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Your search for an App to convert your text into Whispering speech ends here! Get realistic and convincing Whispering voiceovers in no time and for free with our online text to speech converter. Our online text to voice speech generates realistic voices from any text and in many languages. fast, easy and free.

How to convert text into speech?

Our Whispering text to speech tool is very easy to use. Just type some text, select the language, the voice and the speech style and emotion, then hit the Play button. Set back and wait for a few seconds while our AI algorithm does its text to speech magic to convert your text into an awesome voice over. When it is all done, you can click the download button to download your voice over as an mp3 file.

What is the premium voice option?

If you check the 'Use premium voice' option then we will use an advanced algorithm to do the text to speech conversion, the output will sound more realistic and less robotic than the output of the standard algorithm. Please note that Premium voice is not available for all languages and voices, premium voice support is indicated by a icon before the language and voice name in the lists. The premium voice also requires that you have 'premium characters', all users get daily 1k premium characters for free, it is also possible to purchase more characters at any time here .

What are voice emotions aka speech styles?

Texttovoice.online supports speech styles through voice emotions, voice emotions allow you to select the speech style and the narrator's emotion when converting your text into voice. Please note that voice emotions are not available for all languages and voices, emotion voice support is indicated by a icon before the language and voice name in the lists. Voice emotion also requires that you have more than 100K premium characters, you can purchase more characters at any time here .

Why do you need narration in your videos?

You should narrate your videos for a few reasons. A narration will make your video more understandable, give it a more professional feel and help the action points ring through. Video with a text to speech narration is a great way to explain technology in an easy way, especially if you’re not a speaker or if you’re not comfortable talking on camera.

High audio quality

Our free text to speech generator is the best tool for generating audio from text. Anyone can easily recognize each character or word. No one will find it difficult to understand the speech.

Natural Whispering Voices

Our text to speech converter gives you real human voice as an output, and you'll get different options to choose the voice's gender or accent.

Fast Conversion

Our text to speech web-app converts text to speech in less than a second. It depends on your internet connection. But it's very lightweight. So you can get instant results with a slower connection too.

Perfect for Instagram and TikTok

Makes a great Instagram and tiktok voice over. Convert your text into an ai voice and use it as a voice over for your videos on Intagram, Facebook and TikTok.

Cross platfrom text to speech tool for Mac OS and Windows

Whether you are a Macintosh user or a Wnidows user, our web-based text to speech tool will work smoothly on Mac OS and Windows and you will alwyas get the same nice results and save your voice over on Mac or Windows.

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We use random IDs to rename your files on the server. your sound file is generated under a complex file path and it is deleted once the queue is filled on server. We guranteed that no one can access your files except you.

Text to speech calculations is happening on our side

Our text to voice converter app is running on our servers. Our text to speech tool does not perform any calculations on your machine so you can still enjoy a fast and smooth experience.

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Download your generated sound files with a single click and absolutely for free. Once the text to speech conversion is completed, the download button is enabled so you can download your file instantly.

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We are always working on improving our text to speech converter. We are keen to provide an accurate and fast text to voice converter with the most realistic and convincing results.

Microsoft Sam TTS Generator is an online interface for part of Microsoft Speech API 4.0 which was released in 1998.

• Select your voice. Note that BonziBUDDY voice is actually an "Adult Male #2" with a specific pitch and speed.
• Select your pitch and speed. All voices have lower and upper pitch and speed limits.
• Enter your text and press "Say it". Wait for generated audio appear in audio player. It should be done nearly instantly, as the interface tries to generate audio at x16777215 real-time.
• To save generated audio, right click on audio player and press "Save audio as..."

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What Is Whispering Text to Speech/Whisper TTS?

4 best whispering text to speech online websites recommendations, how to make whisper voice text to speech with ai, free unlimited whisper text to speech tool online without registration.

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[Howto] 3 Best Whispering Text to Speech Online Tools Powered by AI

Gary Henderson

Updated on March 28, 2024

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Generate natural, fluent, and emotional whisper text to speech free - the 3 tools can help you, no register, and free download.

AI has overturned the industry of Text to Speech. It adds so many emotions and intelligence to the robotic TTS once prevailing in Microsoft Word/PPT and Google Translate. Another surprise brought by AI is that it develops tons of voices that sound ‘soft, angry, friendly, or terrified.’ This article focuses on introducing readers to one particular TTS voice - the Whispering Text to Speech voice, to address special occasions such as scary film voiceovers, bedtime story narrations, or soothing podcasts.

Let’s take a quick glance at the 4 best whispering Text to Speech AI Tools online no download. 

Whispering TTS refers to a special type of TTS tool powered by AI. By adding pauses, and adjusting pitches, speed and volume, AI creates whispering voices for users to address their particular situations.

You can use the generated MP3 files to replace the narration in your videos or as podcasts for bedtime stories. And for a special group of people who demand more specific types of whispering text to speech such as ‘female whisper text to speech’ or ‘creepy whisper text to speech’, they can also find help from AI. 

The next part is the recommendation of 3 qualified whispering TTS tools online. Let’s read on!

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In this part, you can see 4 brilliant online Whispering text to speech AI tools that offer murmuring TTS services. Some demand you to configure the volume and speed to achieve the whispering effect, and some are natural text to speech tools waiting for you to use. Let’s take a look at the 4 candidates selected by the editor, and see if you can choose a free tool to easily get started with.

This AI Voice Generator Whisper is skilled at offering AI voice effects for Text to Speech online. You can find a slew of celebrity voices as well as special voice effects - affectionate, whispering, embarrassed, envious, or shouting. Free generation, free download, No need to queue in line, and no coins or credits needed. 

The best part of this Whisper tts online website is that it lets you clone your own voice, maintain the tones, pitches, and emotions in your voice, and add extra filters to your cloned voice. Vidnoz lets its users make whispering text-to-speech, a free download. Here are 3 major ways to create whisper text to speech online with Vidnoz: 

#1. Online Whispering text to speech - free tries

#2. Your own whispering voice generator and cloner

#3. Make voiceover videos with templates and digital avatars

#2. Voicemaker

Link: https://voicemaker.in/

This is the most straightforward whisper text to speech online tool that allows you to generate whispering voice audio. You can directly choose the whispering/breathing /soft voices offered in the ‘Voice Effect’ and proceed, or you can also achieve this goal by adjusting the volume of the voice (from -20db to 20db), and the speed of the whisper tts online, so a barely-be-heard-of voice can be made. 

The second whisper TTS is selected here for its clean interfaces and intuitive workflow. Even a green hand knows how to get around this tool in 1 minute. If you are seeking more dispersed voice types other than whisper voice generators, you can also head to the ‘Voice Effects’ to choose its ‘angry, shouting, fearing, terrified’ voices.

#3. Whispering Text to Speech

Link: https://www.texttovoice.online/?emo=whispering

This site is so far the best-ranked AI text to speech site that provides whispering TTS service for us. The most thoughtful part of this online AI voice generator is that it allows you to configure the emotions of your sample voice (this feature is Premium , however). When you hit on the 'Whispering' option, you can see dozens of other voices to choose from. This webpage does not contain massive instructional content to read so it is pretty straightforward. Yet the premium license's limitations are a little bit annoying, if you don't mind the credits thing, you can use this tool to generate whisper text to speech audio files in seconds.

#4. Fasthub.net

Link: https://fasthub.net/

This site is selected from Reddit. 

Doing a good job of reading out loud textual inputs , translating languages, and recording text to speech, Fasthub manages to win the strictest Reddit users’ hearts with its reliable performance. This site is pretty ‘hardcore’ though. Without any traces of commercial interference, its interfaces are outdated. Yet it won’t bother with the generation of whisper text to speech. As you can see in the picture below, you can adjust the pitch and speed of your voice. 

The trick of creating a whispering voice is that you need to select the right ‘Voice type.’ From the dropdown menu, find ‘Whisper.’ Then drag the Speed to the lowest setting. Then you will get what you want. 

Considering the most user-friendly experience, the editor chooses Vidnoz AI to showcase how to generate an audio file featuring a whispering voice/creepy whisper voice text to speech/ female whispering text to speech . This tool is not misleading; you will love its operations and performance. Let’s spend 3 minutes to learn how to use this online tool:

Step 1. Navigate to Vidnoz AI.

Step 2. You type in words into the blank bar, adjust the volume, speed, pitch. The most importantly, hit the circled option to select the Whispering voice effect.

Step 3. Check the option just as the picture below.

Step 4. Now you can hit the ‘Generate’ button to create the whispering TTS audio file.

From the list above, you can see that the AI text to speech tools on this market are not cheap at all. And to get its specially-designed voices (soft/friendly/whispering/angry) you either need to buy its subscription/upload the license, or risk yourself watching ads or clicking redirects. Isn’t there any clean and green Text to Speech online tool that requires no registration, and credits at all? 

Vidnoz Text to Speech is right now one of the rarest websites that offers free services in terms of AI powered TTS. 

Link: https://www.vidnoz.com/text-to-speech.html

Please type in texts into the box below and quickly have a free try.

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This article introduces 3 practical AI powered whispering text to speech tools from the Internet. The 3 TTS tools selected in this article all provide the ‘Whispered voice’ option for users. Yet not all of them are for free, some set this feature as exclusive for only premium users. Yet Vidnoz is a 100% free platform that offers natural text to speech tool to read content aloud, translate language and read with different voices. Please feel free to try it!

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Gary Henderson once was the most-viewed writer under Quora's 'screen recorder' catagory. You can still find him professionally solving people's puzzles that related to videos, screenshots and gaming.

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Text to Speech FAQ

How do i turn my text into voice, what is an ai voice, is using ai voiceover better than a human voice, can ai voices be used for commercial purposes, how long does it take to convert text to speech, what are the most realistic text to speech (tts) providers.

Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.

Using Whisper (speech-to-text) and Tortoise (text-to-speech)

Introduction

This blog post shows you how to perform speech recognition using Whisper (i.e., producing a written transcript of an audio file) and speech generation using Tortoise (i.e., creating an audio file based on someone’s voice for arbitrary text). First, I’ll demonstrate how to download audio from a YouTube video, and then we’ll use it for these speech tasks. Everything will be written in Python .

Some background information:

• Whisper is an automatic speech recognition system released by OpenAI last month, and was trained on 680,000 hours of data (about one-third of its audio data is non-English). Whisper is open source and you can find it on GitHub , read the accompanying paper , or check out the OpenAI blog post .
• Tortoise is a text-to-speech program by James Betker , and was trained on a dataset consisting of audiobooks. Its development prioritized realistic intonation and rhythm in speech as well as multi-voice capabilities. Check out its repo on GitHub .

Getting Audio From YouTube

In order to perform speech tasks, the first step is to download audio from a YouTube video so that we have something to work with.

To do so, I used pytube ( docs ), which is a dependency-free library for downloading YouTube videos. I installed it using conda: conda install pytube . Once installed, we can import the module in Python and start using it. For this step, I used a Jupyter notebook.

Import pytube and define a YouTube object :

Replace the URL above with the URL of any YouTube video that contains the voice that will be cloned. This can be a video of your own voice, for example. A length between 5 to 15 minutes is ideal, so that you have enough audio for the speech generation task but not so much that it slows down the speech recognition task.

Verify that you have the correct video by checking its title :

Query the audio-only stream :

Note that you can view more streams with audio-only tracks with the command yt.streams.filter(only_audio=True) . Additionally, if you wanted to view all streams, use the command yt.streams .

Looking at the output of the above command, it appears that the audio stream has itag of 140. We’ll use that to identify the correct stream to download.

Download the stream :

And that’s it! If you followed the above steps, you should have a downloaded audio file of your chosen YouTube video. We will use this audio file for the speech tasks in the following sections.

Using Whisper (speech-to-text)

OpenAI has made it very simple to use Whisper; it only takes a few lines of code to get a transcript of an audio file.

The first step is to install Whisper . I installed it on my local machine using pip: pip install git+https://github.com/openai/whisper.git

The next step is to select a model. Whisper’s GitHub provides a table (reproduced below) of the different models, sizes, and their speed-accuracy tradeoffs. I chose the base model, and given that my task would be English-only, I chose base.en which typically has better performance than the multilingual version.

Import Whisper and load the model :

Transcribe the audio file :

It took about 1 minute on my CPU to perform inference on a 13-minute audio file. result['text'] contains the transcription.

Now that we’ve shown how to use Whisper to speech-to-text, let’s move on to speech generation in the next section.

Using Tortoise (text-to-speech)

Before using Tortoise, we need some short clips from our downloaded audio file of the voice we want to clone. Each clip should be about 6 to 10 seconds long, and I recommend having 5 to 10 clips total (I used 8 clips). Pick higher-quality clips without background noise, if possible. If you have existing software on your computer that you prefer to use, feel free to use it to create these clips. Since I have a Mac machine, I used Apple’s Voice Memos app to trim my audio file to create short clips (which are saved in ~/Library/Application\ Support/com.apple.voicememos ).

Once you have created these audio clips, convert them to .wav format with a 22,050 sample rate. I used an online M4A to WAV Converter that allowed me to specify the sample rate.

Now we’re ready to use Tortoise! We’ll be running it in inference mode ; we won’t be training or fine-tuning. Note that Tortoise is a slow model (hence the name) and since my local computer doesn’t have an NVIDIA GPU, I decided to run this section’s code in a notebook environment on Google Colab . The added benefit is that I don’t need to mess with anything on my local computer, such as installing a bunch of dependencies or dealing with any installation errors that pop up.

Open a new notebook in Colab, turn on a GPU runtime, and check your GPU :

Looks good.

Install the latest versions of SciPy and Tortoise, plus its dependencies :

These commands should take a bit to run, and will produce a lot of output.

Import the modules we’ll need :

Download models used by Tortoise from HuggingFace :

Now we’re ready to generate speech. Think about what you want your cloned voice to say — I chose a poem from The Lord of the Rings . Note that the longer the text, the longer it will take to generate; I suggest starting with something short.

Specify text and preset mode :

The preset mode determines the quality of the generated audio. The options include ultra_fast , fast , standard , and high_quality .

On Colab, navigate to Files using the left menubar and locate the tortoise/voices folder. Inside that folder, create a subfolder named after your chosen voice, such as michael . Upload all of your .wav clips into the newly created folder.

List all of the available voices, and display one of your audio clips :

You can see that Tortoise comes with a number of other voices you can use, if you decide not to use your custom voice.

Edit the path above to display the audio for one of your clips. If you have trouble playing it, it’s possible that your audio clip isn’t in the correct format. If that’s the case, try a different .wav converter and see if that works.

Specify the voice and generate the audio sample :

This took about 5 minutes on the Colab GPU. Once it’s done, you should see a file called generated.wav in your working directory.

This is my generated audio :

This is one of the 8 clips used to generate the cloned voice :

Sounds like a pretty good clone of the original voice, especially considering how I ran the model in inference mode and did not fine-tune Tortoise to my chosen voice.

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Scan or take a picture of any image and Speechify will read it aloud to you with its cutting-edge OCR technology. Save your images to your library in the cloud and access it anywhere. You can now listen to that note you got from a friend, relative, or other loved one.

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What is text to speech

Text to speech, also known as TTS, read aloud, or even speech synthesis . It simply means using artificial intelligence to read words aloud be; it from a PDF , email, docs, or any website. There isn’t a voice artist recording phrases or words, or even the entire article. Speech generation is done on-the-fly, in real time, with natural sounding AI voices.

And that’s the beauty of it all. You don’t have to wait. You simply press play and artificial intelligence makes the words come alive instantly, in a very natural sounding voice. You can change voices and accents across multiple languages.

Listen to any article. Easily scan any printed material and convert the image to audio.

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I used to hate school because I’d spend hours just trying to read the assignments. Listening has been totally life changing. This app saved my education.

Ana Student with Dyslexia

Speechify has made my editing so much faster and easier when I’m writing. I can hear an error and fix it right away. Now I can’t write without it.

Daniel Writer

Speechify makes reading so much easier. English is my second language and listening while I follow along in a book has seriously improved my skills.

Lou Avid Reader

More text to speech features you’ll love, speechify text to speech online reviews, kate marfori.

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With Speechify’s API, we can offer our users a new and accessible way to consume our content. We’ve seen that readers who choose to listen to articles with Speechify are on average 20% more engaged than users who choose not to listen.

Susy Botello

Thanks for sharing this.I love this feature. I just tweeted at you on how much I like it. The voice is great and not at all like the text-to-speech I am used to listening to. I am a podcaster and I think this will help a lot of people multitask a bit, especially if they are interrupted with incoming emails or whatever. You can read-along but continue reading if your eyes need to go elsewhere. Hope you keep this. It’s already in other web publications. I also see it in some news sites. So I think it could become a standard that readers expect when they read online. Can I vote twice?

Renato Vargas

I just started using Medium more and I absolutely love this feature. I’ve listened to my own stories and the Al does the inflections just as I would. Many complain that they can’t read their own stories, but let’s be honest. How many stories would go without an audio version if you had to do all of them yourself? I certainly appreciate it. Thanks for this!!

Oh! How cool – I love it 🙂 The voice is surprisingly natural sounding! My eyes took a much appreciated rest for a bit. I’ve been a long time subscriber to Audible on Amazon. I think this is Great 🙂 Thank you!

Paola Rios Schaaf

Super excited about this! We are all spending too much time staring at our screens. Using another sense to take in the great content at Medium is awesome.

Hi Warren, I am one of those small, randomly selected people, and I ABSOLUTELY love this feature. I have consumed more ideas than I ever have on Medium. And also as a non-native English speaker, this is really helping me to improve my pronunciation. Keep this forevermore! Love, Ananya:)

This is the single most important feature you can role out for me. I simply don’t have the time to read all the articles I would like to on Medium. If I could listen to the articles I could consume at least 3X the amount of Medium content I do now.

Andrew Picken

Love this feature Warren. I use it when I’m reading, helps me churn through reading and also stay focused on the article (at a good speed) when my willpower is low! Keeping me more engaged..

I was THRILLED the other day when I saw the audio option. I didn’t know how it got there, but I pressed play, and then I was blown away hearing the words that I wrote being narrated

Neeramitra Reddy

LOVE THISSS. As someone who loves audio almost as much as reading, this is absolute gold

What is text to speech (TTS)?

Text-to-speech goes by a few names. Some refer to it as TTS,  read aloud , or even speech synthesis ; for the more engineered name. Today, it simply means using  artificial intelligence  to read words aloud be; it from a PDF, email, docs, or any website. Instantly turn text into audio. Listen in English, Italian, Portuguese,  Spanish , or more and choose your accent and character to personalize your experience.

How does AI text to speech work?

Beautifully. Speech synthesis works by installing an app like Speechify either on your device or as a browser extension. AI scans the words on the page and  reads it out loud , without any lag. You can change the default voice to a custom voice, change accents, languages, and even increase or decrease the speaking rate.

AI has made significant progress in synthesizing voices. It can pick up on formatted text and change tone accordingly. Gone are the days where the voices sounded  robotic . Speechify is revolutionizing that.

Once you install the TTS mobile app, you can easily convert text to speech from any website within your browser, read aloud your email, and more. If you install it as a  browser extension , you can do just the same on your laptop. The web version is OS agnostic. Mac or Windows, no problem.

What is the text-to-speech service?

A text-to-speech service is a tool, like Speechify text to speech, that transforms your written words into spoken words. Imagine typing out a message and having it read out loud by a digital voice – that’s what TTS services, like Speechify TTS do.

What are the benefits of text to speech?

TTS technology offers many benefits, like helping those with reading difficulties, providing rest for your eyes, multitasking by listening to content, improving pronunciation and language learning, and making content accessible to a wider audience.

How is Speechify TTS better than Murf AI text to speech, Google Voice, or TTSReader?

Speechify TTS stands out by offering a more natural and human-like voice quality, a wider range of customization options, and user-friendly integration across devices. Plus, our dedication to accessibility means that we ensure a seamless and inclusive experience for all users.

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How to Use Whisper AI: The Only Guide You Need

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From ChatGPT to Dalle, and now, Whisper, Open AI has set the stage for the revolution of AI with some of the most valuable and mindblowing tools you will ever find. Their youngest child, Whisper, is on a transcription tool that beats all the rest in time, cost, and accuracy.

While it has gained much praise for being the best, one concern remains: few people know how to use it. The fact that you can’t download it like any other software is a big letdown to likely users.

 After conducting my research, some of the concerns  I noted respondents raising were, “It's too technical to use!” and ‘You have to go through numerous developer notes that are tiresome to read!” 

If this is a problem you have encountered, here is an easy step-by-step solution on how to use Whisper OpenAI.

What is OpenAI's Whisper? 

Whisper is an automatic speech recognition system by Open AI, the makers of ChatGPT and Dalle. The project is open source, meaning it is free to use, distribute, and change.

Unlike other speech-to-text systems, Whisper does not have a download site. All its files are in a GitHub repository. You must download some developer tools and run some code to install it in your system.

Who Can Use Open AI Whisper?

Anyone who needs to convert their speech to text can use Whisper AI. For example: 

A student who wants to transcribe their class notes

A meeting head who wants to derive the context of a previously recorded Zoom meeting

A podcaster looking to repurpose their audio content into various formats

A video editor looking to add subtitles to a video and more.

Looking for a better transcription? Notta AI offers accuracy, efficiency, and advanced features which can help you transcribe speech into searchable text. Experience seamless transcription today!

How to Download and Install Whisper

First, it's essential to understand that Whisper is unlike other transcription and translation tools in how it runs and operates. There is no download site with a ready file to download and install in your system. To install and use it, you need a basic understanding of the Windows, Linux, or Mac command line, depending on your device.

Our guide is a step-by-step process for installing Whisper in Windows for offline use. To get started, you need several prerequisites on your computer to ensure a smooth download and install.

NVIDIA CUDA (optional)

Pip (only for older versions of Python)

For this installation, we will use Python version 3.9.9, but its dependencies allow it to work with versions between 3.7 and 3.11.

Head to the Python website and click on the preferred Python, depending on the release date to download.

For this guide, I chose to use the Python 3.9.9. Click on it and scroll to the section with the installation files.

 Click on the file best suited to your systems. The download will start immediately.

Once done, install the software into your system. When installing Pythion for the first time, remember to click "Add to path" at the bottom of the first page of the installer. This allows you to run Python from a terminal. Failure to check this box can cause the entire Whisper installation to fail.

Since the Open AI Whisper files are on a GitHub repository, you need to download, configure, and install Git to your system to access these files.

Visit Git for Windows and choose an installer that suits your device.

Installing Rust in your system will help you avoid errors when building the wheels for tokenizers, a unique requirement when installing (Python) py-based projects.

There are two ways to install Rust into your system.

Head to Rust’s official site and choose an installer that best fits your computer system.

2.Open your command interface and run the following command line: pip install setuptools-rust

N.B: To open a CMD interface, Click ‘Windows+R’ to quick launch an app; type ‘cmd’ then click ‘run.’

NVIDIA CUDA

If you have used any AI tool before, you already know that a lot of computation power is needed to run these tools. Therefore, running the AI tools on devices that run using NVIDIA GPUs and have NVIDIA CUDA installed is highly favorable. CUDa improves the GPUs' processing power, allowing them to be more efficient in processing data than traditional GPUs.

 Unfortunately, you can only install CUDA on devices that run on NVIDIA GPUs. However, this does not mean you cannot use Whisper on CPU devices. As you will see later, Whisper can run on various models from tiny, base, small, medium, and large. The higher the model, the more the computation power and vice versa. Therefore, all models, CPU or GPU users, can benefit.

If your device can support an NVIDIA CUDA, visit the NVIDIA website and download the latest CUDA compatible with PyTorch.

As of this post, PyTorch supports CUDA 11.7 and 11.8.

PIP is a package installer and management tool for Python applications and packages. It’s a necessity if you want to manage all your PyPL installations using the command line.

Newer versions of Python come with an already installed PIP. However, if you are running an older version, you must download it to your computer.

To check if there is an installed PIP on your device, access your cmd and run the command prompt:

If there is a response,  PIP is present in your Python.

However, if you find an error response, you must install it on your device. 

Visit https://pip.pypa.io/en/stable/installation/ for a step-by-step guide on downloading PIP into your system.

Pytorch is a deep-learning library mostly used when running applications that rely on GPUs and CPUs. Developers prefer it due to its speed and flexibility of implementation.

To install it, go to the PyTorch Website and choose your installation preferences based on what you will be using.

Once done, you will get a Command line.

Copy and run the command in your cmd interface to download PyTorch.

N.B: If you use a GPU, select CUDA 11.7 or 11.8. Select the CPU if your device does not have an NVIDIA graphics card. 

FFmpeg is one of the most critical tools in this list since it will help convert audio to the format Whisper can process. To download it: 

Visit the FFmpeg website to download the authentic file.

Scroll down to where the Windows Icon is and click on it. Click on one of the two files that appear below it. I have chosen the ‘ Windows builds by BtbN .’This will open a new page where you will find various ffmpeg assets.

Scroll down and select the one that matches your system. For me, I'll choose the bigger ‘Win64’gpl. Click on it to download the zip folder containing the files.

Extract the files to a folder and open them. In the bin file, you will find three applications you must install on your system.

To do so, head to the local disk C and create a folder. Name this as ‘Path.’ Then, copy your three applications and paste them into the ‘Path file’ on the local disk.

Click at the top of the drive to copy the file path ‘C:\Path’

Next, Click on the Start button and search for “Edit environment variables.” Open it. 

Select ‘Path’ and click on the edit button

Click ‘New’ to add a path and paste the file path, C:\Path, at the end of the list. Then click ‘Okay’ to close the box.

To confirm the installation is successful, open a new cmd prompt window and run ‘ffmpeg.” The installation succeeded if the code appears like that in the image below.

Install Whisper

Since everything is ready, you can now install Whisper. To do so:

Open your command console and run the command lines below:

pip install git+ https://github.com/openai/whisper.git

Two possible scenarios may occur: 

The installation will be successful, as in the image above.

You may encounter an error like “cannot find command git.” 

This error means the pip command cannot locate git in your device. As a result, it cannot connect to the Whisper repository. To correct this problem, click here to download git for Windows, then run the pip install command again. During the git installation, click on the check box that auto-updates the path automatically. This will allow Pip to locate the git on your device.

2. Once the installation is complete, you only need to run Whisper in a command interface:

Here, you will see all the languages the tool can work with alongside other options that can help you run the tool, such as the Whisper model and output format. To get more information on the various commands you can run whisper on, use the command:

N.B: If you encounter an error that says “it’s not a recognized internal or external command ,” add the Python script directory to the Path with your Python installation.

How to Record Your Voice on Mac and Windows

We are done with the hard part: the installation. Everything else that follows from now will be a breeze. To record your voice on Mac or Windows, you need the help of a free tool such as Audacity. If you are not interested in downloading software, you can use a web-based platform like Notta .

Notta not only transcribes but also translates, annotates and collaborates and seamlessly integrates with your favorite tools like Notion and Salesforce. Let Notta improve your productivity today!

For the best results while recording, ensure that you: 

Have a good microphone. 

Record in a silent room without background noise.

When using Audacity:

Download the software from their main site .

2.Open the software and connect your microphone.

3.Click on Audio Setup and set your microphone as the recording device for a crispier take.

4.Click on the Record icon to start recording. Once done, Click the Stop Button to end the recording.

5.Head to ‘File’ and select ‘Export’ to save your recording as MP3, WAV, or OGG.

When using Notta: 

Create a free account with Notta.

2. Click here to download the Chrome Extension

3.Login to your Notta account.

4.Connect your microphone and permit Notta to record.

5.Click on ‘Record an Audio’ in the top right corner of your screen to record straight from your dashboard. To end the recording, click on the ‘Stop’ button. 

The Chrome Extension can allow you to capture audio from a source. 

To use it: 

Identify the Audio or video you want to record. 

Click on the Notta extension icon on your browser toolbar.

Hit ‘Start Recording’ and Play the audio source. Click ‘Stop’ to complete the recording.

N.B: Notta automatically saves all the recordings in the dashboard. To access and export them, navigate to your account dashboard and find the recording you want to export. Notta allows you to export the audio as an MP3.

How to Transcribe Voice to Text with Whisper Open AI

Now that we have the Audio, we can transcribe it using Whisper.

Save the audio file you want to transcribe in a new folder. I will call my folder ‘Transcribe.’

Open a new command prompt from the new folder. To do this, click on the file directory and type ‘cmd.’

In the command prompt window, Type ‘Whisper followed by the file name you want to transcribe. If there are spaces in between the name of the file, remember to add apostrophe marks.

The transcription process will begin, and the time it takes to complete will depend on

The size of your file. 

The speed of your GPU or CPU.

OpenAI Whisper Accuracy

Open AI’s Whisper is among the most accurate language models. 

There are two ways to deduce the accuracy levels: 

Analyzing the transcription quality. 

Whisper claims that the language model has gone through 680,000 hours of multilingual data training. As a result, it shows high levels of accuracy in transcription and translation. This intensive training has improved Whisper AI’s robustness and ability to detect accents and eliminate background and technical noise.

2.A look at the difference in WER

A research paper comparing the Word-Error-Rate (WER) between Whisper and six other current speech recognition models reveals that Whisper outperforms the best open-source model (NVIDIA STT) in every data set. 

As you can tell from the table above, Whisper AI takes the crown of being the most accurate tool among all the other language models.

Still, it's essential to acknowledge that less than five languages have a word error rate lower than 5%, and more than 25 languages have a 50% and above word error rate. Still, it manages to make 50% fewer errors than language models. 

N.B: AI speech technology is constantly improving, and Whisper AI is far from perfect. Some areas it may be lacking include:

It can occasionally leave out some punctuations 

It can transcribe some words incorrectly or fail to transcribe some at all

It does not provide a distinction between the different speakers

Whisper cannot provide real-time transcription. Currently, it only focuses on zero-shot asynchronous transcription. To run Open AI Whisper online, you must use the Whisper API. 

While it shines in performance, we still acknowledge that accuracy is still a concern to all language models, Whisper included, especially when dealing with non-English languages. 

Whisper Speech Recognition Languages

Whisper can transcribe a total of 99 languages and translate them all into English. According to the AI, the most straightforward language to transcribe is Spanish, Italian, English, and Portuguese. All these have a word error rate of less than 5%.

Here is a distribution of how the languages compare in their word error rates:

Cost to Run Whisper

The most significant benefit that comes with using Whisper is that it is free to use! You can run Whisper locally without registering and paying any subscription fees.

But there is a catch. It will cost you time and resources to install and use the software. Considering Open AI does not provide ongoing support and integration assistance, encountering errors will create operational setbacks.

At the same time, to get the best out of the tool, you need to use a device with a good GPU.  How so? 

Whisper provides five language models that you can use for transcription. These include 

Each model requires a certain amount of processing power to operate. For example, tiny and base needs a VRAM of about 1 GB each, small 2GB, medium 5GB, and large 10 GB. The higher the processing power, the faster the result.

Ideally, an Nvidia GPU (GTX970 or any newer version) can serve you well.

Do not confuse speed with accuracy. While the larger models use less time and more GPU resources, they are not necessarily the most accurate.

Whisper Free Alternative- Notta AI Speech Recognition Software

As seen above, Whisper AI is a winner in transcription accuracy. Unfortunately, it lags behind due to its limited features, numerous failure modes, and a lack of assistance. Also, it eliminates users with CPU devices as they cannot maximize the use of the tool.

As such, one tool that may interest the average user that boasts high accuracy and everything else Whisper lacks is Notta.

Notta is a transcription and translation software that can record, transcribe, and translate both audio and video. It is among the best tools for podcasters, students, and marketing teams. Notta is a web app, Chrome extension, and mobile app that allows seamless access across devices.  Some of its most notable features include:

Highly accurate - Notta delivers an accuracy of 99.98%, making it better than most tools in the market.

AI summary - Notta leverages GPT-4 to derive a highly accurate and concise summary from the generated transcription to give you an overview of the whole conversation. 

Extensive language support - It can transcribe 58 languages and translate 42 more than any other AI tool.

Fast turnaround time - The transcription process is very fast. You can get a 2-hour audio in just 5 minutes. Moreover, you don't need an expensive GPU to improve the speed! 

Real-time meeting transcriptions and note-taking - Notta supports real-time transcription of ongoing meetings. You only need to connect the app to your online meeting, and the AI assistant will take care of everything. 

To transcribe an audio file with Notta:

Sign up or log in to your Notta account.

2.At the top right corner, set the transcription language.

3.Click on ‘ Import Audio’ to upload your audio file. You can drag and drop the file from your local files or share a public URL from YouTube, Dropbox, or Google Drive. The transcription will happen immediately after upload.

4.Navigate to the dashboard, click on the transcribed file, and make any necessary edits using the built-in editor.

5.When ready to export, click the ‘Download’ icon at the top right corner.

6.Choose the format you want to export and save your transcript.

From afar, Whisper AI may seem like a tool only for tech-literate individuals, but it is, in fact, easy to use. The only challenge you may encounter is during the set-up. While the steps may seem technical, follow this guide to the letter, and nothing will stand in your way.

Please note that you can only access Whisper AI on the device that you install it. If you want a tool compatible with various devices but still delivers the same level of accuracy as OpenAI’s Whisper model, give Notta a try today.

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Audio to Text Converter

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Voice   Generator

This web app allows you to generate voice audio from text - no login needed, and it's completely free! It uses your browser's built-in voice synthesis technology, and so the voices will differ depending on the browser that you're using. You can download the audio as a file, but note that the downloaded voices may be different to your browser's voices because they are downloaded from an external text-to-speech server. If you don't like the externally-downloaded voice, you can use a recording app on your device to record the "system" or "internal" sound while you're playing the generated voice audio.

Want more voices? You can download the generated audio and then use voicechanger.io to add effects to the voice. For example, you can make the voice sound more robotic, or like a giant ogre, or an evil demon. You can even use it to reverse the generated audio, randomly distort the speed of the voice throughout the audio, add a scary ghost effect, or add an "anonymous hacker" effect to it.

Note: If the list of available text-to-speech voices is small, or all the voices sound the same, then you may need to install text-to-speech voices on your device. Many operating systems (including some versions of Android, for example) only come with one voice by default, and the others need to be downloaded in your device's settings. If you don't know how to install more voices, and you can't find a tutorial online, you can try downloading the audio with the download button instead. As mentioned above, the downloaded audio uses external voices which may be different to your device's local ones.

You're free to use the generated voices for any purpose - no attribution needed. You could use this website as a free voice over generator for narrating your videos in cases where don't want to use your real voice. You can also adjust the pitch of the voice to make it sound younger/older, and you can even adjust the rate/speed of the generated speech, so you can create a fast-talking high-pitched chipmunk voice if you want to.

Note: If you have offline-compatible voices installed on your device (check your system Text-To-Speech settings), then this web app works offline! Find the "add to homescreen" or "install" button in your browser to add a shortcut to this app in your home screen. And note that if you don't have an internet connection, or if for some reason the voice audio download isn't working for you, you can also use a recording app that records your devices "internal" or "system" sound.

Got some feedback? You can share it with me here .

If you like this project check out these: AI Chat , AI Anime Generator , AI Image Generator , and AI Story Generator .

OpenAI debuts Whisper API for speech-to-text transcription and translation

To coincide with the rollout of the ChatGPT API , OpenAI today launched the Whisper API, a hosted version of the open source Whisper speech-to-text model that the company released in September.

Priced at $0.006 per minute, Whisper is an automatic speech recognition system that OpenAI claims enables “robust” transcription in multiple languages as well as translation from those languages into English. It takes files in a variety of formats, including M4A, MP3, MP4, MPEG, MPGA, WAV and WEBM.

Countless organizations have developed highly capable speech recognition systems, which sit at the core of software and services from tech giants like Google, Amazon and Meta. But what makes Whisper different is that it was trained on 680,000 hours of multilingual and “multitask” data collected from the web, according to OpenAI president and chairman Greg Brockman, which lead to improved recognition of unique accents, background noise and technical jargon.

“We released a model, but that actually was not enough to cause the whole developer ecosystem to build around it,” Brockman said in a video call with TechCrunch yesterday afternoon. “The Whisper API is the same large model that you can get open source, but we’ve optimized to the extreme. It’s much, much faster and extremely convenient.”

To Brockman’s point, there’s plenty in the way of barriers when it comes to enterprises adopting voice transcription technology. According to a 2020 Statista survey , companies cite accuracy, accent- or dialect-related recognition issues and cost as the top reasons they haven’t embraced tech like tech-to-speech.

Whisper has its limitations, though — particularly in the area of “next-word” prediction. Because the system was trained on a large amount of noisy data, OpenAI cautions that Whisper might include words in its transcriptions that weren’t actually spoken — possibly because it’s both trying to predict the next word in audio and transcribe the audio recording itself. Moreover, Whisper doesn’t perform equally well across languages, suffering from a higher error rate when it comes to speakers of languages that aren’t well-represented in the training data.

That last bit is nothing new to the world of speech recognition, unfortunately. Biases have long plagued even the best systems, with a 2020 Stanford  study finding systems from Amazon, Apple, Google, IBM and Microsoft made far fewer errors — about 19% — with users who are white than with users who are Black.

Despite this, OpenAI sees Whisper’s transcription capabilities being used to improve existing apps, services, products and tools. Already, AI-powered language learning app Speak is using the Whisper API to power a new in-app virtual speaking companion.

If OpenAI can break into the speech-to-text market in a major way, it could be quite profitable for the Microsoft-backed company. According to one report, the segment could be worth $5.4 billion by 2026, up from $2.2 billion in 2021.

“Our picture is that we really want to be this universal intelligence,” Brockman said. “W e really want to, very flexibly, be able to take in whatever kind of data you have — whatever kind of task you want to accomplish — and be a force multiplier on that attention.”

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Robust Speech Recognition via Large-Scale Weak Supervision

openai/whisper

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[Blog] [Paper] [Model card] [Colab example]

Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification.

A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. These tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing a single model to replace many stages of a traditional speech-processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets.

We used Python 3.9.9 and PyTorch 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.11 and recent PyTorch versions. The codebase also depends on a few Python packages, most notably OpenAI's tiktoken for their fast tokenizer implementation. You can download and install (or update to) the latest release of Whisper with the following command:

Alternatively, the following command will pull and install the latest commit from this repository, along with its Python dependencies:

To update the package to the latest version of this repository, please run:

It also requires the command-line tool ffmpeg to be installed on your system, which is available from most package managers:

You may need rust installed as well, in case tiktoken does not provide a pre-built wheel for your platform. If you see installation errors during the pip install command above, please follow the Getting started page to install Rust development environment. Additionally, you may need to configure the PATH environment variable, e.g. export PATH="$HOME/.cargo/bin:$PATH" . If the installation fails with No module named 'setuptools_rust' , you need to install setuptools_rust , e.g. by running:

Available models and languages

There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and inference speed relative to the large model; actual speed may vary depending on many factors including the available hardware.

The .en models for English-only applications tend to perform better, especially for the tiny.en and base.en models. We observed that the difference becomes less significant for the small.en and medium.en models.

Whisper's performance varies widely depending on the language. The figure below shows a performance breakdown of large-v3 and large-v2 models by language, using WERs (word error rates) or CER (character error rates, shown in Italic ) evaluated on the Common Voice 15 and Fleurs datasets. Additional WER/CER metrics corresponding to the other models and datasets can be found in Appendix D.1, D.2, and D.4 of the paper , as well as the BLEU (Bilingual Evaluation Understudy) scores for translation in Appendix D.3.

Command-line usage

The following command will transcribe speech in audio files, using the medium model:

The default setting (which selects the small model) works well for transcribing English. To transcribe an audio file containing non-English speech, you can specify the language using the --language option:

Adding --task translate will translate the speech into English:

Run the following to view all available options:

See tokenizer.py for the list of all available languages.

Python usage

Transcription can also be performed within Python:

Internally, the transcribe() method reads the entire file and processes the audio with a sliding 30-second window, performing autoregressive sequence-to-sequence predictions on each window.

Below is an example usage of whisper.detect_language() and whisper.decode() which provide lower-level access to the model.

More examples

Please use the 🙌 Show and tell category in Discussions for sharing more example usages of Whisper and third-party extensions such as web demos, integrations with other tools, ports for different platforms, etc.

Whisper's code and model weights are released under the MIT License. See LICENSE for further details.

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• Speech to Text
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Text to Speech with OpenAI

Frequently asked questions, is this app free.

WhisperUI Text to Speech is not free to use. You will need to have a working OpenAI API Key for you to use the app. By using the API Key you will pay directly to OpenAI for the amount of tokens you use.

How do i get an OpenAI API Key?

You can get your API key directly with OpenAI at https://platform.openai.com/account/api-keys

Is my API key safe?

Your API is safe and stored locally on your browser.

What can i do with WhisperUI Text to Speech?

You can transform text into audio by using OpenAI Text to Speech.

How does the Text To Speech transformation process work?

The inputs a text to our web app, which then uses OpenAI to generate a speech.

What types of audio files are supported by WhisperUI Text to Speech?

WhisperUI Text to Speech supports MP3, AAC and FLAC.

How accurate is the transcription process?

OpenAI Whisper is known for its high accuracy, but the final transcription will depend on the quality of the audio file and the clarity of the spoken words.

How long does it take to transform an text into a audio file?

The time it takes to generate an audio file depends on its length and the complexity of the spoken words. However, most audios are generated within a few minutes.

What are the supported languages?

WhisperUI Text to Speech supports several languages including English, Spanish, French, German, Chinese, and more.

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Here’s how voice assisted ai technology can give people a voice again.

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Dutch Whispp app user, Ruud, uses the app to speak clearly with friends.

Johns Hopkins defines stuttering as a voice speech disorder. Stuttering affects more than 80 million people worldwide, and in the United States, more than one million Americans stutter.

A voice disorder is a problem with pitch, volume, tone, and other qualities of your voice that occurs when vocal cords don't vibrate normally. There are several types of voice disorders classified as organic, which include structural and neurological (caused by a neurological disorder like Parkinson's or Alzheimer's), functional (muscle dysfunction), and psychogenic disorders.

A Dutch start-up has created an app designed to give a voice to people with voice disorders or speech disorders, such as stuttering.

Using artificial intelligence (AI), the Whispp app enables them to make understandable and relaxed phone and video calls.

"The app has a real-time assistive voice technology that converts voiceless/vocal cord-impaired speech or whispered speech (speech that does not have a clear pitch) into natural and voiced speech," said Joris Castermans, CEO of Whispp. "People who stutter severely, for example, can reduce their stuttering frequency by an average of 85% while whispering. Additionally, people who suffer from spasmodic dysphonia or recurrent respiratory papillomatosis speak much more relaxed and fluently when they whisper."

In an email interview, Castermans said the Whispp enables users to express themselves better and easier, enhancing their quality of life and allowing them to participate more fully in society.

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"Communication is a fundamental aspect of human existence that presents a daily challenge for people who suffer from a voice disability or stutter severely," said Castermans. "The inability to communicate can lead to social isolation and, in many cases, feelings of inadequacy and depression."

With their own AI models for Whispp, the AI is audio-to-audio based with no textual intermediate, so the company doesn't use language models.

"With this, Whisper converts non-voiced speech with a very low latency," said Castermans. "Whispp's AI converts every 20 milliseconds of audio into a real-time stream.

Whispp uses real-time, audio-to-audio-based assistive voice AI to create real-time speech conversion and accommodates a range of voice types — from whispers to rough esophageal speech. This allows the app to create a tailored solution for several voice conditions.

For example, Castermans says people who stutter severely speak fluently and are relaxed when they whisper. "This is because of a neurological change that occurs while they are speaking; aside from this, people who stutter severely didn't 'learn' to be anxious while whispering."

Castermans says that big tech and assistive speech tech companies predominantly focus on Automatic Speech Recognition, known as speech-to-text (STT), for non-standard speech. "This is very helpful for patients with reduced articulation (ALS, MS, stroke and Parkinson's Disease) who can use text-to-speech to synthesize their speech."

"The disadvantage of this approach, however, is the high latency of two to three seconds, which creates barriers to natural conversation," said Castermans. "As a result, current AI speech technology solutions do not provide an adequate solution for people with voice disorders who have lost their voice but still have good articulation."

The Whispp app is available on Android and IoS.

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Today, we’re launching Universal-1, our most powerful and accurate multilingual speech-to-text model to date—trained on 12.5M hours of multilingual audio data.

Today, AssemblyAI is launching Universal-1 ,  our most capable and highly trained speech recognition model. Trained on over 12.5 million hours of multilingual audio data, Universal-1 achieves best-in-class speech-to-text accuracy, reduces word error rate and hallucinations, improves timestamp estimation, and helps us continue to raise the bar as the industry-leading Speech AI provider. 

Universal-1 is trained on four major languages: English, Spanish, French, and German, and shows extremely strong speech-to-text accuracy in almost all conditions, including heavy background noise, accented speech, natural conversations, and changes in language, while achieving fast turn-around time and improved timestamp accuracy.

In the last few years we've seen an explosion of audio data available online. This coupled with advances in AI technology have allowed organizations to unlock the value of voice data in ways that were previously impossible. As a result, organizations are building new products, services, and capabilities that serve millions of people around the world. By building on AssemblyAI’s Speech AI models, customers have built products that can summarize video calls with clear notes and action items, automate customer service experiences and help organizations understand the voice of their customers with insights from every customer interaction, and create apps that help teachers guide students more effectively as they learn to read.

With Universal-1 we sought to build on the industry-leading performance of our previous models, and designed this new model guided by the idea that accuracy of every word matters. In conversations with customers, it was clear that there was a need in the industry for a model that focused on the nuances of spoken language across accents, tone, dialect, faithfulness, and more. We hope the new capabilities of Universal-1 will help power the next generation of AI products and features built with voice data.

Accuracy is paramount when deciding which speech-to-text model to implement. AssemblyAI's Automatic Speech Recognition (ASR) model is best-in-class, and we are beneficiaries of the constant improvements they implement, like Universal-1. We provide lead intelligence to over 200,000 small businesses. If the transcriptions are not accurate, then the downstream intelligence our customers depend on will also be subpar — garbage in, garbage out.

Ryan Johnson, Chief Product Officer, CallRail

Universal-1 ASR: Pushing the Boundaries of Speech AI

Universal-1 accomplishes the following improvements: 

Accurate and robust multilingual speech-to-text Universal-1 represents another major milestone in our mission to provide accurate, faithful, and robust speech-to-text capabilities for multiple languages, helping our customers and developers worldwide build various Speech AI applications.

• Universal-1 achieves 10% or greater improvement in English, Spanish, and German speech-to-text accuracy, compared to the next-best commercial speech-to-text system we tested.
• Universal-1 reduces hallucination rate by 30% over a widely used open-source model, Whisper Large-v3, providing users with confidence in the results we deliver.
• Humans prefer the outputs from Universal-1 over Conformer-2, our previous generation model, 71% of the time when they have a preference.
• Universal-1 exhibits the ability to code switch, transcribing multiple languages within a single audio file.

Precise timestamp estimation Word-level timestamps are essential for various downstream applications, such as audio and video editing. In conversation analytics and meeting transcription, accurate timestamps are crucial to enable speaker diarization to align speaker labels with recognized words.

• Word-level timestamps are essential for various downstream applications, such as audio and video editing as well as conversation analytics.
• Universal-1 improves our timestamp accuracy by 13% relative to Conformer-2.
• The improvement in timestamp estimation results in a positive impact on speaker diarization, improving concatenated minimum-permutation word error rate (cpWER) by 14% and speaker count estimation accuracy by 71% compared to Conformer-2.

Efficient parallel inference

• Effective parallelization during inference is crucial to achieve very low turnaround processing time for long audio files.
• Universal-1 achieves a 5x speed-up compared to a fast and batch-enabled implementation of Whisper Large-v3 on the same hardware.

See it in action

Paul. It's okay. I'm here.

I'm here. It's been a while since you've had one of those nightmares. Tell me, what was it about? It's only fragments.

Nothing's clear. You've been fighting the Harkonnens for decades. Load.

My family's been fighting them for centuries. Your blood comes from dukes and great houses. Here, we're equal.

What we do, we do for the benefit of all. Well, I'd very much like to be equal to you. Maybe I'll show you the way.

Deal with this prophet. Send assassins. Theodorother, he's psychotic.

I see possible futures all at once. And in so many futures, our enemies prevail. But I do see a way.

There is a narrow way through. My allegiance is to you. Do you believe me? This is a form of power that our world has not yet seen.

The ultimate power. I want you to know I will love you as long as I breathe. You will never lose me as long as you stay who you are.

Consider what you're about to do, Paul Atreides. Silence. This prophecy is how they enslave us.

Journey. You are not prepared for what is done to come.

Entonces le digo yo a Martínez, Martínez, espérame right here cinco minutes que yo tengo que ir al toilet. Pero hay no idea lo que me iba a encontrar yo en ese toilet. Oye, te mando mamá, you cooking for me the sunny side up cuando tú sabes que a mí me gusta scramble.

Emilito. ¿Number one, who told you que esto es para ti? En number dos, lo primero que tú dices en mi cocina es good morning. Ah, good morning, mami.

Pues good morning, mamá. Good morning, mija. Así que no estoy en el toilet doing my business cuando escucho una woman screaming from el toilet de Alao.

Mamá Sonny, side up for me, please. Sony, side up. Pero ya tú no eres vegetarian.

No more lacto. Y aquí podemos ver a mi older sister que todos los días está cambiando el diet pensando que le estaban haciendo daño y boom. I can't believe my eyeball.

Mami. El jefe Kissing in the mouth con Missy Martinez. Oh, my God.

¿Oye, quién me ayuda con algo de mi Instagram? I can't figure it out. Dame acá. Abuelita.

¿What is it? ¿Carolina? That's too la baby. Baja volumen, mi amor. Yo sospechaba algo porque ese jefe Eli's grabbing and touching all the girls en la oficina.

Emilio, Mrs. Martinez no es ninguna santa, you know. Mamá, tú no puedes estar comiendo tu chorizo every morning.

Habías hecho cáncer de colon. Emilio, sé something. ¿What? ¿Cómo que Emilio? ¿Qué falta de respeto es esa? You call me dad.

¿Abuelita, how? ¿Cómo es que tú tienes 100 likes en esta foto? Esa es mi people from bingo. Ay, my salud de colon ideal. So por favor, min, your own business.

Carolina de volume. Wow, abuelita, tú eres una rockstar. ¿Can you like my post emily to bless the table? Yo bendije ayer, papá.

Den tu lilianita. Thank you for all this comida que tu pones en nuestra family table. Bless the hands que prepararon la comida.

Perdónanos por comer dis baby chicken huevos and forgive my papá Emilio for being so gossipy and chismoso. Amén. Amén.

No, no, no, no puedo tomar café. No te hagas el sentido. No, no, no.

My name is Angelica Skyler Alexander Hamilton. Where's your family from? Unimportant. There's a million things I haven't.

Just you wait. Just you wait. So this is what it feels like to match wit for someone at your level.

What the hell is the catch? It's the feeling of freedom. Of seeing the light is Ben Franklin with the key and a kite. You see it, right? The conversation lasted two minutes, maybe three minutes.

Everything we said in total agreement. It's the dream and it's a bit of a dance, a bit of a posture. It's a bit of a stance.

He's a bit of a flirt. But I'm gonna give it a chance. I asked about his family.

Did you see his answer? His hands started fidgeting. He looked askance. He's penniless.

He's flying by the seat of his pants. Handsome boy, does he know it. Peach fuzz.

Then he can't even grow it. Want to take him far away from this place? Then I turn and see my sister's face. And she is helpless.

And I know she is helpless. And her eyes are just helpless. And I realize three fundamental truths at the exact same time.

Universal-1’s training data far exceeds the training data used for most existing speech-to-text models. This training data includes audio from non-native speakers, audio with heavy background noise, conversations involving multiple talkers held in various domains and settings, to better simulate how speech happens in the real world. Universal-1 also builds on our predecessor models, Conformer-1 and Conformer-2, to capture proper nouns and alphanumeric details with high accuracy. 

We’re excited to see the impact that Universal-1 has on applications like:

• Conversational intelligence platforms that are now able to analyze vast amounts of customer data quickly, accurately, and reliably in order to surface critical voice of customer insights and analytics regardless of accent, recording condition, number of speakers, and more.
• AI notetakers that can now generate highly accurate and hallucination-free meeting notes to serve as the basis for LLM-powered summaries, action items, and other metadata generation with accurate proper noun, speaker, and timing information included.
• Creator tool applications that are now able to build AI-powered video editing workflows for their end-users leveraging precise speech-to-text outputs in multiple languages with low error rates and reliable word timing information.
• Telehealth platforms automating clinical note entry and claims submission processes with a high success rate leveraging accurate and faithful speech-to-text outputs, including rare words like prescription names and medical diagnoses, in adversarial and far field recording conditions.

Improving the accuracy of Speech AI across languages

Trained on English, Spanish, German, and French data, Universal-1 is built to support the languages most often used by our customers and their end-users.

Today, Universal-1 is available in English & Spanish, with German and French being made available shortly. We will be adding additional language support within future Universal models over time.

Best & Nano ASR Tiers: More Options to Build with AssemblyAI

Today, we’re also introducing our Best and Nano tiers to give you more options when building with  Speech AI models from AssemblyAI depending on your budget, accuracy needs, and use case. 

At AssemblyAI, we use a combination of models to produce your results. Our Best tier will house our most powerful and accurate models, including Universal-1. This tier is best suited for use cases where accuracy is paramount, and end-users will interact directly with the results generated from our models. 

We are also introducing a Nano tier—a lightweight lower cost speech-to-text option  available in many languages. Nano is best suited for use cases like search and topic detection or for use cases where accuracy is not paramount.

What Comes Next for Universal-1

Universal -1 is available via our API , and you can start building on it today. We’ll continue to improve our Speech AI models over time, so stay tuned for updates as we add new capabilities and languages to Universal-1.

# Frequently Asked Questions

Read our research post here. View all of our research here .

Our Best tier supports 17 languages. Our Nano tier supports 99 languages. As of April 3, 2024, Universal-1 will be supporting English and Spanish requests to our API when selecting Best.

At AssemblyAI, we use a combination of models to produce your results. AssemblyAI’s Best tier is our most robust and accurate offering, housing our most powerful models, and has the broadest range of capabilities. The Best tier is suited for use cases where accuracy and power are paramount. AssemblyAI’s Nano tier is a fast, lightweight offering that gives product and development teams access to Speech AI at an attainable price point across 99 languages. It is best for teams with extensive language needs, and those who are looking for a low-cost Speech AI option.

Visit our Pricing page.

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Our 5 favorite AI voice notes apps for different purposes

The ai voice notes apps listed are not ranked from best to worst; each possesses unique features tailored to meet different needs.

We’ve all been there: a great idea pops up, but it’s just impossible to tap down before it evaporates. Oddly, the market for AI voice notes apps is relatively small against the powerful potential they promise to revolutionize note-taking.

Discovering the ideal AI voice note-taking app can be surprisingly challenging. Despite the evident demand for tools that capture fleeting thoughts with precision and organization, the niche remains underserved. The hunt for an app that not only records but also intelligently organizes and interprets voice notes often leads to a maze of options, each falling short in one way or another. This scarcity points to a gap between the potential impact of such technology on our daily productivity and the current market offerings, emphasizing the need for more advanced solutions in this space.

Top 5 AI voice notes apps

These innovative tools go beyond simple speech-to-text. Just speak your mind. Watch as your words are automatically organized into neat, searchable notes. Need to go back to a specific detail or recapture the emphasis of the speaker? Each app gives the option to flip it back to the original transcribed audio without skipping a beat. It’s the best of both worlds: structured notes for quick reference and raw audio for in-depth review.

The AI voice notes apps listed are not ranked from best to worst ; each possesses unique features tailored to meet different needs.

Cleft Notes

Cleft Notes is an innovative app designed to transform voice memos into shared, structured notes effortlessly. By simply speaking, users can convert scattered thoughts into AI-optimized notes in perfect markdown format, which can then be easily shared. The app not only transcribes but also organizes content with headings and a coherent structure for readability. Additionally, it offers privacy-first features, such as encryption and on-device transcription, ensuring user data remains secure. It’s ideal for ideation, asynchronous communication, and planning, appealing especially to those seeking simplicity and efficiency in note-taking without the need for typing.

ClickUp stands out for its comprehensive project management capabilities, integrating advanced note-taking tools with an AI writing assistant. This versatility makes it suitable for a wide range of users, from individuals to large teams, looking to enhance productivity and creativity in note-taking.

Otter.ai is renowned for its live transcription capabilities, making it a favorite among professionals and students alike. It excels in creating actionable notes from meetings, lectures, and discussions, with features that include summarizing key points and identifying action items​.

Fireflies.ai

Fireflies.ai offers a robust solution for recording and transcribing meetings across various platforms like Zoom, Google Meet, and Teams. Its ability to generate automated summaries and integrate with CRM and team collaboration tools makes it an excellent option for teams focused on efficiency and collaboration​.

Reflect Notes

Reflect focuses on personal note-taking with AI integrations, offering a streamlined, secure, and user-friendly experience. It incorporates Whisper AI for voice note transcriptions and a GPT-4 assistant to help transform your notes, making it ideal for personal use and managing personal knowledge bases​.

Here are some tips for making the most out of AI voice notes apps:

• Utilize voice-to-text features for capturing ideas during brainstorming sessions or while on the move.
• Use AI for organizing notes and identifying key themes, which is especially useful in meetings or lectures.
• Take advantage of real-time transcription services for accurate note-taking during important discussions, ensuring no detail is missed.
• Use collaborative features to share meeting outcomes and action items with team members, streamlining communication.
• Explore customization options to tailor the note-taking process to your personal workflow, making information retrieval quicker and more efficient.

Image credits: Kerem Gülen/Midjourney

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Whisper AI integration for offline voice typing dictation

With the powerful voice typing model of whisper AI, I am wishing that open AI can Make it easy for developers to integrate it in their keyboards like SwiftKey and other Android keyboards to offer a seamless experience for offline voice typing transcription, OR, I’m wishing that open AI can integrate it in their coming voice assistant to provide a voice typing dictation experience through voice using any keyboard and with auto punctuation in a way similar to Google’s assistant voice typing which is unfortunately only exclusive 4 Pixel phones.

joining you here… I wish, too… But I don’t think it will happen in our lifetime)

( How to install and use Whisper offline (no internet required) · openai/whisper · Discussion #1463 · GitHub )

if this is possible than means there is away most likely.

Why not? Almost daily I’m hearing about advances in the efficiency of language models and if tensor processors are technically lower in raw performance then their counter snapdragn CPUs so I think at leastSnapdragon seven and eight series can handle voice typing tasks without an issue. I just hate it when Google is keeping things for their Pixel phones only, and these phones are only available in certain markets and also they are notorious for hardware issues. They should learn from Apple which do not discriminate between their phones like Google!!

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