speech to text library python

• 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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How to Translate Languages in Python

Learn how to make a language translator and detector using Googletrans library (Google Translation API) for translating more than 100 languages with Python.

Speech Recognition using Transformers in Python

Learn how to perform speech recognition using wav2vec2 and whisper transformer models with the help of Huggingface transformers library in Python.

How to Play and Record Audio in Python

Learn how to play and record sound files using different libraries such as playsound, Pydub and PyAudio in Python.

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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 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.

The Only Guide You Need to Speech Recognition in Python with Speech-To-Text API

Rev › Blog › Resources › Other Resources › Speech-to-Text APIs › The Only Guide You Need to Speech Recognition in Python with Speech-To-Text API

The mega tech companies, Microsoft, Google, and Amazon provide speech-to-text-transcription services.  These work well for most use cases, in particular consumer applications like home automation and search.  

But the tech giants cast a wide net. Their solution does not work for niche applications.  They are not always well-suited for or priced correctly for all software applications and devices.  The reasons for that are both architecture and the need for additional features.  In short, the specialized application often needs full control.

Even though they are mega-large, with the world’s top engineers, the tech giants do not have as much quality audio training data as a company like Rev. The big companies vacuum up everything with devices like Alexa turned on all the time.  But doing that sucks up noise and clutter as well as clear speech.  That muddles the picture.

Rev takes a different approach. We employ over 50,000 freelance human transcriptionists to continually transcribe speech to text. Rev uses that highly curated data to train its AI models, making it the best and most accurate speech recognition solution in the world, consistently beating Google, Amazon, Microsoft, and others in accuracy tests .

Serving the Needs of Software and Hardware: Speech-to-Text API Use Cases & Examples

Some common scenarios Rev.ai handles:

Live Captions

Rev.ai can add captions & transcripts to videos in real time streaming media. For example, Rev used Rev.ai to create a live captioning integration for Zoom .

Transcripts of Videos

The video company Loom uses Rev to transcribe videos on their video hosting platform.  

Video or Audio Editing/Production

Hollywood studios & production companies often use transcription for video editing. For example, transcribing all available video footage in order to quickly find the takes or scenes to edit.

Video/Audio Accessibility & Compliance

All companies need to comply with accessibility laws and make video & audio accessible to all individuals. Think about anyone who is deaf or hard of hearing. Rev AI can help with making your software, applications, video, and audio more accessible.

Transcripts of Meetings

Virtual meetings like Zoom meetings are becoming more and more common in all industries. Any recorded meeting can be transcribed also. This is a great replacement for taking meeting notes, or improving meeting experiences for deaf & hard of hearing individuals.

Transcripts of Interviews

Documentary filmmakers, journalists, and media companies use speech recognition for interviews.

Converting massive amounts of audio or video to text creates a ton of data. You can use this data for analysis in a wide range of industries.

Police Body Cameras

Camera manufacturers can add the ability to transcribe video footage.  This meets the legal requirements of the state and makes legal discovery easy, as the user can search for text instead of having to watch many hours of video. Axon uses Rev for this currently . Transcribing video footage has many use cases beyond police body cameras.

Podcast Transcription

Podcasts are blowing up in popularity, and transcriptions of podcasts can create an entirely new asset for any podcast. Converting podcasts to text can improve accessibility and create an SEO asset for any podcast.

Live Depositions

The legal industry is becoming more virtual all the time. Depositions, live court reporting, and more can benefit from speech recognition.

Python Speech Recognition Code Examples

Here we provide a code example, so a developer or CTO can understand the Rev.ai solution.  

In this example we use one of the simplest, albeit most widely used programming languages, Python. 

We also handle JavaScript, Java, and Go, which can all be found in our SDK’s .

Asynchronous API Python Code Example

We call our products asynchronous (pre-recorded) and streaming (realtime).

Here is a simple asynchronous example: We transcribe Dr. Martin Luther King’s 17-minute famous “I Have a Dream” speech.

Asynchronous API Python Code Explained

To get started, log into the portal and generate an API key.  Download the API from the Python public repository or use pip. 

The Python code is rather simple, since Rev.ai does all the heavy lifting.  For example, the API handles all the complexity of working with different 16 kHz audio, a specific set of media formats, uploading a file, issuing a callback, queuing it for processing, etc.  The programmer just:

• Submits the file (base64 encoded) or URL
• Checks the status 
• Downloads the transcription  

Basically, the procedure is to submit an audio file or URL to the rev.ai engine.  Then you poll the system and retrieve the results when the transcription is complete.  Or, you can use callbacks.  Callbacks tell your program when transcription is complete with no time delay.

The Python code below does the following:

• Reads the API key, which we have saved as an environment variable
• Opens a client connection to rev.ai
• Submits a URL or file
• Queries the job status by job.id
• When the job is complete, downloads the results as a text file

There are other options, like submitting audio or video files for transcription.  You can read about those in the documentation .   

The Complete Python Code for the Rev AI Asynchronous API

Here is the complete code:

The output looks like this:

How to Use Rev’s Streaming Speech Recognition API with Python

Working with streaming data is completely different than working with a single file.  

When you work with a streaming file you have to work at a lower level on the network stack.  This is because a stream is not like a file that you open and close.  Rev.ai handles that complexity by working at the websocket layer.  Async uses REST.  Streaming uses RTMP (Real-Time Messaging Protocol).

At first glance, you might think the streaming API will transcribe streaming audio data that is sent to you.  It works the other way around.  You connect to Rev AI using a streaming API such as PyAudio.  That maintains a persistent connection.  Rev.ai processes the audio or video and sends back the transcribed text also as a stream.

The code is similarly simple.  Obviously, you would have to write your own video or audio server.  But that’s to be expected as Rev.ai exists to service this type of application.

Further Reading

You can read more about how to use Rev.ai in our detailed documentation .

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Folders and files, repository files navigation, 🐸coqui.ai news.

• 📣 ⓍTTSv2 is here with 16 languages and better performance across the board.
• 📣 ⓍTTS fine-tuning code is out. Check the example recipes .
• 📣 ⓍTTS can now stream with <200ms latency.
• 📣 ⓍTTS, our production TTS model that can speak 13 languages, is released Blog Post , Demo , Docs
• 📣 🐶Bark is now available for inference with unconstrained voice cloning. Docs
• 📣 You can use ~1100 Fairseq models with 🐸TTS.
• 📣 🐸TTS now supports 🐢Tortoise with faster inference. Docs

🐸TTS is a library for advanced Text-to-Speech generation.

🚀 Pretrained models in +1100 languages.

🛠️ Tools for training new models and fine-tuning existing models in any language.

📚 Utilities for dataset analysis and curation.

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Please use our dedicated channels for questions and discussion. Help is much more valuable if it's shared publicly so that more people can benefit from it.

🔗 Links and Resources

🥇 tts performance.

Underlined "TTS*" and "Judy*" are internal 🐸TTS models that are not released open-source. They are here to show the potential. Models prefixed with a dot (.Jofish .Abe and .Janice) are real human voices.

• Text2Spec models (Tacotron, Tacotron2, Glow-TTS, SpeedySpeech).
• Speaker Encoder to compute speaker embeddings efficiently.
• Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS, ParallelWaveGAN, WaveGrad, WaveRNN)
• Fast and efficient model training.
• Detailed training logs on the terminal and Tensorboard.
• Support for Multi-speaker TTS.
• Efficient, flexible, lightweight but feature complete Trainer API .
• Released and ready-to-use models.
• Tools to curate Text2Speech datasets under dataset_analysis .
• Utilities to use and test your models.
• Modular (but not too much) code base enabling easy implementation of new ideas.

Model Implementations

Spectrogram models.

• Tacotron: paper
• Tacotron2: paper
• Glow-TTS: paper
• Speedy-Speech: paper
• Align-TTS: paper
• FastPitch: paper
• FastSpeech: paper
• FastSpeech2: paper
• SC-GlowTTS: paper
• Capacitron: paper
• OverFlow: paper
• Neural HMM TTS: paper
• Delightful TTS: paper

End-to-End Models

• VITS: paper
• 🐸 YourTTS: paper
• 🐢 Tortoise: orig. repo
• 🐶 Bark: orig. repo

Attention Methods

• Guided Attention: paper
• Forward Backward Decoding: paper
• Graves Attention: paper
• Double Decoder Consistency: blog
• Dynamic Convolutional Attention: paper
• Alignment Network: paper

Speaker Encoder

• GE2E: paper
• Angular Loss: paper
• MelGAN: paper
• MultiBandMelGAN: paper
• ParallelWaveGAN: paper
• GAN-TTS discriminators: paper
• WaveRNN: origin
• WaveGrad: paper
• HiFiGAN: paper
• UnivNet: paper

Voice Conversion

• FreeVC: paper

You can also help us implement more models.

Installation

🐸TTS is tested on Ubuntu 18.04 with python >= 3.9, < 3.12. .

If you are only interested in synthesizing speech with the released 🐸TTS models, installing from PyPI is the easiest option.

If you plan to code or train models, clone 🐸TTS and install it locally.

If you are on Ubuntu (Debian), you can also run following commands for installation.

If you are on Windows, 👑@GuyPaddock wrote installation instructions here .

Docker Image

You can also try TTS without install with the docker image. Simply run the following command and you will be able to run TTS without installing it.

You can then enjoy the TTS server here More details about the docker images (like GPU support) can be found here

Synthesizing speech by 🐸TTS

🐍 python api, running a multi-speaker and multi-lingual model, running a single speaker model, example voice conversion.

Converting the voice in source_wav to the voice of target_wav

Example voice cloning together with the voice conversion model.

This way, you can clone voices by using any model in 🐸TTS.

Example text to speech using Fairseq models in ~1100 languages 🤯.

For Fairseq models, use the following name format: tts_models/<lang-iso_code>/fairseq/vits . You can find the language ISO codes here and learn about the Fairseq models here .

Command-line tts

Synthesize speech on command line.

You can either use your trained model or choose a model from the provided list.

If you don't specify any models, then it uses LJSpeech based English model.

Single Speaker Models

List provided models:

Get model info (for both tts_models and vocoder_models):

Query by type/name: The model_info_by_name uses the name as it from the --list_models.

For example:

Query by type/idx: The model_query_idx uses the corresponding idx from --list_models.

Query info for model info by full name:

Run TTS with default models:

Run TTS and pipe out the generated TTS wav file data:

Run a TTS model with its default vocoder model:

Run with specific TTS and vocoder models from the list:

Run your own TTS model (Using Griffin-Lim Vocoder):

Run your own TTS and Vocoder models:

Multi-speaker Models

List the available speakers and choose a <speaker_id> among them:

Run the multi-speaker TTS model with the target speaker ID:

Run your own multi-speaker TTS model:

Voice Conversion Models

Directory structure, code of conduct, releases 98, used by 1.2k.

Contributors 149

• Python 92.0%
• Jupyter Notebook 7.5%
• Makefile 0.1%
• Cython 0.0%

Python Speech Recognition Module – A Complete Introduction

Hey there! Today let’s learn about converting speech to text using the speech recognition library in Python programming language. So let’s begin!

Introduction to Speech Recognition

Speech recognition is defined as the automatic recognition of human speech and is recognized as one of the most important tasks when it comes to making applications like Alexa or Siri.

Python comes with several libraries which support speech recognition feature. We will be using the speech recognition library because it is the simplest and easiest to learn.

Importing Speech Recognition Module

The first step, as always, is to import the required libraries. In this case, we only need to import the speech_recognition library.

If the statement gives an error, you might need to install the library using the pip command.

Implementing Speech Recognition in Python

To convert speech from our audio to text, we need the Recognizer class from the speech_recognition module to create an object which contains all the necessary functions for further processing.

1. Loading Audio

Before we continue, we’ll need to download an audio file. The one I used to get started is a speech from Emma Watson which can be found here .

We download the audio file and converted it into wav format because it works best to recognize speech. But make sure you save it to the same folder as your Python file.

To load audio we will be using the AudioFile function. The function opens the file, reads its contents and store all the information in an AudioFile instance called source.

We will traverse through the source and do the following things:

• Every audio has some noise involved which can be removed using the adjust_for_ambient_noise function.
• Making use of the record method which reads the audio file and stores certain information into a variable to be read later on.

The complete code to load the audio is mentioned below.

Here we have also mentioned a parameter known as duration because it will take a lot more time to recognize speech for a longer audio. So will will only be taking first 100 seconds of the audio.

2. Reading data from audio

Now that we have successfully loaded the audio, we can now invoke recognize_google() method and recognize any speech in the audio.

The method can take several seconds depending on your internet connection speed. After processing the method returns the best possible speech that the program was able to recognize from the first 100 seconds.

The code for the same is shown below.

The output comes out to be a bunch of sentences from the audio which turn out to be pretty good. The accuracy can be increased by the use of more functions but for now it does the basic functionalities.

Congratulalations! Today in this tutorial you learned about recognizing speech from audio and displaying the same on your screen.

I would also like to mention that speech recognition is a very deep and vast concept, and what we have learned here barely scratches the surface of the whole subject.

Thank you for reading!

• Español – América Latina
• Português – Brasil
• Cloud Speech-to-Text
• Documentation

Speech-to-Text Client Libraries

This page shows how to get started with the Cloud Client Libraries for the Speech-to-Text API. Client libraries make it easier to access Google Cloud APIs from a supported language. Although you can use Google Cloud APIs directly by making raw requests to the server, client libraries provide simplifications that significantly reduce the amount of code you need to write.

Read more about the Cloud Client Libraries and the older Google API Client Libraries in Client libraries explained .

Install the client library

If you are using .NET Core command-line interface tools to install your dependencies, run the following command:

For more information, see Setting Up a C# Development Environment .

For more information, see Setting Up a Go Development Environment .

If you are using Maven , add the following to your pom.xml file. For more information about BOMs, see The Google Cloud Platform Libraries BOM .

If you are using Gradle , add the following to your dependencies:

If you are using sbt , add the following to your dependencies:

If you're using Visual Studio Code, IntelliJ, or Eclipse, you can add client libraries to your project using the following IDE plugins:

• Cloud Code for VS Code
• Cloud Code for IntelliJ
• Cloud Tools for Eclipse

The plugins provide additional functionality, such as key management for service accounts. Refer to each plugin's documentation for details.

For more information, see Setting Up a Java Development Environment .

For more information, see Setting Up a Node.js Development Environment .

For more information, see Using PHP on Google Cloud .

For more information, see Setting Up a Python Development Environment .

For more information, see Setting Up a Ruby Development Environment .

Set up authentication

For production environments, the way you set up ADC depends on the service and context. For more information, see Set up Application Default Credentials .

For a local development environment, you can set up ADC with the credentials that are associated with your Google Account:

Install and initialize the gcloud CLI .

When you initialize the gcloud CLI, be sure to specify a Google Cloud project in which you have permission to access the resources your application needs.

Configure ADC:

A sign-in screen appears. After you sign in, your credentials are stored in the local credential file used by ADC .

Use the client library

The following example shows how to use the client library.

Additional resources

The following list contains links to more resources related to the client library for C#:

• API reference
• Client libraries best practices
• Issue tracker
• google-cloud-speech on Stack Overflow
• Source code

The following list contains links to more resources related to the client library for Go:

The following list contains links to more resources related to the client library for Java:

The following list contains links to more resources related to the client library for Node.js:

The following list contains links to more resources related to the client library for PHP:

The following list contains links to more resources related to the client library for Python:

The following list contains links to more resources related to the client library for Ruby:

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.

Last updated 2024-05-15 UTC.

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• 13 May 2024

Brain-reading device is best yet at decoding ‘internal speech’

• Miryam Naddaf

You can also search for this author in PubMed   Google Scholar

Illustration showing the supramarginal gyrus (orange), a region of the brain involved in speech. Credit: My Box/Alamy

Scientists have developed brain implants that can decode internal speech — identifying words that two people spoke in their minds without moving their lips or making a sound.

Although the technology is at an early stage — it was shown to work with only a handful of words, and not phrases or sentences — it could have clinical applications in future.

Similar brain–computer interface (BCI) devices, which translate signals in the brain into text, have reached speeds of 62–78 words per minute for some people . But these technologies were trained to interpret speech that is at least partly vocalized or mimed.

The latest study — published in Nature Human Behaviour on 13 May 1 — is the first to decode words spoken entirely internally, by recording signals from individual neurons in the brain in real time.

“It's probably the most advanced study so far on decoding imagined speech,” says Silvia Marchesotti, a neuroengineer at the University of Geneva, Switzerland.

“This technology would be particularly useful for people that have no means of movement any more,” says study co-author Sarah Wandelt, a neural engineer who was at the California Institute of Technology in Pasadena at the time the research was done. “For instance, we can think about a condition like locked-in syndrome.”

Mind-reading tech

The researchers implanted arrays of tiny electrodes in the brains of two people with spinal-cord injuries. They placed the devices in the supramarginal gyrus (SMG), a region of the brain that had not been previously explored in speech-decoding BCIs.

Figuring out the best places in the brain to implant BCIs is one of the key challenges for decoding internal speech, says Marchesotti. The authors decided to measure the activity of neurons in the SMG on the basis of previous studies showing that this part of the brain is active in subvocal speech and in tasks such as deciding whether words rhyme.

Two weeks after the participants were implanted with microelectrode arrays in their left SMG, the researchers began collecting data. They trained the BCI on six words (battlefield, cowboy, python, spoon, swimming and telephone) and two meaningless pseudowords (nifzig and bindip). “The point here was to see if meaning was necessary for representation,” says Wandelt.

The rise of brain-reading technology: what you need to know

Over three days, the team asked each participant to imagine speaking the words shown on a screen and repeated this process several times for each word. The BCI then combined measurements of the participants’ brain activity with a computer model to predict their internal speech in real time.

For the first participant, the BCI captured distinct neural signals for all of the words and was able to identify them with 79% accuracy. But the decoding accuracy was only 23% for the second participant, who showed preferential representation for ‘spoon’ and ‘swimming’ and had fewer neurons that were uniquely active for each word. “It's possible that different sub-areas in the supramarginal gyrus are more, or less, involved in the process,” says Wandelt.

Christian Herff, a computational neuroscientist at Maastricht University in the Netherlands, thinks these results might highlight the different ways in which people process internal speech. “Previous studies showed that there are different abilities in performing the imagined task and also different BCI control abilities,” adds Marchesotti.

The authors also found that 82–85% of neurons that were active during internal speech were also active when the participants vocalized the words. But some neurons were active only during internal speech, or responded differently to specific words in the different tasks.

Although the study represents significant progress in decoding internal speech, clinical applications are still a long way off, and many questions remain unanswered.

“The problem with internal speech is we don't know what’s happening and how is it processed,” says Herff. For example, researchers have not been able to determine whether the brain represents internal speech phonetically (by sound) or semantically (by meaning). “What I think we need are larger vocabularies” for the experiments, says Herff.

Marchesotti also wonders whether the technology can be generalized to people who have lost the ability to speak, given that the two study participants are able to talk and have intact brain speech areas. “This is one of the things that I think in the future can be addressed,” she says.

The next step for the team will be to test whether the BCI can distinguish between the letters of the alphabet. “We could maybe have an internal speech speller, which would then really help patients to spell words,” says Wandelt.

doi: https://doi.org/10.1038/d41586-024-01424-7

Wandelt, S. K. et al. Nature Hum. Behav . https://doi.org/10.1038/s41562-024-01867-y (2024).

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pip install gTTS Copy PIP instructions

Released: Jan 29, 2024

gTTS (Google Text-to-Speech), a Python library and CLI tool to interface with Google Translate text-to-speech API

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Unverified details

Project links.

• documentation

GitHub Statistics

• Open issues:

View statistics for this project via Libraries.io , or by using our public dataset on Google BigQuery

License: MIT License (MIT)

Author: Pierre Nicolas Durette

Tags gtts, text to speech, Google Translate, TTS

Requires: Python >=3.7

Classifiers

• OSI Approved :: MIT License
• Microsoft :: Windows
• POSIX :: Linux
• Python :: 3.8
• Python :: 3.9
• Python :: 3.10
• Python :: 3.11
• Python :: 3.12
• Multimedia :: Sound/Audio :: Speech
• Software Development :: Libraries

Project description

gTTS ( Google Text-to-Speech ), a Python library and CLI tool to interface with Google Translate's text-to-speech API. Write spoken mp3 data to a file, a file-like object (bytestring) for further audio manipulation, or stdout . https://gtts.readthedocs.io/

• Customizable speech-specific sentence tokenizer that allows for unlimited lengths of text to be read, all while keeping proper intonation, abbreviations, decimals and more;
• Customizable text pre-processors which can, for example, provide pronunciation corrections;

Installation

Command Line:

See https://gtts.readthedocs.io/ for documentation and examples.

This project is not affiliated with Google or Google Cloud. Breaking upstream changes can occur without notice. This project is leveraging the undocumented Google Translate speech functionality and is different from Google Cloud Text-to-Speech .

• Questions & community
• Contributing

The MIT License (MIT) Copyright © 2014-2024 Pierre Nicolas Durette & Contributors

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