example of speech to text

Speech to Text - Voice Typing & Transcription

Take notes with your voice for free, or automatically transcribe audio & video recordings. amazingly accurate, secure & blazing fast..

~ Proudly serving millions of users since 2015 ~

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Dictate Notes

Start taking notes, on our online voice-enabled notepad right away, for free. Learn more.

Transcribe Recordings

Automatically transcribe (& optionally translate) recordings, audio and video files, YouTubes and more, in no time. Learn more.

Speechnotes is a reliable and secure web-based speech-to-text tool that enables you to quickly and accurately transcribe & translate your audio and video recordings, as well as dictate your notes instead of typing, saving you time and effort. With features like voice commands for punctuation and formatting, automatic capitalization, and easy import/export options, Speechnotes provides an efficient and user-friendly dictation and transcription experience. Proudly serving millions of users since 2015, Speechnotes is the go-to tool for anyone who needs fast, accurate & private transcription. Our Portfolio of Complementary Speech-To-Text Tools Includes:

Voice typing - Chrome extension

Dictate instead of typing on any form & text-box across the web. Including on Gmail, and more.

Transcription API & webhooks

Speechnotes' API enables you to send us files via standard POST requests, and get the transcription results sent directly to your server.

Zapier integration

Combine the power of automatic transcriptions with Zapier's automatic processes. Serverless & codeless automation! Connect with your CRM, phone calls, Docs, email & more.

Android Speechnotes app

Speechnotes' notepad for Android, for notes taking on your mobile, battle tested with more than 5Million downloads. Rated 4.3+ ⭐

iOS TextHear app

TextHear for iOS, works great on iPhones, iPads & Macs. Designed specifically to help people with hearing impairment participate in conversations. Please note, this is a sister app - so it has its own pricing plan.

Audio & video converting tools

Tools developed for fast - batch conversions of audio files from one type to another and extracting audio only from videos for minimizing uploads.

Our Sister Apps for Text-To-Speech & Live Captioning

Complementary to Speechnotes

Reads out loud texts, files & web pages

Listen on the go to any written content, from custom texts to websites & e-books, for free.

Speechlogger

Live Captioning & Translation

Live captions & simultaneous translation for conferences, online meetings, webinars & more.

Need Human Transcription? We Can Offer a 10% Discount Coupon

We do not provide human transcription services ourselves, but, we partnered with a UK company that does. Learn more on human transcription and the 10% discount .

Dictation Notepad

Start taking notes with your voice for free

Speech to Text online notepad. Professional, accurate & free speech recognizing text editor. Distraction-free, fast, easy to use web app for dictation & typing.

Speechnotes is a powerful speech-enabled online notepad, designed to empower your ideas by implementing a clean & efficient design, so you can focus on your thoughts. We strive to provide the best online dictation tool by engaging cutting-edge speech-recognition technology for the most accurate results technology can achieve today, together with incorporating built-in tools (automatic or manual) to increase users' efficiency, productivity and comfort. Works entirely online in your Chrome browser. No download, no install and even no registration needed, so you can start working right away.

Speechnotes is especially designed to provide you a distraction-free environment. Every note, starts with a new clear white paper, so to stimulate your mind with a clean fresh start. All other elements but the text itself are out of sight by fading out, so you can concentrate on the most important part - your own creativity. In addition to that, speaking instead of typing, enables you to think and speak it out fluently, uninterrupted, which again encourages creative, clear thinking. Fonts and colors all over the app were designed to be sharp and have excellent legibility characteristics.

Example use cases

• Voice typing
• Writing notes, thoughts
• Medical forms - dictate
• Transcribers (listen and dictate)

Transcription Service

Start transcribing

Fast turnaround - results within minutes. Includes timestamps, auto punctuation and subtitles at unbeatable price. Protects your privacy: no human in the loop, and (unlike many other vendors) we do NOT keep your audio. Pay per use, no recurring payments. Upload your files or transcribe directly from Google Drive, YouTube or any other online source. Simple. No download or install. Just send us the file and get the results in minutes.

• Transcribe interviews
• Captions for Youtubes & movies
• Auto-transcribe phone calls or voice messages
• Students - transcribe lectures
• Podcasters - enlarge your audience by turning your podcasts into textual content
• Text-index entire audio archives

Key Advantages

Speechnotes is powered by the leading most accurate speech recognition AI engines by Google & Microsoft. We always check - and make sure we still use the best. Accuracy in English is very good and can easily reach 95% accuracy for good quality dictation or recording.

Lightweight & fast

Both Speechnotes dictation & transcription are lightweight-online no install, work out of the box anywhere you are. Dictation works in real time. Transcription will get you results in a matter of minutes.

Super Private & Secure!

Super private - no human handles, sees or listens to your recordings! In addition, we take great measures to protect your privacy. For example, for transcribing your recordings - we pay Google's speech to text engines extra - just so they do not keep your audio for their own research purposes.

Health advantages

Typing may result in different types of Computer Related Repetitive Strain Injuries (RSI). Voice typing is one of the main recommended ways to minimize these risks, as it enables you to sit back comfortably, freeing your arms, hands, shoulders and back altogether.

Saves you time

Need to transcribe a recording? If it's an hour long, transcribing it yourself will take you about 6! hours of work. If you send it to a transcriber - you will get it back in days! Upload it to Speechnotes - it will take you less than a minute, and you will get the results in about 20 minutes to your email.

Saves you money

Speechnotes dictation notepad is completely free - with ads - or a small fee to get it ad-free. Speechnotes transcription is only $0.1/minute, which is X10 times cheaper than a human transcriber! We offer the best deal on the market - whether it's the free dictation notepad ot the pay-as-you-go transcription service.

Dictation - Free

• Online dictation notepad
• Voice typing Chrome extension

Dictation - Premium

• Premium online dictation notepad
• Premium voice typing Chrome extension
• Support from the development team

Transcription

$0.1 /minute.

• Pay as you go - no subscription
• Audio & video recordings
• Speaker diarization in English
• Generate captions .srt files
• REST API, webhooks & Zapier integration

Compare plans

Privacy Policy

We at Speechnotes, Speechlogger, TextHear, Speechkeys value your privacy, and that's why we do not store anything you say or type or in fact any other data about you - unless it is solely needed for the purpose of your operation. We don't share it with 3rd parties, other than Google / Microsoft for the speech-to-text engine.

Privacy - how are the recordings and results handled?

- transcription service.

Our transcription service is probably the most private and secure transcription service available.

• HIPAA compliant.
• No human in the loop. No passing your recording between PCs, emails, employees, etc.
• Secure encrypted communications (https) with and between our servers.
• Recordings are automatically deleted from our servers as soon as the transcription is done.
• Our contract with Google / Microsoft (our speech engines providers) prohibits them from keeping any audio or results.
• Transcription results are securely kept on our secure database. Only you have access to them - only if you sign in (or provide your secret credentials through the API)
• You may choose to delete the transcription results - once you do - no copy remains on our servers.

- Dictation notepad & extension

For dictation, the recording & recognition - is delegated to and done by the browser (Chrome / Edge) or operating system (Android). So, we never even have access to the recorded audio, and Edge's / Chrome's / Android's (depending the one you use) privacy policy apply here.

The results of the dictation are saved locally on your machine - via the browser's / app's local storage. It never gets to our servers. So, as long as your device is private - your notes are private.

Payments method privacy

The whole payments process is delegated to PayPal / Stripe / Google Pay / Play Store / App Store and secured by these providers. We never receive any of your credit card information.

More generic notes regarding our site, cookies, analytics, ads, etc.

• We may use Google Analytics on our site - which is a generic tool to track usage statistics.
• We use cookies - which means we save data on your browser to send to our servers when needed. This is used for instance to sign you in, and then keep you signed in.
• For the dictation tool - we use your browser's local storage to store your notes, so you can access them later.
• Non premium dictation tool serves ads by Google. Users may opt out of personalized advertising by visiting Ads Settings . Alternatively, users can opt out of a third-party vendor's use of cookies for personalized advertising by visiting https://youradchoices.com/
• In case you would like to upload files to Google Drive directly from Speechnotes - we'll ask for your permission to do so. We will use that permission for that purpose only - syncing your speech-notes to your Google Drive, per your request.

How to Turn Audio to Text using OpenAI Whisper

Do you know what OpenAI Whisper is? It’s the latest AI model from OpenAI that helps you to automatically convert speech to text.

Transforming audio into text is now simpler and more accurate, thanks to OpenAI’s Whisper.

This article will guide you through using Whisper to convert spoken words into written form, providing a straightforward approach for anyone looking to leverage AI for efficient transcription.

Introduction to OpenAI Whisper

OpenAI Whisper is an AI model designed to understand and transcribe spoken language. It is an automatic speech recognition (ASR) system designed to convert spoken language into written text.

Its capabilities have opened up a wide array of use cases across various industries. Whether you’re a developer, a content creator, or just someone fascinated by AI, Whisper has something for you.

Let's go over some its key features:

1. Transcription s ervices: Whisper can transcribe audio and video content in real-time or from recordings, making it useful for generating accurate meeting notes, interviews, lectures, and any spoken content that needs to be documented in text form.

2. Subtitling and c losed c aptioning: It can automatically generate subtitles and closed captions for videos, improving accessibility for the deaf and hard-of-hearing community, as well as for viewers who prefer to watch videos with text.

3. Language l earning and t ranslation : Whisper's ability to transcribe in multiple languages supports language learning applications, where it can help in pronunciation practice and listening comprehension. Combined with translation models, it can also facilitate real-time cross-lingual communication.

4. Accessibility t ools: Beyond subtitling, Whisper can be integrated into assistive technologies to help individuals with speech impairments or those who rely on text-based communication. It can convert spoken commands or queries into text for further processing, enhancing the usability of devices and software for everyone.

5. Content s earchability: By transcribing audio and video content into text, Whisper makes it possible to search through vast amounts of multimedia data. This capability is crucial for media companies, educational institutions, and legal professionals who need to find specific information efficiently.

6. Voice- c ontrolled a pplications: Whisper can serve as the backbone for developing voice-controlled applications and devices. It enables users to interact with technology through natural speech. This includes everything from smart home devices to complex industrial machinery.

7. Customer s upport a utomation: In customer service, Whisper can transcribe calls in real time. It allows for immediate analysis and response from automated systems. This can improve response times, accuracy in handling queries, and overall customer satisfaction.

8. Podcasting and j ournalism: For podcasters and journalists, Whisper offers a fast way to transcribe interviews and audio content for articles, blogs, and social media posts, streamlining content creation and making it accessible to a wider audience.

OpenAI's Whisper represents a significant advancement in speech recognition technology.

With its use cases spanning across enhancing accessibility, streamlining workflows, and fostering innovative applications in technology, it's a powerful tool for building modern applications.

How to Work with Whisper

Now let’s look at a simple code example to convert an audio file into text using OpenAI’s Whisper. I would recommend using a Google Collab notebook .

Before we dive into the code, you need two things:

• OpenAI API Key
• Sample audio file

First, install the OpenAI library (Use ! only if you are installing it on the notebook):

Now let’s write the code to transcribe a sample speech file to text:

This script showcases a straightforward way to use OpenAI Whisper for transcribing audio files. By running this script with Python, you’ll see the transcription of your specified audio file printed to the console.

Feel free to experiment with different audio files and explore additional options provided by the Whisper Library to customize the transcription process to your needs.

Tips for Better Transcriptions

Whisper is powerful, but there are ways to get even better results from it. Here are some tips:

• Clear a udio: The clearer your audio file, the better the transcription. Try to use files with minimal background noise.
• Language s election: Whisper supports multiple languages. If your audio isn’t in English, make sure to specify the language for better accuracy.
• Customiz e o utput: Whisper offers options to customize the output. You can ask it to include timestamps, confidence scores, and more. Explore the documentation to see what’s possible.

Advanced Features

Whisper isn’t just for simple transcriptions. It has features that cater to more advanced needs:

• Real- t ime t ranscription : You can set up Whisper to transcribe the audio in real time. This is great for live events or streaming.
• Multi- l anguage s upport: Whisper can handle multiple languages in the same audio file. It’s perfect for multilingual meetings or interviews.
• Fine- t uning: If you have specific needs, you can fine-tune Whisper’s models to suit your audio better. This requires more technical skill but can significantly improve results.

Working with OpenAI Whisper opens up a world of possibilities. It’s not just about transcribing audio – it’s about making information more accessible and processes more efficient.

Whether you’re transcribing interviews for a research project, making your podcast more accessible with transcripts, or exploring new ways to interact with technology, Whisper has you covered.

Hope you enjoyed this article. Visit turingtalks.ai for daily byte-sized AI tutorials.

Cybersecurity & Machine Learning Engineer. Loves building useful software and teaching people how to do it. More at manishmshiva.com

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

All Speech-to-Text code samples

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

Free Speech to Text

Convert your voice to text in real time, no account or payment required.

How to Convert Speech to Text

Access the converter.

Start the speech to text converter and choose a language.

As the speech happens, text will be generated.

Edit and Export

Edit the styling of the text, proofread, and export.

Maestra API

Integrate Maestra’s speech to text converter into your workflow and bridge the gap between audio and text.

Advantages of AI Voice to Text

Free and Online

Accurately convert your voice to text using AI technology , completely for free .

Start up the voice to text software and leave the rest to AI speech recognition technology.

Cloud-Based

No downloads are needed, all you need to do is start the speech to text converter.

Improved Accessibility

Provide free transcripts to live audiences by converting your speech into text in real time.

Free Speech to Text Use Cases

Multiply your viewers by converting voice to text in live streams .

Ensure the comprehension of every student with free voice dictation.

Effortlessly convert your podcast episodes to text as they happen.

Transcriptionists

Have accurate transcripts ready right after any event using free speech to text software.

In Addition to AI Voice Typing

Voice Cloning

Clone your voice and start speaking in 29 languages by using Maestra’s AI voice cloning feature.

YouTube Integration

YouTube integration allows Maestra users to fetch content from their YouTube channel without having to upload files one by one. Maestra serves as a localization station for YouTubers, allowing them to add then edit existing subtitles on their YouTube videos, directly from Maestra’s editor.

Free Speech to Text in 125+ Languages

Full List of Languages

Interactive Text Editor

Proofread and edit the text using our friendly and easy to use text editor. Maestra has a very high accuracy rate, but if needed, the text can be adjusted through the text editor.

*Click image to switch dark/light mode

Maestra’s video dubber offers AI voice cloning and voiceovers with a diverse portfolio of AI speakers. Voices with different dialects and accents further improve your content game, in addition to promoting accessibility.

Maestra Teams & Collab

Create Team-based channels with “View” and “Edit” level permissions for your entire team & company. Collaborate on voiceovers with your colleagues in real-time.

Auto Subtitle Generator

Maestra’s auto subtitle generator provides subtitles in 125+ languages. Pairing voice dictation with subtitles promotes accessibility by allowing sight and hearing-impaired individuals, as well as audiences who watch on mute to consume the content, instantly multiplying viewership through AI speech recognition technology.

Check API Docs

How do I turn my voice to text?

Start up Maestra’s free voice to text converter and start speaking. Text will be generated in real time as speech happens. It is all online and free, no credit card or account required.

How can I convert speech-to-text?

Anyone can use Maestra’s speech to text app for free. Start the converter and start speaking, text will be generated as you speak, no credit card or account required.

Is speech-to-text free?

Yes, Maestra’s speech to text tool is completely free for anyone to use. There is no further payment, account or download requirement. Talk after starting the online tool and speech will be converted to text using AI technology.

Is there a talk to text app?

Start Maestra’s online speech to text tool and start talking to convert your voice into text in real time, completely for free.

Which AI converts voice to text?

Maestra uses AI speech recognition technology to convert voice to text. The process is completely free and online, no account required.

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Customer reviews, 4.7 out of 5 stars, “master the media with maestra”.

The best side of this product is auto subtitling. And most importantly, it supports multiple languages.

“The All In One “over the top” turnkey solution for Automatic Transcripts, Subtitles and Voiceovers”

What comes to mind as Maestra being the go-to solution for our company is that it’s such a time and money saver.

“perfect for anything transcript needs”

The best thing about Maestra is how well it creates transcripts. It’s so useful for me. It makes my day a lot easier.

“MAESTRA IS THE GO-TO FOR SUBTITLING. LOVE IT!”

Maestra is just amazing! We were able to produce subtitles in multiple languages assisted by their platform. Multiple users were able to work and collaborate thanks to their super user-friendly interface.

“Pocket Friendly Content Creator”

It is cloud-based. It allows to automatically transcribe, caption, and voiceover video and audio files to hundreds of languages. It helps to reach and educate people all around the globe.

Audio to Text

Transcribe audio to text automatically, using AI. Over +120 languages supported

319 reviews

Accurate audio transcriptions with AI

Effortlessly convert spoken words into written text with unmatched accuracy using VEED’s AI audio-to-text technology. Get instant transcriptions for your podcasts, interviews, lectures, meetings, and all types of business communications. Say goodbye to manually transcribing your audio and embrace efficiency. Our advanced algorithms use machine learning to ensure contextually relevant transcripts, even for complex recordings.

With customizable options and quick turnaround, you have full control over the transcription process. Join countless professionals who rely on VEED to streamline their work, making every spoken word accessible and searchable. Our text converter also features a built-in video and audio editor to help you achieve a crisp, studio-quality sound for your recordings. Increase your productivity to new heights!

How to transcribe audio to text:

Upload or record

Upload your audio or video to VEED or record one using our online audio recorder .

Auto-transcribe and translate

Auto-transcribe your video from the Subtitles menu. You can also translate your transcript to over 120 languages. Select a language and translate the transcript instantly.

Review and export

Review and edit the transcription if necessary. Just click on a line of text and start typing. Download your transcript in VTT, SRT, or TXT format.

Learn more about our audio-to-text tool in this video:

FOCUS ON WORK

Instant transcription downloads for better documentation

VEED uses cutting-edge technology to transcribe your audio to text at lightning-fast speed. Download your transcript in one click and keep track of your records better—without paying for expensive transcription services. Get a written copy of your recordings instantly and one proofread for 100% accuracy. Downloading transcriptions is available to premium subscribers. Check our pricing page for more info.

Transcribe videos to bump your content in search results

Our audio-to-text tool is part of a robust and powerful video editing software that also lets you edit and transcribe your video content. Transcribe your video and add captions to help your content rank higher in search engine results. Drive traffic to your website, increase engagement in your social media pages, and grow your channel. Animate your captions and captivate viewers in just a few clicks!

Convert audio to text and create globally accessible content

VEED can help your brand create content that caters to a diverse audience. With automatic transcriptions and instant translations , you can publish globally accessible and inclusive content. Translate your audio and video transcriptions to over 100 languages. Reach untapped markets and help your business grow with instant, reliable, and affordable transcriptions.

How do I convert my audio to text?

VEED lets you automatically transcribe your audio to text at lightning-fast speed! Upload your audio file to VEED and click on the Subtitles tool on the left menu. Upload your audio file to VEED and auto-transcribe from the Subtitles menu. Download your transcript in VTT, TXT, or SRT format!

Can I transcribe videos?

Yes, you can! Upload your video file to VEED and our software will transcribe the original audio that was recorded in your video with the help of AI.

Can I download both the TXT file and the video with the subtitles?

Absolutely! When you’re done downloading the TXT, VTT, or SRT file, click on ‘Export’ to download the video with the subtitles on it. Your video will be exported as an MP4 file.

How do I edit the transcription?

Depending on how the speech or recording is spaced out through the video, VEED will separate the transcriptions into different boxes. Just click on each box and start typing or editing the text.

Can I change the text’s color and font of the subtitles?

Yes—but only the subtitles appearing on the video and not the TXT file. You can choose from a wide range of fonts and styles. Change its size, color, and opacity.

How accurate is VEED’s automatic audio-to-text transcription service?

VEED features a 98.5% accuracy in automatic transcriptions and translations with the help of AI. Transcribe your audio to text and translate them to over 100 languages instantly without sacrificing quality.

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• Music Transcription
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• Speech to Text Danish
• Speech to Text Dutch
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• Speech to Text Portuguese
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• Speech to Text Thai
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• Transcribe Recordings to Text
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Loved by creators.

Loved by the Fortune 500

VEED has been game-changing. It's allowed us to create gorgeous content for social promotion and ad units with ease.

Max Alter Director of Audience Development, NBCUniversal

I love using VEED. The subtitles are the most accurate I've seen on the market. It's helped take my content to the next level.

Laura Haleydt Brand Marketing Manager, Carlsberg Importers

I used Loom to record, Rev for captions, Google for storing and Youtube to get a share link. I can now do this all in one spot with VEED.

Cedric Gustavo Ravache Enterprise Account Executive, Cloud Software Group

VEED is my one-stop video editing shop! It's cut my editing time by around 60% , freeing me to focus on my online career coaching business.

Nadeem L Entrepreneur and Owner, TheCareerCEO.com

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When it comes to amazing videos, all you need is VEED

Transcribe audio

No credit card required

Convert audio to text, translate to multiple languages, and more!

VEED is a comprehensive and incredibly easy-to-use video editing software that allows you to do so much more than just transcribe audio to text. Apart from transcribing an audio file, you can transcribe the original recording of a video. Add subtitles to your videos to make them more accessible for everyone. It also has all the video editing tools you need. All tools are accessible online so you don’t need to install any software. Try VEED today and start creating professional-quality, globally accessible content!

• About AssemblyAI

The top free Speech-to-Text APIs, AI Models, and Open Source Engines

This post compares the best free Speech-to-Text APIs and AI models on the market today, including APIs that have a free tier. We’ll also look at several free open-source Speech-to-Text engines and explore why you might choose an API vs. an open-source library, or vice versa.

Choosing the best Speech-to-Text API , AI model, or open-source engine to build with can be challenging. You need to compare accuracy, model design, features, support options, documentation, security, and more.

This post examines the best free Speech-to-Text APIs and AI models on the market today, including ones that have a free tier, to help you make an informed decision. We’ll also look at several free open-source Speech-to-Text engines and explore why you might choose an API or AI model vs. an open-source library, or vice versa.

Looking for a powerful speech-to-text API or AI model?

Learn why AssemblyAI is the leading Speech AI partner.

Free Speech-to-Text APIs and AI Models

APIs and AI models are more accurate, easier to integrate, and come with more out-of-the-box features than open-source options. However, large-scale use of APIs and AI models can come with a higher cost than open-source options.

If you’re looking to use an API or AI model for a small project or a trial run, many of today’s Speech-to-Text APIs and AI models have a free tier. This means that the API or model is free for anyone to use up to a certain volume per day, per month, or per year.

Let’s compare three of the most popular Speech-to-Text APIs and AI models with a free tier: AssemblyAI, Google, and AWS Transcribe.

AssemblyAI is an API platform that offers AI models that accurately transcribe and understand speech, and enable users to extract insights from voice data. AssemblyAI offers cutting-edge AI models such as Speaker Diarization , Topic Detection, Entity Detection , Automated Punctuation and Casing , Content Moderation , Sentiment Analysis , Text Summarization , and more. These AI models help users get more out of voice data, with continuous improvements being made to accuracy .

AssemblyAI also offers LeMUR , which enables users to leverage Large Language Models (LLMs) to pull valuable information from their voice data—including answering questions, generating summaries and action items, and more. 

The company offers a $50 credit to get users started.

Its high accuracy and diverse collection of AI models built by AI experts make AssemblyAI a sound option for developers looking for a free Speech-to-Text API. The API also supports virtually every audio and video file format out-of-the-box for easier transcription.

AssemblyAI has expanded the languages it supports to include English, Spanish, French, German, Japanese, Korean, and much more, with additional languages being released monthly. See the full list here .

AssemblyAI’s easy-to-use models also allow for quick set-up and transcription in any programming language. You can copy/paste code examples in your preferred language directly from the AssemblyAI Docs or use the AssemblyAI Python SDK or another one of its ready-to-use integrations .

• Free to test in the AI playground , plus 100 free hours of asynchronous transcription with an API sign-up
• Speech-to-Text – $0.37 per hour
• Real-time Transcription – $0.47 per hour
• Audio Intelligence – varies, $.01 to $.15 per hour
• LeMUR – varies
• Enterprise pricing is also available

See the full pricing list here .

• High accuracy
• Breadth of AI models available, built by AI experts
• Continuous model iteration and improvement
• Developer-friendly documentation and SDKs
• Enterprise-grade support and security
• Models are not open-source

Google Speech-to-Text is a well-known speech transcription API. Google gives users 60 minutes of free transcription, with $300 in free credits for Google Cloud hosting.

Google only supports transcribing files already in a Google Cloud Bucket, so the free credits won’t get you very far. Google also requires you to sign up for a GCP account and project — whether you're using the free tier or paid.

With good accuracy and 125+ languages supported, Google is a decent choice if you’re willing to put in some initial work.

• 60 minutes of free transcription
• $300 in free credits for Google Cloud hosting
• Decent accuracy
• Multi-language support
• Only supports transcription of files in a Google Cloud Bucket
• Difficult to get started
• Lower accuracy than other similarly-priced APIs
• AWS Transcribe

AWS Transcribe offers one hour free per month for the first 12 months of use.

Like Google, you must create an AWS account first if you don’t already have one. AWS also has lower accuracy compared to alternative APIs and only supports transcribing files already in an Amazon S3 bucket.

However, if you’re looking for a specific feature, like medical transcription, AWS has some options. Its Transcribe Medical API is a medical-focused ASR option that is available today.

• One hour free per month for the first 12 months of use
• Tiered pricing , based on usage, ranges from $0.02400 to $0.00780
• Integrates into existing AWS ecosystem
• Medical language transcription
• Difficult to get started from scratch
• Only supports transcribing files already in an Amazon S3 bucket

Open-Source Speech Transcription engines

An alternative to APIs and AI models, open-source Speech-to-Text libraries are completely free--with no limits on use. Some developers also see data security as a plus, since your data doesn’t have to be sent to a third party or the cloud.

There is work involved with open-source engines, so you must be comfortable putting in a lot of time and effort to get the results you want, especially if you are trying to use these libraries at scale. Open-source Speech-to-Text engines are typically less accurate than the APIs discussed above.

If you want to go the open-source route, here are some options worth exploring:

DeepSpeech is an open-source embedded Speech-to-Text engine designed to run in real-time on a range of devices, from high-powered GPUs to a Raspberry Pi 4. The DeepSpeech library uses end-to-end model architecture pioneered by Baidu.

DeepSpeech also has decent out-of-the-box accuracy for an open-source option and is easy to fine-tune and train on your own data.

• Easy to customize
• Can use it to train your own model
• Can be used on a wide range of devices
• Lack of support
• No model improvement outside of individual custom training
• Heavy lift to integrate into production-ready applications

Kaldi is a speech recognition toolkit that has been widely popular in the research community for many years.

Like DeepSpeech, Kaldi has good out-of-the-box accuracy and supports the ability to train your own models. It’s also been thoroughly tested—a lot of companies currently use Kaldi in production and have used it for a while—making more developers confident in its application.

• Can use it to train your own models
• Active user base
• Can be complex and expensive to use
• Uses a command-line interface

Flashlight ASR (formerly Wav2Letter)

Flashlight ASR, formerly Wav2Letter, is Facebook AI Research’s Automatic Speech Recognition (ASR) Toolkit. It is also written in C++ and usesthe ArrayFire tensor library.

Like DeepSpeech, Flashlight ASR is decently accurate for an open-source library and is easy to work with on a small project.

• Customizable
• Easier to modify than other open-source options
• Processing speed
• Very complex to use
• No pre-trained libraries available
• Need to continuously source datasets for training and model updates, which can be difficult and costly
• SpeechBrain

SpeechBrain is a PyTorch-based transcription toolkit. The platform releases open implementations of popular research works and offers a tight integration with Hugging Face for easy access.

Overall, the platform is well-defined and constantly updated, making it a straightforward tool for training and finetuning.

• Integration with Pytorch and Hugging Face
• Pre-trained models are available
• Supports a variety of tasks
• Even its pre-trained models take a lot of customization to make them usable
• Lack of extensive docs makes it not as user-friendly, except for those with extensive experience

Coqui is another deep learning toolkit for Speech-to-Text transcription. Coqui is used in over twenty languages for projects and also offers a variety of essential inference and productionization features.

The platform also releases custom-trained models and has bindings for various programming languages for easier deployment.

• Generates confidence scores for transcripts
• Large support comunity
• No longer updated and maintained by Coqui

Whisper by OpenAI, released in September 2022, is comparable to other current state-of-the-art open-source options.

Whisper can be used either in Python or from the command line and can also be used for multilingual translation.

Whisper has five different models of varying sizes and capabilities, depending on the use case, including v3 released in November 2023 .

However, you’ll need a fairly large computing power and access to an in-house team to maintain, scale, update, and monitor the model to run Whisper at a large scale, making the total cost of ownership higher compared to other options. 

As of March 2023, Whisper is also now available via API . On-demand pricing starts at $0.006/minute.

• Multilingual transcription
• Can be used in Python
• Five models are available, each with different sizes and capabilities
• Need an in-house research team to maintain and update
• Costly to run

Which free Speech-to-Text API, AI model, or Open Source engine is right for your project?

The best free Speech-to-Text API, AI model, or open-source engine will depend on our project. Do you want something that is easy-to-use, has high accuracy, and has additional out-of-the-box features? If so, one of these APIs might be right for you:

Alternatively, you might want a completely free option with no data limits—if you don’t mind the extra work it will take to tailor a toolkit to your needs. If so, you might choose one of these open-source libraries:

Whichever you choose, make sure you find a product that can continually meet the needs of your project now and what your project may develop into in the future.

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Quickstart: Recognize and convert speech to text

• 3 contributors

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

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

Prerequisites

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

Try real-time speech to text

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

Select Speech from the list of AI services.

Select Real-time speech to text .

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

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

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

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

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

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

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

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

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

Set up the environment

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

Set environment variables

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

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

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

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

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

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

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

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

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

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

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

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

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

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

For more configuration options, see the Xcode documentation .

Recognize speech from a microphone

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

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

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

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

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

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

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

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

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

Here are some other considerations:

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

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

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

Clean up resources

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

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

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

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

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

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

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

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

Follow these steps to create a GO module.

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

Copy the following code into speech-recognition.go :

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

Build and run the code:

Reference documentation | Additional samples on GitHub

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

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

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

Install the Speech SDK and dependencies.

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

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

Copy the following code into SpeechRecognition.java :

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

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

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

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

Recognize speech from a file

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

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

Install the Speech SDK for JavaScript:

Copy the following code into SpeechRecognition.js :

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

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

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

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

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

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

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

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

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

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

Follow these steps to create a console application.

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

Run this command to install the Speech SDK:

Copy the following code into speech_recognition.py :

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

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

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

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

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

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

Follow these steps to recognize speech in a macOS application.

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

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

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

Open the helloworld.xcworkspace workspace in Xcode.

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

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

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

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

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

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

Objective-C

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Learn more about speech recognition

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Additional resources

MelNet - Audio Samples

Audio samples accompanying the paper MelNet: A Generative Model for Audio in the Frequency Domain .

Single-Speaker Speech Generation

Multi-speaker speech generation, music generation, single-speaker text-to-speech, multi-speaker text-to-speech, wavenet baseline, ablation - multiscale modelling.

• Due to the large number of audio samples on this page, all samples have been compressed (96 kb/s mp3). The uncompressed files are available for download at this repository .
• Audio clips which correspond to ground-truth data are generated by inverting ground-truth spectrograms.
• Samples shown here were selected based on diversity and quality. Samples used for quantitative experiments in the paper were randomly drawn.

Samples generated by MelNet trained on the task of unconditional single-speaker speech generation using professionally recorded audiobook data from the Blizzard 2013 dataset.

Samples from the model without biasing or priming.

Biased Samples

Samples from the model using a bias of 1.0.

Primed Samples

The first 5 seconds of each audio clip are from the dataset and the remaining 5 seconds are generated by the model.

Samples generated by MelNet trained on the task of unconditional multi-speaker speech generation using noisy, multispeaker, multilingual speech data from the VoxCeleb2 dataset.

Samples generated by MelNet trained on the task of unconditional music generation using recorded piano performances from the MAESTRO dataset.

Samples generated by MelNet trained on the task of single-speaker TTS using professionally recorded audiobook data from the Blizzard 2013 dataset.

The first audio clip for each text is taken from the dataset and the remaining 3 are samples generated by the model.

“My dear Fanny, you feel these things a great deal too much. I am most happy that you like the chain,”

Looking with a half fantastic curiosity to see whether the tender grass of early spring,

“I like them round,” said Mary. “And they are exactly the color of the sky over the moor.”

Lydia was Lydia still; untamed, unabashed, wild, noisy, and fearless.

“Oh, he has been away from New York—he has been all round the world. He doesn't know many people here, but he's very sociable, and he wants to know every one.”

Each unlabelled audio clip is taken from the dataset and the audio clip that directly follows is a sample generated by the model primed with that sequence.

Write a fond note to the friend you cherish.

Pluck the bright rose without leaves.

Two plus seven is less than ten.

He said the same phrase thirty times.

We frown when events take a bad turn.

Samples generated by MelNet trained on the task of multi-speaker TTS using noisy speech recognition data from the TED-LIUM 3 dataset.

Samples generated by the model conditioned on text and speaker ID. The conditioning text and speaker IDs are taken directly from the validation set (text in the dataset is unnormalized and unpunctuated).

it wasn't like i was asking for the code to a nuclear bunker or anything like that but the amount of resistance i got from this

and what that form is modeling and shaping is not cement

that every person here every decision that you've made today every decision you've made in your life you've not really made that decision but in fact

syria was largely a place of tolerance historically accustomed

and no matter what the rest of the world tells them they should be

the years went by and the princess grew up into a beautiful young woman

i spent so much time learning this language why do i only

and we were down to eating one meal a day running from place to place but wherever we could help we did at a certain point in time in

phrases and words even if you have a phd of chinese language you can't understand them

and when they came back and told us about it we really started thinking about the ways in which we see styrofoam every day

is only a very recent religious enthusiasm it surfaced only in the west

chances are that they are rooted in the productivity crisis

i cannot face your fears or chase your dreams and you can't do that for me but we can be supportive of eachother

the first law of travel and therefore of life you're only as strong

Selected Speakers

Samples generated by the model for selected speakers. Reference audio for each of the speakers can be found on the TED website .

A cramp is no small danger on a swim.

The glow deepened in the eyes of the sweet girl.

Bring your problems to the wise chief.

Clothes and lodging are free to new men.

Port is a strong wine with a smoky taste.

For comparison, we train WaveNet on the same three unconditional audio generation tasks used to evaluate MelNet (single-speaker speech generation, multi-speaker speech generation, and music generation).

Samples without biasing or priming.

Samples with priming: 5 seconds from the dataset followed by 5 seconds generated by WaveNet.

Samples from a two-stage model which separately models MIDI notes and then uses WaveNet to synthesize audio conditioned on the generated MIDI notes.

The following models were trained on the same data, with each model using a different number of tiers.

5-Tier Model

4-tier model, 3-tier model, 2-tier model.

Speech Synthesis, Recognition, and More With SpeechT5

SpeechT5 was originally described in the paper SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing by Microsoft Research Asia. The official checkpoints published by the paper’s authors are available on the Hugging Face Hub.

If you want to jump right in, here are some demos on Spaces:

• Speech Synthesis (TTS)
• Voice Conversion
• Automatic Speech Recognition

Introduction

SpeechT5 is not one, not two, but three kinds of speech models in one architecture.

• speech-to-text for automatic speech recognition or speaker identification,
• text-to-speech to synthesize audio, and
• speech-to-speech for converting between different voices or performing speech enhancement.

The main idea behind SpeechT5 is to pre-train a single model on a mixture of text-to-speech, speech-to-text, text-to-text, and speech-to-speech data. This way, the model learns from text and speech at the same time. The result of this pre-training approach is a model that has a unified space of hidden representations shared by both text and speech.

At the heart of SpeechT5 is a regular Transformer encoder-decoder model. Just like any other Transformer, the encoder-decoder network models a sequence-to-sequence transformation using hidden representations. This Transformer backbone is the same for all SpeechT5 tasks.

To make it possible for the same Transformer to deal with both text and speech data, so-called pre-nets and post-nets were added. It is the job of the pre-net to convert the input text or speech into the hidden representations used by the Transformer. The post-net takes the outputs from the Transformer and turns them into text or speech again.

A figure illustrating SpeechT5’s architecture is depicted below (taken from the original paper ).

During pre-training, all of the pre-nets and post-nets are used simultaneously. After pre-training, the entire encoder-decoder backbone is fine-tuned on a single task. Such a fine-tuned model only uses the pre-nets and post-nets specific to the given task. For example, to use SpeechT5 for text-to-speech, you’d swap in the text encoder pre-net for the text inputs and the speech decoder pre and post-nets for the speech outputs.

Note: Even though the fine-tuned models start out using the same set of weights from the shared pre-trained model, the final versions are all quite different in the end. You can’t take a fine-tuned ASR model and swap out the pre-nets and post-net to get a working TTS model, for example. SpeechT5 is flexible, but not that flexible.

Text-to-speech

SpeechT5 is the first text-to-speech model we’ve added to 🤗 Transformers, and we plan to add more TTS models in the near future.

For the TTS task, the model uses the following pre-nets and post-nets:

Text encoder pre-net. A text embedding layer that maps text tokens to the hidden representations that the encoder expects. Similar to what happens in an NLP model such as BERT.

Speech decoder pre-net. This takes a log mel spectrogram as input and uses a sequence of linear layers to compress the spectrogram into hidden representations. This design is taken from the Tacotron 2 TTS model.

Speech decoder post-net. This predicts a residual to add to the output spectrogram and is used to refine the results, also from Tacotron 2.

The architecture of the fine-tuned model looks like the following.

Here is a complete example of how to use the SpeechT5 text-to-speech model to synthesize speech. You can also follow along in this interactive Colab notebook .

SpeechT5 is not available in the latest release of Transformers yet, so you'll have to install it from GitHub. Also install the additional dependency sentencepiece and then restart your runtime.

First, we load the fine-tuned model from the Hub, along with the processor object used for tokenization and feature extraction. The class we’ll use is SpeechT5ForTextToSpeech .

Next, tokenize the input text.

The SpeechT5 TTS model is not limited to creating speech for a single speaker. Instead, it uses so-called speaker embeddings that capture a particular speaker’s voice characteristics. We’ll load such a speaker embedding from a dataset on the Hub.

The speaker embedding is a tensor of shape (1, 512). This particular speaker embedding describes a female voice. The embeddings were obtained from the CMU ARCTIC dataset using this script , but any X-Vector embedding should work.

Now we can tell the model to generate the speech, given the input tokens and the speaker embedding.

This outputs a tensor of shape (140, 80) containing a log mel spectrogram. The first dimension is the sequence length, and it may vary between runs as the speech decoder pre-net always applies dropout to the input sequence. This adds a bit of random variability to the generated speech.

To convert the predicted log mel spectrogram into an actual speech waveform, we need a vocoder . In theory, you can use any vocoder that works on 80-bin mel spectrograms, but for convenience, we’ve provided one in Transformers based on HiFi-GAN. The weights for this vocoder , as well as the weights for the fine-tuned TTS model, were kindly provided by the original authors of SpeechT5.

Loading the vocoder is as easy as any other 🤗 Transformers model.

To make audio from the spectrogram, do the following:

We’ve also provided a shortcut so you don’t need the intermediate step of making the spectrogram. When you pass the vocoder object into generate_speech , it directly outputs the speech waveform.

And finally, save the speech waveform to a file. The sample rate used by SpeechT5 is always 16 kHz.

The output sounds like this ( download audio ):

That’s it for the TTS model! The key to making this sound good is to use the right speaker embeddings.

You can play with an interactive demo on Spaces.

💡 Interested in learning how to fine-tune SpeechT5 TTS on your own dataset or language? Check out this Colab notebook with a detailed walk-through of the process.

Speech-to-speech for voice conversion

Conceptually, doing speech-to-speech modeling with SpeechT5 is the same as text-to-speech. Simply swap out the text encoder pre-net for the speech encoder pre-net. The rest of the model stays the same.

The speech encoder pre-net is the same as the feature encoding module from wav2vec 2.0 . It consists of convolution layers that downsample the input waveform into a sequence of audio frame representations.

As an example of a speech-to-speech task, the authors of SpeechT5 provide a fine-tuned checkpoint for doing voice conversion. To use this, first load the model from the Hub. Note that the model class now is SpeechT5ForSpeechToSpeech .

We will need some speech audio to use as input. For the purpose of this example, we’ll load the audio from a small speech dataset on the Hub. You can also load your own speech waveforms, as long as they are mono and use a sampling rate of 16 kHz. The samples from the dataset we’re using here are already in this format.

Next, preprocess the audio to put it in the format that the model expects.

As with the TTS model, we’ll need speaker embeddings. These describe what the target voice sounds like.

We also need to load the vocoder to turn the generated spectrograms into an audio waveform. Let’s use the same vocoder as with the TTS model.

Now we can perform the speech conversion by calling the model’s generate_speech method.

Changing to a different voice is as easy as loading a new speaker embedding. You could even make an embedding from your own voice!

The original input ( download ):

The converted voice ( download ):

Note that the converted audio in this example cuts off before the end of the sentence. This might be due to the pause between the two sentences, causing SpeechT5 to (wrongly) predict that the end of the sequence has been reached. Try it with another example, you’ll find that often the conversion is correct but sometimes it stops prematurely.

You can play with an interactive demo here . 🔥

Speech-to-text for automatic speech recognition

The ASR model uses the following pre-nets and post-net:

Speech encoder pre-net. This is the same pre-net used by the speech-to-speech model and consists of the CNN feature encoder layers from wav2vec 2.0.

Text decoder pre-net. Similar to the encoder pre-net used by the TTS model, this maps text tokens into the hidden representations using an embedding layer. (During pre-training, these embeddings are shared between the text encoder and decoder pre-nets.)

Text decoder post-net. This is the simplest of them all and consists of a single linear layer that projects the hidden representations to probabilities over the vocabulary.

If you’ve tried any of the other 🤗 Transformers speech recognition models before, you’ll find SpeechT5 just as easy to use. The quickest way to get started is by using a pipeline.

As speech audio, we’ll use the same input as in the previous section, but any audio file will work, as the pipeline automatically converts the audio into the correct format.

Now we can ask the pipeline to process the speech and generate a text transcription.

Printing the transcription gives:

That sounds exactly right! The tokenizer used by SpeechT5 is very basic and works on the character level. The ASR model will therefore not output any punctuation or capitalization.

Of course it’s also possible to use the model class directly. First, load the fine-tuned model and the processor object. The class is now SpeechT5ForSpeechToText .

Preprocess the speech input:

Finally, tell the model to generate text tokens from the speech input, and then use the processor’s decoding function to turn these tokens into actual text.

Play with an interactive demo for the speech-to-text task .

SpeechT5 is an interesting model because — unlike most other models — it allows you to perform multiple tasks with the same architecture. Only the pre-nets and post-nets change. By pre-training the model on these combined tasks, it becomes more capable at doing each of the individual tasks when fine-tuned.

We have only included checkpoints for the speech recognition (ASR), speech synthesis (TTS), and voice conversion tasks but the paper also mentions the model was successfully used for speech translation, speech enhancement, and speaker identification. It’s very versatile!

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Why i use libreoffice instead of microsoft 365, why everyone should use a gaming keyboard (even non-gamers), quick links, chatgpt is better than google’s voice typing, what makes chatgpt’s speech-to-text function so good, note-taking using chatgpt on my phone, voice typing using chatgpt on my desktop, key takeaways.

• ChatGPT’s speech-to-text is superior to Google’s, eliminating the need to say punctuation out loud.
• WhisperAI neural network powers ChatGPT for flawless transcription, despite a lack of integration with keyboard apps.
• Use ChatGPT effortlessly on Android, iPhone, macOS, and soon on Windows for efficient note-taking and transcription.

You have to speak it to believe it; ChatGPT’s fantastic speech-to-text function, that is. It’s proved to be far smoother and more precise than some of the most established voice-to-text apps.

Google’s voice typing is a tool I’ve used on and off for years. It comes with the SwiftKey keyboard app and Google’s own Gboard keyboard for mobile phones. It was good for a time—above average, in fact—but not anymore. ChatGPT has leaped ahead of the competition, and the results are slick.

If you’ve ever used Google’s voice typing, you will know how awkward it is to say “comma” or “period” out loud each time you want to add punctuation to your text. In ChatGPT, there’s no need. You can speak as naturally as if you’re having a chat with your friend, and it will effortlessly add punctuation where you would expect it to go.

This makes a huge difference. Take this sentence, for example: “I want to go to the supermarket and buy apples, oranges, watermelon, pears, and cherries.” To dictate it using Google’s voice typing, you would need to say “...apples comma oranges comma watermelon comma pears comma and cherries.” Repeating the word “comma” five times is clunky and unnatural.

ChatGPT does an incredible job of converting speech to text thanks to WhisperAI, an advanced neural network. OpenAI released it as an open-source model aimed at people wanting to develop this technology into useful applications. Which brings us to a key sticking point. ChatGPT’s speech-to-text function is not yet integrated into something like a voice typing keyboard.

Despite this, I have begun using it all the time in my workflow. Even though Google’s voice typing is easily accessible from my keyboard, I end up wasting a lot of time fixing its mistakes. At one point, I started speaking in short fragments—think robocalls and computerized speech—to help it pick up my speech better.

That’s why I am happily using ChatGPT’s speech-to-text with a small workaround. In the end, it’s going to save me far more time and effort, besides letting me talk naturally.

ChatGPT is available on Android , iPhone , and macOS (M1 and later).

Those using Windows computers can expect a desktop app for ChatGPT in late 2024.

I write notes for my articles using pen and paper. This is, ironically, a very low-tech approach for a tech writer! While I enjoy it, eventually I need to get those words into a digital format if they are going to be of any use to me.

My preferred place to transfer my ideas to is a note-taking app. Google Keep, for example, is good because it automatically syncs your notes online and between devices. Or there’s Obsidian, my new favorite way to organize my thoughts into long-term storage. In the long run, it’s best to aim for a note-taking app that works on any device for added convenience.

My process is simple. Open ChatGPT and hit the microphone button, then start speaking. After that, press stop to convert the audio to text. Finally, copy the text and paste it into a note-taking app.

At my desktop, I follow the same process. The app looks nearly identical to the mobile version, so you simply need to press the microphone button to start recording, then press the tick button when you’re done. After this, you can copy the text to where it needs to go, such as a Word document or an email.

Sometimes it’s good to have a record of your transcription directly in ChatGPT. In that case, you can add the line, “Do not comment:” immediately before the transcribed text, then hit enter to add it to ChatGPT’s conversation feed. This stops ChatGPT from replying with a long-winded answer, with the added benefit of maintaining a record of your transcriptions.

There are plenty of things you can do with ChatGPT besides converting speech to text, making it a nice multipurpose app to have on hand.

It won’t be long before this speech-to-text AI model makes its way into voice typing apps or transcription tools. Until then, you can use ChatGPT to produce clean and accurate transcriptions for spots of note-taking, brainstorming, or dictation.

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SoundType AI - Voice To Text 4+

Transcribe voice to text, innosquares ltd, designed for ipad.

• 4.7 • 175 Ratings
• Offers In-App Purchases

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Description.

Turn spoken words into written text effortlessly with SoundType AI! Our advanced app for transcribing voice to text and transcribing audio transforms your voice or video files into accurately transcribed text. Its also equipped with innovative audio features and AI-powered summaries. With our standout feature of individual speaker identification, its an ideal choice for transcribing from meetings, interviews, podcasts, and more. Supporting over 90 languages, SoundType AI simplifies transcription of conversations from around the globe. Features: ● AI-Powered Transcribe Voice to Text Accuracy Our AI boasts an unrivaled precision for transcribing voice to text, trained on an impressive 680K hours of multilingual and multitask data. Experience flawless transcriptions each time you use SoundType AI. ● Individual Speaker Recognition for Transcribing Ideal for group meetings and interviews, SoundType AI identifies and tags different speakers in your audio, providing well-structured, easy-to-follow transcriptions. ● Uncomplicated Long Audio Transcription Have lengthy recordings to transcribe? No problem! SoundType AI handles long audio files with ease, ensuring all-inclusive and accurate transcriptions. ● Engaging Transcribe Audio to Text Experience Engage with your transcriptions in unique ways. Ask questions about your audio or video, and our AI will generate responses from the content, enhancing your transcribe experience. ● Summarized Transcriptions Receive the key points and highlights of your audio in a concise, understandable format with SoundType AIs summary feature. ● Comprehensive Voice/Video to Text Transcription Whether its uploading an audio or video file, recording within the app, or importing from YouTube, SoundType AI transcribes it into text for straightforward analysis. ● Broad Language Support for Transcription With our sophisticated AI technology, transcribe in over 90 languages and dialects effortlessly, perfect for international meetings, research work, and global podcasts. Use SoundType AI for transcribing spoken content from: ○ Meeting Notes ○ Negotiations ○ Interviews ○ Language Studies ○ Podcasts ○ Lectures And more, all converted into simple-to-read text! Supported Formats: Our app accepts a broad range of file formats, including MP3, WAV, WMA, M4A, and more. If you have queries about specific file types, our support team is ready to help. Export Formats: Easily export your transcriptions in various formats, such as TXT, SRT, PDF and Docx. Requirements: Internet connection Upgrade your productivity with SoundType AI - the future of transcribe voice to text and transcribe audio to text at your fingertips. Privacy Policy: https://soundtype.ai/privacy-policy Terms of Use: https://soundtype.ai/terms-of-use

Version 1.6.8

- Improve video conversion - Improve youtube support

Ratings and Reviews

175 Ratings

Efficient and Reliable

it cọmes to converting spoken words into text. The interface is simple to navigate, and I appreciate the robust features offered.

I was really considering upgrading bc I like this app. It does a good job transcribing - accuracy is higher than others I’ve tried. Speaker detection is just ok. I like the ability to edit the text, create folders, and so much more. There is a lot to like about the app. Having said that, I uploaded 2 audio files as my testers to see how I would like the app. One is approximately 54 SECONDS - a very short conversation between 2 people meant to see how well voices would be distinguished. The other is a lecture and is about 6 MINUTES long. BUT when I went to my account settings to look at upgrade options, I noticed it shows that my free account has used 172 of the 180 free minutes of transcription!!!! I haven’t deleted anything or used it for more than testing the 2 audio files totaling under 10 minutes! Very shady. I will not be upgrading.

Impressive Accuracy and Speed

I am thoroughly impressed with the ability to transcribe speech accurately and swiftly. This app is definitely worth the download!

App Privacy

The developer, Innosquares Ltd , indicated that the app’s privacy practices may include handling of data as described below. For more information, see the developer’s privacy policy .

Data Linked to You

The following data may be collected and linked to your identity:

• Contact Info
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The following data may be collected but it is not linked to your identity:

• Diagnostics

Privacy practices may vary, for example, based on the features you use or your age. Learn More

Information

• Pro Subscription $9.99
• Pro-Yearly $79.99
• Developer Website
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Harris to Lay Out Economic Message Focused on High Cost of Living

The vice president’s plans represent more of a reboot of President Biden’s economic policies than a radically fresh start.

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By Nicholas Nehamas and Jim Tankersley

Reporting from Washington

Vice President Kamala Harris will unveil the central planks of her economic agenda on Friday in Raleigh, N.C., during her first major policy speech, focusing on how she plans to fight big corporations and bring down costs on necessities like food, housing and raising children.

Ms. Harris’s proposals for her first 100 days in the White House include efforts to combat price gouging at the grocery store , jump-start the construction of more affordable housing, restore an expanded tax credit for parents and lower the cost of prescription drugs, according to a briefing document released by her campaign. She will call for a tax incentive to build starter homes, seek to cap the cost of insulin at $35 for all Americans and attempt to reduce the cost of health insurance through the Affordable Care Act.

Taken together, her plan represents more of a reboot of President Biden’s economic policy than a radically fresh start — a new sales pitch focused on its most popular aspects, not a new vision. Many of the policies reiterate or build on proposals in Mr. Biden’s most recent presidential budget. Harris campaign officials released scattered details, leaving key questions unanswered — like the income cutoff for families to qualify for a new $6,000 child tax credit for newborns, or what exactly would qualify as grocery-store “price gouging” under a federal ban.

Campaign officials did not detail how Ms. Harris would pay for her spending and tax-cut proposals in their release ahead of the speech. But they said her overall plan would reduce projected federal deficits, like Mr. Biden’s latest budget proposed to do, largely by “asking the wealthiest Americans and largest corporations to pay their fair share.”

In terms of emphasis, her speech is expected to shift away from Mr. Biden’s focus on job creation, particularly in manufacturing, and more toward reining in the cost of living.

And she will also try to paint a strong contrast against former President Donald J. Trump, describing him as a friend to billionaires and chief executives who will not help the middle class. Ms. Harris has been attacking Mr. Trump’s proposal to impose new tariffs of up to 20 percent on all imported goods, saying it would amount to a tax increase on working families.

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