how to make a text to speech program in python

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Text to Speech in Python [With Code Examples]

In this article, you will learn how to create text-to-speech programs in Python. You will create a Python program that converts any text you provide into speech.

This is an interesting experiment to discover what can be created with Python and to show you the power of Python and its modules.

How can you make Python speak?

Python provides hundreds of thousands of packages that allow developers to write pretty much any type of program. Two cross-platform packages you can use to convert text into speech using Python are PyTTSx3 and gTTS.

Together we will create a simple program to convert text into speech. This program will show you how powerful Python is as a language. It allows us to do even complex things with very few lines of code.

The Libraries to Make Python Speak

In this guide, we will try two different text-to-speech libraries:

• gTTS (Google text to Speech API)

They are both available on the Python Package Index (PyPI), the official repository for Python third-party software. Below you can see the page on PyPI for the two libraries:

• PyTTSx3: https://pypi.org/project/pyttsx3/
• gTTS: https://pypi.org/project/gTTS/

There are different ways to create a program in Python that converts text to speech and some of them are specific to the operating system.

The reason why we will be using PyTTSx3 and gTTS is to create a program that can run in the same way on Windows, Mac, and Linux (cross-platform).

Let’s see how PyTTSx3 works first…

Text-To-Speech With the PyTTSx3 Module

Before using this module remember to install it using pip:

If you are using Windows and you see one of the following error messages, you will also have to install the module pypiwin32 :

You can use pip for that module too:

If the pyttsx3 module is not installed you will see the following error when executing your Python program:

There’s also a module called PyTTSx (without the 3 at the end), but it’s not compatible with both Python 2 and Python 3.

We are using PyTTSx3 because is compatible with both Python versions.

It’s great to see that to make your computer speak using Python you just need a few lines of code:

Run your program and you will hear the message coming from your computer.

With just four lines of code! (excluding comments)

Also, notice the difference that commas make in your phrase. Try to remove the comma before “and you?” and run the program again.

Can you see (hear) the difference?

Also, you can use multiple calls to the say() function , so:

could be written also as:

All the messages passed to the say() function are not said unless the Python interpreter sees a call to runAndWait() . You can confirm that by commenting the last line of the program.

Change Voice with PyTTSx3

What else can we do with PyTTSx?

Let’s see if we can change the voice starting from the previous program.

First of all, let’s look at the voices available. To do that we can use the following program:

You will see an output similar to the one below:

The voices available depend on your system and they might be different from the ones present on a different computer.

Considering that our message is in English we want to find all the voices that support English as a language. To do that we can add an if statement inside the previous for loop.

Also to make the output shorter we just print the id field for each Voice object in the voices list (you will understand why shortly):

Here are the voice IDs printed by the program:

Let’s choose a female voice, to do that we use the following:

I select the id com.apple.speech.synthesis.voice.samantha , so our program becomes:

How does it sound? 🙂

You can also modify the standard rate (speed) and volume of the voice setting the value of the following properties for the engine before the calls to the say() function.

Below you can see some examples on how to do it:

Play with voice id, rate, and volume to find the settings you like the most!

Text to Speech with gTTS

Now, let’s create a program using the gTTS module instead.

I’m curious to see which one is simpler to use and if there are benefits in gTTS over PyTTSx or vice versa.

As usual, we install gTTS using pip:

One difference between gTTS and PyTTSx is that gTTS also provides a CLI tool, gtts-cli .

Let’s get familiar with gtts-cli first, before writing a Python program.

To see all the language available you can use:

That’s an impressive list!

The first thing you can do with the CLI is to convert text into an mp3 file that you can then play using any suitable applications on your system.

We will convert the same message used in the previous section: “I love Python for text to speech, and you?”

I’m on a Mac and I will use afplay to play the MP3 file.

The thing I see immediately is that the comma and the question mark don’t make much difference. One point for PyTTSx that does a better job with this.

I can use the –lang flag to specify a different language, you can see an example in Italian…

…the message says: “I like programming in Python, and you?”

Now we will write a Python program to do the same thing.

If you run the program you will hear the message.

Remember that I’m using afplay because I’m on a Mac. You can just replace it with any utilities that can play sounds on your system.

Looking at the gTTS documentation, I can also read the text more slowly passing the slow parameter to the gTTS() function.

Give it a try!

Change Voice with gTTS

How easy is it to change the voice with gTTS?

Is it even possible to customize the voice?

It wasn’t easy to find an answer to this, I have been playing a bit with the parameters passed to the gTTS() function and I noticed that the English voice changes if the value of the lang parameter is ‘en-US’ instead of ‘en’ .

The language parameter uses IETF language tags.

The voice seems to take into account the comma and the question mark better than before.

Also from another test it looks like ‘en’ (the default language) is the same as ‘en-GB’.

It looks to me like there’s more variety in the voices available with PyTTSx3 compared to gTTS.

Before finishing this section I also want to show you a way to create a single MP3 file that contains multiple messages, in this case in different languages:

The write_to_fp () function writes bytes to a file-like object that we save as hello_ciao.mp3.

Makes sense?

Work With Text to Speech Offline

One last question about text-to-speech in Python.

Can you do it offline or do you need an Internet connection?

Let’s run the first one of the programs we created using PyTTSx3.

From my tests, everything works well, so I can convert text into audio even if I’m offline.

This can be very handy for the creation of any voice-based software.

Let’s try gTTS now…

If I run the program using gTTS after disabling my connection, I see the following error:

So, gTTS doesn’t work without a connection because it requires access to translate.google.com.

If you want to make Python speak offline use PyTTSx3.

We have covered a lot!

You have seen how to use two cross-platform Python modules, PyTTSx3 and gTTS, to convert text into speech and to make your computer talk!

We also went through the customization of voice, rate, volume, and language that from what I can see with the programs we created here are more flexible with the PyTTSx3 module.

Are you planning to use this for a specific project?

Let me know in the comments below 🙂

Claudio Sabato is an IT expert with over 15 years of professional experience in Python programming, Linux Systems Administration, Bash programming, and IT Systems Design. He is a professional certified by the Linux Professional Institute .

With a Master’s degree in Computer Science, he has a strong foundation in Software Engineering and a passion for robotics with Raspberry Pi.

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1 thought on “Text to Speech in Python [With Code Examples]”

Hi, Yes I was planning to develop a program which would read text in multiple voices. I’m not a programmer and was looking to find the simplest way to achieve this. There are so many programming languages out there, would you say Python would be the best to for this purpose? kind regards Delton

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How to Convert Text to Speech in Python

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Speech synthesis (or Text to Speech) is the computer-generated simulation of human speech. It converts human language text into human-like speech audio. In this tutorial, you will learn how to convert text to speech in Python.

Please note that I will use text-to-speech or speech synthesis interchangeably in this tutorial, as they're essentially the same thing.

In this tutorial, we won't be building neural networks and training the model from scratch to achieve results, as it is pretty complex and hard to do for regular developers. Instead, we will use some APIs, engines, and pre-trained models that offer it.

More specifically, we will use four different techniques to do text-to-speech:

• gTTS : There are a lot of APIs out there that offer speech synthesis; one of the commonly used services is Google Text to Speech; we will play around with the gTTS library.
• pyttsx3 : A library that looks for pre-installed speech synthesis engines on your operating system and, therefore, performs text-to-speech without needing an Internet connection.
• openai : We'll be using the OpenAI Text to Speech API .
• Huggingface Transformers : The famous transformer library that offers a wide range of pre-trained deep learning (transformer) models that are ready to use. We'll be using a model called SpeechT5 that does this.

To clarify, this tutorial is about converting text to speech and not vice versa. If you want to convert speech to text instead, check this tutorial .

Table of contents:

Online Text to Speech

Offline text to speech, speech synthesis using openai api, speech synthesis using 🤗 transformers.

To get started, let's install the required modules:

As you may guess, gTTS stands for Google Text To Speech; it is a Python library that interfaces with Google Translate's text-to-speech API. It requires an Internet connection, and it's pretty easy to use.

Open up a new Python file and import:

It's pretty straightforward to use this library; you just need to pass text to the gTTS object, which is an interface to Google Translate 's Text to Speech API:

Up to this point, we have sent the text and retrieved the actual audio speech from the API. Let's save this audio to a file:

Awesome, you'll see a new file appear in the current directory; let's play it using playsound module installed previously:

And that's it! You'll hear a robot talking about what you just told him to say!

It isn't available only in English; you can use other languages as well by passing the lang parameter:

If you don't want to save it to a file and just play it directly, then you should use tts.write_to_fp() which accepts io.BytesIO() object to write into; check this link for more information.

To get the list of available languages, use this:

Here are the supported languages:

Now you know how to use Google's API, but what if you want to use text-to-speech technologies offline?

Well, pyttsx3 library comes to the rescue. It is a text-to-speech conversion library in Python, and it looks for TTS engines pre-installed in your platform and uses them, here are the text-to-speech synthesizers that this library uses:

• SAPI5 on Windows XP, Windows Vista, 8, 8.1, 10 and 11.
• NSSpeechSynthesizer on Mac OS X.
• espeak on Ubuntu Desktop Edition.

Here are the main features of the pyttsx3 library:

• It works fully offline
• You can choose among different voices that are installed on your system
• Controlling the speed of speech
• Tweaking volume
• Saving the speech audio into a file

Note : If you're on a Linux system and the voice output is not working with this library, then you should install espeak, FFmpeg, and libespeak1:

To get started with this library, open up a new Python file and import it:

Now, we need to initialize the TTS engine:

To convert some text, we need to use say() and runAndWait() methods:

say() method adds an utterance to speak to the event queue, while the runAndWait() method runs the actual event loop until all commands are queued up. So you can call say() multiple times and run a single runAndWait() method in the end to hear the synthesis, try it out!

This library provides us with some properties we can tweak based on our needs. For instance, let's get the details of the speaking rate:

Alright, let's change this to 300 (make the speaking rate much faster):

Another useful property is voices, which allow us to get details of all voices available on your machine:

Here is the output in my case:

As you can see, my machine has three voice speakers. Let's use the second, for example:

You can also save the audio as a file using the save_to_file() method, instead of playing the sound using say() method:

A new MP3 file will appear in the current directory; check it out!

In this section, we'll be using the newly released OpenAI audio models. Before we get started, make sure to update openai library to the latest version:

Next, you must create an OpenAI account and navigate to the API key page to Create a new secret key . Make sure to save this somewhere safe and do not share it with anyone.

Next, let's open up a new Python file and initialize our OpenAI API client:

After that, we can simply use client.audio.speech.create() to perform text to speech:

This is a paid API, and at the time of writing this, there are two models: tts-1 for 0.015$ per 1,000 characters and tts-1-hd for 0.03$ per 1,000 characters. tts-1 is cheaper and faster, whereas tts-1-hd provides higher-quality audio.

There are currently 6 voices you can choose from. I've chosen nova , but you can use alloy , echo , fable , onyx , and shimmer .

You can also experiment with the speed parameter; the default is 1.0 , but if you set it lower than that, it'll generate a slow speech and a faster speech when above 1.0 .

There is another parameter that is response_format . The default is mp3 , but you can set it to opus , aac , and flac .

In this section, we will use the 🤗 Transformers library to load a pre-trained text-to-speech transformer model. More specifically, we will use the SpeechT5 model that is fine-tuned for speech synthesis on LibriTTS . You can learn more about the model in this paper .

To get started, let's install the required libraries (if you haven't already):

Open up a new Python file named tts_transformers.py and import the following:

Let's load everything:

The processor is the tokenizer of the input text, whereas the model is the actual model that converts text to speech.

The vocoder is the voice encoder that is used to convert human speech into electronic sounds or digital signals. It is responsible for the final production of the audio file.

In our case, the SpeechT5 model transforms the input text we provide into a sequence of mel-filterbank features (a type of representation of the sound). These features are acoustic features often used in speech and audio processing, derived from a Fourier transform of the signal.

The HiFi-GAN vocoder we're using takes these representations and synthesizes them into actual audible speech.

Finally, we load a dataset that will help us get the speaker's voice vectors to synthesize speech with various speakers. Here are the speakers:

Next, let's make our function that does all the speech synthesis for us:

The function takes the text , and the speaker (optional) as arguments and does the following:

• It tokenizes the input text into a sequence of token IDs.
• If the speaker is passed, then we use the speaker vector to mimic the sound of the passed speaker during synthesis.
• If it's not passed, we simply make a random vector using torch.randn() . Although I do not think it's a reliable way of making a random voice.
• Next, we use our model.generate_speech() method to generate the speech tensor, it takes the input IDs, speaker embeddings, and the vocoder.
• Finally, we make our output filename and save it with a 16Khz sampling rate. (A funny thing you can do is when you reduce the sampling rate to 12Khz or 8Khz, you'll get a deeper and slower voice, and vice-versa: a higher-pitched and faster voice when you increase it to values like 22050 or 24000)

Let's use the function now:

This will generate a speech of the US female (as it's my favorite among all the speakers). This will generate a speech with a random voice:

Let's now call the function with all the speakers so you can compare speakers:

Listen to 6799-In-his-miracle-year,-he-published.mp3 :

Great, that's it for this tutorial; I hope that will help you build your application or maybe your own virtual assistant in Python!

To conclude, we have used four different methods for text-to-speech:

• Online Text to speech using the gTTS library
• Offline Text to speech using pyttsx3 library that uses an existing engine on your OS.
• The convenient Audio OpenAI API.
• Finally, we used 🤗 Transformers to perform text-to-speech (offline) using our computing resources.

So, to wrap it up, If you want to use a reliable synthesis, you can go for Audio OpenAI API, Google TTS API, or any other reliable API you choose. If you want a reliable but offline method, you can also use the SpeechT5 transformer. And if you just want to make it work quickly and without an Internet connection, you can use the pyttsx3 library.

You can get the complete code for all the methods used in the tutorial here .

Here is the documentation for used libraries:

• gTTS (Google Text-to-Speech)
• pyttsx3 - Text-to-speech x-platform
• OpenAI Text to Speech
• SpeechT5 (TTS task)

Related:  How to Play and Record Audio in Python .

Happy Coding ♥

Just finished the article? Why not take your Python skills a notch higher with our Python Code Assistant ? Check it out!

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Text to speech in python

• machine-learning

Text to speech (TTS) is the conversion of written text into spoken voice.You can create TTS programs in python. The quality of the spoken voice depends on your speech engine.

In this article you’ll learn how to create your own TTS program.

Related course: Complete Python Programming Course & Exercises

Example with espeak

The program ‘espeak’ is a simple speech synthesizer which converst written text into spoken voice. The espeak program does sound a bit robotic, but its simple enough to build a basic program.

TTS with Google

Google has a very natural sounding voices. You can use their TTS engine with the code below. For this program you need the module gTTS installed as well as the program mpg123.

This will output spoken voice / an mp3 file.

Play sound in Python

Convert MP3 to WAV

voicebox-tts 0.0.7

pip install voicebox-tts Copy PIP instructions

Released: Dec 28, 2023

Python text-to-speech library with built-in voice effects and support for multiple TTS engines.

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• Open issues:

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License: MIT License (MIT License Copyright (c) 2023 Austin Bowen Permission is hereby granted, free of charge, to any p...)

Author: Austin Bowen

Requires: Python >=3.8

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• Python :: 3.8
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Project description

| GitHub | Documentation 📘 | Audio Samples 🔉 |

• pip install voicebox-tts
• On Debian/Ubuntu: sudo apt install libportaudio2
• Install dependencies for whichever TTS engine(s) you want to use (see section below).

Supported Text-to-Speech Engines

Classes for supported TTS engines are located in the voicebox.tts.* modules.

Amazon Polly 🌐

Online TTS engine from AWS.

• Class: voicebox.tts.AmazonPolly
• Setup: pip install "voicebox-tts[amazon-polly]"

ElevenLabs 🌐

Online TTS engine with very realistic voices and support for voice cloning.

• Class: voicebox.tts.ElevenLabs
• pip install "voicebox-tts[elevenlabs]"
• Install ffmpeg or libav for pydub ( docs )
• Set environment variable ELEVEN_API_KEY=<api-key> ; or
• Set with import elevenlabs; elevenlabs.set_api_key('<api_key>') ; or
• Pass as parameter to class: voicebox.tts.ElevenLabs(api_key='<api_key>')

eSpeak NG 🌐

Offline TTS engine with a good number of options.

• Class: voicebox.tts.ESpeakNG
• On Debian/Ubuntu: sudo apt install espeak-ng

Google Cloud Text-to-Speech 🌐

Powerful online TTS engine offered by Google Cloud.

• Class: voicebox.tts.GoogleCloudTTS
• Setup: pip install "voicebox-tts[google-cloud-tts]"

Online TTS engine used by Google Translate.

• Class: voicebox.tts.gTTS
• pip install "voicebox-tts[gtts]"

Very basic offline TTS engine.

• Class: voicebox.tts.PicoTTS
• On Debian/Ubuntu: sudo apt install libttspico-utils

Built-in effect classes are located in the voicebox.effects module, and can be imported like:

Here is a non-exhaustive list of fun effects:

• Glitch creates a glitchy sound by randomly repeating small chunks of audio.
• RingMod can be used to create choppy, Doctor Who Dalek-like effects.
• Vocoder is useful for making monotone, robotic voices.

There is also support for all the awesome audio plugins in Spotify's pedalboard library using the special PedalboardEffect wrapper, e.g.:

Some pre-built voiceboxes are available in the voicebox.examples package. They can be imported into your own code, and you can run them to demo:

Command Line Demo

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Making Your Python Programs Speak: A Practical Guide to Text-to-Speech

Learn how to use Python to add text-to-speech capabilities to your projects and create applications that can speak for themselves

Text-to-speech Python libraries

With the rise of the new AI models like GPT-4, being able to communicate with machines in a natural and intuitive way is becoming more and more important. Text-to-speech is a powerful technology that can help bridge the gap between humans and machines by enabling machines to speak and understand human language. In this blog post, we’ll explore some of the possibilities and libraries available for text-to-speech.

In Python, there are several modules available to easily convert text into speech. Today we are going to explore two of the most popular ones: pyttsx3 and gTTS .

pyttsx3 is a comprehensive library that provides support for multiple languages and custom voices, while gTTS is a simpler and easy to use option that uses Google Translate’s services to generate online speech.

TL;DR - I simply want to play out loud some text

The simplest way I could came up with was using the pyttsx3 library.

Initializing the engine and use the commands say and runAndWait would be the standard way to work with the API.

Once we have the engine, we can tune some parameters.

I don’t like the voice, how can I change it?

We can go through all the voices installed in our system with the following:

And then we can set the desired voice like this:

How to save the speech as an audio file?

Tuning parameters.

We can easily change parameters such as rate , volume or voice like this:

How can I stop the audio playback?

When working with text-to-speech in Python, one potential issue you may encounter is the main program becoming stuck or unresponsive while the audio is being played. This can be a frustrating and limiting problem, especially if you’re working on a real-time application where responsiveness is crucial such as an AI voice assistant bot.

This is because the code that generates and plays the audio is typically executed in a sequential manner, meaning that the program has to wait for the audio to finish before moving on to the next task.

To overcome this problem, one solution is to use multiprocessing, which involves creating multiple processes to execute different parts of the program in parallel. That way, the audio generation and playback are handled by a separate process, allowing the main program to continue executing without being blocked.

To make this happen, we need to run the say or speak function in another thread and use is_pressed from the keyboard module as a callback.

Alternative: gTTS

If you’re looking for an alternative to pyttsx3 , you might want to consider using the gTTS (Google Text-to-Speech) module along with the playsound library. Combining these two libraries is a quick way to add text-to-speech capabilities to your project.

Hopefully, this article has given you a brief overview of a couple text-to-speech options available in Python and how you can use them to improve the accessibility and user experience of your projects with just a few lines of code.

Pablo Cánovas

Senior data scientist at spotahome.

Data Scientist, formerly physicist | Tidyverse believer, piping life | Hanging out at TypeThePipe

• Português – Brasil

Using the Text-to-Speech API with Python

1. overview.

The Text-to-Speech API enables developers to generate human-like speech. The API converts text into audio formats such as WAV, MP3, or Ogg Opus. It also supports Speech Synthesis Markup Language (SSML) inputs to specify pauses, numbers, date and time formatting, and other pronunciation instructions.

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

What you'll learn

• How to set up your environment
• How to list supported languages
• How to list available voices
• How to synthesize audio from text

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 Text-to-Speech API, run the following command in Cloud Shell to enable the API:

You should see something like this:

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

Navigate to your home directory:

Create a Python virtual environment to isolate the dependencies:

Activate the virtual environment:

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

Now, you're ready to use the Text-to-Speech 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 and list the supported languages...

4. List supported languages

In this section, you will get the list of all supported languages.

Copy the following code into your IPython session:

Take a moment to study the code and see how it uses the list_voices client library method to build the list of supported languages.

Call the function:

You should get the following (or a larger) list:

The list shows 58 languages and variants such as:

• Chinese and Taiwanese Mandarin,
• Australian, British, Indian, and American English,
• French from Canada and France,
• Portuguese from Brazil and Portugal.

This list is not fixed and grows as new voices are available.

This step allowed you to list the supported languages.

5. List available voices

In this section, you will get the list of voices available in different languages.

Take a moment to study the code and see how it uses the client library method list_voices(language_code) to list voices available for a given language.

Now, get the list of available German voices:

Multiple female and male voices are available, as well as standard, WaveNet, Neural2, and Studio voices:

• Standard voices are generated by signal processing algorithms.
• WaveNet, Neural2, and Studio voices are higher quality voices synthesized by machine learning models and sounding more natural.

Now, get the list of available English voices:

You should get something like this:

In addition to a selection of multiple voices in different genders and qualities, multiple accents are available: Australian, British, Indian, and American English.

Take a moment to list the voices available for your preferred languages and variants (or even all of them):

This step allowed you to list the available voices. You can read more about the supported voices and languages .

6. Synthesize audio from text

You can use the Text-to-Speech API to convert a string into audio data. You can configure the output of speech synthesis in a variety of ways, including selecting a unique voice or modulating the output in pitch, volume, speaking rate, and sample rate .

Take a moment to study the code and see how it uses the synthesize_speech client library method to generate the audio data and save it as a wav file.

Now, generate sentences in a few different accents:

To download all generated files at once, you can use this Cloud Shell command from your Python environment:

Validate and your browser will download the files:

Open each file and hear the result.

In this step, you were able to use Text-to-Speech API to convert sentences into audio wav files. Read more about creating voice audio files .

7. Congratulations!

You learned how to use the Text-to-Speech API using Python to generate human-like speech!

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-texttospeech

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/text-to-speech
• Text-to-Speech documentation: https://cloud.google.com/text-to-speech/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.

Python: Text to Speech

Text-to-Speech (TTS) is a kind of speech synthesis which converts typed text into audible human-like voice.

There are several speech synthesizers that can be used with Python. In this tutorial, we take a look at three of them: pyttsx , Google Text-to-Speech (gTTS) and Amazon Polly .

We first install pip , the package installer for Python.

If you have already installed it, upgrade it.

We will start with the tutorial on pyttsx , a Text-to-Speech (TTS) conversion library compatible with both Python 2 and 3. The best thing about pyttsx is that it works offline without any kind of delay. Install it via pip .

By default, the pyttsx3 library loads the best driver available in an operating system: nsss on Mac, sapi5 on Windows and espeak on Linux and any other platform.

Import the installed pyttsx3 into your program.

Here is the basic program which shows how to use it.

pyttsx3 Female Voices

Now let us change the voice in pyttsx3 from male to female. If you wish for a female voice, pick voices[10] , voices[17] from the voices property of the engine. Of course, I have picked the accents which are easier for me to make out.

You can actually loop through all the available voices and pick the index of the voice you desire.

Google Text to Speech (gTTS)

Now, Google also has developed an application to read text on screen for its Android operating system. It was first released on November 6, 2013.

It has a library and CLI tool in Python called gTTS to interface with the Google Translate text-to-speech API.

We first install gTTS via pip .

gTTS creates an mp3 file from spoken text via the Google Text-to-Speech API.

We will install mpg321 to play these created mp3 files from the command-line.

Using the gtts-cli , we read the text 'Hello, World!' and output it as an mp3 file.

We now start the Python interactive shell known as the Python Shell

You will see the prompt consisting of three greater-than signs ( >>> ), which is known as the Python REPL prompt.

Import the os module and play the created hello.mp3 file.

Putting it all together in a single .py file

The created hello.mp3 file is saved in the very location where your Python program is.

gTTS supports quite a number of languages. You will find the list here .

The below line creates an mp3 file which reads the text "你好" in Chinese.

The below program creates an mp3 file out of text "안녕하세요" in Korean and plays it.

Amazon Polly

Amazon also has a cloud-based text-to-speech service called Amazon Polly .

If you have an AWS account, you can access and try out the Amazon Polly console here:

https://console.aws.amazon.com/polly/

The interface looks as follows.

There is a Language and Region dropdown to choose the desired language from and several male and female voices to pick too. Pressing the Listen to speech button reads out the text typed into the text box. Also, the speech is available to download in several formats like MP3, OGG, PCM and Speech Marks.

Now to use Polly in a Python program, we need an SDK. The AWS SDK for Python is known as Boto .

We first install it.

Now to initiate a boto session, we are going to need two more additional ingredients: Access Key ID and the Secret Access Key .

Login to your AWS account and expand the dropdown menu next to your user name, located on the top right of the page. Next select My Security Credentials from the menu.

A pop-up appears. Click on the Continue to Security Credentials button on the left.

Expand the Access keys tab and click on the Create New Access Key button.

As soon as you click on the Create New Access Key button, it auto creates the two access keys: Access Key ID , a 20-digit hex number, and Secret Access Key , another 40-digit hex number.

Now we have the two keys, here is the basic Python code which reads a given block of text, convert it into mp3 and play it with mpg321 .

There is also another way to configure Access Key ID and the Secret Access Key . You can install awscli , the universal command-line environment for AWS ,

and configure them by typing the following command.

• The latest documentation on pyttsx3 is available here .
• You can also access the updated documentation on gTTS here .

Text to Speech Python: A Comprehensive Guide

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Welcome to the exciting world of text-to-speech (TTS) in Python! This comprehensive guide will take you through everything you need to know about converting...

Welcome to the exciting world of text-to-speech (TTS) in Python! This comprehensive guide will take you through everything you need to know about converting text to speech using Python. Whether you're a beginner or an experienced developer, you'll find valuable insights, practical examples, and real-world applications.

Text-to-speech (TTS) technology converts written text into spoken words. Using various algorithms and Python libraries, this technology has become more accessible and versatile.

To begin, ensure you have Python installed. Python 3 is recommended for its updated features and support. You can download it from the official Python website, suitable for Windows, Linux, or any other operating system.

Setting Up Your Environment

• Install Python and set up your environment.
• Choose an IDE or text editor for Python programming, like Visual Studio Code or PyCharm.

Python offers several libraries for TTS, each with unique features and functionalities.

• pyttsx3 is a Python library that works offline and supports multiple voices and languages like English, French, German, and Hindi.
• Installation: pip install pyttsx3

Basic usage:

import pyttsx3

engine = pyttsx3.init()

engine.say("Hello World")

engine.runAndWait()

• gTTS (Google Text to Speech) is a Python library that converts text into speech using Google's TTS API.
• It requires an internet connection but supports various languages and dialects.
• Installation: pip install gTTS

from gtts import gTTS

tts = gTTS('hello', lang='en')

tts.save('hello.mp3')

Advanced TTS Features in Python

Python TTS libraries offer advanced features for more sophisticated needs.

• Combine TTS with speech recognition for interactive applications.
• Python's speech_recognition library can be used alongside TTS for a comprehensive audio experience.
• Adjust the speaking rate, volume, and voice properties using pyttsx3 .
• Example: Setting a different voice or speaking rate.

Save the output speech as an MP3 file or other audio formats for later use.

Real-World Applications of Python TTS

Python TTS is not just for learning; it has practical applications in various fields.

• Assistive technology for visually impaired students.
• Language learning applications.
• Automated voice responses in customer service.
• System notifications and alerts in software applications.

This guide provides a solid foundation for text-to-speech in Python. For further exploration, check out additional resources and tutorials on GitHub or Python tutorial websites. Remember, the best way to learn is by doing, so start your own Python project today!

Cost : Free to try

Speechify Text to Speech is a groundbreaking tool that has revolutionized the way individuals consume text-based content. By leveraging advanced text-to-speech technology, Speechify transforms written text into lifelike spoken words, making it incredibly useful for those with reading disabilities, visual impairments, or simply those who prefer auditory learning. Its adaptive capabilities ensure seamless integration with a wide range of devices and platforms, offering users the flexibility to listen on-the-go.

Top 5 Speechify TTS Features :

High-Quality Voices : Speechify offers a variety of high-quality, lifelike voices across multiple languages. This ensures that users have a natural listening experience, making it easier to understand and engage with the content.

Seamless Integration : Speechify can integrate with various platforms and devices, including web browsers, smartphones, and more. This means users can easily convert text from websites, emails, PDFs, and other sources into speech almost instantly.

Speed Control : Users have the ability to adjust the playback speed according to their preference, making it possible to either quickly skim through content or delve deep into it at a slower pace.

Offline Listening : One of the significant features of Speechify is the ability to save and listen to converted text offline, ensuring uninterrupted access to content even without an internet connection.

Highlighting Text : As the text is read aloud, Speechify highlights the corresponding section, allowing users to visually track the content being spoken. This simultaneous visual and auditory input can enhance comprehension and retention for many users.

Python Text to Speech FAQ

pyttsx3 and gTTS (Google Text to Speech) are popular free text-to-speech libraries in Python. pyttsx3 works offline across various operating systems like Windows and Linux, while gTTS requires an internet connection.

Yes, gTTS (Google Text to Speech) requires an internet connection as it uses Google's text-to-speech API to convert text into speech.

Yes, gTTS is a Python library that provides an interface to Google's text-to-speech services, enabling the conversion of text to speech in Python programs.

Yes, pyttsx3 is generally considered safe. It's a widely-used Python library for text-to-speech conversion, available on GitHub for transparency and community support.

To perform text-to-speech in Python, you can use libraries like pyttsx3 or gTTS . Simply import the library, initialize the speech engine, and use the say method to convert text to speech. For example:

engine.say("Your text here")

Speech synthesis is the artificial production of human speech. It converts written text into spoken words using algorithms and can be customized in terms of voice, speaking rate, and language, often used in TTS (Text-to-Speech) systems.

The "best" Python text-to-speech library depends on specific needs. pyttsx3 is excellent for offline use and cross-platform compatibility, supporting multiple languages like English, French, and Hindi. gTTS is preferred for its simplicity and reliance on Google's advanced text-to-speech API, offering high-quality speech synthesis in various languages, but requires an internet connection.

Online Tone Generator: The Ultimate Guide to Sound Waves and Audio Testing

Alternatives to Podcastle.ai for Podcast Creators

Cliff Weitzman

Cliff Weitzman is a dyslexia advocate and the CEO and founder of Speechify, the #1 text-to-speech app in the world, totaling over 100,000 5-star reviews and ranking first place in the App Store for the News & Magazines category. In 2017, Weitzman was named to the Forbes 30 under 30 list for his work making the internet more accessible to people with learning disabilities. Cliff Weitzman has been featured in EdSurge, Inc., PC Mag, Entrepreneur, Mashable, among other leading outlets.

Text To Speech Python: Tutorial, Advanced Features & Use Cases

Unreal Speech

Imagine a world where you could turn text into spoken words effortlessly and use it in your applications. The good news is that there is text to speech Python, a fantastic technology that enables just that. With this technology, you can quickly convert written text into audible speech, giving your apps a voice. In this blog post, we will explore the world of text to speech technology , focusing on how to integrate it with Python to create seamless user experiences. Let's dive in!

Table Of Content

• Introduction To Text-To-Speech (TTS) In Python • Text To Speech Python: Installing And Setting Up Python TTS Libraries • Advanced TTS Features In Python • Real-World Applications And Use Cases Of Python TTS • Try Unreal Speech for Free Today — Affordably and Scalably Convert Text into Natural-Sounding Speech with Our Text-to-Speech API

Introduction To Text-To-Speech (TTS) In Python

Text-to-speech technology is a software that converts written text into spoken words using natural language processing and speech synthesizers. TTS engines help in making information accessible to everyone with or without visual impairments. These engines are used in various applications such as navigation systems, virtual assistants, and accessibility tools. TTS uses algorithms and Python libraries to generate human-like speech and has become more accessible.

Python Libraries for Text-to-Speech (TTS)

Python libraries for Text-to-Speech (TTS) provide functionality to convert text into spoken audio. They offer various features and capabilities for generating synthetic speech from textual input. Some popular Python libraries include:

A Python library for offline TTS that supports multiple TTS engines and platforms.

gTTS (Google Text-to-Speech)

A library that converts text to speech using Google Text-to-Speech API.

Another Python library for TTS supporting various TTS engines like SAPI5 on Windows and NSSpeechSynthesizer on macOS.

Cutting-edge Text-to-Speech Solutions

If you are looking for cheap, scalable, realistic TTS to incorporate into your products, try our text-to-speech API for free today. Convert text into natural-sounding speech at an affordable and scalable price. Unreal Speech offers a low-cost, highly scalable text-to-speech API with natural-sounding AI voices, which is the cheapest and most high-quality solution. We cut your text-to-speech costs by up to 90%. Get human-like AI voices with our super-fast, low-latency API, with the option for per-word timestamps.

With our simple, easy-to-use API , you can give your LLM a voice with ease and offer this functionality at scale.

Text To Speech Python: Installing And Setting Up Python TTS Libraries

Installing and Setting Up Python TTS Libraries

To begin, ensure you have Python installed. Python 3 is recommended. Choose an IDE for Python programming like Visual Studio Code or PyCharm.

Installing popular TTS libraries: gTTS and pyttsx3

Gtts: google text to speech.

gTTS is a Python library that converts text into speech using Google’s TTS API.

To install gTTS

pip install gTTS

Basic usage

```python from gtts import gTTS tts = gTTS(‘welcom’, lang=’en’) tts.save(‘welcome.mp3’) ```

pyttsx3: A Cross-Platform Library

pyttsx3 is a Python library that works offline and supports multiple voices and languages.

pip install pyttsx3

```python import pyttsx3 engine = pyttsx3.init() engine.say(“Hello”) engine.runAndWait() ```

Cost-effective Text-to-Speech Solution

Unreal Speech offers a low-cost, highly scalable text-to-speech API with natural-sounding AI voices which is the cheapest and most high-quality solution in the market. We cut your text-to-speech costs by up to 90%. Get human-like AI voices with our super fast / low latency API, with the option for per-word timestamps. If you are looking for cheap, scalable, realistic TTS to incorporate into your products, try our text-to-speech API for free today.

Convert text into natural-sounding speech at an affordable and scalable price.

Advanced TTS Features In Python

Combining speech recognition with TTS in Python can create engaging interactive applications. By using Python's speech recognition library alongside TTS, developers can bring a comprehensive audio experience to their projects. This allows for a two-way interaction, where the application can both speak to the user and listen to their responses.

Customizing Speech Properties

Customizing speech properties in TTS allows developers to tailor the audio output to suit a particular use case or audience. With pyttsx3, developers can adjust the speaking rate, volume, and voice properties. This flexibility enables them to set different voices or speaking rates depending on the context in which the TTS is used.

Saving Speech to Audio Files

Saving TTS output as audio files opens up additional possibilities for using the speech content. By saving the output as an MP3 file or another audio format, developers can reuse the generated speech across multiple sections of an application or website. This feature helps to streamline the development process and create a more consistent user experience .

Affordable and Scalable Text-to-Speech Solution

If you are looking for cheap, scalable, realistic TTS to incorporate into your products, try our text-to-speech API for free today. Convert text into natural-sounding speech at an affordable and scalable price.

Real-World Applications And Use Cases Of Python TTS

Accessibility Solutions

When it comes to making technology more accessible for visually impaired users, TTS is an invaluable tool. With Python, developers can integrate text-to-speech functionality into applications to convert written content into spoken language.

This feature enables those with visual impairments to access digital content more comfortably. By transforming text into voice, Python applications can help visually impaired users navigate websites, read articles, or interact with various digital interfaces.

Language Learning Tools

Python-based language learning tools are taking advantage of TTS to provide learners with an engaging and effective learning experience. These platforms can offer pronunciation guides, audio flashcards, and interactive listening exercises, allowing users to improve their language skills more effectively. By incorporating TTS, Python applications can read out texts, words, or phrases, helping language learners practice pronunciation and listening comprehension.

Virtual Assistants and Chatbots

Virtual assistants and chatbots are becoming increasingly sophisticated thanks to Python and TTS. With the power of natural language processing , Python-powered virtual assistants can provide users with spoken responses and interact with them through text-to-speech capabilities. By integrating TTS into chatbots and virtual agents, developers can create more engaging, human-like interactions with users.

E-Learning Platforms

In the realm of online education, TTS is making learning more accessible and engaging for students. E-learning platforms built with Python can use text-to-speech to narrate course content, provide audio feedback on assessments, and strengthen the overall learning experience. By adding TTS functionality, Python applications can turn written material into spoken content, helping students with different learning styles or preferences.

Customer Service

Businesses are leveraging Python and TTS in customer service applications to provide customers with more interactive and engaging experiences. By integrating text-to-speech capabilities, Python-powered customer service applications can deliver automated voice responses, create interactive voice menus, and utilize virtual agents to enhance customer interactions. With TTS, businesses can provide a more comprehensive customer service experience, catering to customers who prefer voice interactions over text-based communication.

Try Unreal Speech for Free Today — Affordably and Scalably Convert Text into Natural-Sounding Speech with Our Text-to-Speech API

Unreal Speech is a game-changer in the text-to-speech market. It offers a low-cost, highly scalable API with natural-sounding AI voices that can reduce your text-to-speech costs by up to 90%. The quality of the voices is undeniably high, offering human-like AI voices that are not only affordable but also scalable.

Rapid and Responsive Text-to-Speech Conversion

When you utilize Unreal Speech , you get to enjoy super fast and low latency API services. This means you can quickly convert text into natural-sounding speech without any delays. Unreal Speech offers an option for per-word timestamps which can be immensely beneficial.

User-Friendly and Scalable API Integration

One of the most appealing aspects of Unreal Speech is its simplicity and ease of use. The API is designed to be user-friendly so that you can easily give your LLM a voice with minimal effort. Its scalability allows you to offer this functionality at a wider scale without any hassle. If you are looking for a cheap, scalable, and realistic text-to-speech solution for your products, Unreal Speech is definitely worth considering. Give it a try today to experience text-to-speech conversion at an affordable price.

Convert Text to Speech with Deep Learning in Python

A few years ago, the idea of text-to-speech software would have been laughable, if not impossible. However, with deep learning and artificial neural networks on the rise, it’s quite possible to convert text to speech on your own computer. In this guide, you’ll learn how to perform text-to-speech conversion with your own Python script to make speech synthesis more natural sounding than ever before possible.

Application of Text to Speech

Text to speech or TTS has several applications like:

• E-Reader books : This kind of application can read a book or paper for you
• Voice-enabled mobile Apps : A good example of this kind of app is Google Map drive navigation
• Siri : This product of Apple uses TTS in its background
• Amazon Alexa
• Google Assistance
• Youtube videos : Nowadays using deep learning and AI TTS can generate audio like human voice which can use as voiceover for youtube videos.

Text to Speech Python implementation

There are so many text to speech library in Python. I found two of them very promising which can generate natural audio like real human. Let me share those. Those are:

• Google Text to Speech (gTTS)

1. Text to Speech with Google TTS

gTTS (Google Text-to-Speech) is a Python library for interacting with Google Translate’s text-to-speech API. It supports several languages including Indian voices like Hindi, Tamil, Bengali (Bangla), Kannada and many more. You can find the complete list of languages supported by gtts with their language code below:

gtts languages list

The speech can be delivered in either of two audio speeds: fast or slow. However, in the most recent version, changing the voice of the produced audio is no longer available.

Installation

For installing the gTTS API, Open a terminal or command prompt and type

This is applicable to any platform.

We’re now ready to write a sample code that can translate text to speech.

English Language

After hearing the output you can understand gTTS cannot generate sound like humans (Sounds like robotic). But since this library is free and open source and easy to use you can use it in your fun project.

Let’s try some Indian languages.

Bengali Language

Hindi language, 2. text to speech with tecotron 2.

If you want to use Text to speech in any advanced project where you want to produce natural sound, you must use a deep learning model.

In this tutorial, I will show you the best deep-learning model to synthesize audio from text which is called Tecotron 2 .

Tecotron 2 is an open-source deep-learning TTS model developed by NVIDIA . You can download their pre-trained easily and use that in your local system for your project.

Before we proceed to install the required modules for Tacotron 2 I will highly recommend you create a virtual environment and install Tecotron 2 inside there.

To create virtual environment run below command:

Here tts_python is the virtual environment name

Note: Don’t change the python version else you may get the below TensorFlow error:

Inside the virtual environment execute the below commands one by one :

Setup Tecotron 2

To set up Tecotron 2 execute the following commands:

Install Additional Packages

You need to install some additional packages. To install them run below commands:

Note: If you want to install tensorflow for GPU you can follow this tutorial: Install TensorFlow GPU with Jupiter notebook for Windows

Install Pytorch

To work with Tecotron, you have to install PyTorch with CUDA. To install Pytorch with CUDA run the below command

Setup Jupyter Notebook

To configure jupyter notebook for your virtual environment execute below commands inside your environment:

Solve some error

You may face below errors while configuring Tecotron 2:

To solve the above error just uninstall librosa and joblib and install librosa again running below commands.

Download Pre-trained model

NVIDIA published their pre-trained model to use in your TTS project with Python freely. You need to download two models:

• Tacotron 2: Download link
• WaveGlow: Download link

Once downloaded paste them inside tacotron2 folder (git cloned folder)

Those models are trained using LJ Speech Dataset which contains 24 hours of audio clips. You can understand the power of this model.

Generate Audio using Tecotron 2

Now we are all set to generate realistic audio like human voice from text using deep learning model called Tecotro

First, create a jupyter notebook inside tacotron2 folder (git cloned folder) then execute below codes there.

Import Required libraries

Define some parameters, load pre-trained tts models.

Now let’s load two models which we have already downloaded

Convert Text to Speech

After hearing this voice you can understand, this TTS audio quality is near to human natural voice. This is clearly better than gTTS.

Though Tacotron generates natural sounds like a real human but the only drawback of Tacotron 2 is that it supports only English language . In this part gTTS is one step ahead.

In this tutorial, I show you two TTS framework to generate Text to speech in Python which you can use in your project.

The only disadvantage of those two tools (gTTS & Tecotron 2) is that they can only produce default female voice. There is no option to produce audio for male voice.

That’s all for this tutorial. If you have any questions or suggestions regarding this tutorial, feel free to mention those in the comment section below.

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3 thoughts on “Convert Text to Speech with Deep Learning in Python”

Thank you, exactly what I’ve been looking for!

# Define parameter hparams = create_hparams() hparams.sampling_rate = 22050

This chunk of code isn’t working for me. I am working in google colab and it gives me following error:

AttributeError: module ‘tensorflow’ has no attribute ‘contrib’

I followed your tutorial step by step. KIndly answer me ASAP. Thank you.

This issue is already discussed in this article, Please crosscheck which python and tensorflow version you are using?

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Introduction to gpt-4o

GPT-4o ("o" for "omni") is designed to handle a combination of text, audio, and video inputs, and can generate outputs in text, audio, and image formats.

Before GPT-4o, users could interact with ChatGPT using Voice Mode, which operated with three separate models. GPT-4o will integrate these capabilities into a single model that's trained across text, vision, and audio. This unified approach ensures that all inputs—whether text, visual, or auditory—are processed cohesively by the same neural network.

Current API Capabilities

Currently, the API supports {text, image} inputs only, with {text} outputs, the same modalities as gpt-4-turbo . Additional modalities, including audio, will be introduced soon. This guide will help you get started with using GPT-4o for text, image, and video understanding.

Getting Started

Install openai sdk for python, configure the openai client and submit a test request.

To setup the client for our use, we need to create an API key to use with our request. Skip these steps if you already have an API key for usage.

You can get an API key by following these steps:

• Create a new project
• Generate an API key in your project
• (RECOMMENDED, BUT NOT REQUIRED) Setup your API key for all projects as an env var

Once we have this setup, let's start with a simple {text} input to the model for our first request. We'll use both system and user messages for our first request, and we'll receive a response from the assistant role.

Image Processing

GPT-4o can directly process images and take intelligent actions based on the image. We can provide images in two formats:

• Base64 Encoded

Let's first view the image we'll use, then try sending this image as both Base64 and as a URL link to the API

Base64 Image Processing

Url image processing, video processing.

While it's not possible to directly send a video to the API, GPT-4o can understand videos if you sample frames and then provide them as images. It performs better at this task than GPT-4 Turbo.

Since GPT-4o in the API does not yet support audio-in (as of May 2024), we'll use a combination of GPT-4o and Whisper to process both the audio and visual for a provided video, and showcase two usecases:

• Summarization
• Question and Answering

Setup for Video Processing

We'll use two python packages for video processing - opencv-python and moviepy.

These require ffmpeg , so make sure to install this beforehand. Depending on your OS, you may need to run brew install ffmpeg or sudo apt install ffmpeg

Process the video into two components: frames and audio

Example 1: summarization.

Now that we have both the video frames and the audio, let's run a few different tests to generate a video summary to compare the results of using the models with different modalities. We should expect to see that the summary generated with context from both visual and audio inputs will be the most accurate, as the model is able to use the entire context from the video.

Visual Summary

Audio summary.

• Visual + Audio Summary

The visual summary is generated by sending the model only the frames from the video. With just the frames, the model is likely to capture the visual aspects, but will miss any details discussed by the speaker.

The results are as expected - the model is able to capture the high level aspects of the video visuals, but misses the details provided in the speech.

The audio summary is generated by sending the model the audio transcript. With just the audio, the model is likely to bias towards the audio content, and will miss the context provided by the presentations and visuals.

{audio} input for GPT-4o isn't currently available but will be coming soon! For now, we use our existing whisper-1 model to process the audio

The audio summary is biased towards the content discussed during the speech, but comes out with much less structure than the video summary.

Audio + Visual Summary

The Audio + Visual summary is generated by sending the model both the visual and the audio from the video at once. When sending both of these, the model is expected to better summarize since it can perceive the entire video at once.

After combining both the video and audio, we're able to get a much more detailed and comprehensive summary for the event which uses information from both the visual and audio elements from the video.

Example 2: Question and Answering

For the Q&A, we'll use the same concept as before to ask questions of our processed video while running the same 3 tests to demonstrate the benefit of combining input modalities:

• Visual Q&A
• Audio Q&A
• Visual + Audio Q&A

Comparing the three answers, the most accurate answer is generated by using both the audio and visual from the video. Sam Altman did not discuss the raising windows or radio on during the Keynote, but referenced an improved capability for the model to execute multiple functions in a single request while the examples were shown behind him.

Integrating many input modalities such as audio, visual, and textual, significantly enhances the performance of the model on a diverse range of tasks. This multimodal approach allows for more comprehensive understanding and interaction, mirroring more closely how humans perceive and process information.

Currently, GPT-4o in the API supports text and image inputs, with audio capabilities coming soon.

Spanish Speech-to-Text with Python

Speech-to-Text, also known as Automatic Speech Recognition, is a technology that converts spoken audio into text. The technology has a wide range of applications, from video transcription to hands-free user interfaces.

While many cloud Speech-to-Text APIs are available on the market, most can only transcribe in English. Picovoice's Leopard Speech-to-Text engine , however, supports 8 different languages and achieves state-of-the-art performance, all while running locally on-device.

In this tutorial, we will walk through the process of using the Leopard Speech-to-Text Python SDK to transcribe Spanish audio in just a few lines of code.

Prerequisites

Sign up for a free Picovoice Console account. Once you've created an account, copy your AccessKey on the main dashboard.

Install Python (version 3.7 or higher) and ensure it is successfully installed:

Install the pvleopard Python SDK package:

Leopard Speech-to-Text Model File

To initialize Leopard Speech-to-Text, we will need a Leopard Speech-to-Text model file. The Leopard Speech-to-Text model files for all supported languages are publicly available on GitHub . For Spanish Speech-to-Text, download the leopard_params_es.pv model file.

Implementation

After completing the setup, the actual implementation of the Speech-to-Text system can be written in just a few lines of code.

Import the pvleopard package:

Set the paths for all the required files. Make sure to replace ${ACCESS_KEY} with your actual AccessKey from the Picovoice Console , ${MODEL_FILE} with the Spanish Leopard Speech-to-Text model file and ${AUDIO_FILE} with the audio file you want to transcribe:

Initialize Leopard Speech-to-Text and transcribe the audio file:

Leopard Speech-to-Text also provides start and end time-stamps, as well as confidence scores for each word:

Additional Languages

Leopard Speech-to-Text supports 8 different languages, all of which are equally straightforward to use. Simply download the corresponding model file from GitHub , initialize Leopard Speech-to-Text with the file, and begin transcribing.

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AI + Machine Learning , Announcements , Azure AI Content Safety , Azure AI Studio , Azure OpenAI Service , Partners

Introducing GPT-4o: OpenAI’s new flagship multimodal model now in preview on Azure

By Eric Boyd Corporate Vice President, Azure AI Platform, Microsoft

Posted on May 13, 2024 2 min read

• Tag: Copilot
• Tag: Generative AI

Microsoft is thrilled to announce the launch of GPT-4o, OpenAI’s new flagship model on Azure AI. This groundbreaking multimodal model integrates text, vision, and audio capabilities, setting a new standard for generative and conversational AI experiences. GPT-4o is available now in Azure OpenAI Service, to try in preview , with support for text and image.

Azure OpenAI Service

A step forward in generative AI for Azure OpenAI Service

GPT-4o offers a shift in how AI models interact with multimodal inputs. By seamlessly combining text, images, and audio, GPT-4o provides a richer, more engaging user experience.

Launch highlights: Immediate access and what you can expect

Azure OpenAI Service customers can explore GPT-4o’s extensive capabilities through a preview playground in Azure OpenAI Studio starting today in two regions in the US. This initial release focuses on text and vision inputs to provide a glimpse into the model’s potential, paving the way for further capabilities like audio and video.

Efficiency and cost-effectiveness

GPT-4o is engineered for speed and efficiency. Its advanced ability to handle complex queries with minimal resources can translate into cost savings and performance.

Potential use cases to explore with GPT-4o

The introduction of GPT-4o opens numerous possibilities for businesses in various sectors: 

• Enhanced customer service : By integrating diverse data inputs, GPT-4o enables more dynamic and comprehensive customer support interactions.
• Advanced analytics : Leverage GPT-4o’s capability to process and analyze different types of data to enhance decision-making and uncover deeper insights.
• Content innovation : Use GPT-4o’s generative capabilities to create engaging and diverse content formats, catering to a broad range of consumer preferences.

Exciting future developments: GPT-4o at Microsoft Build 2024 

We are eager to share more about GPT-4o and other Azure AI updates at Microsoft Build 2024 , to help developers further unlock the power of generative AI.

Get started with Azure OpenAI Service

Begin your journey with GPT-4o and Azure OpenAI Service by taking the following steps:

• Try out GPT-4o in Azure OpenAI Service Chat Playground (in preview).
• If you are not a current Azure OpenAI Service customer, apply for access by completing this form .
• Learn more about  Azure OpenAI Service  and the  latest enhancements.  
• Understand responsible AI tooling available in Azure with Azure AI Content Safety .
• Review the OpenAI blog on GPT-4o.

Let us know what you think of Azure and what you would like to see in the future.

Provide feedback

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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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