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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.

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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.

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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 up to 100 free transcription hours for audio files or video streams, with a concurrency limit of 5, before transitioning to an affordable paid tier.

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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Automatic speech-to-text recognition involves converting an audio file to editable text. Computer algorithms facilitate this process in four steps: analyze the audio, break it down into parts, convert it into a computer-readable format, and use the algorithm again to match it into a text-readable format.

In the past, this was a task only reserved for proprietary systems. This was disadvantageous to the user due to high licensing and usage fees, limited features, and a lack of transparency. 

As more people researched these tools, creating your language processing models with the help of open-source voice recognition systems became possible . These systems, made by the community for the community, are easy to customize, cheap to use, and transparent, giving the user control over their data.

Best 13 Open-Source Speech Recognition Systems

An open-source speech recognition system is a library or framework consisting of the source code of a speech recognition system. These community-based projects are made available to the public under an open-source license. Users can contribute to these tools, customize them, or even tailor them to their needs.

Here are the top open-source speech recognition engines you can start on: 

Whisper is Open AI’s newest brainchild that offers transcription and translation services.  Released in September 2022, this AI tool is one of the most accurate automatic speech recognition models. It stands out from the rest of the tools in the market due to the large number of training data sets it was trained on: 680 thousand hours of audio files from the internet. This diverse range of data improves the human-level robustness of the tool.

You must install Python or the command line interface to transcribe using Whisper. Five models are available to work with; all have different sizes and capabilities. These include tiny, base, small, medium, and large. The larger the model, the faster the transcription speed. Still, you must invest in a good CPU and GPU device to maximize their use.

Whisper AI falls short compared to models proficient in LibriSpeech performance (one of the most common speech recognition benchmarks). However, its zero-shot performance reveals that the API has 50% fewer errors than the same models.

It supports content formats such as MP3, MP4, M4A, Mpeg, MPGA, WEBM, and WAV.

It can transcribe 99 languages and translate them all into English.

The tool is free to use.

The larger the model, the more GPU resources it consumes, which can be costly. 

It will cost you time and resources to install and use the tool.

It does not provide real-time transcription.

2. Project DeepSpeech

Project DeepSearch is an open-source speech-to-text engine by Mozilla. This voice-to-text command and library is released under the Mozilla Public License (MPL). Its model follows the Baidu Deep Speech research paper, making it end-to-end trainable and capable of transcribing audio in several languages. It is also trained and implemented using Google’s TensorFlow. 

Download the source code from GitHub and install it in your Python to use it. The tool comes when already pre-trained on an English model. However, you can still train the model with your data. Alternatively, you can get a pre-trained model and improve it using custom data.

DeepSpeech is easy to customize since it’s a code-native solution.

It provides special wrappers for Python, C, .Net Framework, and Javascript, allowing you to use the tool regardless of the language.

It can function on various gadgets, including a Raspberry Pi device. 

Its per-word error rate is remarkably low at 7.5%.

Mozilla takes a serious approach to privacy concerns.

Mozilla is reportedly ending the development of DeepSpeech. This means there will be less support in case of bugs and implementation problems.

Kaldi is a speech recognition tool purposely created for speech recognition researchers. It’s written in C++ and released under the Apache 2.0 license, one of the least restrictive licenses. Unlike tools like Whisper and DeepSpeech, which focus on deep learning, Kaldi primarily focuses on speech recognition models that use old-school, reliable tools. These include models like HMMs (Hidden Markov Models), GMMs (Gaussian Mixture Models), and FSTs (Finite State Transducers.)

Kaldi is very reliable. Its code is thoroughly tested and verified. 

Although its focus is not on deep learning, it has some models that can help with transcription services.

It is perfect for academic and industry-related research, allowing users to test their models and techniques.

It has an active forum that provides the right amount of support.

There are also resources and documentation available to help users address any issues.

Being open-source, users with privacy or security concerns can inspect the code to understand how it works.

Its classical approach to models may limit its accuracy levels. 

Kaldi is not user-friendly since it operates on a Command-line interface.

It's pretty complex to use, making it suitable for users with technical experience.

You need lots of computation power to use the toolkit.

4. SpeechBrain

SpeechBrain is an open-source toolkit that facilitates the research and development of speech-related tech. It supports a variety of tasks, including speech recognition, enhancement, separation, speaker diarization, and microphone signal processing. Speechbrain uses PyTorch as its foundation, taking advantage of its flexibility and ease of use. Developers and researchers can also benefit from Pytorch’s expensive ecosystem and support to build and train their neural networks.

Users can choose between both traditional and deep-leaning-based ASR models.

It's easy to customize a model to adapt to your needs. 

Its integration with Pytorch makes it easier to use.  

There are available pre-trained models users can use to get started with speech-to-text tasks.

The SpeechBrain documentation is not as extensive as that of Kaldi.

Its pre-trained models are limited.

You may need particular expertise to use the tool. Without it, you may need to undergo a steep learning curve.

Coqui is an advanced deep learning toolkit perfect for training and deploying stt models. Licensed under the Mozilla Public License 2.0, you can use it to generate multiple transcripts, each with a confidence score. It provides pre-trained models alongside example audio files you can use to test the engine and help with further fine-tuning. Moreover, it has well-detailed documentation and resources that can help you use and solve any arising problems.

The STT models it provides are highly trained with high-quality data. 

The models support multiple languages.

There is a friendly support community where you can ask questions and get any details relating to STT.

It supports real-time transcription with extremely low latency in seconds. 

Developers can customize the models to various use cases, from transcription to acting as voice assistants. 

Coqui stopped to maintain the STT project to focus on their text-to-speech toolkit. This means you may have to solve any problems that arise by yourself without any help from support.

Julius is one of the oldest speech-to-text projects, dating back to 1997, with roots in Japan. It is available under the BSD -3-license, making it accessible to developers. It strongly supports Japanese ASR, but being a language-independent program, the model can understand and process multiple languages, including English, Slovenian, French, Thai, and others. The transcription accuracy largely depends on whether you have the right language and acoustic model. The project is written in the most common language, C, allowing it to work in Windows, Linux, Android, and macOS systems.

Julius can perform real-time speech-to-text transcription with low memory usage.

It has an active community that can help with ASR problems.

The models trained in English are readily available on the web for download.

It does not need internet access for speech recognition, making it suitable for users needing privacy.

Like any other open-source program, you need users with technical experience to make it work.

It has a huge learning curve.

7. Flashlight ASR (Formerly Wav2Letter++)

Flashlight ASR is an open-source speech recognition toolkit designed by the Facebook AI research team. Its capability to handle large datasets, speed, and efficiency stands out. You can attribute the speed to using only convolutional neural networks in the language modeling, machine translation, and speech synthesis. 

Ideally, most speech recognition engines use convolutionary and recurrent neural networks to understand and model the language. However, recurrent networks may need high computation power, thus affecting the speed of the engine.

The Flashlight ASR is compiled using modern C++, an easy language on your device’s CPU and GPU. It’s also built on Flashlight, a stand-alone library for machine learning.

It's one of the fastest machine learning speech-to-text systems.

You can adapt its use to various languages and dialects.

The model does not consume a lot of GPU and CPU resources.

It does not provide any pre-trained language models, including English.

You need to have deep coding expertise to operate the tool.

It has a steep learning curve for new users.

8. PaddleSpeech (Formerly DeepSpeech2)

This open-source speech-to-text toolkit is available on the Paddlepaddle platform and provided under the Apache 2.0 license. PaddleSpeech is one of the most versatile toolkits capable of performing speech recognition, speech-to-text conversion, keyword spotting, translation, and audio classification. Its transcription quality is so good that it won the NAACL2022 Best Demo Award .

This speech-to-text engine supports various language models but prioritizes Chinese and English models. The Chinese model, in particular, features text normalization and pronunciation to make it adapt to the rules of the Chinese language.

The toolkit delivers high-end and ultra-lightweight models that use the best technology in the market.

The speech-to-text engine provides both command-line and server options, making it user-friendly to adopt.

It is very convenient for users by both developers and researchers.

Its source code is written in Python, one of the most commonly used languages.

Its focus on Chinese leads to the limitation of resources and support for other languages.

It has a steep learning curve.

You need to have certain expertise to integrate and use the tool.

9. OpenSeq2Seq

Like its name, OpenSeq2Seq is an open-source speech-to-text tool kit that helps train different types of sequence-to-sequence models. Developed by Nvidia, this toolkit is released under the Apache 2.0 license, meaning it's free for everyone. It trains language models that perform transcription, translation, automatic speech recognition, and sentiment analysis tasks.

To use it, use the default models or train your own, depending on your needs. OpenSeq2Seq performs best when you use many graphics cards and computers simultaneously. It works best on Nvidia-powered devices.

The tool has multiple functions, making it very versatile.

It can work with the most recent Python, TensorFlow, and CUDA versions. 

Developers and researchers can access the tool, collaborate, and make their innovations.

Beneficial to users with Nvidia-powered devices.

It can consume significant computer resources due to its parallel processing capability.

Community support has reduced over time as Nvidia paused the project development.

Users without access to Nvidia hardware can be at a disadvantage.

One of the most compact and lightweight speech-to-text engines today is Vosk . This open-source toolkit works offline on multiple devices, including Android, iOS, and Raspberry Pi. It supports over 20 languages and dialects, including English, Chinese, Portuguese, Polish and German.

Vosk provides users with small language models that do not take up much space. Ideally, around 50MB. However, a few large models can take up to 1.4GB. The tool is quick to respond and can convert speech to text continuously.

It can work with various programming languages such as Java, Python, C++, Kotlyn, and Shell, making it a versatile addition for developers. 

It has various use cases, from transcriptions to developing chatbots and virtual assistants. 

It has a fast response time. 

The engine's accuracy can vary depending on the language and accent.

You need coding expertise to integrate and use the tool.

Athena is another sequence-to-sequence-based speech-to-text open-source engine released under the Apache 2.0 license. This toolkit suits researchers and developers with their end-to-end speech processing needs. Some tasks the models can handle include automatic speech recognition (ASR), speech synthesis, voice detection, and keyword spotting. All the language models are implemented on TensorFlow, making the toolkit accessible to more developers.

Athena is versatile in its use, from transcription services to speech synthesis.

It does not depend on Kaldi since it has its pythonic feature extractor.

The tool is well maintained with regular updates and new features.

It is open source, free to use, and available to various users.

It has a deep learning curve for new users.

Although it has a WeChat group for community support, it limits the accessibility to only those who can access the platform.

ESPnet is an open-source speech-to-text software released under the Apache 2.0 license. It provides end-to-end speech processing capabilities that cover tasks ranging from ASR, translation, speech synthesis, enhancement, and diarization. The toolkit stands out for leveraging Pytorch as its deep learning framework and following the Kaldi data processing style. As a result, you get comprehensive recipes for various language-processing tasks. The tool is also multi-lingual as it is capable of handling various languages. Use it with the readily available pre-trained models or create your own according to your needs.

The toolkit delivers a stand-out performance compared to other speech-to-text software.

It can process audio in real time, making it suitable for live transcription services.

Suitable for use by researchers and developers.

It is one of the most versatile tools to deliver various speech-processing tasks.

It can be complex to integrate and use for new users.

You must be familiar with Pytorch and Python to run the toolkit.

13. Tensorflow ASR

Our last feature on this list of free speech-to-text open-source engines is the Tensorflow ASR . This GitHub project is released under the Apache 2.0 license and uses Tensorflow 2.0 as the deep learning framework to implement various speech processing models.

Tensorflow has an incredible accuracy rate, with the author claiming it to be an almost ‘state-of-the-art’ model. It’s also one of the most well-maintained tools that undergo regular updates to improve its functionality. For example, the toolkit now supports language training on TPUs (a special hardware).

Tensorflow also supports using specific models such as Conformer, ContextNet, DeepSpeech2, and Jasper. You can choose the tool depending on the tasks you intend to handle. For example, for general tasks, consider DeepSpeech2, but for precision, use Conformer.

The language models are accurate and highly efficient when processing speech-to-text.

You can convert the models to a TFlite format to make it lightweight and easy to deploy.

It can deliver on various speech-to-text-related tasks. 

It Supports multiple languages and provides pre-trained English, Vietnamese, and German models.

The installation process can be quite complex for beginners. Users need to have a particular expertise.

There is a learning curve to using advanced models.

TPUs do not allow testing, limiting the tool's capabilities.

Top 3 Speech-to-Text APIs and AI Models

A Speech-to-text API and AI model is a tech solution that helps users convert their speech or audio files into text. Most of these solutions are cloud-based. You need to access the internet and make an API request to use them. The decision to use either APIs, AI models, or open-source engines largely depends on your needs. An API or AI model is the most preferred for small-scale tasks that are needed quickly. However, for large-scale use, consider using an open-source engine. 

Several other differences exist between speech-to-text APIs /AI models and open-source engines. Let's take a look at the most common in the table below:

After considerable research, here are our top three speech-to-text API and AI models:

The Google Cloud Speech-to-text API is one of the most common speech recognition technologies for developers looking to integrate the service into their applications. It automatically detects and converts audio to text using neural network models. Initially, the purpose of this toolkit was for use on Google’s home voice assistant, as its focus is on short command and response applications. Although the accuracy level is not that high, it does an excellent job of transcribing with minimal errors. However, the quality of the transcript is dependent on the audio quality.

Google Cloud speech-to-text API uses a pay-as-you-go subscription, priced according to the number of audio files processed per month measured per second. Users get 60 free transcription minutes plus Google Cloud hosting credits worth $300 for the first 90 days. Any audio over 60 minutes will cost you an additional $0.006 per 15 seconds.

The API can transcribe more than 125 languages and variants.

You can deploy the tool in the cloud and on-premise.

It provides automatic language transcription and translation services.

You can configure it to transcribe your phone and video conversations.

It is not free to use.

It has a limited vocabulary builder.

2. AWS Transcribe

AWS transcribe is an on-demand voice-to-text API allowing users to generate audio transcriptions. If you have heard of the Alexa voice assistant, it's the tool behind the development. Unlike every other consumer-oriented transcription tool, the AWS API has a daily good accuracy level. It can also distinguish voices in a conversation and provide timestamps to the transcript. This tool supports 37 languages, including English, German, Hebrew, Japanese, and Turkish.

Integrating it into an existing AWS ecosystem is effortless.

It is one of the best short audio commands and response options.

It is highly scalable.

It has a reasonably good accuracy level.

It is expensive to use.

It only supports cloud deployment.

It has limited support.

The tool can be slow at times.

3. AssemblyAI

AssemblyAI API is one of the best solutions for users looking to transcribe speech without many technical terms, jargon, or accents. This API model automatically detects audio, transcribes it, and even creates a summary. It also provides services such as speaker diarization, sentiment analysis, topic detection, content moderation, and entity detection.

AssemblyAI has a simple and open pricing model, where you pay for only what you use. For example, you may need to pay $0.650016 per hour to get the core transcription service, while real-time transcription costs $0.75024 per hour.

It is not expensive to use.

Accuracy levels are high for not-technical languages.

It provides helpful documentation.

The toolkit is easy to set up, even for beginners.

Its deployment speed is slow.

Its accuracy levels drop when dealing with technical terms.

What is the Best Open Source Speech Recognition System?

As you can see above, every tool from this list has benefits and disadvantages. Choosing the best open-source speech recognition system depends on your needs and available resources. For example, if you are looking for a lightweight toolkit compatible with almost every device, Voskand Julius beat the rest of the tools in this list. You can use them on Android, iOS, and even Raspberry Pi. Moreover, they don’t consume much space.

For users who want to train their models, you can use toolkits such as Whisper, OpenSeq2Seq, Flashlight ASR, and Athena.

The best approach to choosing an open-source voice recognition software is to review its documentation to understand the necessary resources and test it to see if it works for your case.

Introducing the Notta AI Model 

As shown above, AI models differ from open-source engines. They are fast, more efficient, easy to use, and can deliver high accuracy. Moreover, their use is not only limited to users with experience. Anyone can operate the tools and generate transcripts in minutes. 

Here is where we come in. Notta is one of the leading speech-to-text AI models that can transcribe and summarize your audio and video recordings. This AI tool supports 58 languages and can deliver transcripts with an impressive accuracy rate of 98.86%. The tool is available for use both on mobile and web.

Notta is easy to set up and use.

It supports multiple video and audio formats.

Its transcription speed is lightning-fast.

It adopts rigorous security protocols to protect user data.

It's free to use.

There is a limit to the file size you can upload to transcribe.

The free version supports only a limited number of transcriptions per month.

The advancement of speech recognition technology has been impressive over the years. What was once a world of proprietary software has shifted to one led by open-source toolkits and APIs/AI.

It's too early to say which is the clear winner, as they are all improving. You can, however, take advantage of their services, which include transcription, translation, dictation, speech synthesis, keyword spotting, diarization, and language enhancement.

There is no right or wrong tool in the options above. Every one of them has its strengths and weaknesses. Carefully assess your needs and resources before choosing a tool to make an informed decision.

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The Best Speech-to-Text APIs in 2024

, Jose Nicholas Francisco

If you've been shopping for a speech-to-text (STT) solution for your business, you're not alone. In our recent  State of Voice Technology  report, 82% of respondents confirmed their current utilization of voice-enabled technology, a 6% increase from last year.

The vast number of options for speech transcription can be overwhelming, especially if you're unfamiliar with the space. From Big Tech to open source options, there are many choices, each with different price points and feature sets. While this diversity is great, it can also be confusing when you're trying to compare options and pick the right solution.

This article breaks down the leading speech-to-text APIs available today, outlining their pros and cons and providing a ranking that accurately represents the current STT landscape. Before getting to the ranking, we explain exactly what an STT API is, and the core features you can expect an STT API to have, and some key use cases for speech-to-text APIs.

What is a speech-to-text API?

At its core, a speech-to-text (also known as automatic speech recognition, or ASR) application programming interface (API) is simply the ability to call a service to transcribe audio containing speech into written text. The STT service will take the provided audio data, process it using either machine learning or legacy techniques (e.g. Hidden Markov Models), and then provide a transcript of what it has inferred was said.

What are the most important things to consider when choosing a speech-to-text API?

What makes the best speech-to-text API? Is the fastest speech-to-text API the best? Is the most accurate speech-to-text API the best? Is the most affordable speech-to-text API the best? The answers to these questions depend on your specific project and are thus certainly different for everybody. There are a number of aspects to carefully consider in the evaluation and selection of a transcription service and the order of importance is dependent on your target use case and end user needs.

Accuracy - A speech-to-text API should produce highly accurate transcripts, even while dealing with varying levels of speaking conditions (e.g. background noise, dialects, accents, etc.). “Garbage in, garbage out,” as the saying goes. The vast majority of voice applications require highly accurate results from their transcription service to deliver value and a good customer experience to their users.

Speed - Many applications require quick turnaround times and high throughput. A responsive STT solution will deliver value with low latency and fast processing speeds.

Cost - Speech-to-text is a foundational capability in the application stack, and cost efficiency is essential. Solutions that fail to deliver adequate ROI and a good price-to-performance ratio will be a barrier to the overall utility of the end user application.

Modality - Important input modes include support for pre-recorded or real-time audio:

Batch or pre-recorded transcription capabilities - Batch transcription won't be needed by everyone, but for many use cases, you'll want a service that you can send batches of files to to be transcribed, rather than having to do it one-by-one on your end.

Real-time streaming - Again, not everyone will need real-time streaming. However, if you want to use STT to create, for example, truly conversational AI that can respond to customer inquiries in real time, you'll need to use a STT API that returns its results as quickly as possible.

Features & Capabilities - Developers and companies seeking speech processing solutions require more than a bare transcript. They also need rich features that help them build scalable products with their voice data, including sophisticated formatting and speech understanding capabilities to improve readability and utility by downstream tasks.

Scalability and Reliability - A good speech-to-text solution will accommodate varying throughput needs, adequately handling a range of audio data volumes from small startups to large enterprises. Similarly, ensuring reliable, operational integrity is a hard requirement for many applications where the effects from frequent or lengthy service interruption could result in revenue impacts and damage to brand reputation. 

Customization, Flexibility, and Adaptability - One size, fits few. The ability to customize STT models for specific vocabulary or jargon as well as flexible deployment options to meet project-specific privacy, security, and compliance needs are important, often overlooked considerations in the selection process.

Ease of Adoption and Use - A speech-to-text API only has value if it can be integrated into an application. Flexible pricing and packaging options are critical, including usage-based pricing with volume discounts. Some vendors do a better job than others to provide a good developer experience by offering frictionless self-onboarding and even including free tiers with an adequate volume of credits to help developers test the API and prototype their applications before choosing the best subscription option to choose.

Support and Subject Matter Expertise - Domain experts in AI, machine learning, and spoken language understanding are an invaluable resource when issues arise. Many solution providers outsource their model development or offer STT as a value-add to their core offering. Vendors for whom speech AI is their core focus are better equipped to diagnose and resolve challenge issues in a timely fashion. They are also more inclined to make continuous improvements to their STT service and avoid issues with stagnating performance over time.

What are the most important features of a speech-to-text API?

In this section, we'll survey some of the most common features that STT APIs offer. The key features that are offered by each API differ, and your use cases will dictate your priorities and needs in terms of which features to focus on.

Multi-language support - If you're planning to handle multiple languages or dialects, this should be a key concern. And even if you aren't planning on multilingual support now, if there's any chance that you would in the future, you're best off starting with a service that offers many languages and is always expanding to more.

Formatting - Formatting options like punctuation, numeral formatting, paragraphing, speaker labeling (or speaker diarization), word-level timestamping, profanity filtering, and more, all to improve readability and utility for data science

Automatic punctuation & capitalization - Depending on what you're planning to do with your transcripts, you might not care if they're formatted nicely. But if you're planning on surfacing them publicly, having this included in what the STT API provides can save you time.

Profanity filtering or redaction - If you're using STT as part of an effort for community moderation, you're going to want a tool that can automatically detect profanity in its output and censor it or flag it for review.

Understanding - A primary motivation for employing a speech-to-text API is to gain understanding of who said what and why they said it. Many applications employ natural language and spoken language understanding tasks to accurately identify, extract, and summarize conversational audio to deliver amazing customer experiences. 

Topic detection - Automatically identify the main topics and themes in your audio to improve categorization, organization, and understanding of large volumes of spoken language content..

Intent detection - Similarly, intent detection is used to determine the purpose or intention behind the interactions between speakers, enabling more efficient handling by downstream agents or tasks in a system in order to determine the next best action to take or response to provide.

Sentiment analysis - Understand the interactions, attitudes, views, and emotions in conversational audio by quantitatively scoring the overall and component sections as being positive, neutral, or negative. 

Summarization - Deliver a concise summary of the content in your audio, retaining the most relevant and important information and overall meaning, for responsive understanding, analysis, and efficient archival.

Keywords (a.k.a. Keyword Boosting) - Being able to include an extended, custom vocabulary is helpful if your audio has lots of specialized terminology, uncommon proper nouns, abbreviations, and acronyms that an off-the-shelf model wouldn't have been exposed to. This allows the model to incorporate these custom terms as possible predictions.

Custom models - While keywords provide inclusion of a small set of specialized, out-of-vocabulary words, a custom model trained on representative data will always give the best performance. Vendors that allow you to tailor a model for your specific needs, fine-tuned on your own data, give you the ability to boost accuracy beyond what an out-of-the-box solution alone provides.

Accepts multiple audio formats - Another concern that won't be present for everyone is whether or not the STT API can process audio in different formats. If you have audio coming from multiple sources that aren't encoded in the same format, having a STT API that removes the need for converting to different types of audio can save you time and money.

What are the top speech-to-text use cases?

As noted at the outset, voice technology that's built on the back of STT APIs is a critical part of the future of business. So what are some of the most common use cases for speech-to-text APIs? Let's take a look.

Smart assistants  - Smart assistants like Siri and Alexa are perhaps the most frequently encountered use case for speech-to-text, taking spoken commands, converting them to text, and then acting on them.

Conversational AI  - Voicebots let humans speak and, in real time, get answers from an AI. Converting speech to text is the first step in this process, and it has to happen quickly for the interaction to truly feel like a conversation.

Sales and support enablement  - Sales and support digital assistants that provide tips, hints, and solutions to agents by transcribing, analyzing and pulling up information in real time. It can also be used to gauge sales pitches or sales calls with a customer.

Contact centers  - Contact centers can use STT to create transcripts of their calls, providing more ways to evaluate their agents, understand what customers are asking about, and provide insight into different aspects of their business that are typically hard to assess.

Speech analytics  - Broadly speaking, speech analytics is any attempt to process spoken audio to extract insights. This might be done in a call center, as above, but it could also be done in other environments, like meetings or even speeches and talks.

Accessibility  - Providing transcriptions of spoken speech can be a huge win for accessibility, whether it's  providing captions for classroom lectures  or creating badges that transcribe speech on the fly.

How do you evaluate performance of a speech-to-text API?

All speech-to-text solutions aim to produce highly accurate transcripts in a user-friendly format. We advise performing side-by-side accuracy testing using files that resemble the audio you will be processing in production to determine the best speech solution for your needs. The best evaluation regimes employ a holistic approach that includes a mix of quantitative benchmarking and qualitative human preference evaluation across the most important dimensions of quality and performance, including accuracy and speed.

The generally accepted industry metric for measuring transcription quality is Word Error Rate (WER). Consider WER in relation to the following equation:

WER + Accuracy Rate = 100%

Thus, an 80% accurate transcript corresponds to a WER of 20%

WER is an industry standard focusing on error rate rather than accuracy as the error rate can be subdivided into distinct error categories. These categories provide valuable insights into the nature of errors present in a transcript. Consequently, WER can also be defined using the formula:

WER = (# of words inserted + # of words deleted + # of words substituted) / total # of words.

We suggest a degree of skepticism towards vendor claims about accuracy. This includes the qualitative claim that OpenAI’s model “approaches human level robustness on accuracy in English,” and the WER statistics published in Whisper’s documentation.

Jose Nicholas Francisco

Apr 12, 2024

Samuel Adebayo

Apr 8, 2024

Zian (Andy) Wang

Apr 5, 2024

Apr 3, 2024

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Optimal Free Text-to-Speech & Speech-to-Text APIs, AI Models, and Open Source Solutions

• Unreal Speech

This article presents a comprehensive evaluation of the leading free Text-to-Speech and Speech-to-Text APIs, AI models, and open source engines, with a particular focus on those offering a free tier. We aim to explore the nuances of choosing between an API, an AI model, and an open source library, highlighting the unique benefits and considerations of each.

Choosing the right Text-to-Speech or Speech-to-Text solution for your project involves balancing various factors such as accuracy, model architecture, features, support, documentation, and security. This decision can be especially challenging for smaller or experimental projects where you might be testing an API or AI model, or considering an API for development purposes.

Our article aims to simplify this decision-making process by providing a detailed comparison of the top free options available in the market. Whether you're leaning towards an API, an AI model, or an open source engine, this guide will help you make an informed choice, taking into account the specific needs and scope of your project.

Free Text-to-Speech and Speech-to-Text APIs and AI Models

APIs and AI models typically deliver higher accuracy, easier integration, and a broader range of ready-to-use features compared to their open source counterparts. However, it's important to note that extensive use of these APIs and AI models can lead to additional costs.

For smaller projects or for those in the experimental or trial phase, many of today's Text-to-Speech and Speech-to-Text APIs and AI models offer a free usage tier. This allows users to access the API or model without any charges up to a certain limit, which could be daily, monthly, or annually.

In this article, we will delve into five prominent Text-to-Speech and Speech-to-Text APIs and AI models that provide a free tier, including Unreal Speech, Eleven Labs, PlayHT, Google, and AWS Transcribe.

Unreal Speech is a cutting-edge text-to-speech (TTS) API designed to significantly reduce costs associated with TTS services. It stands out in the market for its affordability, being up to 10 times cheaper than competitors like Eleven Labs and Play.ht, and up to twice as economical as solutions from tech giants such as Amazon, Microsoft, and Google. This cost-effectiveness is a key feature, especially for high-volume users.

One of the notable aspects of Unreal Speech is its pricing structure, which is designed to become more cost-effective the more you use it. This makes it an attractive option for businesses or projects where large-scale text-to-speech conversion is a regular requirement. The service offers volume discounts, starting free and scaling up based on usage, with the cost per million characters decreasing as usage increases. This scalable pricing model is particularly beneficial for users with fluctuating or growing needs.

Unreal Speech provides a high-quality listening experience, as evidenced by testimonials from users who have experienced significant cost savings without compromising on audio quality. In fact, some users have noted that it offers better sound quality than Amazon Polly, a well-known player in the TTS market.

The platform is also user-friendly, offering a straightforward API that allows for easy integration into various applications. It supports a range of customizable options, including different voice types, bitrates, speech speeds, pitches, and codecs. This flexibility ensures that users can tailor the speech output to meet their specific requirements, whether for different types of content or varying audience needs.

Currently, Unreal Speech focuses on English-speaking voices, but there are plans to expand into multilingual support. This future expansion could make it an even more versatile tool for global applications. Additionally, while it does not currently offer custom voice cloning, this is another area where development is anticipated.

In terms of usage rights, audio generated with Unreal Speech can be used commercially. The terms vary based on the subscription plan, with free plans requiring attribution to Unreal Speech, while paid plans do not require any attribution.

In summary, Unreal Speech positions itself as a highly cost-effective, scalable, and user-friendly text-to-speech solution. Its focus on quality, combined with a flexible and affordable pricing model, makes it a compelling choice for a wide range of users, from individual creators to large-scale enterprises.

Play.ht presents itself as a sophisticated AI voice generator and text-to-speech (TTS) platform, offering a wide array of features designed to create realistic and human-like voice performances. This platform is particularly notable for its extensive library of AI voices and its ability to cater to a variety of languages and accents, making it a versatile tool for various applications.

One of the key strengths of Play.ht is its expansive selection of over 800 natural-sounding AI voices. These voices are enhanced by advanced machine learning technology, ensuring that they deliver humanlike intonation and expression. This extensive range includes voices suitable for different types of content, such as conversational voices for podcasts and audiobooks, narrative voices for documentaries, and even character voices for gaming and creative videos. Additionally, the platform supports 142 languages and accents, enabling users to create content that resonates with a global audience.

Play.ht is designed to be contextually aware, offering emotional and expressive text-to-speech models. This feature is particularly useful for creating content that requires a specific tone or emotional resonance, such as marketing videos, explainer content, or entertainment. The platform's ability to generate conversational, long-form, or short-form voice content with consistent quality makes it a reliable tool for a wide range of users, from individual creators to large enterprises.

The platform also emphasizes security and privacy in voice generations, assuring users of the safety of their content. Additionally, it provides full commercial rights and copyrights for the generated audio, which is a crucial aspect for users intending to use the content for commercial purposes.

In terms of accessibility and ease of use, Play.ht addresses common questions about AI voice generation and text-to-speech technology, providing users with a comprehensive understanding of how to effectively use the platform. This includes information on customizations, commercial usage, and the realistic quality of AI-generated voices.

In summary, Play.ht stands out as a comprehensive and versatile AI voice generator and text-to-speech platform. Its wide range of voices, language support, and advanced features make it a suitable choice for a variety of applications, from audio publishing and e-learning to gaming and voice accessibility.

Google Speech-to-Text is recognized as a prominent speech transcription API in the industry. Google generously offers users an initial 60 minutes of free transcription, complemented by $300 in free credits applicable for Google Cloud hosting services.

However, it's important to note that Google's transcription service is primarily designed to work with files that are already stored in a Google Cloud Bucket. This specific requirement means that the provided free credits might not stretch as far as one might initially expect. Additionally, getting started with Google's service can present some challenges. To access even the free tier, users are required to set up a Google Cloud Platform (GCP) account and project. This process can be unexpectedly intricate and may pose a hurdle for those unfamiliar with Google's cloud services.

Despite these initial setup complexities, Google Speech-to-Text stands out for its high accuracy and extensive language support, covering over 63 languages. This makes it a viable option for users who are prepared to navigate the initial setup process. The effort invested in getting started can be worthwhile, especially for those who require reliable and accurate speech transcription across a diverse range of languages.

• AWS Transcribe

AWS Transcribe is another notable player in the field of speech transcription services, offering users one hour of free transcription each month for the initial 12 months after signing up.

Similar to Google's offering, AWS requires users to first set up an AWS account, which can be a somewhat intricate process, especially for those who are new to Amazon's cloud services. This setup might be seen as a barrier for some users. Additionally, it's important to note that AWS Transcribe generally requires that files for transcription be located in an Amazon S3 bucket, which adds an extra step in the preparation process.

While AWS Transcribe is known to have slightly lower accuracy in comparison to some other transcription APIs, it still holds its ground with a set of unique features. Particularly noteworthy is its Transcribe Medical API, which is specifically tailored for medical transcription. This specialized Automatic Speech Recognition (ASR) service is currently available and offers a focused solution for healthcare professionals and organizations. This medical-focused transcription service is an example of AWS's commitment to catering to niche requirements, making it an appealing choice for users with specific needs like medical transcription.

• Eleven Labs

Eleven Labs is revolutionizing digital interaction with its advanced generative voice AI technology. This platform enables users to easily clone or create synthetic voices, converting text to speech in an impressive range of 29 languages. Its AI voice generator excels in producing high-quality audio that captures human intonation and inflections, adjusting to context for a realistic experience. This feature is invaluable for content creators, enhancing videos, storytelling, and gaming experiences with lifelike speech.

The technology also significantly benefits the publishing industry by transforming written content into engaging audiobooks with natural voice and tone. Additionally, Eleven Labs is enhancing digital communication by enabling the creation of AI chatbots with human-like voices, improving user interactions in digital platforms.

A key feature of Eleven Labs is its VoiceLab, which allows voice cloning in one language and its use in others, offering versatility for various projects. The platform also provides a comprehensive workflow for long-form voice generation, ideal for audiobooks and other extensive content, with customizable speech pacing and audio editing.

Driven by cutting-edge research and a commitment to ethical AI, Eleven Labs is not just a voice generation tool but a pioneering platform reshaping how we engage with digital content across various industries.

https://elevenlabs.io/

Open Source Speech-to-Text Transcription Tools

As an alternative to using APIs and AI models, open source Speech-to-Text tools offer a completely free solution without usage limitations. A key advantage for some developers is the aspect of data security, as it eliminates the need to transmit data to external parties or cloud services.

However, it's important to note that utilizing open source engines requires significant effort. If you're prepared to invest considerable time and resources, especially for large-scale applications, these tools can be viable. Generally, open source Speech-to-Text tools may not match the accuracy levels of the previously mentioned APIs.

For those interested in exploring open source options, there are several noteworthy choices available.

DeepSpeech, an open-source embedded Speech-to-Text engine, is engineered to operate in real-time across various devices, from robust GPUs to a Raspberry Pi 4. This library employs an end-to-end model architecture initially developed by Baidu.

As an open-source solution, DeepSpeech offers commendable accuracy right from the start. Additionally, it is user-friendly in terms of fine-tuning and training with custom data sets.

Kaldi, a speech recognition toolkit, enjoys longstanding popularity within the research community. It shares similarities with DeepSpeech in terms of initial accuracy and the capability to train custom models. Kaldi's extensive testing and widespread use in production by numerous companies have bolstered its reputation and reliability among developers.

Developed by Facebook AI Research, Wav2Letter is an Automatic Speech Recognition (ASR) Toolkit. It's crafted in C++ and utilizes the ArrayFire tensor library. Wav2Letter, akin to DeepSpeech, offers respectable accuracy for an open-source tool and is user-friendly for smaller-scale projects.

• SpeechBrain

SpeechBrain is a transcription toolkit based on PyTorch. This platform provides open implementations of significant research works and integrates closely with HuggingFace, facilitating easy access. It's well-structured and regularly updated, making it an efficient tool for both training and fine-tuning purposes.

Coqui, another deep learning toolkit for Speech-to-Text transcription, supports over twenty languages and includes various features essential for inference and production. The platform regularly releases custom-trained models and features bindings for multiple programming languages, simplifying deployment.

OpenAI's Whisper, launched in September 2022, stands on par with other leading open-source options in the field. It can be operated via Python or command line and is capable of multilingual translation. Whisper offers five distinct models, each suited to different use cases. However, running Whisper, especially on a large scale, requires a fast GPU and an in-house team for maintenance, scaling, and updates, which can increase the total cost of ownership. As of March 2023, Whisper is also available through an API, offering faster and more cost-effective solutions, with pricing starting at $0.006 per minute.

In conclusion

Choosing the Best Free Speech-to-Text API, Text-to-Speech AI Model, or Open Source Engine for Your Project

The selection of an appropriate free Speech-to-Text API, Text-to-Speech AI model, or open source engine largely depends on your project's specific needs. If you have a smaller-scale project and need a solution that is user-friendly, highly accurate, and comes with pre-built features, then one of these APIs could be an ideal choice:

On the other hand, if your priority is a completely free option without data usage restrictions and you're willing to invest more effort in customizing a toolkit, an open source library could be more appropriate. In this case, consider these options:

When making your decision, it's crucial to select a tool that not only fulfills your current project needs but also has the potential to accommodate the future evolution of your project.

Top Free Speech to Text tools, APIs, and Open Source models

What is speech to text api .

Speech recognition technology, also known as Automatic Speech Recognition (ASR) or computer speech recognition, allows users to transcribe audio content into written text. The conversion of speech from a verbal to a written format is accomplished through acoustic and language modeling processes. It's important not to confuse speech recognition technology with voice recognition; while the former translates audio to text, the latter is used to identify an individual user's voice.

This technology is utilized across multiple industries, from transcription services and voice assistants to accessibility features and beyond.

Top Open Source (Free) AI Speech Recognition models on the market

For users seeking a cost-effective engine, opting for an open-source model is the recommended choice. Here is the list of best Automatic Speech Recognition Open Source Models:

1. DeepSpeech

DeepSpeech is an open-source, embedded speech-to-text engine that operates in real-time on a variety of devices, ranging from high-powered GPUs to a Raspberry Pi 4. The DeepSpeech library utilises an end-to-end model architecture pioneered by Baidu.

Kaldi is a speech recognition software package highly regarded by researchers for many years. Similar to DeepSpeech, it boasts good initial accuracy and is capable of facilitating model training.

Kaldi has an extensive history of testing and is currently employed by numerous companies in their production environments, bolstering developer confidence in its effectiveness.

‍3. Wav2Letter

Wav2Letter is an Automatic Speech Recognition (ASR) Toolkit developed by Facebook AI Research. It is written in C++ and employs the ArrayFire tensor library. Wav2Letter is a moderately precise open-source library that is user-friendly for minor projects.

‍4. SpeechBrain

SpeechBrain is a transcription toolkit based on PyTorch. The platform provides open-source implementations of popular research projects and tightly integrates with HuggingFace, enabling easy access. In general, the platform is clearly defined and regularly updated, making it an uncomplicated tool for training and fine-tuning.

Coqui is a remarkable toolkit for deep learning in Speech-to-Text transcription. It is developed to be utilized in more than twenty language projects with an array of inference and productionization features.

Furthermore, the platform provides custom trained models and has bindings for numerous programming languages, making it easier for deployment.

‍6. Whisper

Whisper, which was released by OpenAI in September 2022, can be considered as one of the leading open source options. This tool can be used in Python or from the command line and allows for multilingual translation.

Additionally, Whisper boasts five different models, each with its own size and capabilities, for users to choose from based on their specific use case.

Probably one of the oldest speech recognition software packages ever, as its development began in 1991 at the University of Kyoto. Julius offers a range of features, such as real-time speech-to-text processing, low memory consumption (less than 64MB for 20,000 words), and the ability to generate N-best/Word-graph outputs. It can also function as a server unit and boasts additional advanced features.

8. OpenSeq2Seq

Developed by NVIDIA for training sequence-to-sequence models, this engine has versatile applications beyond speech recognition. It is a dependable option for this use case. Users have the option to create their own training models or use pre-existing ones. It facilitates parallel processing through the use of multiple GPUs or CPUs.

An end-to-end speech recognition engine implementing ASR is written in Python and licensed under the Apache 2.0 license. It supports unsupervised pre-training and multi-GPU training, on the same or multiple machines. The engine is built on top of TensorFlow and has a large model available for both English and Chinese languages.

Cons of Using Open Source AI models

‍While open source models offer many advantages, they also come with some potential drawbacks and challenges. Here are some cons of using open source models:

• Not Entirely Cost Free: Open-source models, while providing valuable resources to users, may not always be entirely free of cost. Users often need to bear expenses related to hosting and server usage, especially when dealing with large or resource-intensive data sets.
• Lack of Support : Open source models may not come with official support channels or dedicated customer support teams. If you encounter issues or need assistance, you might have to rely on community forums or the goodwill of volunteers, which can be less reliable than commercial support.
• Limited Documentation : Some open source models may have incomplete or poorly maintained documentation. This can make it difficult for developers to understand how to use the model effectively, leading to frustration and wasted time.
• Security Concerns : Security vulnerabilities can exist in open source models, and it may take longer for these issues to be addressed compared to commercially supported models. Users of open source models may need to actively monitor for security updates and patches.
• Scalability and Performance : Open source models may not be as optimized for performance and scalability as commercial models. If your application requires high performance or needs to handle a large number of requests, you may need to invest more time in optimization.

Why choose Eden AI?

Given the potential costs and challenges related to open-source models, one cost-effective solution is to use APIs. Eden AI smoothens the incorporation and implementation of AI technologies with its API, connecting to multiple AI engines.

Eden AI presents a broad range of AI APIs on its platform, customized to suit your specific needs and financial limitations. These technologies include data parsing, language identification, sentiment analysis, logo recognition, question answering, data anonymization, speech recognition, and numerous other capabilities.

To get started, we offer free $10 credits for you to explore our APIs.

Access ASR providers with one API

Our standardized API enables you to integrate Speech to Text APIs into your system with ease by utilizing various providers on Eden AI. Here is the list (in alphabetical order):

• Amazon Transcribe
• NeuralSpace
• Speechmatics

1. Amazon Transcribe- Available on Eden AI

Amazon Transcribe simplifies the process for developers to incorporate speech to text capabilities in their applications. It employs Automatic Speech Recognition (ASR), a deep learning method, to promptly and accurately transform speech into text.

This technology can effectively transcribe customer service calls, automate subtitling, and generate media file metadata, establishing a searchable archive.

2. AssemblyAI- Available on Eden AI

Assembly AI enables accurate transcription of audio and video files through its simple API. The Speech-to-Text technology is bolstered by advanced AI models, with features including batch asynchronous transcription, real-time transcription, speaker diarization, and the ability to accept all audio and video formats.

Notably, Assembly AI maintains top-rated accuracy, an automatic punctuation and casing function, word timings, confidence scores, and paragraph detection.

3. Deepgram- Available on Eden AI

Deepgram offers developers the tools required for effortless implementation of AI speech recognition in applications. We possess the ability to manage nearly all audio file formats and provide lightning-fast processing for premium voice experiences.

Deepgram's Automatic Speech Recognition facilitates optimal voice application creation with superior, faster, and more cost-effective transcription on a large scale.

4. Gladia- Available on Eden AI

Gladia's Audio Intelligence API facilitates the capture, enrichment, and utilization of hidden insights within audio data. It is a highly accurate audio transcription solution for real-world business use cases. The API also includes speaker separation and language alternation detection.

5. Google - Available on Eden AI

Speech-to-Text allows for simple integration of Google's speech recognition technologies into applications for developers. Submit an audio file and receive a textual transcription from Speech-to-Text's API service.

6. IBM- Available on Eden AI

IBM Watson's Speech to Text technology facilitates rapid and precise transcription of speech in various languages for a range of applications, not excluding customer self-help, agent aid, and speech analytics.

The technology offers pre-built advanced machine learning models and optional configurations to adapt to your specific requirements.

7. Microsoft- Available on Eden AI

The Universal language model is the default choice for Microsoft Azure Speech-to-Text service. It was developed by Microsoft and is hosted in the cloud. This model is best suited for conversational and dictation scenarios.

However, for unique environments, it is possible to devise and educate bespoke acoustic, language, and pronunciation models for enhanced performance.

8. NeuralSpace- Available on Eden AI

NeuralSpace's Speech To Text (STT) API serves as a bridge to facilitate audio transcriptions. It utilizes state-of-the-art AI models to offer precise transcriptions of all kinds of speech, whether in conversations or alternative forms.

The API caters to diverse languages worldwide, including those with limited digital representation. You can use the API for various use cases, including captioning videos or meetings, voice bots, and automatic transcription.

9. OpenAI- Available on Eden AI

OpenAI has developed and introduced a neural network named Whisper, which achieves high levels of robustness and accuracy similar to humans. It has been trained on 680,000 hours of multilingual and multitasking supervised data gathered from the internet.

The research demonstrates that the utilization of a broad and varied dataset results in enhanced resilience to accents, ambient sound, and specialized terminology. Furthermore, it allows transcription and translation from multiple languages into English.

10. Rev- Available on Eden AI

Rev's STT engine is the most precise speech-to-text model worldwide. It has been trained on over 50,000 hours of relevant data. Streamline your creation process by implementing a universal model that encompasses all accents, dialects, languages, and audio formats. With a smooth API integration, you can remove redundant stages to achieve the desired outcome.

11. Speechmatics- Available on Eden AI

Speechmatics provides speech recognition technology for mission-critical applications, utilizing its any-context recognition engine. Our technology is used by a wide range of enterprises in contact centers, CRM, consumer electronics, security, media & entertainment, and software. Speechmatics transcribes millions of hours globally in over 30 languages each month.

12. Symbl- Available on Eden AI

The Symbl API utilizes cutting-edge machine learning techniques to transcribe speech in real-time and deliver supplementary context-aware analyses, including speaker identification, sentiment analysis, and topic detection.

13. Voci- Available on Eden AI

Voci provides highly advanced and precise transcription services for a range of purposes. Their API is capable of real-time speech recognition, processing vast audio files, and handling various languages and accents, all thanks to Voci's deep neural networks.

As well as this, Voci's services cover text analytics, speaker diarization, and keyword spotting, with exceptional accuracy and minimal lag time. The API can be incorporated into different types of applications including call centers, transcription services, and voice-enabled devices.

Pricing Structure for Speech to Text API Providers

Eden AI offers a user-friendly platform for evaluating pricing information from diverse API providers and monitoring price changes over time. As a result, keeping up-to-date with the latest pricing is crucial. The pricing chart below outlines the rates for smaller quantities for October 2023, as well as you can get discounts for potentially large volumes.

How Eden AI can help you?

Eden AI is the future of AI usage in companies: our app allows you to call multiple AI APIs.

• Centralized and fully monitored billing on Eden AI for STT APIs
• Unified API for all providers: simple and standard to use, quick switch between providers, access to the specific features of each provider
• Standardized response format: the JSON output format is the same for all suppliers thanks to Eden AI's standardization work. The response elements are also standardized thanks to Eden AI's powerful matching algorithms.
• The best Artificial Intelligence APIs in the market are available: big cloud providers (Google, AWS, Microsoft, and more specialized engines)
• Data protection: Eden AI will not store or use any data. Possibility to filter to use only GDPR engines.

You can see Eden AI documentation here .

Next step in your project

The Eden AI team can help you with your Speech to Text integration project. This can be done by :

• Organizing a product demo and a discussion to better understand your needs. You can book a time slot on this link: Contact
• By testing the public version of Eden AI for free: however, not all providers are available on this version. Some are only available on the Enterprise version.
• By benefiting from the support and advice of a team of experts to find the optimal combination of providers according to the specifics of your needs
• Having the possibility to integrate on a third-party platform: we can quickly develop connectors.

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5 Best Speech-to-Text APIs

Voice search is becoming increasingly prevalent as the years tick on, as increasing amounts of users access the Internet via mobile devices and with the help of voice assistants like Alexa. 41% of adults report using voice search on a daily basis.

Voice search is becoming an essential component of eCommerce, as well. 50% of consumers report making a purchase using voice search in the last year. Neglecting voice is like leaving money on the table, not to mention potentially alienating your audience.

Voice is also highly useful for segmenting your audience. Voice search is used most widely by affluent, highly-educated consumers . You could potentially integrate voice into a digital marketing campaign, as part of your marketing funnel, segmenting your audience in all manner of useful ways.

The fact that voice search could possibly alert you to members of your audience with money to burn and a willingness to spend is reason enough to investigate voice and integrate it into your existing workflow.

But how do you go about integrating voice recognition into your website or app? Isn’t that the domain of uber-rich companies with heavy investments in machine learning and virtual reality?

Not necessarily.

There are numerous speech-to-text web APIs you can use to power your app or website. We’re going to dig into some of our favorite, most useful APIs for voice search.

The 5 Best APIs For Speech-To-Text

Ranking tech solutions from best to worst is always going to be subjective. What constitutes the best API will largely depend on what you’re going to be using voice recognition for.

We’ll be segmenting our favorite speech-to-text APIs by application, as a way to help you figure out which API will best suit your particular needs.

Speech-To-Text APIs for Short Online Searches

The phrases people tend to use to look things up online tend to be short, sweet, and to the point. Voice search APIs for online applications won’t need to be as thorough or have as many technical considerations, like grammar or syntax, to consider. This means these APIs tend to be lighter, faster, and quicker to load.

1. Google Speech-To-Text

Google Speech-To-Text was unveiled in 2018 , just one week after their text-to-speech update. Google’s Speech-To-Text API makes some audacious claims, reducing word errors by 54% in test after test. In certain areas, the results are even more encouraging.

One of the reasons for the APIs impressive accuracy is the ability to select between different machine learning models , depending on what your application’s being used for. This also makes Google Speech-To-Text a suitable solution for applications other than short web searches. It can also be configured for audio from phone calls or videos. There’s a fourth setting, as well, which Google recommends using as default.

The Speech-To-Text API also features an impressive update for extended punctuation options. This is designed to make more useful transcriptions, with fewer run-on sentences or punctuation errors.

The newest update also allows developers to tag their transcribed audio or video with basic metadata . This is more for the company’s benefit than for the developers, however, as it will allow Google to decide which features are most useful for programmers.

The Google Speech-To-Text API isn’t free, however. It is free for speech recognition for audio less than 60 minutes. For audio transcriptions longer than that, it costs $0.006 per 15 seconds.

For video transcriptions, it costs $0.006 per 15 seconds for videos up to 60 minutes in length. For video longer than one hour, it costs $0.012 for every 15 seconds. Make sure you factor that into your pricing models when developing applications and web services.

• Recognizes over 120 languages
• Multiple machine learning models for increased accuracy
• Automatic language recognition
• Text transcription
• Proper noun recognition
• Data privacy
• Noise cancellation for audio from phone calls and video
• Costs money
• Limited custom vocabulary builder

2. Microsoft Cognitive Services

Microsoft is also a major player in the world of voice recognition APIs. Microsoft Cognitive Services is more than just another speech recognition API, however. It’s also a part of the Microsoft Trust Services which offer unparalleled security options for developers looking for the most secure data for their applications.

The main thing that separates Microsoft Cognitive Services’ Speech to Text API  is the Speaker Recognition function. This is the auditory version of security software like face recognition . Think of it as a retina scan for the sound of the user’s voice. It makes it incredibly easy for different levels of users.

This same voice recognition capability allows software to adapt to specific user’s speech styles and patterns. It also offers more custom vocabulary options than Google, as an additional benefit.

Beyond that, Microsoft Cognitive Service’s speech recognition API has many of the same benefits of other voice APIs. It can perform real-time transcription , as well as converting text-into-speech. Thus, Microsoft Cognitive Services can cover most of your text and speech-based needs. It can also be used for call center log analysis, if you’ve got large amounts of audio that needs to be analyzed.

Considering the widespread popularity of Microsoft products and services, Microsoft Cognitive Services is growing faster than many of the other APIs on our list. If you’re looking to join in with a vibrant, active community of developers, Microsoft Cognitive Services could be a good fit.

• Enhanced data security via voice-recognition algorithms
• Real-time transcription
• Real-time translation
• Customizable vocabulary
• Text-to-speech capabilities for natural speech patterns
• Built-in constraints due to the API being created for general purposes
• Uses microservices, which can be useful for solving individual problems but falls short for larger problems

3. Dialogflow (Formerly API.AI, Speaktoit)

Dialogflow is also owned by Google. The main advantage over other voice APIs is Dialogflow’s ability to take context into consideration when analyzing speech, which makes for more accurate transcriptions. It also allows developers to customize their voice-based commands for different devices, such as smart devices, phones, wearables, cars, and smart speakers.

Dialogflow’s earlier incarnation, Api.ai, was used to power the Assistant app, one of the earliest virtual voice-based assistants, way back in 2014. It’s since been discontinued but demonstrates that Dialogflow has been in the AI/machine learning/voice recognition game for longer than most.

The Dialogflow voice recognition API also has a number of analytics built into the platform. You can measure user engagement or session metrics, as well as usage patterns or latency issues. This is bound to be helpful when getting investors, sales and marketing teams, and developers on the same page.

Dialogflow currently only supports 14 languages, however. This makes it less useful for multilingual software than Google Speech-To-Text or Microsoft Cognitive Services.

• Easy to use
• Easy to set up
• Integrates with a wide variety of software
• Easily integrated with other web services
• Can integrate with non-Google devices like Amazon’s Alexa
• Cannot handle math functions
• Cannot match intent with common phrases
• Cannot create clickable links in the text box
• Cannot search across intents
• Can only provide one webhook

Voice Recognition APIs for Longform and Offline Processing

4. ibm watson.

It’s no secret we’re generating, processing, and analyzing larger quantities of data than any other time in history. Not all of that data is going to be clean and well-organized, especially if you’re designing or developing an API. As API developers, it’s our job to make sure that the data is organized and usable.

IBM Watson is perhaps one of the purest expressions of AI as a virtual assistant . IBM Watson is very adept at processing natural language patterns, which is one of the holy grails of AI and machine learning developers.

The I BM Watson Speech to Text API is particularly robust in understanding context, relying on hypothesis generation and evaluation in its response formulation. It’s also able to differentiate between multiple speakers, which makes it suitable for most transcription tasks. You can even set a number of filters, eliminating profanities, adding word confidence, and formatting options for speech-to-text applications.

IBM Watson offers three different interfaces for developers. There’s a WebSocket interface, an HTTP REST interface, and an asynchronous HTTP interface.

IBM Watson is simple to set up and implement, which makes it a wonderful option for those looking for a Speech-To-Text API but aren’t completely technically proficient. IBM provides  extensive documentation and one of the most thorough API reference manuals on the market. If you’re looking for a speech-to-text API that’s simple to set up and start using immediately, IBM Watson might be a good fit.

Of course, IBM Watson is more than just a speech-to-text API. It’s one of the most fully-developed machine learning libraries in existence. It continues to learn and evolve, the more you use it. This makes it suitable for preventing outages and disruptions as well as accelerating research and data . Most applications that would benefit from structuring unstructured data will benefit from using the IBM Watson API.

As one of the best-developed machine learning APIs out there, IBM Watson isn’t cheap. It is quick to get up and running, however, meaning you won’t waste money on downtime or having to hire multiple developers just to get started. The peace of mind of a nearly plug-and-play Speech-To-Text API may be worth the cost of admission alone.

• Processes unstructured data
• Assists humans instead of replacing them
• Helps overcome human limitations
• Improves productivity be delivering relevant data
• Improves user experience
• Can process large quantities of data
• Easy to set up and get started with
• Doesn’t directly support structured data
• Expensive to switch to
• Requires maintenance
• Only supports a limited number of languages
• Takes time to implement fully
• Requires education and training to make full use of its resources

5. Speechmatics

Speechmatics offers an easy-to-use cloud-based API for automatic transcription services. Its main claim to fame is that it supports a wide range of file formats, meaning it can be used for offline file processing.

The Speechmatics API is also highly adept at speaker recognition . It processes an impressive array of different variables, from confidence values to timing and speaker indications. This makes Speechmatics useful for machine learning applications, as it gets to know a speaker more thoroughly with each iteration.

Speechmatics has been found to be one of the fastest and most reliable automatic transcription APIs available for developers. It also supports nine languages, including different variants on English, including British and Australian English.

There are a couple of drawbacks to the Speechmatics API, however, although none of them are major enough to be a dealbreaker. First and most notably, there’s no app interface. If you’ll be using the transcription services, you’ll need to upload the audio to the website.

Secondly, each query does cost money. It costs .06 GBP per 1 minute of processed audio. If you’re going to be using the Speechmatics API for any sort of commercial app or web service, make sure to consider that when setting your processing. They do offer a discount for over 1000 minutes of processed audio. Perhaps you can work out some sort of bulk rate if you’re going to be using the Speechmatics API extensively.

• Supports multiple languages
• Supports multiple English variants
• Multi-speaker support
• Multiple file formats supported
• Does well with noisy audio
• Easily integrated via REST API
• Speaker recognition
• Can be used for cloud-based transcription services and private usage, using the same API
• No app interface
• Costs money for each query

Final Thoughts

Not all Voice-To-Text APIs are created equal. In fact, think of a voice recognition API as a toolbox rather than a product you’d buy off the shelf. Each one has different strengths and weaknesses. Knowing which Speech-To-Text API is right for your product largely depends on what you’ll be using it for.

These five APIs certainly aren’t the only ones you can use for voice-related functions, either. Some other noteworthy voice recognition APIs are worthy of a look.

Other Noteworthy Voice Recognition APIs include:

• Speech Engine by iFlyTek
• UWP Speech Recognition by Microsoft
• CMU Sphinx Speech Recognition Toolkit (open source)
• Kaldi Speech Recognition Toolkit For Research (open source)

Each one of the speech-to-text APIs has its strengths. If you need transcription or to decode noisy audio, Google Speech-To-Text is an excellent contender. If you’re looking for real-time translation and transcription functionality, Microsoft Cognitive Services is probably going to be your best bet. If you’re looking for a plug-and-play voice recognition API that easily configures for numerous devices and software environments, Dialogflow might be right for you.

If you’re going to be dealing with large amounts of unstructured data, however, IBM Watson is going to be the best suited for your particular needs. If you’re going to be needing speaker separation or easy integration with additional software, Speechmatics will make your life as easy as possible, with its convenient REST API.

Considering the rise of mobile and hands-free devices, virtual assistants, and AI, it’s safe to say that voice integration isn’t going anywhere. It’s only going to get more prevalent, as technology continues to intertwine with the fabric of our daily lives.

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J. Simpson lives at the crossroads of logic and creativity. He writes and researches tech-related topics extensively for a wide variety of publications, including Forbes Finds. He is also a graphic designer, journalist, and academic writer, writing on the ways that technology is shaping our society while using the most cutting-edge tools and techniques to aid his path. He lives in Portland, Or.

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Using the Web Speech API

Speech recognition.

Speech recognition involves receiving speech through a device's microphone, which is then checked by a speech recognition service against a list of grammar (basically, the vocabulary you want to have recognized in a particular app.) When a word or phrase is successfully recognized, it is returned as a result (or list of results) as a text string, and further actions can be initiated as a result.

The Web Speech API has a main controller interface for this — SpeechRecognition — plus a number of closely-related interfaces for representing grammar, results, etc. Generally, the default speech recognition system available on the device will be used for the speech recognition — most modern OSes have a speech recognition system for issuing voice commands. Think about Dictation on macOS, Siri on iOS, Cortana on Windows 10, Android Speech, etc.

Note: On some browsers, such as Chrome, using Speech Recognition on a web page involves a server-based recognition engine. Your audio is sent to a web service for recognition processing, so it won't work offline.

To show simple usage of Web speech recognition, we've written a demo called Speech color changer . When the screen is tapped/clicked, you can say an HTML color keyword, and the app's background color will change to that color.

To run the demo, navigate to the live demo URL in a supporting mobile browser (such as Chrome).

HTML and CSS

The HTML and CSS for the app is really trivial. We have a title, instructions paragraph, and a div into which we output diagnostic messages.

The CSS provides a very simple responsive styling so that it looks OK across devices.

Let's look at the JavaScript in a bit more detail.

Prefixed properties

Browsers currently support speech recognition with prefixed properties. Therefore at the start of our code we include these lines to allow for both prefixed properties and unprefixed versions that may be supported in future:

The grammar

The next part of our code defines the grammar we want our app to recognize. The following variable is defined to hold our grammar:

The grammar format used is JSpeech Grammar Format ( JSGF ) — you can find a lot more about it at the previous link to its spec. However, for now let's just run through it quickly:

• The lines are separated by semicolons, just like in JavaScript.
• The first line — #JSGF V1.0; — states the format and version used. This always needs to be included first.
• The second line indicates a type of term that we want to recognize. public declares that it is a public rule, the string in angle brackets defines the recognized name for this term ( color ), and the list of items that follow the equals sign are the alternative values that will be recognized and accepted as appropriate values for the term. Note how each is separated by a pipe character.
• You can have as many terms defined as you want on separate lines following the above structure, and include fairly complex grammar definitions. For this basic demo, we are just keeping things simple.

Plugging the grammar into our speech recognition

The next thing to do is define a speech recognition instance to control the recognition for our application. This is done using the SpeechRecognition() constructor. We also create a new speech grammar list to contain our grammar, using the SpeechGrammarList() constructor.

We add our grammar to the list using the SpeechGrammarList.addFromString() method. This accepts as parameters the string we want to add, plus optionally a weight value that specifies the importance of this grammar in relation of other grammars available in the list (can be from 0 to 1 inclusive.) The added grammar is available in the list as a SpeechGrammar object instance.

We then add the SpeechGrammarList to the speech recognition instance by setting it to the value of the SpeechRecognition.grammars property. We also set a few other properties of the recognition instance before we move on:

• SpeechRecognition.continuous : Controls whether continuous results are captured ( true ), or just a single result each time recognition is started ( false ).
• SpeechRecognition.lang : Sets the language of the recognition. Setting this is good practice, and therefore recommended.
• SpeechRecognition.interimResults : Defines whether the speech recognition system should return interim results, or just final results. Final results are good enough for this simple demo.
• SpeechRecognition.maxAlternatives : Sets the number of alternative potential matches that should be returned per result. This can sometimes be useful, say if a result is not completely clear and you want to display a list if alternatives for the user to choose the correct one from. But it is not needed for this simple demo, so we are just specifying one (which is actually the default anyway.)

Starting the speech recognition

After grabbing references to the output <div> and the HTML element (so we can output diagnostic messages and update the app background color later on), we implement an onclick handler so that when the screen is tapped/clicked, the speech recognition service will start. This is achieved by calling SpeechRecognition.start() . The forEach() method is used to output colored indicators showing what colors to try saying.

Receiving and handling results

Once the speech recognition is started, there are many event handlers that can be used to retrieve results, and other pieces of surrounding information (see the SpeechRecognition events .) The most common one you'll probably use is the result event, which is fired once a successful result is received:

The second line here is a bit complex-looking, so let's explain it step by step. The SpeechRecognitionEvent.results property returns a SpeechRecognitionResultList object containing SpeechRecognitionResult objects. It has a getter so it can be accessed like an array — so the first [0] returns the SpeechRecognitionResult at position 0. Each SpeechRecognitionResult object contains SpeechRecognitionAlternative objects that contain individual recognized words. These also have getters so they can be accessed like arrays — the second [0] therefore returns the SpeechRecognitionAlternative at position 0. We then return its transcript property to get a string containing the individual recognized result as a string, set the background color to that color, and report the color recognized as a diagnostic message in the UI.

We also use the speechend event to stop the speech recognition service from running (using SpeechRecognition.stop() ) once a single word has been recognized and it has finished being spoken:

Handling errors and unrecognized speech

The last two handlers are there to handle cases where speech was recognized that wasn't in the defined grammar, or an error occurred. The nomatch event seems to be supposed to handle the first case mentioned, although note that at the moment it doesn't seem to fire correctly; it just returns whatever was recognized anyway:

The error event handles cases where there is an actual error with the recognition successfully — the SpeechRecognitionErrorEvent.error property contains the actual error returned:

Speech synthesis

Speech synthesis (aka text-to-speech, or TTS) involves receiving synthesizing text contained within an app to speech, and playing it out of a device's speaker or audio output connection.

The Web Speech API has a main controller interface for this — SpeechSynthesis — plus a number of closely-related interfaces for representing text to be synthesized (known as utterances), voices to be used for the utterance, etc. Again, most OSes have some kind of speech synthesis system, which will be used by the API for this task as available.

To show simple usage of Web speech synthesis, we've provided a demo called Speak easy synthesis . This includes a set of form controls for entering text to be synthesized, and setting the pitch, rate, and voice to use when the text is uttered. After you have entered your text, you can press Enter / Return to hear it spoken.

To run the demo, navigate to the live demo URL in a supporting mobile browser.

The HTML and CSS are again pretty trivial, containing a title, some instructions for use, and a form with some simple controls. The <select> element is initially empty, but is populated with <option> s via JavaScript (see later on.)

Let's investigate the JavaScript that powers this app.

Setting variables

First of all, we capture references to all the DOM elements involved in the UI, but more interestingly, we capture a reference to Window.speechSynthesis . This is API's entry point — it returns an instance of SpeechSynthesis , the controller interface for web speech synthesis.

Populating the select element

To populate the <select> element with the different voice options the device has available, we've written a populateVoiceList() function. We first invoke SpeechSynthesis.getVoices() , which returns a list of all the available voices, represented by SpeechSynthesisVoice objects. We then loop through this list — for each voice we create an <option> element, set its text content to display the name of the voice (grabbed from SpeechSynthesisVoice.name ), the language of the voice (grabbed from SpeechSynthesisVoice.lang ), and -- DEFAULT if the voice is the default voice for the synthesis engine (checked by seeing if SpeechSynthesisVoice.default returns true .)

We also create data- attributes for each option, containing the name and language of the associated voice, so we can grab them easily later on, and then append the options as children of the select.

Older browser don't support the voiceschanged event, and just return a list of voices when SpeechSynthesis.getVoices() is fired. While on others, such as Chrome, you have to wait for the event to fire before populating the list. To allow for both cases, we run the function as shown below:

Speaking the entered text

Next, we create an event handler to start speaking the text entered into the text field. We are using an onsubmit handler on the form so that the action happens when Enter / Return is pressed. We first create a new SpeechSynthesisUtterance() instance using its constructor — this is passed the text input's value as a parameter.

Next, we need to figure out which voice to use. We use the HTMLSelectElement selectedOptions property to return the currently selected <option> element. We then use this element's data-name attribute, finding the SpeechSynthesisVoice object whose name matches this attribute's value. We set the matching voice object to be the value of the SpeechSynthesisUtterance.voice property.

Finally, we set the SpeechSynthesisUtterance.pitch and SpeechSynthesisUtterance.rate to the values of the relevant range form elements. Then, with all necessary preparations made, we start the utterance being spoken by invoking SpeechSynthesis.speak() , passing it the SpeechSynthesisUtterance instance as a parameter.

In the final part of the handler, we include an pause event to demonstrate how SpeechSynthesisEvent can be put to good use. When SpeechSynthesis.pause() is invoked, this returns a message reporting the character number and name that the speech was paused at.

Finally, we call blur() on the text input. This is mainly to hide the keyboard on Firefox OS.

Updating the displayed pitch and rate values

The last part of the code updates the pitch / rate values displayed in the UI, each time the slider positions are moved.

Welcom to SpeechFlow

Best Text to Speech APIs

List of the Top Text-to-Speech APIs (also known as TTS APIs) available on RapidAPI.

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Text to speech apis, about tts apis.

TTS APIs (text to speech APIs) can be used to enable speech-based text output in an app or program in addition to providing text on a screen.

What is text to speech?

Text to speech (TTS), also known as speech synthesis, is the process of converting written text to spoken audio. In most cases, text to speech refers specifically to text on a computer or other device.

How does a text-to-speech API work?

First, a program sends text to the API as a request, typically in JSON format. Optionally, text can often be formatted using SSML, a type of markup language created to improve the efficiency of speech synthesis programs.

Once the API receives the request, it will return the equivalent audio object. This object can then be integrated into the program which made the request and played for the user.

The best text to speech APIs also allow selection of accent and gender, as well as other options.

Who is text to speech for?

Text to speech is crucial for some users with disabilities. Users with vision problems may be unable to read text and interpret figures that rely on sight alone, so the ability to have content spoken to them instead of reading can mean the difference between an unusable program and a usable one.

While screen readers and other types of adaptive hardware and software exist to allow users with disabilities to use inaccessible programs, these can be complicated and expensive. It’s almost always better to provide a native text-to-speech solution within your program or app.

Text-to-speech APIs can also help nondisabled users, however. There are many use cases for text to speech, including safer use of an app or program in situations where looking at a screen might be dangerous, distracting or just inconvenient. For example, a sighted user following a recipe on their phone could have it read aloud to them instead of constantly having to clean their hands to check the next step.

Why is a text-to-speech API important?

Using an API for text to speech can make programs much more effective.

Especially because speech synthesis is such a specialized and complex field, an API can free up developers to focus on the unique strengths of their own program.

Users with disabilities also have higher expectations than in the past, and developers are better off meeting their needs with a robust, established text to speech API rather than using a homegrown solution.

What you can expect from the best text to speech APIs?

Any text to speech API will return an audio file.

The best produce seamless audio that sounds like it was spoken by a real human being. In some cases, APIs even allow developers to create their own voice model for the audio output they request.

High-quality APIs of any sort should also include support and extensive documentation.

Are there examples of the best free TTS APIs?

• Text to Speech
• IBM Watson TTS
• Robomatic.ai
• Text to Speech - TTS
• Microsoft Text Translator
• Text-to-Speech

Text to Speech API SDKs

All text to speech APIs are supported and made available in multiple developer programming languages and SDKs including:

• Objective-C
• Java (Android)

Just select your preference from any API endpoints page.

Sign up today for free on RapidAPI to begin using Text to Speech APIs!

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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

Reads out loud texts, PDFs, e-books & websites for free

Speechlogger

Live Captioning & Translation

Live captions & translations for online meetings, webinars, and conferences.

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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
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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.

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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.

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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.

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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.

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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.

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• Pay as you go - no subscription
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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.

The world’s most accurate API for AI- and human-generated transcripts

Trained from the most diverse collection of voices in the world, Rev AI sets the accuracy standard for video and voice applications.

• Submit audio or video files and get machine-generated transcripts in minutes
• High accuracy
• 58+ languages available
• Generates transcription in real-time as audio or video is streamed
• 9 languages available
• Get the highest level of accuracy from human-created transcripts
• ~24 hour turnaround time
• English only
• Predicts the dominant language used in an audio or video file
• 22 languages available
• Get positive, negative, and neutral statements from text
• Identify key topics in text
• Great for auto-tagging
• Transform voice content into concise, actionable summaries
• Communicate across languages with context-aware translations
• 11 languages available
• Precise timestamps enhance content searchability and analysis
• English, Spanish, and French available

Transcend barriers of communication with Rev AI

How to Get Started With Google Cloud’s Text-to-Speech API

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• Introducing Google’s for Text-to-Speech API
• Using Google’s for Text-to-Speech API
• Finetuning Google’s Text-To-Speech Parameters
• Frequently Asked Questions (FAQs) about Google Cloud’s Text-to-Speech API

In this tutorial, we’ll walk you through the process of setting up and using Google Cloud’s Text-to-Speech API, including examples and code snippets .

Introducing Google’s for Text-to-Speech API

As a software engineer, you often need to integrate various APIs into your applications to enhance their functionality. Google Cloud’s Text-to-Speech API is a powerful tool that converts text into natural-sounding speech.

The most common use cases for the Google TTS API include:

• Accessibility : One of the primary applications of TTS technology is to improve accessibility for individuals with visual impairments or reading difficulties. By converting text into speech, the API enables users to access digital content through audio, making it easier for them to navigate websites, read articles, and engage with online services
• Virtual Assistants : The TTS API is often used to power virtual assistants and chatbots, providing them with the ability to communicate with users in a more human-like manner. This enhances user experience and enables developers to create more engaging and interactive applications.
• E-Learning : In the education sector, the Google TTS API can be utilized to create audio versions of textbooks, articles, and other learning materials. This enables students to consume educational content while on the go, multitasking, or simply preferring to listen rather than read.
• Audiobooks : The Google TTS API can be used to convert written content into audiobooks, providing an alternative way for users to enjoy books, articles, and other written materials. This not only saves time and resources on manual narration but also allows for rapid content creation and distribution.
• Language Learning : The API supports multiple languages, making it a valuable tool for language learning applications. By generating accurate and natural-sounding speech, the TTS API can help users improve their listening skills, pronunciation, and overall language comprehension.
• Content Marketing : Businesses can leverage the TTS API to create audio versions of their blog posts, articles, and other marketing materials. This enables them to reach a broader audience, including those who prefer listening to content over reading it.
• Telecommunications : The TTS API can be integrated into Interactive Voice Response (IVR) systems, enabling businesses to automate customer service calls, provide information to callers, and route them to the appropriate departments. This helps companies save time and resources while maintaining a high level of customer satisfaction.

Using Google’s for Text-to-Speech API

Prerequisites.

Before we start, ensure that you have the following:

• A Google Cloud Platform (GCP) account. If you don’t have one, sign up for a free trial here .
• Basic knowledge of Python programming.
• A text editor or integrated development environment of your choice.

Step 1: Enable the Text-to-Speech API

• Log in to your GCP account and navigate to the GCP console .
• Click on the project dropdown and create a new project or select an existing one.
• In the left sidebar, click on APIs & Services > Library .
• Search for Text-to-Speech API and click on the result.
• Click Enable to enable the API for your project.

Step 2: Create API credentials

• In the left sidebar, click on APIs & Services > Credentials .
• Click Create credentials and select Service account .
• Fill in the required details and click Create .
• On the Grant this service account access to project page, select the Cloud Text-to-Speech API User role and click Continue .
• Click Done to create the service account.
• In the Service Accounts list, click on the newly created service account.
• Under Keys , click Add Key and select JSON .
• Download the JSON key file and store it securely, as it contains sensitive information.

Step 3: Set up your Python environment

Install the Google Cloud SDK by following the instructions here .

Install the Google Cloud Text-to-Speech library for Python:

Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of the JSON key file you downloaded earlier:

(Replace /path/to/your/keyfile.json with the actual path to your JSON key file.)

Step 4: Create a Python Script

Create a new Python script (such as text_to_speech.py ) and add the following code:

This script defines a synthesize_speech function that takes a text string and an output filename as arguments. It uses the Google Cloud Text-to-Speech API to convert the text into speech and saves the resulting audio as an MP3 file.

Step 5: Run the script

Execute the Python script from the command line:

This will create an output.mp3 file containing the spoken version of the input text “Hello, world!”.

Step 6 (optional): Customize the voice and audio settings

You can customize the voice and audio settings by modifying the voice and audio_config variables in the synthesize_speech function. For example, to change the language, replace en-US with a different language code (such as es-ES for Spanish). To change the gender, replace texttospeech.SsmlVoiceGender.FEMALE with texttospeech.SsmlVoiceGender.MALE . For more options, refer to the Text-to-Speech API documentation .

Finetuning Google’s Text-To-Speech Parameters

Google’s Speech-to-Text API offers a wide range of configuration parameters that allow developers to fine-tune the API’s behavior to meet specific use cases. Some of the most common configuration parameters and their use cases include:

• Audio Encoding : specifies the encoding format of the audio file being sent to the API. The supported encoding formats include FLAC , LINEAR16 , MULAW , AMR , AMR_WB , OGG_OPUS , and SPEEX_WITH_HEADER_BYTE . Developers can choose the appropriate encoding format based on the input source, audio quality, and the target application.
• Audio Sample Rate : specifies the rate at which the audio file is sampled. The supported sample rates include 8000, 16000, 22050, and 44100 Hz. Developers can select the appropriate sample rate based on the input source and the target application’s requirements.
• Language Code : specifies the language of the input speech. The supported languages include a wide range of options such as English, Spanish, French, German, Mandarin, and many others. Developers can use this parameter to ensure that the API accurately transcribes the input speech in the appropriate language.
• Model : allows developers to choose between different transcription models provided by Google. The available models include default, video, phone_call , and command_and_search . Developers can choose the appropriate model based on the input source and the target application’s requirements.
• Speech Contexts : allows developers to specify specific words or phrases that are likely to appear in the input speech. This can improve the accuracy of the transcription by providing the API with context for the input speech.

These configuration parameters can be combined in various ways to create custom configurations that best suit specific use cases. For example, a developer could configure the API to transcribe a phone call in Spanish using a specific transcription model and a custom list of speech contexts to improve accuracy.

Overall, Google’s Speech-to-Text API is a powerful tool for transcribing speech to text, and the ability to customize its configuration makes it even more versatile. By carefully selecting the appropriate configuration parameters, developers can optimize the API’s performance and accuracy for a wide range of use cases.

In this tutorial, we’ve shown you how to get started with Google Cloud’s Text-to-Speech API, including setting up your GCP account, creating API credentials, installing the necessary libraries, and writing a Python script to convert text or SSML to speech. You can now integrate this functionality into your applications to enhance user experience, create audio content, or support accessibility features.

Frequently Asked Questions (FAQs) about Google Cloud’s Text-to-Speech API

What are the key features of google cloud’s text-to-speech api.

Google Cloud’s Text-to-Speech API is a powerful tool that converts text into natural-sounding speech. It offers a wide range of features including over 200 voices across 40+ languages and variants, giving you a lot of flexibility in terms of language support. It also provides a selection of neural network-powered voices for incredibly realistic speech. The API supports SSML tags, allowing you to add pauses, numbers, date and time formatting, and other pronunciation instructions. It also offers a high level of customization, including pitch, speaking rate, and volume gain control.

How can I get started with Google Cloud’s Text-to-Speech API?

To get started with Google Cloud’s Text-to-Speech API, you first need to set up a Google Cloud project and enable the Text-to-Speech API for that project. You can then authenticate your project and start making requests to the API. The API uses a simple syntax for converting text into speech, and you can customize the voice and format of the speech output.

Is Google Cloud’s Text-to-Speech API free to use?

Google Cloud’s Text-to-Speech API is not entirely free. It comes with a pricing model based on the number of characters you convert into speech. However, Google does offer a free tier for the API, which allows you to convert a certain number of characters per month for free.

How can I integrate Google Cloud’s Text-to-Speech API into my application?

You can integrate Google Cloud’s Text-to-Speech API into your application by making HTTP POST requests to the API. You need to include the text you want to convert into speech in the request, along with any customization options you want to apply. The API will then return an audio data response, which you can play or save as an audio file.

Can I use Google Cloud’s Text-to-Speech API for commercial purposes?

Yes, you can use Google Cloud’s Text-to-Speech API for commercial purposes. However, you should be aware that usage of the API is subject to Google’s terms of service, and you may need to pay for the API if you exceed the free tier limits.

What languages does Google Cloud’s Text-to-Speech API support?

Google Cloud’s Text-to-Speech API supports over 40 languages and variants, including English, Spanish, French, German, Italian, Dutch, Russian, Chinese, Japanese, and Korean. This makes it a versatile tool for applications that need to support multiple languages.

How can I customize the voice in Google Cloud’s Text-to-Speech API?

You can customize the voice in Google Cloud’s Text-to-Speech API by specifying a voice name, language code, and SSML gender in your API request. You can also adjust the pitch, speaking rate, and volume gain of the voice.

Can I use Google Cloud’s Text-to-Speech API offline?

No, Google Cloud’s Text-to-Speech API is a cloud-based service and requires an internet connection to function. You need to make HTTP requests to the API, and the API returns audio data over the internet.

What is the audio quality of the speech generated by Google Cloud’s Text-to-Speech API?

The audio quality of the speech generated by Google Cloud’s Text-to-Speech API is very high. The API uses advanced neural networks to generate natural-sounding speech that is almost indistinguishable from human speech.

Can I use Google Cloud’s Text-to-Speech API to create an audiobook?

Yes, you can use Google Cloud’s Text-to-Speech API to create an audiobook. You can convert large amounts of text into high-quality speech, and you can customize the voice to suit the content of the book. However, you should be aware that creating an audiobook with the API may involve a significant amount of data and may incur costs if you exceed the free tier limits.

Matt is the co-founder of SitePoint, 99designs and Flippa. He lives in Vancouver, Canada.

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Transcribe speech to text by using the API

This page shows you how to send a speech recognition request to Speech-to-Text using the REST interface and the curl command.

Before you begin

Before you can send a request to the Speech-to-Text API, you must have completed the following actions. See the before you begin page for details.

• Make sure billing is enabled for Speech-to-Text.

Install the Google Cloud CLI, then initialize it by running the following command:

• (Optional) Create a new Google Cloud Storage bucket to store your audio data.

Make an audio transcription request

Now you can use Speech-to-Text to transcribe an audio file to text. Use the following code sample to send a recognize REST request to the Speech-to-Text API.

Create a JSON request file with the following text, and save it as a sync-request.json plain text file:

Use curl to make a speech:recognize request, passing it the filename of the JSON request you set up in step 1:

The sample curl command uses the gcloud auth print-access-token command to get an authentication token.

Note that to pass a filename to curl you use the -d option (for "data") and precede the filename with an @ sign. This file should be in the same directory in which you execute the curl command.

You should see a response similar to the following:

Congratulations! You've sent your first request to Speech-to-Text.

If you receive an error or an empty response from Speech-to-Text, take a look at the troubleshooting and error mitigation steps.

To avoid incurring charges to your Google Cloud account for the resources used on this page, follow these steps.

• Use the Google Cloud console to delete your project if you do not need it.

What's next

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2024-04-05 UTC.

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Gemini 1.5 Pro Now Available in 180+ Countries; With Native Audio Understanding, System Instructions, JSON Mode and More

April 09, 2024

Grab an API key in Google AI Studio , and get started with the Gemini API Cookbook

Less than two months ago, we made our next-generation Gemini 1.5 Pro model available in Google AI Studio for developers to try out. We’ve been amazed by what the community has been able to debug , create and learn using our groundbreaking 1 million context window.

Today, we’re making Gemini 1.5 Pro available in 180+ countries via the Gemini API in public preview, with a first-ever native audio (speech) understanding capability and a new File API to make it easy to handle files. We’re also launching new features like system instructions and JSON mode to give developers more control over the model’s output. Lastly, we’re releasing our next generation text embedding model that outperforms comparable models. Go to Google AI Studio to create or access your API key, and start building.

Unlock new use cases with audio and video modalities

We’re expanding the input modalities for Gemini 1.5 Pro to include audio (speech) understanding in both the Gemini API and Google AI Studio. Additionally, Gemini 1.5 Pro is now able to reason across both image (frames) and audio (speech) for videos uploaded in Google AI Studio, and we look forward to adding API support for this soon.

Gemini API Improvements

Today, we’re addressing a number of top developer requests:

1. System instructions : Guide the model’s responses with system instructions, now available in Google AI Studio and the Gemini API. Define roles, formats, goals, and rules to steer the model's behavior for your specific use case. Set System Instructions easily in Google AI Studio 2. JSON mode : Instruct the model to only output JSON objects. This mode enables structured data extraction from text or images. You can get started with cURL, and Python SDK support is coming soon. 3. Improvements to function calling : You can now select modes to limit the model’s outputs, improving reliability. Choose text, function call, or just the function itself.

A new embedding model with improved performance

Starting today, developers will be able to access our next generation text embedding model via the Gemini API. The new model, text-embedding-004 , (text-embedding-preview-0409 in Vertex AI ), achieves a stronger retrieval performance and outperforms existing models with comparable dimensions, on the MTEB benchmarks .

These are just the first of many improvements coming to the Gemini API and Google AI Studio in the next few weeks. We’re continuing to work on making Google AI Studio and the Gemini API the easiest way to build with Gemini. Get started today in Google AI Studio with Gemini 1.5 Pro, explore code examples and quickstarts in our new Gemini API Cookbook , and join our community channel on Discord .

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5 Best AI Voice Generators: AI Text-To-Speech in 2024

In search of the best AI voice generator? Discover the leading AI text-to-speech platforms available in 2024.

eWEEK content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More .

An AI voice generator is a specialized type of generative AI technology that enables users to create new voices or manipulate existing vocal audio with no audio engineering expertise. Instead, they simply insert text, or some other media, with requested parameters to direct the vocal generator to create a relevant voice or voice product.

In this guide, we’ll take a closer look at the five best AI voice generators available today, but first, here’s a glance at where each of these tools differentiates itself the most:

• Murf : Best for Multichannel Content Creation
• PlayHT : Best for AI Voice Agents
• LOVO : Best Combined AI Voice and Video Platform
• ElevenLabs : Best for Enterprise AI Scalability
• Speechify : Best for AI Narration

Featured Partners: AI Software

Top AI Voice Generator Software Comparison

In addition to text-to-speech and voice cloning capabilities, we’ll primarily compare these tools across these key criteria for generative AI voice generation software:

TABLE OF CONTENTS

Murf: Best for Multichannel Content Creation

Murf is one of the top generative AI voice tools available to both casual and business users, providing them with an accessible user interface and a range of scalable voice generation and editing features. Its primary focus areas include text-to-speech content generation, no-code voice editing, AI-powered translation, AI voice deployment to apps via API, voice cloning, and an AI dubbing feature that is currently in beta for more than 20 languages.

Many business users select this tool for its wide range of collaborative features, its enterprise-level security and compliance expertise and features, its vocal quality and variety, and its comprehensive support for various enterprise use cases.

In addition to its easy-to-use enterprise integrations with various creative and product development tools, Murf also offers free creative guides and resources on the following topics: e-learning, explainer videos, YouTube videos, Spotify ads, corporate videos, advertisements, audiobooks, podcasts, video games, training videos, presentations, product demos, IVR voices, animation character voices, and documentaries.

Pros and Cons

• Creator Lite: $23 per month billed annually, or $29 billed monthly for one editor to access up to five projects and 24 hours per year of voice generation.
• Creator Plus: $39 per month billed annually, or $49 billed monthly for one editor to access up to 30 projects and four hours per month of voice generation (up to 48 hours per year).
• Business Lite: $79 per month billed annually, or $99 billed monthly for up to three editors and five viewers to access up to 50 projects and eight hours per month of voice generation (up to 96 hours per year). Free trial access to this plan’s features is available for one editor, up to two projects, and up to 10 minutes of voice generation.
• Business Plus: $159 per month billed annually, or $199 billed monthly for up to three editors and five viewers to access up to 200 projects and 20 hours per month of voice generation (up to 240 hours per year). Free trial access to this plan’s features is available for one editor, up to two projects, and up to 10 minutes of voice generation.
• Enterprise: Pricing information available upon request. This plan is designed for more than five editors and unlimited viewers to create custom projects with unlimited voice generation access.
• Murf API: Pricing information available upon request.
• AI Translation: Add-on for Enterprise and Business plan users. Pricing information available upon request.
• Integrations: Integrations are available for Canva, Google Slides, Adobe Audition, Adobe Captivate and Captivate Classic, and HTML Embed Code. Users can also download Murf Voices Installer to directly incorporate Murf voices into Windows apps.
• Vocal library: More than 200 voices, styles, and tonalities in more than 20 languages are available to users.
• Team collaboration and project organization: Folders, sub-folders, shareable links, and private folders and projects all support controlled collaboration.
• Enterprise compliance: Depending on the plan selected, users can benefit from GDPR, SOC2, and EU compliance support as well as SSO, access logs, custom contracts, and security reviews.
• Visual voice editing: Easy-to-use buttons and clickability to adjust pitch, emphasis, speed, interjections, pauses, pronunciation, and more.

To see a list of the leading generative AI apps, read our guide: Top 20 Generative AI Tools and Apps 2024

PlayHT: Best for AI Voice Agents

PlayHT has been a favorite artificial intelligence voice generation tool for a few years now, extending to users a highly accessible and scalable tool for multilingual AI voice generation. Compared to other AI voice generation tools, PlayHT first and foremost sets itself apart with its range of voice and language options: All plans, including the free plan, can access 907 voices and 142 different languages and accents. The tool also comes with limited instant voice clones and will soon offer high-fidelity clones to enterprise users.

Beyond its more conventional AI voice features and tools, PlayHT has set its sights on a very specific enterprise use case: AI voice agents. With its new feature set, Play Agents, users can create their own AI voice agent avatars with specific parameters and prompts about how they should greet and respond to user interactions. The tool also comes with several prebuilt agent templates, API-driven agent training and tracking for developers, and a simple table for tracking agent conversation history.

Pricing for PlayHT depends on whether you select PlayHT Studio, AI voice agents, or the API subscription plans:

PlayHT Studio

• Free Plan: $0 for non-commercial access to all voices and languages, one instant voice clone, and up to 12,500 characters.
• Creator: $31.20 per month billed annually, or $39 billed monthly.
• Unlimited: Typically $99 per month, billed annually or monthly. A special discount is currently running for the annual plan for $29 per month.
• Enterprise: Custom pricing.

AI Voice Agents

• Free Plan: $0 for non-commercial access to 30 minutes of agent content creation.
• Pro: $20 billed monthly plus $0.05 per each minute used over 400 minutes.
• Business: $99 billed monthly plus $0.05 per each minute used over 2,000 minutes.
• Growth: $499 billed monthly plus $0.05 per each minute used over 10,000 minutes.
• Enterprise: Custom pricing for unlimited limits and other advanced features.
• Hacker: $5 billed monthly plus $0.25 per every additional 1,000 characters over 25,000 characters per month.
• Startup: $299 billed monthly plus $0.20 per every additional 1,000 characters over 1.5 million characters per month.
• Growth: $999 billed monthly plus $0.10 per every additional 1,000 characters over 10 million characters per month.
• Business: Custom pricing for large volume discounts and custom rate limits.
• Multilingual voice library: PlayHT’s voice library includes 907 text-to-speech voices and 142 languages and accents.
• Pronunciation library: This feature allows users to define specific pronunciations and save these rules for future projects.
• Multi-voice content creation: A single audio file and project can include multiple voices, which is useful for AI conversational projects .
• Play Agents feature: Custom AI voice agents and preconfigured agent templates for healthcare, hotels, restaurants, front desks, and e-commerce can be used to create more intelligent customer service AI chatbots/agents.
• Real-time streaming API: Character-based pricing for API access, which scales up to include dedicated enterprise clusters and other advanced features.

For more information about generative AI providers, read our in-depth guide: Generative AI Companies: Top 20 Leaders

LOVO: Best Combined AI Voice and Video Platform

LOVO offers its users a suite of useful AI features that not only support AI voice generation and voiceover initiatives but also other creative tasks related to video and image creation . LOVO’s flagship platform, Genny, is a user-friendly tool that uses its own generative AI technologies to enable video editing, subtitle generation, voice generation, and voice cloning tasks. With the help of ChatGPT and Stable Diffusion models , users can also generate shortform and longform text and AI art projects at no additional cost and with no third-party tooling requirements.

Users most appreciate that this tool supports multiple languages and unique vocal tones, is easy to use, and offers high-quality voice outputs compared to many competitors. Many users also appreciate that they can purchase affordable, lifetime deals through AppSumo.

Pricing for LOVO depends on whether you select an All in One or Subtitles subscription plan:

• Basic: $24 per month billed annually, or $29 per user billed monthly. Limited to one user per plan subscription.
• Pro: $48 per user per month, billed annually, with a 50% discount for the first year, or $48 per user billed monthly. A 14-day free trial is also available for this plan’s features.
• Pro +: $149 per user per month, billed annually, with a 50% discount for the first year, or $149 per user billed monthly.
• Enterprise: Pricing information available upon request.
• Free: $0 for limited features.
• Subtitles: $12 per user per month, billed annually, or $18 per user billed monthly.
• Genny: All-in-one video creation platform with voice generation, voice cloning, subtitle generation, art generation, text generation, and video editing capabilities.
• Multilingual voice library: The text-to-speech library includes more than 500 voices and more than 100 languages. LOVO also caters voices to 30 different emotions.
• Built-in voice recorder: For voice cloning, users can record their voices directly within the LOVO tool. They also have the option to upload a prerecorded clip, if preferred.
• Simple Mode: For shorter voice generation and voiceover projects (between 2,000 and 5,000 characters), users can work with the lightweight, faster Simple Mode format.
• API access: LOVO voice application development features are available in all plans.

For an in-depth comparison of two leading AI art generators, see our guide: Midjourney vs. Dall-E: Best AI Image Generator 2024

ElevenLabs: Best for Enterprise AI Scalability

ElevenLabs is an artificial intelligence research firm that has developed comprehensive AI voice technologies for text to speech, speech to speech, dubbing, voice cloning, and multilingual content generation. Users frequently compliment ElevenLabs on the quality of the voice products it produces, noting that the vocal tone and overall quality feel more realistic than what most other competitors are producing.

ElevenLabs is one of the most business-friendly AI voice tools on the market today, offering advanced features at different price points. Its free plan is fairly comprehensive, including access to 29 languages and thousands of voices, automated dubbing, custom voices, and API. Six different pricing tiers are available, with the top tier offering unique enterprise draws like custom terms and SSO, unlimited concurrency, and volume-based discounts.

Additionally, ElevenLabs offers a grant program designed for the unique needs of business startups. Eligible startup applicants who can convince the vendor of their longterm strategy and growth potential will be given three months of free access with 11 million characters per month and enterprise features.

• Free: $0 for 10,000 monthly characters, or approximately 10 minutes of audio per month.
• Starter: $50 per year, billed annually, with the first two months free, or $5 billed monthly with 80% off the first month.
• Creator: $220 per year, billed annually, with the first two months free, or $22 billed monthly with 50% off the first month.
• Pro: $990 per year, billed annually, with the first two months free, or $99 billed monthly.
• Scale: $3,300 per year, billed annually, with the first two months free, or $330 billed monthly.
• Custom Enterprise Plans: Pricing information available upon request.
• Precision voice tuning: With this drag-and-drop editing feature, users can adjust vocal stability and variability, vocal clarity, and style exaggerations on a scale.
• Multilingual voice library: More than 1,000 voices across 29 different languages are available for text-to-speech content generation.
• Speech to speech: Users can upload an audio file or record their voice for voice changing, custom voices, and voice cloning capabilities.
• Dubbing Studio: Video translation and dubbing available in 29 different languages. Speaker. Studio interface allows users to granularly adjust specs.
• AI Speech Classifier: This unique feature allows users to upload an audio file so the vendor can evaluate if the clip was created by ElevenLabs AI.

Speechify: Best for AI Narration

Speechify is an AI voice solution that specializes in text-to-speech technology for mobile platforms and more casual use cases, like audiobook narration. With the Speechify AI platform, users can select from a wide variety of AI voices, including voices that mimic celebrities like Gwyneth Paltrow and Snoop Dogg. All of this is available in various mobile and online locations, including through browser extensions that are accessible and favorably reviewed by users.

While Speechify’s core audience is recreational users, students, and other more casual users who want a convenient solution for reading off text in various formats, the platform offers some key enterprise AI usability features through its Voice Over Studio for Business. With this suite of Speechify solutions, business users can benefit from unlimited video and voice downloads, commercial rights, collaborative project management features, dozens of voices, and enterprise security and compliance features.

Pricing for Speechify all depends on how you want to use the tool. Here are some of the options you have as a Speechify user:

• Speechify Limited (text to speech): $0 for 10 standard reading voices and limited text-to-speech features.
• Speechify Premium: $139 per year for advanced text-to-speech features and capabilities.
• Speechify Studio Free: $0 for access to basic AI voice and video features with no downloads.
• Speechify Studio Basic: $24 per user per month, billed annually, or $69 per user billed monthly.
• Speechify Studio Professional: $32.08 per user per month, billed annually, or $99 per user billed monthly.
• Speechify Studio Enterprise: Pricing information available upon request.
• Text to Speech API: Users can join the waitlist.
• Speechify Audiobooks: $9.99 per month, or $120 billed annually.

Custom pricing and discounts may also be available for business teams and educational organizations.

• Browser extensions and app: Users can access Speechify through the Chrome extension, Edge Add-on, Android, iOS, and PDF readers like Adobe Acrobat.
• Multilingual voice library: More than 100 voices in over 40 languages are available for enterprise users.
• AI dubbing: Dubbing is available in multiple languages, with the ability to adjust voice, tone, and speed.
• AI video generator: Users can combine Speechify’s AI voiceovers with avatars to create AI videos.
• Various upload and download formats: Content can be uploaded in .txt, .docx, .srt, and YouTube URL formats; Speechify projects can be downloaded as video, audio, or text.

Key Features of AI Voice Generator Software

AI voice generator software typically includes features that help users transform text, existing audio, and other media into voices with adjustable qualities to meet their needs. Additionally, many of these generative AI tools come with features to make enterprise-level collaboration and content creation run more smoothly. In general, expect to find the following features in AI voice generators:

Text to Speech

Text to speech (TTS) is a type of AI technology that changes written text into spoken audio. Most AI voice generator software allows users to upload text of different lengths and in different languages in order to generate a vocal version of the same content.

Voice Cloning

With voice cloning, AI technology can capture the content, tonality, speed, and other characteristics of a person’s voice in a recording and use that information to create a faithful replica or clone of that unique voice. With this capability, users can generate entirely new content and recordings that sound like they were spoken by that person.

Custom Voices or Voice Changing

On some AI voice platforms, if you submit your own voice clip or directly record your voice into the app, you can then change that voice into a completely different character, adjusting the tone, accent, mood, and other features. Many users want this feature for creative projects like video game development.

Multilingual Voice Library

Most generative AI voice tools give users access to a diverse, multilingual library of predeveloped voice models. Through extensive training, these TTS models are prepared to create voice transcripts and recordings that accurately adhere to each language’s specific pronunciations, tonalities, pauses, and other characteristics of that language’s speech patterns.

Dubbing and Translation

Taking TTS a step further, dubbing and translation with AI make the effort to translate an existing text or voice recording into a different spoken language. For dubbing specifically, existing recordings — often movies, commercials, and other visual media — receive a new vocal overlay, typically dubbed in a different language by an AI model.

APIs and Third-Party Integrations

With the help of APIs and built-in third-party integrations, users can more easily add AI voice creation and editing capabilities directly into their app and product development workflows. A growing number of AI voice tools are adding relevant third-party integrations to creative platforms as well as social and distribution channels.

To learn about today’s top generative AI tools for the video market, see our guide:  5 Best AI Video Generators

How We Evaluated AI Voice Generators

To evaluate these AI voice generators and other leaders in this AI market sector, we looked at each tool’s standard and unique features while focusing on the following criteria. Each criterion is weighted based on its importance to the typical business user:

Vocal Quality – 30%

Needless to say, vocal quality, fidelity, and usability are the most important aspects of an AI voice generator. Within this criterion, we evaluated each tool based on the realistic quality of AI voices, the accuracy of AI voice generations, the availability of different voices and languages, and the ability to granularly edit generated voice products. We also considered whether a tool offered users the ability to customize or record their own voices and voiceovers.

Enterprise Scalability – 30%

Enterprise scalability is hugely important for AI voice generators since many companies invest in this type of platform to create global marketing, sales, and product content at scale.

For enterprise scalability, we assessed each tool’s global library of voices and dialects, its adherence to enterprise security and compliance standards, features that go beyond voice content production, collaboration and sharing capabilities, integrations with relevant third-party tools and platforms, and the scalability of APIs. We placed a special emphasis on each tool’s enterprise-level plans and the additional features that are available at this level.

Pricing – 20%

Pricing is a crucial factor when considering AI voice technology, as the cost of these tools varies widely for the features you get at that price point. As part of this evaluation, we identified whether each tool offered a free plan option, we compared how prices scale from package to package, we considered how many price points were available to users, and we looked at the value of the features added to each tier, particularly enterprise-level tiers.

Ease of Use – 20%

AI voice tools are supposed to make content creation a simpler task; for this reason, ease of use and accessibility were also important factors in how we judged each of these tools. We looked at each tool’s no-code features, the user-friendliness of voice editing tools, the quality of customer support at each subscription tier, and the availability of self-service resources and community forums for getting started and troubleshooting.

AI Voice Generators: Frequently Asked Questions (FAQs)

Learn more about AI voice generator technology and the top solutions available through these frequently asked questions:

What is the best AI voice generator?

The best AI voice generator will depend on your particular needs and project plans, but Murf is consistently a top choice for its flexibility, with a wide range of general use cases.

Is there a free AI voice generator?

Yes, several AI voice generators are free or are available in free, limited versions.

What is the best free AI voice generator?

The best free AI voice generator options will vary based on your exact requirements. ElevenLabs is the best free solution for users who require API access and interoperability with other resources, while Speechify is the most generous for users who don’t require downloads or more complex features.

Bottom Line: AI Voice Generators Are Affordable and Customizable

AI voice technology has grown in popularity for content creators of all backgrounds and budgets. These type of generative AI tools enable creative scalability for videos, podcasts, audiobooks, customer service interactions, and a slew of other enterprise use cases that require consistent and original voice content. What’s more, this technology is frequently customizable and available in affordable plans, meaning users of all stripes can try out these tools to figure out their potential for their projects.

If you’re not sure which of the AI voice tools in this guide is the best fit for your organization, take some time to test out the free plans or trials that are available for each tool. You’ll quickly discover if the software meets your particular needs, if it’s user friendly, and if it has the features necessary to keep up with your organization’s security and compliance requirements.

For a full portrait of the AI vendors serving a wide array of business needs, read our in-depth guide:  150+ Top AI Companies 2024

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