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Python: Convert Speech to text and text to Speech

Speech Recognition is an important feature in several applications used such as home automation, artificial intelligence, etc. This article aims to provide an introduction on how to make use of the SpeechRecognition and pyttsx3 library of Python. Installation required:  

• Python Speech Recognition module:  
• PyAudio: Use the following command for linux users 
• Windows users can install pyaudio by executing the following command in a terminal 
• Python pyttsx3 module:  

Speech Input Using a Microphone and Translation of Speech to Text  

• Allow Adjusting for Ambient Noise: Since the surrounding noise varies, we must allow the program a second or too to adjust the energy threshold of recording so it is adjusted according to the external noise level. 
• Speech to text translation: This is done with the help of Google Speech Recognition. This requires an active internet connection to work. However, there are certain offline Recognition systems such as PocketSphinx, but have a very rigorous installation process that requires several dependencies. Google Speech Recognition is one of the easiest to use. 

Translation of Speech to Text: First, we need to import the library and then initialize it using init() function. This function may take 2 arguments. 

• drivername: [Name of available driver] sapi5 on Windows | nsss on MacOS  
• debug: to enable or disable debug output 

After initialization, we will make the program speak the text using say() function.  This method may also take 2 arguments.  

• text: Any text you wish to hear. 
• name: To set a name for this speech. (optional) 

Finally, to run the speech we use runAndWait() All the say() texts won’t be said unless the interpreter encounters runAndWait(). Below is the implementation. 

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Using the Speech-to-Text API with C#

1. overview.

Google Cloud Speech-to-Text API enables developers to convert audio to text in 120 languages and variants, by applying powerful neural network models in an easy to use API.

In this codelab, you will focus on using the Speech-to-Text API with C#. You will learn how to send an audio file in English and other languages to the Cloud Speech-to-Text API for transcription.

What you'll learn

• How to use the Cloud Shell
• How to enable the Speech-to-Text API
• How to Authenticate API requests
• How to install the Google Cloud client library for C#
• 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 Platform Project
• A Browser, such Chrome or Firefox
• Familiarity using C#

How will you use this tutorial?

How would you rate your experience with c#, how would you rate your experience with using google cloud platform 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 Google 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. Enable the Speech-to-Text API

Before you can begin using the Speech-to-Text API, you must enable the API. You can enable the API by using the following command in the Cloud Shell:

4. Install the Google Cloud Speech-to-Text API client library for C#

First, create a simple C# console application that you will use to run Speech-to-Text API samples:

You should see the application created and dependencies resolved:

Next, navigate to SpeechToTextApiDemo folder:

And add Google.Cloud.Speech.V1 NuGet package to the project:

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

5. Transcribe Audio Files

In this section, you will transcribe a pre-recorded audio file in English. The audio file is available on Google Cloud Storage.

To transcribe an audio file, open the code editor from the top right side of the Cloud Shell:

Navigate to the Program.cs file inside the SpeechToTextApiDemo folder and replace the code with the following:

Take a minute or two to study the code and see it is used to transcribe an audio file*.*

The Encoding parameter tells the API which type of audio encoding you're using for the audio file. Flac is the encoding type for .raw files (see the doc for encoding type for more details).

In the RecognitionAudio object, you can pass the API either the uri of our audio file in Cloud Storage or the local file path for the audio file. Here, we're using a Cloud Storage uri.

Back in Cloud Shell, run the app:

You should see the following output:

In this step, you were able to transcribe an audio file in English and print out the result. Read more about Transcribing .

6. Transcribe with word timestamps

Speech-to-Text can detect time offset (timestamp) 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 time offsets, navigate to the Program.cs file inside the SpeechToTextApiDemo folder and replace the code with the following:

Take a minute or two to study the code and see it is used to transcribe an audio file with word timestamps*.* The EnableWordTimeOffsets parameter tells the API to enable time offsets (see the doc for more details).

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

7. Transcribe different languages

Speech-to-Text API supports transcription in over 100 languages! You can find a list of supported languages here .

In this section, you will transcribe a pre-recorded audio file in French. The audio file is available on Google Cloud Storage.

To transcribe the French audio file, navigate to the Program.cs file inside the SpeechToTextApiDemo folder and replace the code with the following:

Take a minute or two to study the code and see how it is used to transcribe an audio file*.* The LanguageCode parameter tells the API what language the audio recording is in.

This is a sentence from a popular French children's tale .

In this step, you were able to transcribe an audio file in French and print out the result. Read more about supported languages .

8. Congratulations!

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

To avoid incurring charges to your Google Cloud Platform account for the resources used in this quickstart:

• Go to the Cloud Platform Console .
• Select the project you want to shut down, then click ‘Delete' at the top: this schedules the project for deletion.
• Google Cloud Speech-to-Text API: https://cloud.google.com/speech-to-text/docs
• C#/.NET on Google Cloud Platform: https://cloud.google.com/dotnet/
• Google Cloud .NET client: https://googlecloudplatform.github.io/google-cloud-dotnet/

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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All Speech-to-Text code samples

This page contains code samples for Speech-to-Text. To search and filter code samples for other Google Cloud products, see the Google Cloud sample browser .

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Quickstart: Recognize and convert speech to text

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Some of the features described in this article might only be available in preview. This preview is provided without a service-level agreement, and we don't recommend it for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews .

In this quickstart, you try real-time speech to text in Azure AI Studio .

Prerequisites

• Azure subscription - Create one for free .
• Some AI services features are free to try in AI Studio. For access to all capabilities described in this article, you need to connect AI services to your hub in AI Studio .

Try real-time speech to text

Go to the Home page in AI Studio and then select AI Services from the left pane.

Select Speech from the list of AI services.

Select Real-time speech to text .

In the Try it out section, select your hub's AI services connection. For more information about AI services connections, see connect AI services to your hub in AI Studio .

Select Show advanced options to configure speech to text options such as:

• Language identification : Used to identify languages spoken in audio when compared against a list of supported languages. For more information about language identification options such as at-start and continuous recognition, see Language identification .
• Speaker diarization : Used to identify and separate speakers in audio. Diarization distinguishes between the different speakers who participate in the conversation. The Speech service provides information about which speaker was speaking a particular part of transcribed speech. For more information about speaker diarization, see the real-time speech to text with speaker diarization quickstart.
• Custom endpoint : Use a deployed model from custom speech to improve recognition accuracy. To use Microsoft's baseline model, leave this set to None. For more information about custom speech, see Custom Speech .
• Output format : Choose between simple and detailed output formats. Simple output includes display format and timestamps. Detailed output includes more formats (such as display, lexical, ITN, and masked ITN), timestamps, and N-best lists.
• Phrase list : Improve transcription accuracy by providing a list of known phrases, such as names of people or specific locations. Use commas or semicolons to separate each value in the phrase list. For more information about phrase lists, see Phrase lists .

Select an audio file to upload, or record audio in real-time. In this example, we use the Call1_separated_16k_health_insurance.wav file that's available in the Speech SDK repository on GitHub . You can download the file or use your own audio file.

You can view the real-time speech to text results in the Results section.

Reference documentation | Package (NuGet) | Additional samples on GitHub

In this quickstart, you create and run an application to recognize and transcribe speech to text in real-time.

To instead transcribe audio files asynchronously, see What is batch transcription . If you're not sure which speech to text solution is right for you, see What is speech to text?

• An Azure subscription. You can create one for free .
• Create a Speech resource in the Azure portal.
• Get the Speech resource key and region. After your Speech resource is deployed, select Go to resource to view and manage keys.

Set up the environment

The Speech SDK is available as a NuGet package and implements .NET Standard 2.0. You install the Speech SDK later in this guide. For any other requirements, see Install the Speech SDK .

Set environment variables

You need to authenticate your application to access Azure AI services. For production, use a secure way to store and access your credentials. For example, after you get a key for your Speech resource, write it to a new environment variable on the local machine that runs the application.

If you use an API key, store it securely somewhere else, such as in Azure Key Vault . Don't include the API key directly in your code, and never post it publicly.

For more information about AI services security, see Authenticate requests to Azure AI services .

To set the environment variables for your Speech resource key and region, open a console window, and follow the instructions for your operating system and development environment.

• To set the SPEECH_KEY environment variable, replace your-key with one of the keys for your resource.
• To set the SPEECH_REGION environment variable, replace your-region with one of the regions for your resource.

If you only need to access the environment variables in the current console, you can set the environment variable with set instead of setx .

After you add the environment variables, you might need to restart any programs that need to read the environment variables, including the console window. For example, if you're using Visual Studio as your editor, restart Visual Studio before you run the example.

Edit your .bashrc file, and add the environment variables:

After you add the environment variables, run source ~/.bashrc from your console window to make the changes effective.

Edit your .bash_profile file, and add the environment variables:

After you add the environment variables, run source ~/.bash_profile from your console window to make the changes effective.

For iOS and macOS development, you set the environment variables in Xcode. For example, follow these steps to set the environment variable in Xcode 13.4.1.

• Select Product > Scheme > Edit scheme .
• Select Arguments on the Run (Debug Run) page.
• Under Environment Variables select the plus (+) sign to add a new environment variable.
• Enter SPEECH_KEY for the Name and enter your Speech resource key for the Value .

To set the environment variable for your Speech resource region, follow the same steps. Set SPEECH_REGION to the region of your resource. For example, westus .

For more configuration options, see the Xcode documentation .

Recognize speech from a microphone

Follow these steps to create a console application and install the Speech SDK.

Open a command prompt window in the folder where you want the new project. Run this command to create a console application with the .NET CLI.

This command creates the Program.cs file in your project directory.

Install the Speech SDK in your new project with the .NET CLI.

Replace the contents of Program.cs with the following code:

To change the speech recognition language, replace en-US with another supported language . For example, use es-ES for Spanish (Spain). If you don't specify a language, the default is en-US . For details about how to identify one of multiple languages that might be spoken, see Language identification .

Run your new console application to start speech recognition from a microphone:

Make sure that you set the SPEECH_KEY and SPEECH_REGION environment variables . If you don't set these variables, the sample fails with an error message.

Speak into your microphone when prompted. What you speak should appear as text:

Here are some other considerations:

This example uses the RecognizeOnceAsync operation to transcribe utterances of up to 30 seconds, or until silence is detected. For information about continuous recognition for longer audio, including multi-lingual conversations, see How to recognize speech .

To recognize speech from an audio file, use FromWavFileInput instead of FromDefaultMicrophoneInput :

For compressed audio files such as MP4, install GStreamer and use PullAudioInputStream or PushAudioInputStream . For more information, see How to use compressed input audio .

Clean up resources

You can use the Azure portal or Azure Command Line Interface (CLI) to remove the Speech resource you created.

The Speech SDK is available as a NuGet package and implements .NET Standard 2.0. You install the Speech SDK later in this guide. For other requirements, see Install the Speech SDK .

Create a new C++ console project in Visual Studio Community named SpeechRecognition .

Select Tools > Nuget Package Manager > Package Manager Console . In the Package Manager Console , run this command:

Replace the contents of SpeechRecognition.cpp with the following code:

Build and run your new console application to start speech recognition from a microphone.

Reference documentation | Package (Go) | Additional samples on GitHub

Install the Speech SDK for Go. For requirements and instructions, see Install the Speech SDK .

Follow these steps to create a GO module.

Open a command prompt window in the folder where you want the new project. Create a new file named speech-recognition.go .

Copy the following code into speech-recognition.go :

Run the following commands to create a go.mod file that links to components hosted on GitHub:

Build and run the code:

Reference documentation | Additional samples on GitHub

To set up your environment, install the Speech SDK . The sample in this quickstart works with the Java Runtime .

Install Apache Maven . Then run mvn -v to confirm successful installation.

Create a new pom.xml file in the root of your project, and copy the following code into it:

Install the Speech SDK and dependencies.

Follow these steps to create a console application for speech recognition.

Create a new file named SpeechRecognition.java in the same project root directory.

Copy the following code into SpeechRecognition.java :

To recognize speech from an audio file, use fromWavFileInput instead of fromDefaultMicrophoneInput :

Reference documentation | Package (npm) | Additional samples on GitHub | Library source code

You also need a .wav audio file on your local machine. You can use your own .wav file (up to 30 seconds) or download the https://crbn.us/whatstheweatherlike.wav sample file.

To set up your environment, install the Speech SDK for JavaScript. Run this command: npm install microsoft-cognitiveservices-speech-sdk . For guided installation instructions, see Install the Speech SDK .

Recognize speech from a file

Follow these steps to create a Node.js console application for speech recognition.

Open a command prompt window where you want the new project, and create a new file named SpeechRecognition.js .

Install the Speech SDK for JavaScript:

Copy the following code into SpeechRecognition.js :

In SpeechRecognition.js , replace YourAudioFile.wav with your own .wav file. This example only recognizes speech from a .wav file. For information about other audio formats, see How to use compressed input audio . This example supports up to 30 seconds of audio.

Run your new console application to start speech recognition from a file:

The speech from the audio file should be output as text:

This example uses the recognizeOnceAsync operation to transcribe utterances of up to 30 seconds, or until silence is detected. For information about continuous recognition for longer audio, including multi-lingual conversations, see How to recognize speech .

Recognizing speech from a microphone is not supported in Node.js. It's supported only in a browser-based JavaScript environment. For more information, see the React sample and the implementation of speech to text from a microphone on GitHub.

The React sample shows design patterns for the exchange and management of authentication tokens. It also shows the capture of audio from a microphone or file for speech to text conversions.

Reference documentation | Package (PyPi) | Additional samples on GitHub

The Speech SDK for Python is available as a Python Package Index (PyPI) module . The Speech SDK for Python is compatible with Windows, Linux, and macOS.

• For Windows, install the Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017, 2019, and 2022 for your platform. Installing this package for the first time might require a restart.
• On Linux, you must use the x64 target architecture.

Install a version of Python from 3.7 or later . For other requirements, see Install the Speech SDK .

Follow these steps to create a console application.

Open a command prompt window in the folder where you want the new project. Create a new file named speech_recognition.py .

Run this command to install the Speech SDK:

Copy the following code into speech_recognition.py :

To change the speech recognition language, replace en-US with another supported language . For example, use es-ES for Spanish (Spain). If you don't specify a language, the default is en-US . For details about how to identify one of multiple languages that might be spoken, see language identification .

This example uses the recognize_once_async operation to transcribe utterances of up to 30 seconds, or until silence is detected. For information about continuous recognition for longer audio, including multi-lingual conversations, see How to recognize speech .

To recognize speech from an audio file, use filename instead of use_default_microphone :

Reference documentation | Package (download) | Additional samples on GitHub

The Speech SDK for Swift is distributed as a framework bundle. The framework supports both Objective-C and Swift on both iOS and macOS.

The Speech SDK can be used in Xcode projects as a CocoaPod , or downloaded directly and linked manually. This guide uses a CocoaPod. Install the CocoaPod dependency manager as described in its installation instructions .

Follow these steps to recognize speech in a macOS application.

Clone the Azure-Samples/cognitive-services-speech-sdk repository to get the Recognize speech from a microphone in Swift on macOS sample project. The repository also has iOS samples.

Navigate to the directory of the downloaded sample app ( helloworld ) in a terminal.

Run the command pod install . This command generates a helloworld.xcworkspace Xcode workspace containing both the sample app and the Speech SDK as a dependency.

Open the helloworld.xcworkspace workspace in Xcode.

Open the file named AppDelegate.swift and locate the applicationDidFinishLaunching and recognizeFromMic methods as shown here.

In AppDelegate.m , use the environment variables that you previously set for your Speech resource key and region.

To make the debug output visible, select View > Debug Area > Activate Console .

Build and run the example code by selecting Product > Run from the menu or selecting the Play button.

After you select the button in the app and say a few words, you should see the text that you spoke on the lower part of the screen. When you run the app for the first time, it prompts you to give the app access to your computer's microphone.

This example uses the recognizeOnce operation to transcribe utterances of up to 30 seconds, or until silence is detected. For information about continuous recognition for longer audio, including multi-lingual conversations, see How to recognize speech .

Objective-C

The Speech SDK for Objective-C shares client libraries and reference documentation with the Speech SDK for Swift. For Objective-C code examples, see the recognize speech from a microphone in Objective-C on macOS sample project in GitHub.

Speech to text REST API reference | Speech to text REST API for short audio reference | Additional samples on GitHub

You also need a .wav audio file on your local machine. You can use your own .wav file up to 60 seconds or download the https://crbn.us/whatstheweatherlike.wav sample file.

Open a console window and run the following cURL command. Replace YourAudioFile.wav with the path and name of your audio file.

You should receive a response similar to what is shown here. The DisplayText should be the text that was recognized from your audio file. The command recognizes up to 60 seconds of audio and converts it to text.

For more information, see Speech to text REST API for short audio .

Follow these steps and see the Speech CLI quickstart for other requirements for your platform.

Run the following .NET CLI command to install the Speech CLI:

Run the following commands to configure your Speech resource key and region. Replace SUBSCRIPTION-KEY with your Speech resource key and replace REGION with your Speech resource region.

Run the following command to start speech recognition from a microphone:

Speak into the microphone, and you see transcription of your words into text in real-time. The Speech CLI stops after a period of silence, 30 seconds, or when you select Ctrl + C .

To recognize speech from an audio file, use --file instead of --microphone . For compressed audio files such as MP4, install GStreamer and use --format . For more information, see How to use compressed input audio .

To improve recognition accuracy of specific words or utterances, use a phrase list . You include a phrase list in-line or with a text file along with the recognize command:

To change the speech recognition language, replace en-US with another supported language . For example, use es-ES for Spanish (Spain). If you don't specify a language, the default is en-US .

For continuous recognition of audio longer than 30 seconds, append --continuous :

Run this command for information about more speech recognition options such as file input and output:

Learn more about speech recognition

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Additional resources

Speech to Text Conversion Using Python

In this tutorial from Subhasish Sarkar, learn how to build a very 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.

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 conversion of the voice into text. 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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How to convert live real time audio from mic to text?

I need to build a speech to text converter using Python and Google speech to text API. I want to do this real-time as in this example link . So far I have tried following code:

This code first listens through the microphone then it converts to the text format. What I want to achieve here is while listening it should start converting to text in real time instead of waiting for it to complete.

• speech-recognition
• speech-to-text
• google-speech-api

• Possible duplicate of Google Streaming Speech Recognition on an Audio Stream Python –  Nikolay Shmyrev Commented Aug 24, 2019 at 21:35

2 Answers 2

You can use the below code to convert the real time audio from mic to real text.

If you're looking for an environment you could clone and get started with the Speech API you can check the realtime-transcription-playground repository. It's a React<>Python implementation for real-time transcription.

It also includes the Python code that streams the audio data to the Speech API, should you only be interested in that https://github.com/saharmor/realtime-transcription-playground/blob/main/backend/google_speech_wrapper.py . Specifically, the following methods are relevant: start_listen , listen_print_loop , and generator .

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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

install SpeechRecognition

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

install 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.

speech_recognition as sr     # import the library = sr.Recognizer()                 # initialize recognizer sr.Microphone() as source:     # mention source it will be either Microphone or audio files. print("Speak Anything :") audio = r.listen(source)        # listen to the source try: text = r.recognize_google(audio)    # use recognizer to convert our audio into text part. print("You said : {}".format(text)) except: print("Sorry could not recognize your voice")    # In case of voice not recognized  clearly

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 to Code - Enables you to code using just your voice.

pedrooaugusto/speech-to-code

Folders and files.

Name		Name
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Speech to code   .

Code using your voice

You can try a live demo of Speech2Code here: https://pedrooaugusto.github.io/speech-to-code/webapp

You can also check this video on how to solve the FizzBuzz problem using Speech2Code: https://www.youtube.com/watch?v=I71ETEeqa5E

(for this demo the app was ported to the web, to run directly on the browser)

Speech2Code is an application that enables you to code using just voice comands, with Speech2Code instead of using the keyboard to write code in the code editor like a caveman you can just express in natural language what you wish to do and that will be automatically written, as code, in the code editor.

Using Speech2Code instead of using the mouse and keyboard to navigate to line 42 of a file, you can just say: "line 42" , "go to line 42" or even "please go to line 42" . It's possible to say stuff like:

new variable answer equals the string john was the eggman string

• let answer = "john was the eggman"

call function max with arguments variable answer and expression gap plus number 42 on namespace Math

• Math . max ( answer , gap + 42 ) // 'gap' can later be replaced later by an actual value

This project can be divided into 3 main modules:

Webapp , Server and Client : are responsible for the application UI, capture audio and transform audio into text.

Spoken : is responsible for testing if a given phrase is a valid voice command and to extract important information out of it (parse).

Spoken VSCode Extension : is a Visual Studio Code extension able to receive commands to manipulate VSCode. Is through this extension that Speech2Code is able to control the Visual Studio Code.

Those modules interact as follows:

Voice Commands

Voice commands are transformed into text using the Azure Speech to Text service and later parsed by Spoken , which makes use of several pushdown automaton to extract information of the text.

Currently, Speech2Code only supports voice commands for the JavaScript language, a list of all those commands can be found here . All commands can be said in both english and portuguese HU3BR .

Controlling Visual Studio Code

Speech2Code was designed to work with any IDE that implements its interface , this is usually done through plugins and extensions. Currently, it has support for Visual Studio Code and CodeMirror.

For example, the voice command "call function fish with two arguments" will eventually call for editor.write(...) where editor can be any IDE/Editor like vscode, codemirror and sublime and each will have a different implementation for write(...) . The only common thing is that calling that function will write something in the current open file, no matter the IDE. Here you have an example of different implementations of the same function: VSCode.write(...) x CodeMirror.write(...)

The connection between VSCode and Speech2Code is done through a custom VSCode extension and Inter-Process Communication.

Running this project

First, install all the required dependencies with:

node scripts.js install

Then, you can start the server with:

A web based demo of Speech2Code will be accessible through: http://localhost:3000/webapp

Finnaly, if you wish to start the actual application run (make sure that VSCode is running before doing that):

npm --prefix client start

Dont forget to edit server/.env with your azure speech-to-text API keys.

Non code-like material produced in the creation of this project:

• Undergratuate dissertation on this project .
• Figma design: application screens, icons and images used in the dissertation .
• Trello board used before everything went south .
• TypeScript 78.0%
• JavaScript 11.3%
• Python 0.2%

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Join the community, add a new evaluation result row, speech recognition.

1184 papers with code • 234 benchmarks • 89 datasets

Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.

( Image credit: SpecAugment )

Benchmarks Add a Result

--> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> -->

Trend	Dataset	Best Model	Paper	Code	Compare
		FAdam
		parakeet-rnnt-1.1b
		wav2vec 2.0
		IBM (LSTM+Conformer encoder-decoder)
		Speechstew 100M
		Qwen-Audio
		wav2vec 2.0 XLS-R 1B + TEVR (5-gram)
		ConformerCTC-L (4-gram)
		ConformerCTC-L (5-gram)
		Quartznet
		Conformer-Transducer (no LM)
		W2V2-L-LL60K (+ TED-LIUM 3 LM)
		XLSR-53-Viet
		IBM (LSTM+Conformer encoder-decoder)
		Paraformer-large
		LAS + SpecAugment (with LM, Switchboard mild policy)
		wav2vec 2.0 Large-10h-LV-60k
		wav2vec 2.0 Large-10h-LV-60k
		ReVISE (bf)
		ConformerXXL-PS + G-Augment
		CTC-CRF ST-NAS
		Deep Speech 2
		Triphone (39 features) + LDA and MLLT + SGMM
		ConformerXXL-PS + G-Augment
		Whisper (Large v2)
		Icefall - zipformer transducer
		ConformerXXL-P + Downstream NST
		parakeet-rnnt-1.1b
		Vietnamese end-to-end speech recognition using wav2vec 2.0 by VietAI
		wav2vec2-base-vietnamese-160h (No Language Model)
		ConformerXXL-P + Downstream NST
		ConformerXXL-P
		parakeet-rnnt-1.1b
		mllp_2021_offline_verb
		mllp_2021_offline_filt
		Liquid-S4
		AV-HuBERT Large
		TS-SEP
		Paraformer-large
		End-to-end LF-MMI
		Espresso
		CTC-CRF
		wav2vec_wav2letter
		wav2vec_wav2letter
		XLSR53 Wav2Vec2 Portuguese by Orlem Santos
		Whisper (Large v2)
		wav2vec2-large-xls-r-1b-frisian
		Whisper (Large v2)
		Conformer/Transformer-AED
		Conformer/Transformer-AED
		Conformer/Transformer-AED
		SpeechStew (100M)
		SpeechStew (100M)
		ImportantAug
		RAVEn Large
		TDT 0-2
		TDT 0-4
		Qwen-Audio
		Qwen-Audio
		Qwen-Audio
		ConformerXXL-PS
		WavLM Large & EEND-vector clustering

Most implemented papers

Listen, attend and spell.

Unlike traditional DNN-HMM models, this model learns all the components of a speech recognizer jointly.

Deep Speech 2: End-to-End Speech Recognition in English and Mandarin

We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages.

Communication-Efficient Learning of Deep Networks from Decentralized Data

Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device.

Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

Describes an audio dataset of spoken words designed to help train and evaluate keyword spotting systems.

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

On LibriSpeech, we achieve 6. 8% WER on test-other without the use of a language model, and 5. 8% WER with shallow fusion with a language model.

Deep Speech: Scaling up end-to-end speech recognition

We present a state-of-the-art speech recognition system developed using end-to-end deep learning.

Conformer: Convolution-augmented Transformer for Speech Recognition

Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs).

wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler.

Recurrent Neural Network Regularization

wojzaremba/lstm • 8 Sep 2014

We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units.

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, among others.

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Trump flunks basic science yet again in speech insulting Harris' intelligence

By Ja'han Jones

Donald Trump promised an “intellectual” speech during his campaign stop in North Carolina on Wednesday. True to form, Trump broke his vow. Instead, what rallygoers got were some mind-numbingly misinformed ramblings from a septuagenarian nominee.

Over the last several weeks, Trump has lobbed all kinds of puerile insults at Vice President Kamala Harris, attempting to undermine her intelligence . During this very speech, which Trump claimed would focus on the economy, Trump claimed Harris is “not smart.” But it's hard to take such insults seriously when Trump himself fails to grasp some fairly basic concepts involving science and economics.

For example, he went on a rant arguing that Harris wants to “abolish oil, coal and natural gas,” and he suggested people who use wind power can't use their electronics when it’s not windy. In reality, the vice president is currently serving in an administration that has overseen a record boom in domestic oil production. And wind turbines are capable of storing power , so people who rely on them do not need to experience some "Wizard of Oz"-level wind storm to use their appliances and gadgets.

But this is not the scenario Trump envisioned in his speech :

Trump has long demonstrated his ignorance of and aversion to wind power and other climate-conscious policies. During another rant against wind power back in 2019, Trump admitted he “never understood wind.” Evidently, he still doesn’t. That this confusion comes from a man who’s called climate change a “hoax” and repeatedly claimed that the primary consequence of rising sea levels will be more beachfront property doesn’t inspire confidence in his capacity to confront the issues of climate change or encourage an expansion of renewable energy. 

Trump also admitted on Wednesday that he doesn't know what “net zero” means, referring to “Kamala’s extreme high-cost energy policy known as net zero.” But then he took it further :

“They have no idea what it means, by the way. It’s net zero — what does that mean? Nobody knows. Ask her what it means. ‘We’re gonna go to a net zero policy.’ What does that mean? Uhh, I have no idea.”

In reality, many people — all over the world — are familiar with the term (but if you're not, “net zero” refers to the point at which the amount of greenhouse gas being released into the atmosphere is equal to the amount being removed from the atmosphere). In fact, there are even quick, eye-catching videos online that explain the concept in simple terms for people like Trump who don’t know what it means.

So much for science. On the economics front, Trump demonstrated his grasp of the subject by holding up a large package of Tic Tac mints next to a smaller one and saying, “ This is inflation .” He didn’t elaborate. He went on to talk about how inflation, which has actually been slowing as of late , is destroying our country. How the existence of different size packages of mints connects to inflation was left for the audience to guess at. 

Trump is the leader of an entire political party, with staff and advisers who could help fill in the gaps in his knowledge, to educate him and his followers. But Trump seems perfectly content to wallow in ignorance — and to pull the MAGA faithful into the misinformed muck with him.

Ja'han Jones is The ReidOut Blog writer. He's a futurist and multimedia producer focused on culture and politics. His previous projects include "Black Hair Defined" and the "Black Obituary Project."

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Title: code-switching in text and speech reveals information-theoretic audience design.

Abstract: In this work, we use language modeling to investigate the factors that influence code-switching. Code-switching occurs when a speaker alternates between one language variety (the primary language) and another (the secondary language), and is widely observed in multilingual contexts. Recent work has shown that code-switching is often correlated with areas of high information load in the primary language, but it is unclear whether high primary language load only makes the secondary language relatively easier to produce at code-switching points (speaker-driven code-switching), or whether code-switching is additionally used by speakers to signal the need for greater attention on the part of listeners (audience-driven code-switching). In this paper, we use bilingual Chinese-English online forum posts and transcripts of spontaneous Chinese-English speech to replicate prior findings that high primary language (Chinese) information load is correlated with switches to the secondary language (English). We then demonstrate that the information load of the English productions is even higher than that of meaning equivalent Chinese alternatives, and these are therefore not easier to produce, providing evidence of audience-driven influences in code-switching at the level of the communication channel, not just at the sociolinguistic level, in both writing and speech.

Comments:	Submitted to Journal of Memory and Language on 7 June 2024
Subjects:	Computation and Language (cs.CL)
Cite as:	[cs.CL]
	(or [cs.CL] for this version)
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How Well Does Llama 3.1 Perform for Text and Speech Translation?

Meta’s research team introduced Llama 3.1 on July 23, 2023, calling it “the world’s largest and most capable openly available foundation model.”

Llama 3.1 is available in various parameter sizes — 8B, 70B, and 405B — providing flexibility for deployment based on computational resources and specific application needs. On April 18, 2024, Meta announced the Llama 3 family of large language models , which initially included only the 8B and 70B sizes. This latest release introduced the 405B model along with upgraded versions of the 8B and 70B models.

Llama 3.1 models represent a significant advancement over their predecessor, Llama 2, being pre-trained on an extensive corpus of 15 trillion multilingual tokens, a substantial increase from Llama 2’s 1.8 trillion tokens. With a context window of up to 128k tokens — previously limited to 8k tokes — they offer notable improvements in multilinguality, coding, reasoning, and tool usage.

Llama 3.1 maintains a similar architecture to Llama and Llama 2 but achieves performance improvements through enhanced data quality, diversity, and increased training scale. 

Meta’s research team tested Llama 3.1 on over 150 benchmark datasets covering a wide range of languages. They found that their “flagship model” with 405B parameters is competitive with leading models across various tasks and is close to matching the state-of-the-art performance. The smaller models are also “best-in-class,” outperforming alternative models with comparable numbers of parameters. 

SOTA Capabilities in Multilingual Translation

In multilingual tasks, the small Llama 3.1 8B model surpassed Gemma 2 9B and Mistral 7B, while Llama 3.1 70B outperformed Mixtral 8Xx22B and GPT 3.5 Turbo. Llama 3.1 405B is on par with Claude 3.5 Sonnet and outperformed GPT-4 and GPT 4o .

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Strong Performance on Speech Translation

In addition to language processing, the development of Llama 3.1 included multimodal extensions that enable image recognition, video recognition, and speech understanding capabilities.

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In speech translation tasks, where the model was asked to translate non-English speech into English text, Llama 3.1 again outperformed Whisper and SeamlesM4T. “The performance of our models in speech translation highlights the advantages of multimodal foundation models for tasks such as speech translation,” Meta’s team said.

They also shared details of the development process to help the research community understand the key factors of multimodal foundation model development and encourage informed discussions about the future of these models. “We hope sharing our results early will accelerate research in this direction,” they said.

Early Use Cases

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Publicly Available

Meta has released all Llama 3.1 models under an updated community license, promoting further innovation and responsible development towards artificial general intelligence (AGI).

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