speech to text java code

Easy Way to Learn Speech Recognition in Java With a Speech-To-Text API

Rev › Blog › Resources › Other Resources › Speech-to-Text APIs › Easy Way to Learn Speech Recognition in Java With a Speech-To-Text API

Here we explain show how to use a speech-to-text API with two Java examples.

We will be using the Rev AI API ( free for your first 5 hours ) that has two different speech-to-text API’s:

• Asynchronous API – For pre-recorded audio or video
• Streaming API – For live (streaming) audio or video

Asynchronous Rev AI API Java Code Example

We will use the Rev AI Java SDK located here .  We use this short audio , on the exciting topic of HR recruiting.

First, sign up for Rev AI for free and get an access token.

Create a Java project with whatever editor you normally use.  Then add this dependency to the Maven pom.xml manifest:

The code sample below is here . We explain it and show the output.

Submit the job from a URL:

Most of the Rev AI options are self-explanatory, for the most part.  You can use the callback to kick off downloading the transcription in another program that is on standby, listening on http, if you don’t want to use the polling method we use in this example.

Put the program in a loop and check the job status.  Download the transcription when it is done.

The SDK returns captions as well as text.

Here is the complete code:

It responds:

You can get the transcript with Java.

Or go get it later with curl, noting the job id from stdout above.

This returns the transcription in JSON format: 

Streaming Rev AI API Java Code Example

A stream is a websocket connection from your video or audio server to the Rev AI audio-to-text entire.

We can emulate this connection by streaming a .raw file from the local hard drive to Rev AI.

One Ubuntu run:

Download the audio then convert it to .raw format as shown below.  Converted it from wav to raw with the following ffmpeg command:

As you run that is gives key information about the audio file:

To explain, first we set a websocket connection and start streaming the file:

The important items to set here are the  sampling rate (not bit rate) and format.  We match this information from ffmpeg:    Audio: pcm_f32le, 48000 Hz , 

After the client connects, the onConnected event sends a message.  We can get the jobid from there.  This will let us download the transcription later if we don’t want to get it in real-time.

To get the transcription in real time, listen for the onHypothesis event:

Here is what the output looks like:

What is the Best Speech Recognition API for Java?

Accuracy is what you want in a speech-to-text API, and Rev AI is a one-of-a-kind speech-to-text API in that regard.

You might ask, “So what?  Siri and Alexa already do speech-to-text, and Google has a speech cloud API.”

That’s true.  But there’s one game-changing difference: 

The data that powers Rev AI is manually collected and carefully edited .  Rev pays 50,000 freelancers to transcribe audio & caption videos for its 99% accurate transcription & captioning services . Rev AI is trained with this human-sourced data, and this produces transcripts that are far more accurate than those compiled simply by collecting audio, as Siri and Alexa do.

Rev AI’s accuracy is also snowballing, in a sense. Rev’s speech recognition system and API is constantly improving its accuracy rates as its dataset grows and the world-class engineers constantly improve the product.

Labelled Data and Machine Learning

Why is human transcription important?

If you are familiar with machine learning then you know that converting audio to text is a classification problem.  

To train the computer to transcribe audio ML programmers feed feature-label data into their model.  This data is called a training set .

Features (sound) are input and labels (the corresponding letter) are output, calculated by the classification algorithm.

Alexa and Siri vacuum up this data all day long.  So you would think they would have the largest and therefore most accurate training data.  

But that’s only half of the equation.  It takes many hours of manual work to type in the labels that correspond to the audio.  In other words, a human must listen to the audio and type the corresponding letter and word.  

This is what Rev AI has done.

It’s a business model that has taken off, because it fills a very specific need.

For example, look at closed captioning on YouTube.  YouTube can automatically add captions to it’s audio.  But it’s not always clear.  You will notice that some of what it says is nonsense. It’s just like Google Translate: it works most of the time, but not all of the time.

The giant tech companies use statistical analysis, like the frequency distribution of words, to help their models.

But they are consistently outperformed by manually trained audio-to-voice training models.

More Caption & Subtitle Articles

Everybody’s favorite speech-to-text blog.

We combine AI and a huge community of freelancers to make speech-to-text greatness every day. Wanna hear more about it?

Building an application with sphinx4

Using sphinx4 in your projects, configuration, livespeechrecognizer, streamspeechrecognizer, speechaligner, speechresult, building from source, troubleshooting.

Caution! This tutorial uses the sphinx4 API from the 5 pre-alpha release . The API described here is not supported in earlier versions.

Sphinx4 is a pure Java speech recognition library. It provides a quick and easy API to convert the speech recordings into text with the help of CMUSphinx acoustic models. It can be used on servers and in desktop applications. Besides speech recognition, Sphinx4 helps to identify speakers, to adapt models, to align existing transcription to audio for timestamping and more.

Sphinx4 supports US English and many other languages.

As any library in Java all you need to do to use sphinx4 is to add the jars to the dependencies of your project and then you can write code using the API.

The easiest way to use sphinx4 is to use modern build tools like Apache Maven or Gradle . Sphinx-4 is available as a maven package in the Sonatype OSS repository .

In gradle you need the following lines in build.gradle :

To use sphinx4 in your maven project specify this repository in your pom.xml :

Then add sphinx4-core to the project dependencies:

Add sphinx4-data to the dependencies as well if you want to use the default US English acoustic and language models:

Many IDEs like Eclipse, Netbeans or Idea have support for Gradle either through plugins or with built-in features. In that case you can just include sphinx4 libraries into your project with the help of your IDE. Please check the relevant part of your IDE documentation, for example the IDEA documentation on Gradle .

You can also use Sphinx4 in a non-maven project. In this case you need to download the jars from the repository manually. You might also need to download the dependencies (which we try to keep small) and include them in your project. You need the sphinx4-core jar and the sphinx4-data jar if you are going to use US English acoustic model:

Here is an example for how to include the jars in Eclipse:

Basic Usage

To quickly start with sphinx4, create a java project as described above, add the required dependencies and type the following simple code:

This simple code snippet transcribes the file test.wav – just make sure it exists in the project root.

There are several high-level recognition interfaces in sphinx4:

For most of the speech recognition jobs high-level interfaces should be sufficient. Basically, you will only have to setup four attributes:

• Acoustic model
• Grammar/Language model
• Source of speech

The first three attributes are set up using a Configuration object which is then passed to a recognizer. The way to connect to a speech source depends on your concrete recognizer and usually is passed as a method parameter.

A Configuration is used to supply the required and optional attributes to the recognizer.

The LiveSpeechRecognizer uses a microphone as the speech source.

The StreamSpeechRecognizer uses an InputStream as the speech source. You can pass the data from a file, a network socket or from an existing byte array.

Please note that the audio for this decoding must have one of the following formats:

The decoder does not support other formats. If the audio format does not match, you will not get any results. This means, you need to convert your audio to a proper format before decoding. E.g. if you want to decode audio in telephone quality with a sample rate of 8000 Hz, you would need to call

You can retreive multiple results until the end of the file is reached:

A SpeechAligner time-aligns text with audio speech.

A SpeechResult provides access to various parts of the recognition result, such as the recognized utterance, a list of words with timestamps, the recognition lattice, etc.:

A number of sample demos are included in the sphinx4 sources in order to give you an understanding how to run sphinx4. You can run them from the sphinx4-samples jar:

• Transcriber - demonstrates how to transcribe a file
• Dialog - demonstrates how to lead a dialog with a user
• SpeakerID - speaker identification
• Aligner - demonstration of audio to transcription timestamping

If you are going to start with a demo please do not modify the demo inside the sphinx4 sources. Instead, copy the code into your project and modify it there.

If you want to develop sphinx4 itself you might want to build it from source. Sphinx4 uses the Gradle build system. In order to compile and install everything, including the dependencies, simply type ‘gradle build’ in the root directory.

If you are going to use an IDE, make sure it supports Gradle projects. Then simply import the sphinx4 source tree.

You might experience the one or the other problem while using sphinx4. Please check the FAQ first before asking any new questions on the forum.

In case you have issues with the accuracy, you need to provide the audio recording you are trying to recognize along with all models you use. Additionally, you need to describe in which way your results differ from your expectations.

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text-to-speech

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ikfly / java-tts

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sdsb8432 / TextToSpeech-Android

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BullShark / JSpeak

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yp2211 / gTTS4j

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Java Text to Speech Tutorial Using FreeTTS | Eclipse

Hello friends, Welcome to my new tutorial and in this tutorial, we will learn about how we can convert Java Text to Speech  using  the FreeTTS  Jar file.

So Let’s start our tutorial freetts speech to text example and convert Java text to Speech using the Eclipse IDE. To know how to download Eclipse IDE, you can click here .

Also Read –   How to Play Mp3 File in Java 

• 1.1 What is FreeTTS?
• 1.2 Downloading FreeTTS Jar file
• 1.3 Creating a Java Project in Eclipse
• 1.4 Adding FreeTTS JAR Files to Eclipse
• 1.5 Adding Voice to Text in Java
• 1.6 Java Text to Speech Source Code Download

Converting Java Text to Speech Using Eclipse IDE

What is freetts.

• FreeTTS is entirely written in Java programming language, which is nothing but an open-source  Speech Synthesis system by which we can make our computer speak.
• In simple words, we can say that it is an artificial production of human speech which converts normal language text into speech. So in this tutorial, We will learn about how to convert text to speech in Java using the Eclipse IDE.

Downloading FreeTTS Jar file

• In the first step, we need to download the FreeTTS JAR file to include it in our program.
• You can download the FreeTTS JAR file from the download link given below.
• After downloading this ZIP file, extract the files and navigate to the lib folder.
• After going to the lib folder for your convenience, copy   all the JAR files and store them in any new folder on your PC.
• As we have finally downloaded and extracted our JAR files now, the next thing we have to do is create a Java project in Eclipse IDE.

So let’s get started with our tutorial about text to speech in Java.

Creating a Java Project in Eclipse

• Open Eclipse IDE and click on the Java Project under the new section of File Menu ( File>>New>>Java Project ).

• Now give a name to your project ( TextToAudio  in this example) and click on “Finish”.

• Now right click on the project and create a new Java class ( New>>Class ).

• Now give a name to your class ( TextToSpeech   in this Example), tick mark on the public static void main(String[] args), and then click on the Finish button as shown in the figure below.

Adding FreeTTS JAR Files to Eclipse

• To convert Java text to speech in Eclipse IDE, you need to include FreeTTS Jar files to the Eclipse.
• I have given the download link of the zip file in the above.
• Extract the Zip Archive and navigate to the lib folder.
• Right-click on the project, go to the properties section, select Java Build Path, and click on the Add External JARs button.

• After clicking on the button, a pop-up window will appear to select and open the Jar files.

• You can see the added Jar files as shown in the figure below. Now click on the  Apply and Close  button.

Adding Voice to Text in Java

• First of all, we need to create an object of the Voice class.
• Now we have to get the voice of the person using the getVoice() method and it takes a String value in its parameter(in this example I am using   kevin ).
• Next, we have to allocate the voice using the allocate() method.
• Now to speak the voice, we will call the method speak() using the object of the Voice class and it takes the text value that we want to be spoken in its argument.
• We can also set the rate, pitch and volume of the voice according to our requirements.
• The programming example is given below.
• Now run your program.
• As soon as you run your program, the written texts with the speak() method will be spoken. You can try it by yourself. It will act fine.
• You can also download the source code of this project from the link given below.

Java Text to Speech Source Code Download

• The link of the file is given below

So Friends, this was all from this tutorial. If you have any queries regarding this post then you can comment below and you can also check my previous post about how to create Login Form in Java Swing . Thank You

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5 thoughts on “Java Text to Speech Tutorial Using FreeTTS | Eclipse”

Thank you Vimalraj

Thank you sir for this easy to follow example.

You are welcome Doug and thank you for the appreciation

Thanks.Your tutorial is easy to understand .

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10 Best Whisper AI Alternatives for Speech-to-Text Services in 2024

Today, performing multilingual transcription, speech translation, and language detection are made easy with AI-powered speech recognition tools. This software’s API (Application Programming Interface) provides the ability to call a service to transcribe audio-containing speech into written text.

One of the most well-known choices among speech recognition tools is Whisper AI. The platform converts spoken language into text and is used as a chatbot, voice assistant, speech translator, and transcriptor. It is also known for automating the process of taking notes during meetings.

With so many features, still, this tool may not be an ideal choice for your organization if your project involves real-time processing of streaming voice data or if you need to train a custom model.

The vast number of speech transcription options can be overwhelming and make it difficult to make an informed choice. This article breaks down the best Whisper AI alternatives , outlining their top features, pros and cons, and pricing. So, let’s check out the ranking of all these leading speech-to-text APIs.

10 Best Whisper AI Alternatives in 2024

Google speech-to-text, microsoft azure, speechmatics, amazon transcribe, what is the best speech-to-text tool in 2024.

Here are some of the best Whisper AI Alternatives for you to look at:

Google Speech-to-Text is provided as a part of the Google Cloud Platform. It processes over 1 billion voices every month and boasts close to the human level of understanding of numerous languages. It enables developers to translate the audio from text by applying robust neural network models in an easy-to-use API.

• It integrates well with Google Drive, Google Meet, Google Docs, etc.
• This platform provides multi-channel recognition
• It is powered by machine learning.

It offers 0-60 minutes/month for free. The premium plan is for Speech Recognition (without data logging – default):

• Standard Plan- $0.024 / minute
• Medical Plan- $0.078 / minute
• Speech Recognition (with data logging opt-in)- $0.016 / minute.

Link: https://cloud.google.com/speech-to-text

Microsoft Azure allows you to translate text swiftly and accurately in over 90 languages. It is one of the most advanced voice-recognition platforms around. The platform uses deep learning algorithms to overcome poor sound quality and adapt to numerous speaking styles to deliver accurate audio transcriptions.

• Its speaker recognition feature allows to recognize who’s speaking in a meeting
• You can customize translations for the organization’s specific terms in a preferred programming language
• Allows you to deploy your endpoint to use in your application.

It offers a free plan. After you use free credits, move to pay as you go to keep using the same services.

Link: https://azure.microsoft.com/en-us/products/ai-services/speech-to-text

AssemblyAI’s speech-to-text APIs enable you to translate audio and video files and live audio streams into text. This tool offers faster transcription speed than public cloud service providers and decent across. It is an all-in-one speech recognition platform built to serve startups, SMBs, SMEs, and agencies.

• Large Language Models, or LLMs, allow the creation of Generative AI tools on top of voice data
• It offers a speech summarization feature
• Quickly detects and monitors sensitive content, such as hate speech

It offers a free plan. The premium plan starts at $0.12/hr.

Link: https://www.assemblyai.com/

Rev AI is one of the best Whisper AI alternatives that offers automated speech-to-text services powered by advanced machine learning algorithms. It is a wonderful option for highly accurate English language use cases that deliver high accuracy when essential text-to-speech software does not.

• It provides online integrations that improve workflow
• The tool generates transcription in real-time
• You can get positive, negative, and neutral statements from the text.

It offers three pay-as-you-go plans:

• Machine Translation: $0.02/minute
• Human Transcription: $1.50/minute
• Forced Alignment: $0.02/minute
• You can also opt for the Enterprise plan which can be customized.

Link: https://www.rev.ai/

Speechmatics is the most accurate and inclusive speech-to-text API engine that provides accurate and flexible solutions. It is one of the leading experts in the field as it combines the best technologies, i.e., AI and ML, to unlock the business value of human speech. Whether you need transcription or translation, the platform provides a solution that can be integrated into your organization without any trouble.

• It offers real-time transcription, translation, and summarization
• It also provides numeral formatting
• The tool includes profanity and disfluency detection.

It offers a free plan. There are two premium plans:

• Pay as you grow- Starts at $0.30/hour
• Enterprise Plan- Contact the sales team.

IBM Watson is one of the best Whisper AI alternatives , enabling fast and accurate transcriptions in various languages. It provides keyword spotting and profanity filtering to filter specific words or inappropriate content. The best thing is that it is deployable on any cloud—public, private, hybrid, multi-cloud, or on-premises.

• It provides an automatic speech recognition option
• Allows you to analyze and correct weak audio signals before transcription starts
• It can detect up to 6 different speakers

The tool offers 30-day free trial. There are 4 paid price plans:

• Plus- Starting at $500
• Enterprise- Starts at $5000
• Premium- Customized (Contact the sales team)
• IBM Cloud Pak for Data Cartridge- Customized (Contact the sales team)

Link : https://www.ibm.com/products/speech-to-text

Kaldi is an excellent speech recognition tool famous in the research community for numerous years. It is highly accurate and allows you to train your own models.

• Supports multiple languages
• It provides real-time streaming support

It is free to use.

Link : https://kaldi-asr.org/

LumenVox is one of the best Whisper AI alternatives , as its flexible speech-enabling technology allows you to create a solution that caters to your specific requirements.

• Accurate speech detection with speech tuning
• Easy implementation for any network architecture
• Accelerated ability to add new languages and dialects

Its free to use.

Link: https://www.lumenvox.com/

Power your apps with real-time speech recognition (speech-to-text and text-to-speech) with Deepgram. It is one of the best Whisper alternatives known for its low latency, data labeling and flexible deployment options.

• It is a developer-focused provider with a rich ecosystem, dedicated support, and diverse SDK options.
• The tool is proficient in handling pre-recorded audio and real-time streams from numerous sources.
• Deepgram supports smart formatting, multiple languages, filler words, and speaker diarization.

It offers a pay-as-you-go plan that gives you $200 in credit absolutely free. You can also opt for its 2 other annual plans :

• Growth-$4k – 10k per year
• Enterprise- Contact the sales team to customize the pricing as per your requirements

Link: https://deepgram.com/

Amazon Transcribe model is part of the AWS platform that supports over 100 languages. It produces easy-to-read transcripts, improves accuracy with customization, ingests diverse audio input, and filters content to enhance customer privacy.

• Easy to integrate if you are already in the AWS ecosystem
• Its Amazon Transcribe API enables you to analyze audio files stored in Amazon S3 and have the service return a text file of the transcribed speech.
• The tool offers domain-specific models tuned to telephone calls or multimedia video content.

Sign up and get started for free for the first 12 months. The Amazon Transcribe Free Tier allows you to analyze up to 60 audio minutes monthly. However, if you want more minutes, you can choose other paid plans:

• T1- $0.02400 (First 250,000 minutes)
• T2- $0.01500 (Next 750,000 minutes)
• T3- $0.01020 (Next 4,000,000 minutes)
• T4- $0.00780 (Over 5,000,000 minutes)

Link: https://aws.amazon.com/transcribe/?nc=sn&loc=0

Considering all factors, Google Speech-to-Text offers the most convenient and flexible solution that can be integrated with other Google Cloud services. This model is best utilized by a GCP customer who wants to keep everything within one ecosystem. The tool is also known for its machine learning algorithms that reduce errors by 64% compared to other regular models and for adding real-time subtitles in your streaming content.

The mechanisms for evaluating a speech-to-text API have remained constant, including speed, accuracy, and price. These tools must match the cutting-edge offerings of a new company to bring value to the table.

We hope this list of 10 best Whisper AI alternatives has demystified the confusion by helping you choose the right speech recognition tool for your particular use case. These easy-to-use platforms offer a highly accurate transcription feature and support customization to suit your industry.

Is there a better model than Whisper AI?

Some leading speech recognition tools supporting multilingual recognition, spoken language identification, and translation include Google Speech-to-Text, Microsoft Azure, and AssemblyAI.

What is the fastest Whisper AI?

Whisper JAX is known as the fastest Whisper AI. It is an optimized implementation of the Whisper model that runs on JAX with a TPU v4-8 in the backend.

Is Whisper Open AI free?

Before March 2023, Whisper AI used to offer its services for free. However, today it costs $0.006 per minute or $0.10 per 1000 seconds.

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