installed in your environment.

SpeechRecognition is compatible with Python 2.6, 2.7 and 3.3+, but requires some additional installation steps for Python 2 . For this tutorial, I’ll assume you are using Python 3.3+.

You can install SpeechRecognition from a terminal with pip:

Once installed, you should verify the installation by opening an interpreter session and typing:

Note: The version number you get might vary. Version 3.8.1 was the latest at the time of writing.

Go ahead and keep this session open. You’ll start to work with it in just a bit.

SpeechRecognition will work out of the box if all you need to do is work with existing audio files. Specific use cases, however, require a few dependencies. Notably, the PyAudio package is needed for capturing microphone input.

You’ll see which dependencies you need as you read further. For now, let’s dive in and explore the basics of the package.

All of the magic in SpeechRecognition happens with the Recognizer class.

The primary purpose of a Recognizer instance is, of course, to recognize speech. Each instance comes with a variety of settings and functionality for recognizing speech from an audio source.

Creating a Recognizer instance is easy. In your current interpreter session, just type:

Each Recognizer instance has seven methods for recognizing speech from an audio source using various APIs. These are:

• recognize_bing() : Microsoft Bing Speech
• recognize_google() : Google Web Speech API
• recognize_google_cloud() : Google Cloud Speech - requires installation of the google-cloud-speech package
• recognize_houndify() : Houndify by SoundHound
• recognize_ibm() : IBM Speech to Text
• recognize_sphinx() : CMU Sphinx - requires installing PocketSphinx
• recognize_wit() : Wit.ai

Of the seven, only recognize_sphinx() works offline with the CMU Sphinx engine. The other six all require an internet connection.

A full discussion of the features and benefits of each API is beyond the scope of this tutorial. Since SpeechRecognition ships with a default API key for the Google Web Speech API, you can get started with it right away. For this reason, we’ll use the Web Speech API in this guide. The other six APIs all require authentication with either an API key or a username/password combination. For more information, consult the SpeechRecognition docs .

Caution: The default key provided by SpeechRecognition is for testing purposes only, and Google may revoke it at any time . It is not a good idea to use the Google Web Speech API in production. Even with a valid API key, you’ll be limited to only 50 requests per day, and there is no way to raise this quota . Fortunately, SpeechRecognition’s interface is nearly identical for each API, so what you learn today will be easy to translate to a real-world project.

Each recognize_*() method will throw a speech_recognition.RequestError exception if the API is unreachable. For recognize_sphinx() , this could happen as the result of a missing, corrupt or incompatible Sphinx installation. For the other six methods, RequestError may be thrown if quota limits are met, the server is unavailable, or there is no internet connection.

Ok, enough chit-chat. Let’s get our hands dirty. Go ahead and try to call recognize_google() in your interpreter session.

What happened?

You probably got something that looks like this:

You might have guessed this would happen. How could something be recognized from nothing?

All seven recognize_*() methods of the Recognizer class require an audio_data argument. In each case, audio_data must be an instance of SpeechRecognition’s AudioData class.

There are two ways to create an AudioData instance: from an audio file or audio recorded by a microphone. Audio files are a little easier to get started with, so let’s take a look at that first.

Working With Audio Files

Before you continue, you’ll need to download an audio file. The one I used to get started, “harvard.wav,” can be found here . Make sure you save it to the same directory in which your Python interpreter session is running.

SpeechRecognition makes working with audio files easy thanks to its handy AudioFile class. This class can be initialized with the path to an audio file and provides a context manager interface for reading and working with the file’s contents.

Currently, SpeechRecognition supports the following file formats:

• WAV: must be in PCM/LPCM format
• FLAC: must be native FLAC format; OGG-FLAC is not supported

If you are working on x-86 based Linux, macOS or Windows, you should be able to work with FLAC files without a problem. On other platforms, you will need to install a FLAC encoder and ensure you have access to the flac command line tool. You can find more information here if this applies to you.

Type the following into your interpreter session to process the contents of the “harvard.wav” file:

The context manager opens the file and reads its contents, storing the data in an AudioFile instance called source. Then the record() method records the data from the entire file into an AudioData instance. You can confirm this by checking the type of audio :

You can now invoke recognize_google() to attempt to recognize any speech in the audio. Depending on your internet connection speed, you may have to wait several seconds before seeing the result.

Congratulations! You’ve just transcribed your first audio file!

If you’re wondering where the phrases in the “harvard.wav” file come from, they are examples of Harvard Sentences. These phrases were published by the IEEE in 1965 for use in speech intelligibility testing of telephone lines. They are still used in VoIP and cellular testing today.

The Harvard Sentences are comprised of 72 lists of ten phrases. You can find freely available recordings of these phrases on the Open Speech Repository website. Recordings are available in English, Mandarin Chinese, French, and Hindi. They provide an excellent source of free material for testing your code.

What if you only want to capture a portion of the speech in a file? The record() method accepts a duration keyword argument that stops the recording after a specified number of seconds.

For example, the following captures any speech in the first four seconds of the file:

The record() method, when used inside a with block, always moves ahead in the file stream. This means that if you record once for four seconds and then record again for four seconds, the second time returns the four seconds of audio after the first four seconds.

Notice that audio2 contains a portion of the third phrase in the file. When specifying a duration, the recording might stop mid-phrase—or even mid-word—which can hurt the accuracy of the transcription. More on this in a bit.

In addition to specifying a recording duration, the record() method can be given a specific starting point using the offset keyword argument. This value represents the number of seconds from the beginning of the file to ignore before starting to record.

To capture only the second phrase in the file, you could start with an offset of four seconds and record for, say, three seconds.

The offset and duration keyword arguments are useful for segmenting an audio file if you have prior knowledge of the structure of the speech in the file. However, using them hastily can result in poor transcriptions. To see this effect, try the following in your interpreter:

By starting the recording at 4.7 seconds, you miss the “it t” portion a the beginning of the phrase “it takes heat to bring out the odor,” so the API only got “akes heat,” which it matched to “Mesquite.”

Similarly, at the end of the recording, you captured “a co,” which is the beginning of the third phrase “a cold dip restores health and zest.” This was matched to “Aiko” by the API.

There is another reason you may get inaccurate transcriptions. Noise! The above examples worked well because the audio file is reasonably clean. In the real world, unless you have the opportunity to process audio files beforehand, you can not expect the audio to be noise-free.

Noise is a fact of life. All audio recordings have some degree of noise in them, and un-handled noise can wreck the accuracy of speech recognition apps.

To get a feel for how noise can affect speech recognition, download the “jackhammer.wav” file here . As always, make sure you save this to your interpreter session’s working directory.

This file has the phrase “the stale smell of old beer lingers” spoken with a loud jackhammer in the background.

What happens when you try to transcribe this file?

So how do you deal with this? One thing you can try is using the adjust_for_ambient_noise() method of the Recognizer class.

That got you a little closer to the actual phrase, but it still isn’t perfect. Also, “the” is missing from the beginning of the phrase. Why is that?

The adjust_for_ambient_noise() method reads the first second of the file stream and calibrates the recognizer to the noise level of the audio. Hence, that portion of the stream is consumed before you call record() to capture the data.

You can adjust the time-frame that adjust_for_ambient_noise() uses for analysis with the duration keyword argument. This argument takes a numerical value in seconds and is set to 1 by default. Try lowering this value to 0.5.

Well, that got you “the” at the beginning of the phrase, but now you have some new issues! Sometimes it isn’t possible to remove the effect of the noise—the signal is just too noisy to be dealt with successfully. That’s the case with this file.

If you find yourself running up against these issues frequently, you may have to resort to some pre-processing of the audio. This can be done with audio editing software or a Python package (such as SciPy ) that can apply filters to the files. A detailed discussion of this is beyond the scope of this tutorial—check out Allen Downey’s Think DSP book if you are interested. For now, just be aware that ambient noise in an audio file can cause problems and must be addressed in order to maximize the accuracy of speech recognition.

When working with noisy files, it can be helpful to see the actual API response. Most APIs return a JSON string containing many possible transcriptions. The recognize_google() method will always return the most likely transcription unless you force it to give you the full response.

You can do this by setting the show_all keyword argument of the recognize_google() method to True.

As you can see, recognize_google() returns a dictionary with the key 'alternative' that points to a list of possible transcripts. The structure of this response may vary from API to API and is mainly useful for debugging.

By now, you have a pretty good idea of the basics of the SpeechRecognition package. You’ve seen how to create an AudioFile instance from an audio file and use the record() method to capture data from the file. You learned how to record segments of a file using the offset and duration keyword arguments of record() , and you experienced the detrimental effect noise can have on transcription accuracy.

Now for the fun part. Let’s transition from transcribing static audio files to making your project interactive by accepting input from a microphone.

Working With Microphones

To access your microphone with SpeechRecognizer, you’ll have to install the PyAudio package . Go ahead and close your current interpreter session, and let’s do that.

The process for installing PyAudio will vary depending on your operating system.

Debian Linux

If you’re on Debian-based Linux (like Ubuntu) you can install PyAudio with apt :

Once installed, you may still need to run pip install pyaudio , especially if you are working in a virtual environment .

For macOS, first you will need to install PortAudio with Homebrew, and then install PyAudio with pip :

On Windows, you can install PyAudio with pip :

Testing the Installation

Once you’ve got PyAudio installed, you can test the installation from the console.

Make sure your default microphone is on and unmuted. If the installation worked, you should see something like this:

Shell A moment of silence, please... Set minimum energy threshold to 600.4452854381937 Say something! Copied! Go ahead and play around with it a little bit by speaking into your microphone and seeing how well SpeechRecognition transcribes your speech.

Note: If you are on Ubuntu and get some funky output like ‘ALSA lib … Unknown PCM’, refer to this page for tips on suppressing these messages. This output comes from the ALSA package installed with Ubuntu—not SpeechRecognition or PyAudio. In all reality, these messages may indicate a problem with your ALSA configuration, but in my experience, they do not impact the functionality of your code. They are mostly a nuisance.

Open up another interpreter session and create an instance of the recognizer class.

Now, instead of using an audio file as the source, you will use the default system microphone. You can access this by creating an instance of the Microphone class.

If your system has no default microphone (such as on a Raspberry Pi ), or you want to use a microphone other than the default, you will need to specify which one to use by supplying a device index. You can get a list of microphone names by calling the list_microphone_names() static method of the Microphone class.

Note that your output may differ from the above example.

The device index of the microphone is the index of its name in the list returned by list_microphone_names(). For example, given the above output, if you want to use the microphone called “front,” which has index 3 in the list, you would create a microphone instance like this:

For most projects, though, you’ll probably want to use the default system microphone.

Now that you’ve got a Microphone instance ready to go, it’s time to capture some input.

Just like the AudioFile class, Microphone is a context manager. You can capture input from the microphone using the listen() method of the Recognizer class inside of the with block. This method takes an audio source as its first argument and records input from the source until silence is detected.

Once you execute the with block, try speaking “hello” into your microphone. Wait a moment for the interpreter prompt to display again. Once the “>>>” prompt returns, you’re ready to recognize the speech.

If the prompt never returns, your microphone is most likely picking up too much ambient noise. You can interrupt the process with Ctrl + C to get your prompt back.

To handle ambient noise, you’ll need to use the adjust_for_ambient_noise() method of the Recognizer class, just like you did when trying to make sense of the noisy audio file. Since input from a microphone is far less predictable than input from an audio file, it is a good idea to do this anytime you listen for microphone input.

After running the above code, wait a second for adjust_for_ambient_noise() to do its thing, then try speaking “hello” into the microphone. Again, you will have to wait a moment for the interpreter prompt to return before trying to recognize the speech.

Recall that adjust_for_ambient_noise() analyzes the audio source for one second. If this seems too long to you, feel free to adjust this with the duration keyword argument.

The SpeechRecognition documentation recommends using a duration no less than 0.5 seconds. In some cases, you may find that durations longer than the default of one second generate better results. The minimum value you need depends on the microphone’s ambient environment. Unfortunately, this information is typically unknown during development. In my experience, the default duration of one second is adequate for most applications.

Try typing the previous code example in to the interpeter and making some unintelligible noises into the microphone. You should get something like this in response:

Audio that cannot be matched to text by the API raises an UnknownValueError exception. You should always wrap calls to the API with try and except blocks to handle this exception .

Note : You may have to try harder than you expect to get the exception thrown. The API works very hard to transcribe any vocal sounds. Even short grunts were transcribed as words like “how” for me. Coughing, hand claps, and tongue clicks would consistently raise the exception.

Now that you’ve seen the basics of recognizing speech with the SpeechRecognition package let’s put your newfound knowledge to use and write a small game that picks a random word from a list and gives the user three attempts to guess the word.

Here is the full script:

Let’s break that down a little bit.

The recognize_speech_from_mic() function takes a Recognizer and Microphone instance as arguments and returns a dictionary with three keys. The first key, "success" , is a boolean that indicates whether or not the API request was successful. The second key, "error" , is either None or an error message indicating that the API is unavailable or the speech was unintelligible. Finally, the "transcription" key contains the transcription of the audio recorded by the microphone.

The function first checks that the recognizer and microphone arguments are of the correct type, and raises a TypeError if either is invalid:

The listen() method is then used to record microphone input:

The adjust_for_ambient_noise() method is used to calibrate the recognizer for changing noise conditions each time the recognize_speech_from_mic() function is called.

Next, recognize_google() is called to transcribe any speech in the recording. A try...except block is used to catch the RequestError and UnknownValueError exceptions and handle them accordingly. The success of the API request, any error messages, and the transcribed speech are stored in the success , error and transcription keys of the response dictionary, which is returned by the recognize_speech_from_mic() function.

You can test the recognize_speech_from_mic() function by saving the above script to a file called “guessing_game.py” and running the following in an interpreter session:

The game itself is pretty simple. First, a list of words, a maximum number of allowed guesses and a prompt limit are declared:

Next, a Recognizer and Microphone instance is created and a random word is chosen from WORDS :

After printing some instructions and waiting for 3 three seconds, a for loop is used to manage each user attempt at guessing the chosen word. The first thing inside the for loop is another for loop that prompts the user at most PROMPT_LIMIT times for a guess, attempting to recognize the input each time with the recognize_speech_from_mic() function and storing the dictionary returned to the local variable guess .

If the "transcription" key of guess is not None , then the user’s speech was transcribed and the inner loop is terminated with break . If the speech was not transcribed and the "success" key is set to False , then an API error occurred and the loop is again terminated with break . Otherwise, the API request was successful but the speech was unrecognizable. The user is warned and the for loop repeats, giving the user another chance at the current attempt.

Once the inner for loop terminates, the guess dictionary is checked for errors. If any occurred, the error message is displayed and the outer for loop is terminated with break , which will end the program execution.

If there weren’t any errors, the transcription is compared to the randomly selected word. The lower() method for string objects is used to ensure better matching of the guess to the chosen word. The API may return speech matched to the word “apple” as “Apple” or “apple,” and either response should count as a correct answer.

If the guess was correct, the user wins and the game is terminated. If the user was incorrect and has any remaining attempts, the outer for loop repeats and a new guess is retrieved. Otherwise, the user loses the game.

When run, the output will look something like this:

In this tutorial, you’ve seen how to install the SpeechRecognition package and use its Recognizer class to easily recognize speech from both a file—using record() —and microphone input—using listen(). You also saw how to process segments of an audio file using the offset and duration keyword arguments of the record() method.

You’ve seen the effect noise can have on the accuracy of transcriptions, and have learned how to adjust a Recognizer instance’s sensitivity to ambient noise with adjust_for_ambient_noise(). You have also learned which exceptions a Recognizer instance may throw— RequestError for bad API requests and UnkownValueError for unintelligible speech—and how to handle these with try...except blocks.

Speech recognition is a deep subject, and what you have learned here barely scratches the surface. If you’re interested in learning more, here are some additional resources.

For more information on the SpeechRecognition package:

• Library reference
• Troubleshooting page

A few interesting internet resources:

• Behind the Mic: The Science of Talking with Computers . A short film about speech processing by Google.
• A Historical Perspective of Speech Recognition by Huang, Baker and Reddy. Communications of the ACM (2014). This article provides an in-depth and scholarly look at the evolution of speech recognition technology.
• The Past, Present and Future of Speech Recognition Technology by Clark Boyd at The Startup. This blog post presents an overview of speech recognition technology, with some thoughts about the future.

Some good books about speech recognition:

• The Voice in the Machine: Building Computers That Understand Speech , Pieraccini, MIT Press (2012). An accessible general-audience book covering the history of, as well as modern advances in, speech processing.
• Fundamentals of Speech Recognition , Rabiner and Juang, Prentice Hall (1993). Rabiner, a researcher at Bell Labs, was instrumental in designing some of the first commercially viable speech recognizers. This book is now over 20 years old, but a lot of the fundamentals remain the same.
• Automatic Speech Recognition: A Deep Learning Approach , Yu and Deng, Springer (2014). Yu and Deng are researchers at Microsoft and both very active in the field of speech processing. This book covers a lot of modern approaches and cutting-edge research but is not for the mathematically faint-of-heart.

Throughout this tutorial, we’ve been recognizing speech in English, which is the default language for each recognize_*() method of the SpeechRecognition package. However, it is absolutely possible to recognize speech in other languages, and is quite simple to accomplish.

To recognize speech in a different language, set the language keyword argument of the recognize_*() method to a string corresponding to the desired language. Most of the methods accept a BCP-47 language tag, such as 'en-US' for American English, or 'fr-FR' for French. For example, the following recognizes French speech in an audio file:

Only the following methods accept a language keyword argument:

• recognize_bing()
• recognize_google()
• recognize_google_cloud()
• recognize_ibm()
• recognize_sphinx()

To find out which language tags are supported by the API you are using, you’ll have to consult the corresponding documentation . A list of tags accepted by recognize_google() can be found in this Stack Overflow answer .

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Using the Speech-to-Text API with Python

1. overview.

The Speech-to-Text API enables developers to convert audio to text in over 125 languages and variants, by applying powerful neural network models in an easy to use API.

In this tutorial, you will focus on using the Speech-to-Text API with Python.

What you'll learn

• How to set up your environment
• How to transcribe audio files in English
• How to transcribe audio files with word timestamps
• How to transcribe audio files in different languages

What you'll need

• A Google Cloud project
• A browser, such as Chrome or Firefox
• Familiarity using Python

How will you use this tutorial?

How would you rate your experience with python, how would you rate your experience with google cloud services, 2. setup and requirements, self-paced environment setup.

• Sign-in to the Google Cloud Console and create a new project or reuse an existing one. If you don't already have a Gmail or Google Workspace account, you must create one .

• The Project name is the display name for this project's participants. It is a character string not used by Google APIs. You can always update it.
• The Project ID is unique across all Google Cloud projects and is immutable (cannot be changed after it has been set). The Cloud Console auto-generates a unique string; usually you don't care what it is. In most codelabs, you'll need to reference your Project ID (typically identified as PROJECT_ID ). If you don't like the generated ID, you might generate another random one. Alternatively, you can try your own, and see if it's available. It can't be changed after this step and remains for the duration of the project.
• For your information, there is a third value, a Project Number , which some APIs use. Learn more about all three of these values in the documentation .
• Next, you'll need to enable billing in the Cloud Console to use Cloud resources/APIs. Running through this codelab won't cost much, if anything at all. To shut down resources to avoid incurring billing beyond this tutorial, you can delete the resources you created or delete the project. New Google Cloud users are eligible for the $300 USD Free Trial program.

Start Cloud Shell

While Google Cloud can be operated remotely from your laptop, in this codelab you will be using Cloud Shell , a command line environment running in the Cloud.

Activate Cloud Shell

If this is your first time starting Cloud Shell, you're presented with an intermediate screen describing what it is. If you were presented with an intermediate screen, click Continue .

It should only take a few moments to provision and connect to Cloud Shell.

This virtual machine is loaded with all the development tools needed. It offers a persistent 5 GB home directory and runs in Google Cloud, greatly enhancing network performance and authentication. Much, if not all, of your work in this codelab can be done with a browser.

Once connected to Cloud Shell, you should see that you are authenticated and that the project is set to your project ID.

• Run the following command in Cloud Shell to confirm that you are authenticated:

Command output

• Run the following command in Cloud Shell to confirm that the gcloud command knows about your project:

If it is not, you can set it with this command:

3. Environment setup

Before you can begin using the Speech-to-Text API, run the following command in Cloud Shell to enable the API:

You should see something like this:

Now, you can use the Speech-to-Text API!

Navigate to your home directory:

Create a Python virtual environment to isolate the dependencies:

Activate the virtual environment:

Install IPython and the Speech-to-Text API client library:

Now, you're ready to use the Speech-to-Text API client library!

In the next steps, you'll use an interactive Python interpreter called IPython , which you installed in the previous step. Start a session by running ipython in Cloud Shell:

You're ready to make your first request...

4. Transcribe audio files

In this section, you will transcribe an English audio file.

Copy the following code into your IPython session:

Take a moment to study the code and see how it uses the recognize client library method to transcribe an audio file*.* The config parameter indicates how to process the request and the audio parameter specifies the audio data to be recognized.

Send a request:

You should see the following output:

Update the configuration to enable automatic punctuation and send a new request:

In this step, you were able to transcribe an audio file in English, using different parameters, and print out the result. You can read more about transcribing audio files .

5. Get word timestamps

Speech-to-Text can detect time offsets (timestamps) for the transcribed audio. Time offsets show the beginning and end of each spoken word in the supplied audio. A time offset value represents the amount of time that has elapsed from the beginning of the audio, in increments of 100ms.

To transcribe an audio file with word timestamps, update your code by copying the following into your IPython session:

Take a moment to study the code and see how it transcribes an audio file with word timestamps*.* The enable_word_time_offsets parameter tells the API to return the time offsets for each word (see the doc for more details).

In this step, you were able to transcribe an audio file in English with word timestamps and print the result. Read more about getting word timestamps .

6. Transcribe different languages

The Speech-to-Text API recognizes more than 125 languages and variants! You can find a list of supported languages here .

In this section, you will transcribe a French audio file.

To transcribe the French audio file, update your code by copying the following into your IPython session:

In this step, you were able to transcribe a French audio file and print the result. You can read more about the supported languages .

7. Congratulations!

You learned how to use the Speech-to-Text API using Python to perform different kinds of transcription on audio files!

To clean up your development environment, from Cloud Shell:

• If you're still in your IPython session, go back to the shell: exit
• Stop using the Python virtual environment: deactivate
• Delete your virtual environment folder: cd ~ ; rm -rf ./venv-speech

To delete your Google Cloud project, from Cloud Shell:

• Retrieve your current project ID: PROJECT_ID=$(gcloud config get-value core/project)
• Make sure this is the project you want to delete: echo $PROJECT_ID
• Delete the project: gcloud projects delete $PROJECT_ID
• Test the demo in your browser: https://cloud.google.com/speech-to-text
• Speech-to-Text documentation: https://cloud.google.com/speech-to-text/docs
• Python on Google Cloud: https://cloud.google.com/python
• Cloud Client Libraries for Python: https://github.com/googleapis/google-cloud-python

This work is licensed under a Creative Commons Attribution 2.0 Generic License.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

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Software Developer & Professional Explainer

Python Speech Recognition - a Step-by-Step Guide

Have you used Shazam, the app that identifies music that is playing around you?

If yes, how often have you wondered about the technology that shapes this application?

How about products like Google Home or Amazon Alexa or your digital assistant Siri?

Many modern IoT products use speech recognition . This both adds creative functionality to the product and improves its accessibility features.

Python supports speech recognition and is compatible with many open-source speech recognition packages.

In this tutorial, I will teach you how to write Python speech recognition applications use an existing speech recognition package available on PyPI . We will also build a simple Guess the Word game using Python speech recognition.

Table of Contents

You can skip to a specific section of this Python speech recognition tutorial using the table of contents below:

How does speech recognition work?

Available python speech recognition packages, installing and using the speechrecognition package, the recognizer class, speech recognition from a live microphone recording, final thoughts.

Modern speech recognition software works on the Hidden Markov Model (HMM) .

According to the Hidden Markov Model, a speech signal that is broken down into fragments that are as small as one-hundredth of a second is a stationary process whose properties do not change with respect to time.

Your computer goes through a series of complex steps during speech recognition as it converts your speech to an on-screen text.

When you speak, you create an analog wave in the form of vibrations. This analog wave is converted into a digital signal that the computer can understand using a converter.

This signal is then divided into segments that are as small as one-hundredth of a second. The small segments are then matched with predefined phonemes.

Phonemes are the smallest element of a language. Linguists believe that there are around 40 phonemes in the English language.

Though this process sounds very simple, the trickiest part here is that each speaker pronounces a word slightly differently. Therefore, the way a phoneme sounds varies from speaker-to-speaker. This difference becomes especially significant across speakers from different geographical locations.

As Python developers, we are lucky to have speech recognition services that can be easily accessed through an API. Said differently, we do not need to build the infrastructure to recognize these phonemes from scratch!

Let's now look at the different Python speech recognition packages available on PyPI.

There are many Python speech recognition packages available today. Here are some of the most popular:

• google-cloud-speech
• google-speech-engine
• IBM speech to text
• Microsoft Bing voice recognition
• pocketsphinx
• SpeechRecognition
• watson-developer-cloud

In this tutorial, we will use the SpeechRecognition package, which is open-source and available on PyPI.

In this tutorial, I am assuming that you will be using Python 3.5 or above.

You can install the SpeechRecognition package with pyenv , pipenv , or virtualenv . In this tutorial, we will install the package with pipenv from a terminal.

Verify the installation of the speech recognition module using the below command.

Note: If you are using a microphone input instead of audio files present in your computer, you'll want to install the PyAudio (0.2.11 +) package as well.

The recognizer class from the speech\_recognition module is used to convert our speech to text format. Based on the API used that the user selects, the Recognizer class has seven methods. The seven methods are described in the following table:

{:.blueTable}

In this tutorial, we will use the Google Speech API . The Google Speech API is shipped in SpeechRecognition with a default API key. All the other APIs will require an API key with a username and a password.

First, create a Recognizer instance.

AudioFile is a class that is part of the speech\_recognition module and is used to recognize speech from an audio file present in your machine.

Create an object of the AudioFile class and pass the path of your audio file to the constructor of the AudioFile class. The following file formats are supported by SpeechRecognition:

Try the following script:

In the above script, you'll want to replace D:/Files/my_audio.wav with the location of your audio file.

Now, let's use the recognize_google() method to read our file. This method requires us to use a parameter of the speech_recognition() module, the AudioData object.

The Recognizer class has a record() method that can be used to convert our audio file to an AudioData object. Then, pass the AudioFile object to the record() method as shown below:

Check the type of the audio variable. You will notice that the type is speech_recognition.AudioData .

Now, use the recognize google() to invoke the audio object and convert the audio file into text.

Now that you have converted your first audio file into text, let's see how we can take only a portion of the file and convert it into text. To do this, we first need to understand the offset and duration keywords in the record() method.

The duration keyword of the record() method is used to set the time at which the speech conversion should end. That is, if you want to end your conversion after 5 seconds, specify the duration as 5. Let's see how this is done.

The output will be as follows:

It's important to note that inside a with block, the record() method moves ahead in the file stream. That is, if you record twice, say once for five seconds and then again for four seconds, the output you get for the second recording will after the first five seconds.

What if we want the audio to start from the fifth second and for a duration of 10 seconds?

This is where the offset attribute of the record() method comes to our aid. Here's how to use the offset attribute to skip the first four seconds of the file and then print the text for the next 5 seconds.

The output is as follows:

To get the exact phrase from the audio file that you are looking for, use precise values for both offset and duration attributes.

Removing Noise

The file we used in this example had very little background noise that disrupted our conversion process. However, in reality, you will encounter a lot of background noise in your speech files.

Fortunately, you can use the adjust_for_ambient_noise() method of the Recognizer class to remove any unwanted noise. This method takes the AudioData object as a parameter.

Let's see how this works:

As mentioned above, our file did not have much noise. This means that the output looks very similar to what we got earlier.

Now that we have seen speech recognition from an audio file, let's see how to perform the same function when the input is provided via a microphone. As mentioned earlier, you will have to install the PyAudio library to use your microphone.

After installing the PyAudio library, create an object of the microphone class of the speech_recognition module.

Create another instance of the Recognizer class like we did for the audio file.

Now, instead of specifying the input from a file, let us use the default microphone of the system. Access the microphone by creating an instance of the Microphone class.

Similar to the record() method, you can use the listen() method of the Recognizer class to capture input from your microphone. The first argument of the listen() method is the audio source. It records input from the microphone until it detects silence.

Execute the script and try speaking into the microphone.

The system is ready to translate your speech if it displays the You can speak now message. The program will begin translation once you stop speaking. If you do not see this message, it means that the system has failed to detect your microphone.

Python speech recognition is slowly gaining importance and will soon become an integral part of human computer interaction.

This article discussed speech recognition briefly and discussed the basics of using the Python SpeechRecognition library.

Automatic Speech Recognition with Transformer

Author: Apoorv Nandan Date created: 2021/01/13 Last modified: 2021/01/13 Description: Training a sequence-to-sequence Transformer for automatic speech recognition.

Introduction

Automatic speech recognition (ASR) consists of transcribing audio speech segments into text. ASR can be treated as a sequence-to-sequence problem, where the audio can be represented as a sequence of feature vectors and the text as a sequence of characters, words, or subword tokens.

For this demonstration, we will use the LJSpeech dataset from the LibriVox project. It consists of short audio clips of a single speaker reading passages from 7 non-fiction books. Our model will be similar to the original Transformer (both encoder and decoder) as proposed in the paper, "Attention is All You Need".

References:

• Attention is All You Need
• Very Deep Self-Attention Networks for End-to-End Speech Recognition
• Speech Transformers
• LJSpeech Dataset

Define the Transformer Input Layer

When processing past target tokens for the decoder, we compute the sum of position embeddings and token embeddings.

When processing audio features, we apply convolutional layers to downsample them (via convolution strides) and process local relationships.

Transformer Encoder Layer

Transformer decoder layer, complete the transformer model.

Our model takes audio spectrograms as inputs and predicts a sequence of characters. During training, we give the decoder the target character sequence shifted to the left as input. During inference, the decoder uses its own past predictions to predict the next token.

Download the dataset

Note: This requires ~3.6 GB of disk space and takes ~5 minutes for the extraction of files.

Preprocess the dataset

Callbacks to display predictions, learning rate schedule, create & train the end-to-end model.

In practice, you should train for around 100 epochs or more.

Some of the predicted text at or around epoch 35 may look as follows:

DEV Community

Posted on Sep 28, 2020 • Updated on May 22, 2022

How to convert Speech to text in Python

Speech Recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to textual information.

You have probably seen it on Sci-fi, and personal assistants like Siri , Cortana , and Google Assistant , and other virtual assistants that interact with through voice.

These AI assistants in order to understand your voice they need to do speech recognition so as to understand what you have just said.

Speech Recognition is a complex process, well I'm not going to teach you how to train a Machine Learning/Deep Learning Model to that, instead, I instruct you how to do that using google speech recognition API.

As long as you have the basics of Python you can successfully complete this tutorial and build your own fully functioning speech recognition programs in Python.

Requirements

To successfully complete this tutorial, you need to have the following Python library installed on your Machine

-PyAudio Library -SpeechRecognition Library

Installation

SpeechRecognition library allows you to can perform speech recognition with support for several engines and APIs, online and offline.

Below are some of the supported Engines

• CMU Sphinx (works offline)
• Google Speech Recognition
• Google Cloud Speech API
• Microsoft Bing Voice Recognition
• Houndify API
• IBM Speech to Text

Snowboy Hotword Detection (works offline)

On this tutorial, we are going to use Google Speech recognition API which is free for basic uses perhaps it has a limit of requests you can send over a certain time.

Throughout this tutorial, you will learn performing Speech Recognition using sound that is directly fed from Microphone also using Audio Source from File

Speech Recognition from Microphone

When Performing Speech Recognition from Microphone, we need to record the audio from the microphone and then send it to Google Speech to text recognition engine and which will perform the recognition and return out transcribed text

* Steps involved *

• Recording Audio from Microphone ( PyAudio)
• Sending Audio to the Speech recognition engine
• Printing the Recognized text to the screen

Below is a sample app.py code just to do that you can lookout, its straight forward

Speech Recognition from Audio File

When it comes to performing Speech Recognition from Audio line only one line of code is going to change instead of using a Microphone as a source of Audio, we will give a path to our Audio File we want to transcribe to text

On Demo, I have used the below sample audio

Sample Audio

The below code is a sample script to perform speech recognition of audio in a file.

Speech Recognition from Long Audio Source

When you have very long audio, loading the whole audio to Memory and sending it over API it can be a very slow process, to overcome that we have to split the long audio source into small chunks and then performing speech recognition on those individual chunks

We are going to use pydub to split the Long Audio Source into those small chunks

To install pydub just use pip

To use the below link to download sample long audio

Long Sample Audio

The Below is a sample Python code that loads the long Audio, Split into the segment, and then performing the Speech recognition on those individual chunks to to learn more about splitting the audio you can check out DataCamp Tutorial

Congrats you now know how to do, can't wait to see what you're going to build with the knowledge

The Original Article can be found on kalebujordan.dev

In case of any comment, suggestion, or difficulties comment below and I will get back to you ASAP

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• Joined Nov 7, 2020

hello Mr. Jordan Thank you a lot for this article, it helped me a lot in my project, I have a question that I still didn't find an answer for it on google, could you help me with that, please

I am using the record method to record live audio from the microphone as the following: audio = r.record(source, duration=200) so the system will record for 200 minutes, then will stop recording and return the result Is there a way to make the recording stop when user do action? like when user click "Stop" button, system will stop even if the 200 min is not finished?

Thanks a lot in advanced

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Convert Speech to Text using Python | Speech Recognition | Machine Learning Project Tutorial

Updated: May 31, 2023

Unlock the power of speech-to-text conversion with Python! This comprehensive tutorial explores speech recognition techniques and machine learning. Learn to transcribe spoken words into written text using cutting-edge algorithms and models. Enhance your skills in natural language processing and optimize your applications with this hands-on project tutorial. #SpeechToText #Python #SpeechRecognition #MachineLearning #NLP

In this project tutorial we will install the Google Speech Recognition module and covert real-time audio to text and also convert an audio file to text data.

You can watch the step by step explanation video tutorial down below

Project Information

The objective of the project is to convert speech to text in real time and convert audio file to text. It uses google speech API to convert the audio to text.

speech_recognition

Google Speech API

We install the module to proceed

Requirement already satisfied: speechrecognition in c:\programdata\anaconda3\lib\site-packages (3.8.1) Collecting PyAudio Using cached PyAudio-0.2.11.tar.gz (37 kB) Building wheels for collected packages: PyAudio Building wheel for PyAudio (setup.py): started Building wheel for PyAudio (setup.py): finished with status 'error' Running setup.py clean for PyAudio Failed to build PyAudio Installing collected packages: PyAudio Running setup.py install for PyAudio: started Running setup.py install for PyAudio: finished with status 'error'

Now we import the module

We initialize the module

Convert Speech to Text in Real time

We will convert real time audio from a microphone into text

Speak now Speaker: welcome to the channel Speak now Speaker: testing speech recognition Speak now Speaker: quit

Microphone() - Receive audio input from microphone

adjust_for_ambient_noise(source, duration=0.3) - Clear any background noise from the real time input

listen(source) - Capture the audio from the source

recognize_google(audio) - Google Speech recognition function to convert audio into text

text == 'quit' - Condition to quit the while loop

Convert Audio to Text

Now we will process and convert an audio file into text

listening to audio Audio: welcome to speech recognition

Displayed text is the same as the speech in the audio file

For larger audio files you need to split them in smaller segments for better processing

Final Thoughts

Very useful tool for converting real time recordings into text which can help in chats, interviews, narration, captions, etc.

You can also use this process for Emotional Speech recognition and further analyze the text for sentiment analysis.

The Google Speech recognition is a very effective and precise module, you may implement any other module to convert speech into text as per your preference.

In this project tutorial we have explored Convert Speech to Text process using the Google Speech Recognition module. We have installed the module and processed real time audio recording and an audio file converting into text data.

Get the project notebook from here

Thanks for reading the article!!!

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pyttsx3 2.91

pip install pyttsx3 Copy PIP instructions

Released: Aug 15, 2024

Text to Speech (TTS) library for Python 3. Works without internet connection or delay. Supports multiple TTS engines, including Sapi5, nsss, and espeak.

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• License: Mozilla Public License 2.0 (MPL 2.0)
• Author: Natesh M Bhat
• Tags pyttsx, ivona, pyttsx for python3, TTS for python3, pyttsx3, text to speech for python, tts, text to speech, speech, speech synthesis, offline text to speech, offline tts, gtts

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Project description

pyttsx3 is a text-to-speech conversion library in Python. Unlike alternative libraries, it works offline, and is compatible with both Python 2 and 3.

Installation

> If you get installation errors , make sure you first upgrade your wheel version using : pip install –upgrade wheel

Linux installation requirements :

Install espeak , ffmpeg and libespeak1 as shown below:

Changing Voice , Rate and Volume :

Full documentation of the Library

https://pyttsx3.readthedocs.io/en/latest/

Included TTS engines:

Feel free to wrap another text-to-speech engine for use with pyttsx3 .

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Speech Recognition Module Python

Speech recognition, a field at the intersection of linguistics, computer science, and electrical engineering, aims at designing systems capable of recognizing and translating spoken language into text. Python, known for its simplicity and robust libraries, offers several modules to tackle speech recognition tasks effectively. In this article, we’ll explore the essence of speech recognition in Python, including an overview of its key libraries, how they can be implemented, and their practical applications.

Key Python Libraries for Speech Recognition

• SpeechRecognition : One of the most popular Python libraries for recognizing speech. It provides support for several engines and APIs, such as Google Web Speech API, Microsoft Bing Voice Recognition, and IBM Speech to Text. It’s known for its ease of use and flexibility, making it a great starting point for beginners and experienced developers alike.
• PyAudio : Essential for audio input and output in Python, PyAudio provides Python bindings for PortAudio, the cross-platform audio I/O library. It’s often used alongside SpeechRecognition to capture microphone input for real-time speech recognition.
• DeepSpeech : Developed by Mozilla, DeepSpeech is an open-source deep learning-based voice recognition system that uses models trained on the Baidu’s Deep Speech research project. It’s suitable for developers looking to implement more sophisticated speech recognition features with the power of deep learning.

Implementing Speech Recognition with Python

A basic implementation using the SpeechRecognition library involves several steps:

• Audio Capture : Capturing audio from the microphone using PyAudio.
• Audio Processing : Converting the audio signal into data that the SpeechRecognition library can work with.
• Recognition : Calling the recognize_google() method (or another available recognition method) on the SpeechRecognition library to convert the audio data into text.

Here’s a simple example:

Practical Applications

Speech recognition has a wide range of applications:

• Voice-activated Assistants: Creating personal assistants like Siri or Alexa.
• Accessibility Tools: Helping individuals with disabilities interact with technology.
• Home Automation: Enabling voice control over smart home devices.
• Transcription Services: Automatically transcribing meetings, lectures, and interviews.

Challenges and Considerations

While implementing speech recognition, developers might face challenges such as background noise interference, accents, and dialects. It’s crucial to consider these factors and test the application under various conditions. Furthermore, privacy and ethical considerations must be addressed, especially when handling sensitive audio data.

Speech recognition in Python offers a powerful way to build applications that can interact with users in natural language. With the help of libraries like SpeechRecognition, PyAudio, and DeepSpeech, developers can create a range of applications from simple voice commands to complex conversational interfaces. Despite the challenges, the potential for innovative applications is vast, making speech recognition an exciting area of development in Python.

FAQ on Speech Recognition Module in Python

What is the speech recognition module in python.

The Speech Recognition module, often referred to as SpeechRecognition, is a library that allows Python developers to convert spoken language into text by utilizing various speech recognition engines and APIs. It supports multiple services like Google Web Speech API, Microsoft Bing Voice Recognition, IBM Speech to Text, and others.

How can I install the Speech Recognition module?

You can install the Speech Recognition module by running the following command in your terminal or command prompt: pip install SpeechRecognition For capturing audio from the microphone, you might also need to install PyAudio. On most systems, this can be done via pip: pip install PyAudio

Do I need an internet connection to use the Speech Recognition module?

Yes, for most of the supported APIs like Google Web Speech, Microsoft Bing Voice Recognition, and IBM Speech to Text, an active internet connection is required. However, if you use the CMU Sphinx engine, you do not need an internet connection as it operates offline.

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Speech Recognition Python – Converting Speech to Text

Are you surprised about how the modern devices that are non-living things listen your voice, not only this but they responds too. Yes,Its looks like a fantasy, but now-a-days technology are doing the surprising things that were not possible in past. So guys, welcome to my new tutorial Speech Recognition Python .This is a very awesome tutorial having lots of interesting stuffs. In this tutorial we will learn about concept of speech recognition and it’s implementation in python. So let’s gets started.

As the technologies are growing more rapidly and new features are emerging in this way speech recognition is one of them. Speech recognition is a technology that have evolved exponentially over the past few years. Speech recognition is one of the popular and best feature in computer world. It have numerous applications that can boost convenience, enhance security, help law enforcement efforts, that are the few examples. Let’s start understanding the concept of speech recognition, it’s working and  applications.

What is Speech Recognition?

• Speech Recognition is a process in which a computer or device record the speech of humans and convert it into text format.
• It is also known as A utomatic Speech Recognition ( ASR ),  computer speech recognition  or S peech To Text  ( STT ).
• Linguistics, computer science, and electrical engineering are some fields that are associated with Speech Recognition.

Working Nature of Speech Recognition

Now we will discuss how it actually works?

The above pictures shows the working principle of Speech Recognition very clearly.Now let’s understand the concept behind it.

It is based on the algorithm of   acoustic and language modeling. So now the question is -what is acoustic and language modeling?

• Acoustic modeling represents the relationship between linguistic units of speech and audio signals.
• Language modeling matches sounds with word sequences to help distinguish between words that sound similar.

Any speech recognition program is evaluated using two factors:

• Accuracy (percentage error in converting spoken words to digital data).
• Speed (extent to which the program can keep up with a human speaker).

Applications

The most frequent applications of speech recognition are following:

• In-car systems.
• Health care –  Medical documentation and Therapeutic use
• Military – High performance fighter aircraft ,Helicopters,Training air traffic controllers.
• Telephony and other domains
• Usage in Education and Daily life

Speech Recognition Python

Have you ever wondered how to add speech recognition to your Python project? If so, then keep reading! It’s easier than you might think.

Implementing Speech Recognition in Python is very easy and simple. Here we will be using two libraries which are Speech Recognition and PyAudio.

Creating new project

Create a new project and name it as SpeechRecognitionExample (Though the name doesn’t matter at all it can be anything). And then create a python file inside the project. I hope you already know about creating new project in python.

Installing Libraries

we have to install two library for implementing speech recognition.

• SpeechRecognition

Installing SpeechRecognition

• Go to terminal and type

SpeechRecognition is a library that helps in performing speech  recognition in python. It support for several engines and APIs, online and offline e.g. Google Cloud Speech API, Microsoft Bing Voice Recognition, IBM Speech to Text etc.

Installing PyAudio

PyAudio provides  Python  bindings for  PortAudio , the cross-platform audio I/O library. With PyAudio, you can easily use Python to play and record audio on a variety of platforms, such as GNU/Linux, Microsoft Windows, and Apple Mac OS X / macOS.

Performing Speech Recognition

Now let’s jump into the coding part.

So this is the code for speech recognition in python.As you are seeing, it is quite simple and easy.

Explanation of code

So now we will start understanding the code line-by-line.

• first of all we will import speech_recognition as sr.
• Notice that we have speech_recognition in such  format  whereas earlier we have installed it in this way SpeechRecognition , so you need to have a look around the cases because this is case sensitive.
• Now we have used as notation  because writing  speech_recognition whole every time is not a good way.
• Now we have to initialize  r = sr.Recognizer() , this will work as a recognizer to recognize our voice.
• So,  with sr.Microphone() as source: which means that we are initialising our source to sr.Microphone ,we can also use some audio files to convert into text but in this tutorial i am using Microphone voice.
• Next we will print a simple statement that recommend the user to speak anything.
• Now we have to use r.listen(source) command and we have to listen the source.So, it will listen to the source and store it in the audio.
• It may happen some time the audio is not clear and you might not get it correctly ,so we can put it inside the try and except block .
• So inside the try block, our text will be text = r.recognize_google(audio) , now we have various options like recognize_bing(),recognize_google_cloud(),recognize_ibm(), etc.But for this one i am using recognize_google().And lastly we have to pass our audio.
• And this will convert our audio into text.
• Now we just have to print  print(“You said : {}”.format(text))  ,this will print whatever you have said.
• In the except block we can just write  print(“Sorry could not recognize your voice”) , this will message you if your voice is not recorded clearly.

The output of the above code will be as below.

So, its working fine.Obviously You must have enjoyed it, yeah am i right or not?

If you are working on a desktop that do not have a mic you can try some android apps like Wo Mic , from play store to use your smartphone as a mic. And if you’ve got a real mic or headphones with mic then you can try them too.

Finally Speech Recognition Python Tutorial  completed successfully. So friends If you have any question, then leave your comments. If you found this tutorial helpful, then please SHARE it with your friends. Thank You 🙂

25 thoughts on “Speech Recognition Python – Converting Speech to Text”

Errors on pip install pyaudio

[1] Easily install SpeechRecognition 3.8.1 with !pip install SpeechRecognition the leading ! since I am within a cell in Jupyter Notebook on Microsoft Azure ( http://www.notebooks.azure.com )

[2] Errors on !pip install pyaudio Looks like it gcc build failed since there is no portaudio.h Any hints about pyaudio? DETAILS: Collecting pyaudio Downloading https://files.pythonhosted.org/packages/ab/42/b4f04721c5c5bfc196ce156b3c768998ef8c0ae3654ed29ea5020c749a6b/PyAudio-0.2.11.tar.gz Building wheels for collected packages: pyaudio Running setup.py bdist_wheel for pyaudio … error Complete output from command /home/nbuser/anaconda3_501/bin/python -u -c “import setuptools, tokenize;__file__=’/tmp/pip-install-hgcg4y3h/pyaudio/setup.py’;f=getattr(tokenize, ‘open’, open)(__file__);code=f.read().replace(‘\r\n’, ‘\n’);f.close();exec(compile(code, __file__, ‘exec’))” bdist_wheel -d /tmp/pip-wheel-xnk_drv5 –python-tag cp36: running bdist_wheel running build running build_py creating build creating build/lib.linux-x86_64-3.6 copying src/pyaudio.py -> build/lib.linux-x86_64-3.6 running build_ext building ‘_portaudio’ extension creating build/temp.linux-x86_64-3.6 creating build/temp.linux-x86_64-3.6/src gcc -pthread -B /home/nbuser/anaconda3_501/compiler_compat -Wl,–sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/home/nbuser/anaconda3_501/include/python3.6m -c src/_portaudiomodule.c -o build/temp.linux-x86_64-3.6/src/_portaudiomodule.o src/_portaudiomodule.c:29:23: fatal error: portaudio.h: No such file or directory compilation terminated. error: command ‘gcc’ failed with exit status 1 <<<<<<<<<<<<<<<<<<<< build/lib.linux-x86_64-3.6 running build_ext building ‘_portaudio’ extension creating build/temp.linux-x86_64-3.6 creating build/temp.linux-x86_64-3.6/src gcc -pthread -B /home/nbuser/anaconda3_501/compiler_compat -Wl,–sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/home/nbuser/anaconda3_501/include/python3.6m -c src/_portaudiomodule.c -o build/temp.linux-x86_64-3.6/src/_portaudiomodule.o src/_portaudiomodule.c:29:23: fatal error: portaudio.h: No such file or directory compilation terminated. error: command ‘gcc’ failed with exit status 1

—————————————- Command “/home/nbuser/anaconda3_501/bin/python -u -c “import setuptools, tokenize;__file__=’/tmp/pip-install-hgcg4y3h/pyaudio/setup.py’;f=getattr(tokenize, ‘open’, open)(__file__);code=f.read().replace(‘\r\n’, ‘\n’);f.close();exec(compile(code, __file__, ‘exec’))” install –record /tmp/pip-record-ftuiec6_/install-record.txt –single-version-externally-managed –compile” failed with error code 1 in /tmp/pip-install-hgcg4y3h/pyaudio/

which operating system you are using?

You can try this, I think it will help. https://stackoverflow.com/questions/5921947/pyaudio-installation-error-command-gcc-failed-with-exit-status-1 And again if you get something like unmet dependencies then you should run sudo apt-get install -f and then try to install pyaudio.

Your real problem is with portaudio.h which has no available python wheel or libraries and this is currently not available on Python 3.7 so to remove that error downgrade the python version to 3.6 and run the same command pip install pyAudio it will work

Just install python 3.6 and pip install PyAudio will work

This is on some Microsoft server that hosts Microsoft Azure and Jupyter Notebooks.

I am using using Chrome browser on Windows 10, but that should not matter.

I login at https://notebooks.azure.com/

In a Jupyter Notebook, the 2 Python commands:

‘posix’

Hope that helps.

Edward Bujak

This is awesome update in Python

Thanks for the post, it is very helpful. I tried and it worked fine for me. But it converted only the first 4-5s of the audio file. (1 short sentence) What if I want to convert longer audio files? Do you have any recommendations?

Thanks in advance.

hello sir thank you so much i tried with this code its working fine…i have one query that with this code its taking some time to give response(text) back .can i add loop in this code if(can u tell me the code) or any other methods how best i can improve the speed .please help f=me for this sir….WAITING FOR RESPONSE Thanks in advance.

First of all thanks for your comment.Yes it takes some time to response.It may be depends upon your internet speed or speaker’s quality.

it shows the error message “module ‘speech_recognition’ has no attribute ‘Recognizer’ “

May be your file name is speech_recognition.py .You need simple to rename your module (file) like speech-recog.py.

Thanks for sharing it worked for me

If voice is unclear to read , how can it eliminate around noisy things to get distinguished voice for returning text. Do you have any way?

hello sir! I run the code and it show no error but when i try to say something it can’t hear me, I try this in my laptop vaio sony core i3. It can’t record my voice, I am really in a trouble please help me. to solve this shit.. Thanks

Hi i am unable to install pyaudio i am getting the following error:

ERROR: Command “‘c:\users\ganesh.marella\appdata\local\programs\python\python37\python.exe’ -u -c ‘import setuptools, tokenize;__file__='”‘”‘C:\\Users\\GANESH~1.MAR\\AppData\\Local\\Temp\\pip-install-afndru1v\\pyaudio\\setup.py'”‘”‘;f=getattr(tokenize, ‘”‘”‘open'”‘”‘, open)(__file__);code=f.read().replace(‘”‘”‘\r\n'”‘”‘, ‘”‘”‘\n'”‘”‘);f.close();exec(compile(code, __file__, ‘”‘”‘exec'”‘”‘))’ install –record ‘C:\Users\GANESH~1.MAR\AppData\Local\Temp\pip-record-lqg1dul4\install-record.txt’ –single-version-externally-managed –compile” failed with error code 1 in C:\Users\GANESH~1.MAR\AppData\Local\Temp\pip-install-afndru1v\pyaudio\

Please help me with this.

I want to use this functionality on web application using django, how can I do it? Please reply

Since we are using speech speech to text API, is this free cost?

First install portaudio and then install ‘pyaudio’ on any OS that works as expected.

on MAC : brew install portaudio pip install pyaudio

While installing speech recognition it is showing that pip is not an internal or external command .why it is showing that

Because you have not installed pip on your system. Search on youtube how to install pip according to your system type. Thanks

It is easy to write “import SpeechRecognition”, but it only works if you have your system set up to provide it. The hard part is to tell people precisely how to collect the libraries on all those platforms. Its not just “pip install SpeechRecognition”.

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Speech Recognition in Python

Speech recognition allows software to recognize speech within audio and convert it into text. There are many interesting use-cases for speech recognition and it is easier than you may think to add it your own applications.

We just published a course on the freeCodeCamp.org YouTube channel that will teach you how to implement speech recognition in Python by building 5 projects.

Misra Turp & Patrick Loeber teach this course. Patrick is an experienced software engineer and Mirsra is an experienced data scientist. And they are both developer advocates at Assembly AI.

Assembly AI is a deep learning company that creates a speech-to-text API. You will learn how to use the API in this course. Assembly AI provided a grant that made this course possible.

In the first project you will learn the basics of audio processing by learning how to record audio from a microphone with pyaudio and write it to a wave file. You will also learn how to plot the sound waves with matplotlib.

In the second project you will learn how to implement simple speech recognition. You will learn how to use the AssemblyAI API and how to work with APIs with the requests module.

In the third project you will learn how to perform sentiment analysis on iPhone reviews from YouTube. You will learn how to use youtube-dl and how to implement sentiment classification.

In the fourth project you will write a program that will create automatic summarizations of podcasts using the Listen Notes API and Streamlit.

In the final project you will create a voice assistant with real-time speech recognition using websockets and the OpenAI API.

Watch the full course below or on the freeCodeCamp.org YouTube channel (2-hour watch).

I'm a teacher and developer with freeCodeCamp.org. I run the freeCodeCamp.org YouTube channel.

If this article was helpful, share it .

Learn to code for free. freeCodeCamp's open source curriculum has helped more than 40,000 people get jobs as developers. Get started

Speech-to-Text Conversion Using Python

In this tutorial from Subhasish Sarkar, learn how to build a basic speech-to-text engine using simple Python script

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In today’s world, voice technology has become very prevalent. The technology has grown, evolved and matured at a tremendous pace. Starting from voice shopping on Amazon to routine (and growingly complex) tasks performed by the personal voice assistant devices/speakers such as Amazon’s Alexa at the command of our voice, voice technology has found many practical uses in different spheres of life.

Speech-to-Text Conversion

One of the most important and critical functionalities involved with any voice technology implementation is a speech-to-text (STT) engine that performs voice recognition and speech-to-text conversion. We can build a very basic STT engine using a simple Python script. Let’s go through the sequence of steps required.

NOTE : I worked on this proof-of-concept (PoC) project on my local Windows machine and therefore, I assume that all instructions pertaining to this PoC are tried out by the readers on a system running Microsoft Windows OS.

Step 1: Installation of Specific Python Libraries

We will start by installing the Python libraries, namely: speechrecognition, wheel, pipwin and pyaudio. Open your Windows command prompt or any other terminal that you are comfortable using and execute the following commands in sequence, with the next command executed only after the previous one has completed its successful execution.

Step 2: Code the Python Script That Implements a Very Basic STT Engine

Let’s name the Python Script file  STT.py . Save the file anywhere on your local Windows machine. The Python script code looks like the one referenced below in Figure 1.

Figure 1 Code:

Figure 1 Visual:

The while loop makes the script run infinitely, waiting to listen to the user voice. A KeyboardInterrupt (pressing CTRL+C on the keyboard) terminates the program gracefully. Your system’s default microphone is used as the source of the user voice input. The code allows for ambient noise adjustment.

Depending on the surrounding noise level, the script can wait for a miniscule amount of time which allows the Recognizer to adjust the energy threshold of the recording of the user voice. To handle ambient noise, we use the adjust_for_ambient_noise() method of the Recognizer class. The adjust_for_ambient_noise() method analyzes the audio source for the time specified as the value of the duration keyword argument (the default value of the argument being one second). So, after the Python script has started executing, you should wait for approximately the time specified as the value of the duration keyword argument for the adjust_for_ambient_noise() method to do its thing, and then try speaking into the microphone.

The SpeechRecognition documentation recommends using a duration no less than 0.5 seconds. In some cases, you may find that durations longer than the default of one second generate better results. The minimum value you need for the duration keyword argument depends on the microphone’s ambient environment. The default duration of one second should be adequate for most applications, though.

The translation of speech to text is accomplished with the aid of Google Speech Recognition ( Google Web Speech API ), and for it to work, you need an active internet connection.

Step 3: Test the Python Script

The Python script to translate speech to text is ready and it’s now time to see it in action. Open your Windows command prompt or any other terminal that you are comfortable using and CD to the path where you have saved the Python script file. Type in  python "STT.py"  and press enter. The script starts executing. Speak something and you will see your voice converted to text and printed on the console window. Figure 2 below captures a few of my utterances.

Figure 2 . A few of the utterances converted to text; the text “hai” corresponds to the actual utterance of “hi,” whereas “hay” corresponds to “hey.”

Figure 3 below shows another instance of script execution wherein user voice was not detected for a certain time interval or that unintelligible noise/audio was detected/recognized which couldn’t be matched/converted to text, resulting in outputting the message “No User Voice detected OR unintelligible noises detected OR the recognized audio cannot be matched to text !!!”

Figure 3 . The “No User Voice detected OR unintelligible noises detected OR the recognized audio cannot be matched to text !!!” output message indicates that our STT engine didn’t recognize any user voice for a certain interval of time or that unintelligible noise/audio was detected/recognized which couldn’t be matched/converted to text.

Note : The response from the Google Speech Recognition engine can be quite slow at times. One thing to note here is, so long as the script executes, your system’s default microphone is constantly in use and the message “Python is using your microphone” depicted in Figure 4 below confirms the fact.

Finally, press CTRL+C on your keyboard to terminate the execution of the Python script. Hitting CTRL+C on the keyboard generates a KeyboardInterrupt exception that has been handled in the first except block in the script which results in a graceful exit of the script. Figure 5 below shows the script’s graceful exit.

Figure 5 . Pressing CTRL+C on your keyboard results in a graceful exit of the executing Python script.

Note : I noticed that the script fails to work when the VPN is turned on. The VPN had to be turned off for the script to function as expected. Figure 6 below demonstrates the erroring out of the script with the VPN turned on.

Figure 6 . The Python script fails to work when the VPN is turned on.

When the VPN is turned on, it seems that the Google Speech Recognition API turns down the request. Anybody able to fix the issue is most welcome to get in touch with me here and share the resolution.

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Speech To Speech: an effort for an open-sourced and modular GPT4-o

huggingface/speech-to-speech

Folders and files.

Repository files navigation

📖 Quick Index

• Docker Server approach
• Server/Client approach
• Local approach
• Model parameters
• Generation parameters
• Notable parameters

This repository implements a speech-to-speech cascaded pipeline with consecutive parts:

• Voice Activity Detection (VAD) : silero VAD v5
• Speech to Text (STT) : Whisper checkpoints (including distilled versions )
• Language Model (LM) : Any instruct model available on the Hugging Face Hub ! 🤗
• Text to Speech (TTS) : Parler-TTS 🤗

The pipeline aims to provide a fully open and modular approach, leveraging models available on the Transformers library via the Hugging Face hub. The level of modularity intended for each part is as follows:

• VAD : Uses the implementation from Silero's repo .
• STT : Uses Whisper models exclusively; however, any Whisper checkpoint can be used, enabling options like Distil-Whisper and French Distil-Whisper .
• LM : This part is fully modular and can be changed by simply modifying the Hugging Face hub model ID. Users need to select an instruct model since the usage here involves interacting with it.
• TTS : The mini architecture of Parler-TTS is standard, but different checkpoints, including fine-tuned multilingual checkpoints, can be used.

The code is designed to facilitate easy modification. Each component is implemented as a class and can be re-implemented to match specific needs.

Clone the repository:

Install the required dependencies using uv :

The pipeline can be run in two ways:

• Server/Client approach : Models run on a server, and audio input/output are streamed from a client.
• Local approach : Runs locally.

Docker Server

Install the nvidia container toolkit.

https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html

Start the docker container

docker compose up

Server/Client Approach

To run the pipeline on the server:

Then run the pipeline locally:

Running on Mac

To run on mac, we recommend setting the flag --local_mac_optimal_settings :

You can also pass --device mps to have all the models set to device mps. The local mac optimal settings set the mode to be local as explained above and change the models to:

• LightningWhisperMLX

Recommended usage with Cuda

Leverage Torch Compile for Whisper and Parler-TTS:

For the moment, modes capturing CUDA Graphs are not compatible with streaming Parler-TTS ( reduce-overhead , max-autotune ).

Command-line Usage

Model parameters.

model_name , torch_dtype , and device are exposed for each part leveraging the Transformers' implementations: Speech to Text, Language Model, and Text to Speech. Specify the targeted pipeline part with the corresponding prefix:

• stt (Speech to Text)
• lm (Language Model)
• tts (Text to Speech)

For example:

Generation Parameters

Other generation parameters of the model's generate method can be set using the part's prefix + _gen_ , e.g., --stt_gen_max_new_tokens 128 . These parameters can be added to the pipeline part's arguments class if not already exposed (see LanguageModelHandlerArguments for example).

Notable Parameters

Vad parameters.

• --thresh : Threshold value to trigger voice activity detection.
• --min_speech_ms : Minimum duration of detected voice activity to be considered speech.
• --min_silence_ms : Minimum length of silence intervals for segmenting speech, balancing sentence cutting and latency reduction.

Language Model

• --init_chat_role : Defaults to None . Sets the initial role in the chat template, if applicable. Refer to the model's card to set this value (e.g. for Phi-3-mini-4k-instruct you have to set --init_chat_role system )
• --init_chat_prompt : Defaults to "You are a helpful AI assistant." Required when setting --init_chat_role .

Speech to Text

--description : Sets the description for Parler-TTS generated voice. Defaults to: "A female speaker with a slightly low-pitched voice delivers her words quite expressively, in a very confined sounding environment with clear audio quality. She speaks very fast."

--play_steps_s : Specifies the duration of the first chunk sent during streaming output from Parler-TTS, impacting readiness and decoding steps.

Distil-Whisper

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How to continuously to do speech recognition while outputting the recognized word as soon as possible

I am working on a project that involves speech recognition using the SpeechRecognition module. One thing I want to do to improve my speech recognition is to be able to output the words that have been recognized as soon as possible. I want it to be similar to whenever you speak into Google Translate, as soon as you say a word it outputs it on the screen to let you know that you have said it.

Some of the things I have tried are to have an array that stores separate audio recordings and have speech recognition iterate through the array recognizing each audio recording and then outputting that. This did not work because different words take different times to say.

I looked further into the Google API for speech recognition given to me by the SpeechRecognition module and wanted to see how I could adjust the actual library by adding print statements in some places to achieve the goal. I did not know where to put, as I am a beginner in speech recognition and that I do not know much about the Google Speech Recognition API.

Here is the google api code, it accesses the cloud to do sr.

Here is my base code (the things I previously tried are not shown here): It is able to successfully do speech recognition.

The final method you should know is the recording function to make audio files or objects:

I would like to achieve software that is able to take the current code and implement in such a way it outputs the recognized word as soon as possible. It would be similar to when you speak in Google Translate.

• speech-recognition
• speech-to-text

4 Answers 4

Have you seen this? https://web.archive.org/web/20220705065027/https://speech-to-text-demo.ng.bluemix.net/ just click on "record audio", you will see the hypotheses in the screen while you speak. That demo is open source, you can just fork the code in GitHub. The continuous speech recognition effect can be achieved by calling the service using the WebSocket API using your favorite programming language.

What you are asking for is a continuous speech recognizer . These exist, but a speech recognizer needs to be specifically built for this application, as it needs to respond very quickly, and to be able to correctly handle utterances that are not yet complete.

The Google speech API you are using ( https://www.google.com/speech-api/v2/recognize ) is not a continuous speech recognizer. You cannot turn it into a continuous recognizer by modifying the code calling it.

you could use Pocketsphinx that another speech engine and they dont use over the internet connection

https://pypi.org/project/pocketsphinx/

you can use threading it's built-in python module

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Google Cloud Text-to-Speech API

Overview of the APIs available for Google Cloud Text-to-Speech API.

All entries

Classes, methods and properties & attributes for Google Cloud Text-to-Speech API.

properties and attributes

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Last updated 2024-08-21 UTC.