how to build a speech to text engine

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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.

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

Learn why AssemblyAI is the leading Speech AI partner.

Free Speech-to-Text APIs and AI Models

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

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

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

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

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

The company offers 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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Learn how to Build your own Speech-to-Text Model (using Python)

• Learn how to build your very own speech-to-text model using Python in this article
• The ability to weave deep learning skills with NLP is a coveted one in the industry; add this to your skillset today
• We will use a real-world dataset and build this speech-to-text model so get ready to use your Python skills!
• Introduction

“Hey Google. What’s the weather like today?”

This will sound familiar to anyone who has owned a smartphone in the last decade. I can’t remember the last time I took the time to type out the entire query on Google Search. I simply ask the question – and Google lays out the entire weather pattern for me.

It saves me a ton of time and I can quickly glance at my screen and get back to work. A win-win for everyone! But how does Google understand what I’m saying? And how does Google’s system convert my query into text on my phone’s screen?

This is where the beauty of speech-to-text models comes in. Google uses a mix of deep learning and Natural Language Processing (NLP) techniques to parse through our query, retrieve the answer and present it in the form of both audio and text.

The same speech-to-text concept is used in all the other popular speech recognition technologies out there, such as Amazon’s Alexa, Apple’s Siri, and so on. The semantics might vary from company to company, but the overall idea remains the same.

I have personally researched quite a bit on this topic as I wanted to understand how I could build my own speech-to-text model using my Python and deep learning skills. It’s a fascinating concept and one I wanted to share with all of you.

So in this article, I will walk you through the basics of speech recognition systems (AKA an introduction to signal processing). We will then use this as the core when we implement our own speech-to-text model from scratch in Python.

Looking for a place to start your deep learning and/or NLP journey? We’ve got the perfect resources for you:

• Computer Vision using Deep Learning 2.0 Course
• Natural Language Processing (NLP) using Python

Table of contents

A brief history of speech recognition through the decades, what is an audio signal, parameters of an audio signal, different types of signals, what is sampling the signal and why is it required, time-domain, frequency domain, spectrogram, understanding the problem statement for our speech-to-text project, import the libraries, duration of recordings, frequently asked questions.

You must be quite familiar with speech recognition systems. They are ubiquitous these days – from Apple’s Siri to Google Assistant. These are all new advents though brought about by rapid advancements in technology.

Did you know that the exploration of speech recognition goes way back to the 1950s? That’s right – these systems have been around for over 50 years! We have prepared a neat illustrated timeline for you to quickly understand how Speech Recognition systems have evolved over the decades:

• The first speech recognition system, Audrey , was developed back in 1952 by three Bell Labs researchers. Audrey was designed to recognize only digits
• Just after 10 years, IBM introduced its first speech recognition system IBM Shoebox , which was capable of recognizing 16 words including digits. It could identify commands like  “Five plus three plus eight plus six plus four minus nine, total,” and would print out the correct answer, i.e., 17
• The Defense Advanced Research Projects Agency (DARPA) contributed a lot to speech recognition technology during the 1970s. DARPA funded for around 5 years from 1971-76 to a program called Speech Understanding Research and finally, Harpy was developed which was able to recognize 1011 words. It was quite a big achievement at that time.
• In the 1980s, the Hidden Markov Model (HMM) was applied to the speech recognition system. HMM is a statistical model which is used to model the problems that involve sequential information. It has a pretty good track record in many real-world applications including speech recognition. 
• In 2001, Google introduced the Voice Search application that allowed users to search for queries by speaking to the machine.  This was the first voice-enabled application which was very popular among the people. It made the conversation between the people and machines a lot easier. 
• By 2011, Apple launched Siri that offered a real-time, faster, and easier way to interact with the Apple devices by just using your voice. As of now, Amazon’s Alexa and Google’s Home are the most popular voice command based virtual assistants that are being widely used by consumers across the globe. 

Wouldn’t it be great if we can also work on such great use cases using our machine learning skills? That’s exactly what we will be doing in this tutorial!

Introduction to Signal Processing

Before we dive into the practical aspect of speech-to-text systems, I strongly recommend reading up on the basics of signal processing first. This will enable you to understand how the Python code works and make you a better NLP and deep learning professional!

So, let us first understand some common terms and parameters of a signal.

This is pretty intuitive – any object that vibrates produces sound waves. Have you ever thought of how we are able to hear someone’s voice? It is due to the audio waves. Let’s quickly understand the process behind it.

When an object vibrates, the air molecules oscillate to and fro from their rest position and transmits its energy to neighboring molecules. This results in the transmission of energy from one molecule to another which in turn produces a sound wave.

• Amplitude: Amplitude refers to the maximum displacement of the air molecules from the rest position
• Crest and Trough: The crest is the highest point in the wave whereas trough is the lowest point
• Wavelength: The distance between 2 successive crests or troughs is known as a wavelength

• Cycle: Every audio signal traverses in the form of cycles. One complete upward movement and downward movement of the signal form a cycle
• Frequency: Frequency refers to how fast a signal is changing over a period of time

The below GIF wonderfully depicts the difference between a high and low-frequency signal:

In the next section, I will discuss different types of signals that we encounter in our daily life.

We come across broadly two different types of signals in our day-to-day life – Digital and Analog.

Digital signal

A digital signal is a discrete representation of a signal over a period of time. Here, the finite number of samples exists between any two-time intervals.

For example, the batting average of top and middle-order batsmen year-wise forms a digital signal since it results in a finite number of samples.

Analog signal

An analog signal is a continuous representation of a signal over a period of time. In an analog signal, an infinite number of samples exist between any two-time intervals.

For example, an audio signal is an analog one since it is a continuous representation of the signal.

Wondering how we are going to store the audio signal since it has an infinite number of samples?  Sit back and relax! We will touch on that concept in the next section.

An audio signal is a continuous representation of amplitude as it varies with time. Here, time can even be in picoseconds. That is why an audio signal is an analog signal.

Analog signals are memory hogging since they have an infinite number of samples and processing them is highly computationally demanding. Therefore, we need a technique to convert analog signals to digital signals so that we can work with them easily.

Sampling the signal is a process of converting an analog signal to a digital signal by selecting a certain number of samples per second from the analog signal. Can you see what we are doing here? We are converting an audio signal to a discrete signal through sampling so that it can be stored and processed efficiently in memory.

I really like the below illustration. It depicts how the analog audio signal is discretized and stored in the memory:

The key thing to take away from the above figure is that we are able to reconstruct an almost similar audio wave even after sampling the analog signal since I have chosen a high sampling rate. The sampling rate or sampling frequency is defined as the number of samples selected per second. 

Different Feature Extraction Techniques for an Audio Signal

The first step in speech recognition is to extract the features from an audio signal which we will input to our model later. So now, l will walk you through the different ways of extracting features from the audio signal.

Here, the audio signal is represented by the amplitude as a function of time. In simple words, it is a plot between amplitude and time . The features are the amplitudes which are recorded at different time intervals.

The limitation of the time-domain analysis is that it completely ignores the information about the rate of the signal which is addressed by the frequency domain analysis. So let’s discuss that in the next section.

In the frequency domain, the audio signal is represented by amplitude as a function of frequency. Simply put – it is a plot between frequency and amplitude . The features are the amplitudes recorded at different frequencies.

The limitation of this frequency domain analysis is that it completely ignores the order or sequence of the signal which is addressed by time-domain analysis.

Time-domain analysis completely ignores the frequency component whereas frequency domain analysis pays no attention to the time component.

We can get the time-dependent frequencies with the help of a spectrogram.

Ever heard of a spectrogram? It’s a 2D plot between time and frequency where each point in the plot represents the amplitude of a particular frequency at a particular time in terms of intensity of color. In simple terms, the spectrogram is a spectrum (broad range of colors) of frequencies as it varies with time. 

The right features to extract from audio depends on the use case we are working with. It’s finally time to get our hands dirty and fire up our Jupyter Notebook!

Let’s understand the problem statement of our project before we move into the implementation part.

We might be on the verge of having too many screens around us. It seems like every day, new versions of common objects are “re-invented” with built-in wifi and bright touchscreens. A promising antidote to our screen addiction is voice interfaces. 

TensorFlow recently released the Speech Commands Datasets. It includes 65,000 one-second long utterances of 30 short words, by thousands of different people. We’ll build a speech recognition system that understands simple spoken commands.

You can download the dataset from here .

Implementing the Speech-to-Text Model in Python

The wait is over! It’s time to build our own Speech-to-Text model from scratch.

First, import all the necessary libraries into our notebook. LibROSA and SciPy are the Python libraries used for processing audio signals.

Python Code:

Visualization of Audio signal in time series domain

Now, we’ll visualize the audio signal in the time series domain:

Sampling rate

Let us now look at the sampling rate of the audio signals:

From the above, we can understand that the sampling rate of the signal is 16,000 Hz. Let us re-sample it to 8000 Hz since most of the speech-related frequencies are present at 8000 Hz:

Now, let’s understand the number of recordings for each voice command:

What’s next? A look at the distribution of the duration of recordings:

Preprocessing the audio waves

In the data exploration part earlier, we have seen that the duration of a few recordings is less than 1 second and the sampling rate is too high. So, let us read the audio waves and use the below-preprocessing steps to deal with this.

Here are the two steps we’ll follow:

• Removing shorter commands of less than 1 second

Let us define these preprocessing steps in the below code snippet:

Convert the output labels to integer encoded:

Now, convert the integer encoded labels to a one-hot vector since it is a multi-classification problem :

Reshape the 2D array to 3D since the input to the conv1d must be a 3D array:

Split into train and validation set

Next, we will train the model on 80% of the data and validate on the remaining 20%:

Model Architecture for this problem

We will build the speech-to-text model using conv1d . Conv1d is a convolutional neural network which performs the convolution along only one dimension. 

Here is the model architecture:

Model building

Let us implement the model using Keras functional API.

Define the loss function to be categorical cross-entropy since it is a multi-classification problem:

Early stopping and model checkpoints are the callbacks to stop training the neural network at the right time and to save the best model after every epoch:

Let us train the model on a batch size of 32 and evaluate the performance on the holdout set:

Diagnostic plot

I’m going to lean on visualization again to understand the performance of the model over a period of time:

Loading the best model

Define the function that predicts text for the given audio:

Prediction time! Make predictions on the validation data:

The best part is yet to come! Here is a script that prompts a user to record voice commands . Record your own voice commands and test it on the model:

Let us now read the saved voice command and convert it to text:

Here is an awesome video that I tested on one of my colleague’s voice commands:

Congratulations! You have just built your very own speech-to-text model!

A. One popular NLP model for speech-to-text is the Listen, Attend and Spell (LAS) model. It utilizes an attention mechanism to align acoustic features with corresponding output characters, allowing for accurate transcription of spoken language. LAS models typically consist of an encoder, an attention mechanism, and a decoder, and have been successful in various speech recognition tasks.

A. ASR (Automatic Speech Recognition) models are designed to convert spoken language into written text. They use techniques from both speech processing and natural language processing to transcribe audio recordings or real-time speech. ASR models can be based on various architectures such as Hidden Markov Models (HMM), Deep Neural Networks (DNN), or end-to-end models like Connectionist Temporal Classification (CTC) or Listen, Attend and Spell (LAS).

Find the notebook here

Got to love the power of deep learning and NLP. This is a microcosm of the things we can do with deep learning. I encourage you to try it out and share the results with our community. 🙂

In this article, we covered all the concepts and implemented our own speech recognition system from scratch in Python.

I hope you have learned something new today. I will see you in the next article. If you have any queries/feedback, please free to share in the below comments section!

Natural Language Processing

Introduction to nlp, text pre-processing, nlp libraries, regular expressions, string similarity, spelling correction, topic modeling, text representation, information retrieval system, word vectors, word senses, dependency parsing, language modeling, getting started with rnn, different variants of rnn, machine translation and attention, self attention and transformers, transfomers and pretraining, question answering, text summarization, named entity recognition, coreference resolution, audio separation.

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Responses From Readers

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That was quite nice, but you should have warned the speech recognition beginner that speech recognition and understanding goes a long way beyond small vocabulary isolated word recognition. Perhaps your next instalments could look at medium vocabulary continuous speech recognition, then robust open vocabulary continuous speech recognition, then start combining that with get into NLP. However, I expect python could run into problems doing that kind of thing in real-time. Perhaps that's one reason why you decided to downsample from 16 to 8 kHz, when 16 kHz is known to be more accurate for speech recognition.

Hi Andrew, Thanks. I completely agree with you. The next task would be to build a continuous speech recognition for medium vocabulary. However, the article was designed by keeping beginners in mind.

Hi I am unable to download dataset from kaggle . please help me out

This is a wonderful learning opportunity for anyone starting to learn NLP and ML. Unfortunately, the kernel keeps crashing. Gotta fix my environment. Thanks Aravind for the good start.

sir please can you tell me why we added stop.wav file to the filepath at the end ,if we add like that then it will predict only stop.wav file samples only but does it works for a normal english voice record to convert into text??

This article is really usefull and interesting, but I was wondering if there's a way to use the predict function with live audio from a microphone. How could it be done?

My model is getting trained properly and testing it with the data set used to train the model proves to be successful too. I have saved the trained model (using joblib) by the following code, <> But while loading this saved model (using joblib) and using the model on a recording of my voice of the trained words, I seem to get an array of numbers as the output (from the predict function). Output I am receiving: Text: [[7.2750112e-07 3.9977379e-04 3.2177421e-01 1.1283716e-04 3.0543706e-01 4.8851152e-04 6.3216076e-03 3.8301587e-04 3.6399230e-01 1.0899495e-03]] Output to be received: Text: No

Hey Thanks for the code man. It's working . Do u have any idea of converting this model to tensor flow lite format for supporting android devices?. Thanks in advance

Thanks, Aravind for this great article. But I am not able to download the data. Can you please share the train and test data?

Hi Aravind, Many thank for this wonderful article. I am a beginner in Python, whenever I am trying to read the .wav file into my Jupyter notebook, I am getting the below error. Not sure, how to fix this? Can you please help? samples, sample_rate = librosa.load(train_audio_path+'\yes\0a7c2a8d_nohash_0.wav', sr = 16000) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) in ----> 1 samples, sample_rate = librosa.load(train_audio_path+'\yes\0a7c2a8d_nohash_0.wav', sr = 16000) ~\Anaconda3\lib\site-packages\librosa\core\audio.py in load(path, sr, mono, offset, duration, dtype, res_type) 117 118 y = [] --> 119 with audioread.audio_open(os.path.realpath(path)) as input_file: 120 sr_native = input_file.samplerate 121 n_channels = input_file.channels ~\Anaconda3\lib\site-packages\audioread\__init__.py in audio_open(path, backends) 109 for BackendClass in backends: 110 try: --> 111 return BackendClass(path) 112 except DecodeError: 113 pass ~\Anaconda3\lib\site-packages\audioread\rawread.py in __init__(self, filename) 60 """ 61 def __init__(self, filename): ---> 62 self._fh = open(filename, 'rb') 63 64 try: ValueError: embedded null character

Reading Buddy Software is advanced, speech recognition reading software that listens, responds, and teaches as your child reads. It’s like having a tutor in your computer Tool : Buddy Software

Hi Andrew, I just read your comment and assume that you have good knowledge related to NLP. I am planning to develop a software that basically finds traits the confidence level, ability to be a leader, etc., from a given speech of a person. Just want to know your thoughts as to how, to begin with this problem statement. I am new to ML/ NLP but have good knowledge of other computer science domains.

You mention: "Let us re-sample it to 8000 Hz since most of the speech-related frequencies are present at 8000 Hz:" But due to the Nyquist theorem that would mean only frequencies below 4000Hz would be present in your samples. Keeping it at 16kHz would achieve what you are saying. Additionally, I'm not sure why you are even resampling. Since the model is performing a frequency domain conversion (I assume at least, not entirely clear) having a higher sample rate shouldn't affect the size of your tables, they will be the same size as your FFT window regardless of your sample rate. Are you doing this because of the speed? I think FFT is nlog(n) so is that small gain in speed worth the loss of frequency?

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Speech to Text Conversion Using Python

In this tutorial from Subhasish Sarkar, learn how to build a very basic speech to text engine using simple Python script

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In today’s world, voice technology has become very prevalent. The technology has grown, evolved and matured at a tremendous pace. Starting from voice shopping on Amazon to routine (and growingly complex) tasks performed by the personal voice assistant devices/speakers such as Amazon’s Alexa at the command of our voice, voice technology has found many practical uses in different spheres of life.

One of the most important and critical functionalities involved with any voice technology implementation is a speech to text (STT) engine that performs voice recognition and conversion of the voice into text. We can build a very basic STT engine using a simple Python script. Let’s go through the sequence of steps required.

NOTE : I worked on this proof-of-concept (PoC) project on my local Windows machine and therefore, I assume that all instructions pertaining to this PoC are tried out by the readers on a system running Microsoft Windows OS.

Step 1: Installation of Specific Python Libraries

We will start by installing the Python libraries, namely: speechrecognition, wheel, pipwin and pyaudio. Open your Windows command prompt or any other terminal that you are comfortable using and execute the following commands in sequence, with the next command executed only after the previous one has completed its successful execution.

Step 2: Code the Python Script That Implements a Very Basic STT Engine

Let’s name the Python Script file  STT.py . Save the file anywhere on your local Windows machine. The Python script code looks like the one referenced below in Figure 1.

Figure 1 Code:

Figure 1 Visual:

The while loop makes the script run infinitely, waiting to listen to the user voice. A KeyboardInterrupt (pressing CTRL+C on the keyboard) terminates the program gracefully. Your system’s default microphone is used as the source of the user voice input. The code allows for ambient noise adjustment.

Depending on the surrounding noise level, the script can wait for a miniscule amount of time which allows the Recognizer to adjust the energy threshold of the recording of the user voice. To handle ambient noise, we use the adjust_for_ambient_noise() method of the Recognizer class. The adjust_for_ambient_noise() method analyzes the audio source for the time specified as the value of the duration keyword argument (the default value of the argument being one second). So, after the Python script has started executing, you should wait for approximately the time specified as the value of the duration keyword argument for the adjust_for_ambient_noise() method to do its thing, and then try speaking into the microphone.

The SpeechRecognition documentation recommends using a duration no less than 0.5 seconds. In some cases, you may find that durations longer than the default of one second generate better results. The minimum value you need for the duration keyword argument depends on the microphone’s ambient environment. The default duration of one second should be adequate for most applications, though.

The translation of speech to text is accomplished with the aid of Google Speech Recognition ( Google Web Speech API ), and for it to work, you need an active internet connection.

Step 3: Test the Python Script

The Python script to translate speech to text is ready and it’s now time to see it in action. Open your Windows command prompt or any other terminal that you are comfortable using and CD to the path where you have saved the Python script file. Type in  python “STT.py”  and press enter. The script starts executing. Speak something and you will see your voice converted to text and printed on the console window. Figure 2 below captures a few of my utterances.

Figure 2 . A few of the utterances converted to text; the text “hai” corresponds to the actual utterance of “hi,” whereas “hay” corresponds to “hey.”

Figure 3 below shows another instance of script execution wherein user voice was not detected for a certain time interval or that unintelligible noise/audio was detected/recognized which couldn’t be matched/converted to text, resulting in outputting the message “No User Voice detected OR unintelligible noises detected OR the recognized audio cannot be matched to text !!!”

Figure 3 . The “No User Voice detected OR unintelligible noises detected OR the recognized audio cannot be matched to text !!!” output message indicates that our STT engine didn’t recognize any user voice for a certain interval of time or that unintelligible noise/audio was detected/recognized which couldn’t be matched/converted to text.

Note : The response from the Google Speech Recognition engine can be quite slow at times. One thing to note here is, so long as the script executes, your system’s default microphone is constantly in use and the message “Python is using your microphone” depicted in Figure 4 below confirms the fact.

Finally, press CTRL+C on your keyboard to terminate the execution of the Python script. Hitting CTRL+C on the keyboard generates a KeyboardInterrupt exception that has been handled in the first except block in the script which results in a graceful exit of the script. Figure 5 below shows the script’s graceful exit.

Figure 5 . Pressing CTRL+C on your keyboard results in a graceful exit of the executing Python script.

Note : I noticed that the script fails to work when the VPN is turned on. The VPN had to be turned off for the script to function as expected. Figure 6 below demonstrates the erroring out of the script with the VPN turned on.

Figure 6 . The Python script fails to work when the VPN is turned on.

When the VPN is turned on, it seems that the Google Speech Recognition API turns down the request. Anybody able to fix the issue is most welcome to get in touch with me here and share the resolution.

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How to build a Speech-To-Text application with Python (1/3)

A tutorial to create and build your own Speech-To-Text application with Python .

At the end of this first article, your Speech-To-Text application will be able to receive an audio recording and will generate its transcript!

Final code of the app is available in our dedicated GitHub repository .

Overview of our final app

Overview of our final Speech-To-Text application

In the previous notebook tutorials , we have seen how to translate speech into text , how to punctuate the transcript and summarize it. We have also seen how to distinguish speakers and how to generate video subtitles , all the while managing potential memory problems .

Now that we know how to do all this, let’s combine all these features together into a Speech-To-Text application using Python !

➡ To create this app, we will use Streamlit , a Python framework that turns scripts into a shareable web application. If you don’t know this tool, don’t worry, it is very simple to use.

This article is organized as follows:

Import code from previous tutorials

• Write the Streamlit App

Run your app!

In the following articles, we will see how to implement the more advanced features (diarization, summarization, punctuation, …), and we will also learn how to build and use a custom Docker image for a Streamlit application, which will allow us to deploy our app on AI Deploy !

⚠️ Since this article uses code already explained in the previous notebook tutorials , we will not re-explain its usefulness here. We therefore recommend that you read the notebooks first.

1. Set up the environment

To start, let’s create our Python environment. To do this, create a file named requirements.txt and add the following text to it. This will allow us to specify each version of the libraries required by our Speech to text project.

Then, you can install all these elements in only one command. To do so, you just have to open a terminal and enter the following command :

2. Import libraries

Once your environment is ready, create a file named app.py and import the required libraries we used in the notebooks .

They will allow us to use artificial intelligence models, to manipulate audio files, times, …

3. Functions

We also need to use some previous functions, you will probably recognize some of them.

⚠️ Reminder: All this code has been explained in the notebook tutorials . That’s why we will not re-explain its usefulness here.

To begin, let’s create the function that allows you to transcribe an audio chunk .

Then, create the four functions that allow silence detection method , which we have explained in the first notebook tutorial .

Get the timestamps of the silences

Get the middle value of each timestamp

Create a regular distribution , which merges the timestamps according to a min_space and a max_space value.

Add automatic “time cuts” to the silence list till end value depending on min_space and max_space values:

Create a function to clean the directory where we save the sounds and the audio chunks, so we do not keep them after transcribing:

Write the Streamlit application code

1. configuration of the application.

Now that we have the basics, we can create the function that allows to configure the app . It will give a title and an icon to our app, and will create a data directory so that the application can store sounds files in it. Here is the function:

As you can see, this data directory is located at the root of the parent directory (indicated by the ../ notation). It will only be created if the application is launched locally on your computer, since AI Deploy has this folder pre-created .

➡️ We recommend that you do not change the location of the data directory (../) . Indeed, this location makes it easy to juggle between running the application locally or on AI Deploy.

2. Load the speech to text model

Create the function that allows to load the speech to text model .

As we are starting out, we only import the transcription model for the moment. We will implement the other features in the following article 😉.

⚠️ Here, the use case is English speech recognition, but you can do it in another language thanks to one of the many models available on the Hugging Face website . In this case, just keep in mind that you won’t be able to combine it with some of the models we will use in the next article, since some of them only work on English transcripts.

We use a @st.cache(allow_output_mutation=True) here. This tells Streamlit to run the function and stores the results in a local cache , so next time we call the function (app refreshment), Streamlit knows it can skip executing this function . Indeed, since we have already imported the model(s) one time (initialization of the app), we must not waste time to reload them each time we want to transcribe a new file.

However, downloading the model when initializing the application takes time since it depends on certain factors such as our Internet connection. For one model, this is not a problem because the download time is still quite fast. But with all the models we plan to load in the next article, this initialization time may be longer, which would be frustrating 😪.

➡️ That’s why we will propose a way to solve this problem in a next blog post .

3. Get an audio file

Once we have loaded the model, we need an audio file to use it 🎵!

For this we will realize two features. The first one will allow the user to import his own audio file . The second one will allow him to indicate a video URL for which he wants to obtain the transcript.

3.1. Allow the user to upload a file (mp3/mp4/wav)

Let the user upload his own audio file thanks to a st.file_uploader() widget:

As you can see, if the uploaded_file variable is not None, which means the user has uploaded an audio file, we launch the transcribe process by calling the transcription() function that we will soon create.

3.2. Transcribe a video from YouTube

Create the function that allows to download the audio from a valid YouTube link :

⚠️ If you are not the administrator of your computer, this function may not work for local execution.

Then, we need to display an element that allows the user to indicate the URL they want to transcribe.

We can do it thanks to the st.text_input() widget . The user will be able to type in the URL of the video that interests him. Then, we make a quick verification : if the entered link seems correct (contains the pattern of a YouTube link : “youtu”), we try to extract the audio from the URL’s video, and then transcribe it.

This is what the following function does:

4. Transcribe the audio file

Now, we have to write the functions that links the majority of those we have already defined.

To begin, we write the code of the init_transcription() function. It informs the user that the transcription of the audio file is starting and that it will transcribe the audio from start seconds to end seconds. For the moment, these values correspond to the temporal ends of the audio (0s and the audio length). So it is not really interesting, but it will be useful in the next episode 😌!

This function also initializes some variables. Among them, s rt_text and save_results are variables that we will also use in the following article. Do not worry about them for now.

We have the functions that perform the silences detection method and that transcribe an audio file . But now we need to link all these functions . The function transcription_non_diarization() will do it for us:

You will notice that this function calls the display_transcription() function, which displays the right elements according to the parameters chosen by the user.

For the moment, the display is basic since we have not yet added the user’s parameters . This is why we will modify this function in the next article, in order to be able to handle different display cases, depending on the selected parameters.

You can add it to your app.py file:

Once this is done, all you have to do is display all the elements and link them using the transcription() function:

This huge function looks like our main block code . It almost gathers all the implemented functionalities .

First of all, it retrieves the length of the audio file and allows the user to play it with a st.audio() , a widget that displays an audio player . Then, if the audio length is greater than 0s and the user clicks on the “ Transcribe ” button, the transcription is launched.

The user knows that the code is running since all the script is placed in a st.spinner() , which is displayed as a loading spinner on the app.

In this code, we initialize some variables. For the moment, we set the srt_token to False , since we are not going to generate subtitles (we will do it in next tutorials as I mentioned).

Then, the location of the audio file is indicated (remember it is in our ../data directory). The transcription process is at that time really started as the function transcription_non_diarization() is called. The audio file is transcribed from chunk to chunk, and the transcript is displayed part by part, with the corresponding timestamps.

Once finished, we can clean up the directory where all the chunks are located, and the final text is displayed.

All that remains is to define the main , global architecture of our application.

We just need to create a st.radio () button widget so the user can either choose to transcribe his own file by importing it, or an external file by entering the URL of a video. Depending on the radio button value, we launch the right function (transcript from URL or from file).

We can already try our program ! Indeed, run your code and enter the following command in your terminal. The Streamlit application will open in a tab of your browser.

⚠️⚠️ If this is the first time you manipulate audio files on your computer, you may get some OSErrors about the libsndfile , ffprobe and ffmpeg libraries.

Don’t worry, you can easily fix these errors by installing them. The command will be different depending on the OS you are using. For example, on Linux , you can use apt-get :

If you have Conda or Miniconda installed on your OS, you can use:

If the application launches without error, congratulations 👏 ! You are now able to choose a YouTube video or import your own audio file into the application and get its transcript!

😪 Unfortunately, local resources may not be powerful enough to get a transcript in just a few seconds, which is quite frustrating.

➡️ To save time , you can run your app on GPUs thanks to AI Deploy . To do this, please refer to this documentation to boot it up.

You can see what we have built on the following video:

Quick demonstration of our Speech-To-Text application after completing this first tutorial

Well done 🥳 ! You are now able to import your own audio file on the app and get your first transcript!

You could be satisfied with that, but we can do so much better !

Indeed, our Speech-To-Text application is still very basic . We need to implement new functions like speakers differentiation , transcripts summarization , or punctuation , and also other essential functionalities like the possibility to trim/cut an audio , to download the transcript , interact with the timestamps , justify the text , …

➡️ If you want to improve your Streamlit application, follow the next article 😉.

Mathieu Busquet

I am an engineering student who has been working at OVHcloud for a few months. I am familiar with several computer languages, but within my studies, I specialized in artificial intelligence and Python is therefore my main working tool.

It is a growing field that allows me to discover and understand things, to create but also as you see to explain them :)!

• Mathieu Busquet https://blog.ovhcloud.com/author/mathieu-busquet/ How to build a Speech-To-Text Application with Python (2/3)
• Mathieu Busquet https://blog.ovhcloud.com/author/mathieu-busquet/ How to build a Speech-To-Text Application with Python (3/3)
• Mathieu Busquet https://blog.ovhcloud.com/author/mathieu-busquet/ Image segmentation: Train a U-Net model to segment brain tumors
• Mathieu Busquet https://blog.ovhcloud.com/author/mathieu-busquet/ Fine-Tuning LLaMA 2 Models using a single GPU, QLoRA and AI Notebooks

Speech to text

An AI Speech feature that accurately transcribes spoken audio to text.

Make spoken audio actionable

Quickly and accurately transcribe audio to text in more than 100 languages and variants. Customize models to enhance accuracy for domain-specific terminology. Get more value from spoken audio by enabling search or analytics on transcribed text or facilitating action—all in your preferred programming language.

High-quality transcription

Get accurate audio to text transcriptions with state-of-the-art speech recognition.

Customizable models

Add specific words to your base vocabulary or build your own speech-to-text models.

Flexible deployment

Run Speech to Text anywhere—in the cloud or at the edge in containers.

Production-ready

Access the same robust technology that powers speech recognition across Microsoft products.

Accurately transcribe speech from various sources

Convert audio to text from a range of sources, including  microphones ,  audio files , and  blob storage . Use speaker diarisation to determine who said what and when. Get readable transcripts with automatic formatting and punctuation.

Customize speech models to your needs

Tailor your speech models to understand organization- and industry-specific terminology. Overcome speech recognition barriers such as background noise, accents, or unique vocabulary.  Customize your models  by uploading audio data and transcripts. Automatically  generate custom models using Office 365 data  to optimize speech recognition accuracy for your organization.

Deploy anywhere

Run Speech to Text wherever your data resides. Build speech applications that are optimized for robust cloud capabilities and on-premises using  containers .

Fuel App Innovation with Cloud AI Services

Learn 5 key ways your organization can get started with AI to realize value quickly.

Comprehensive privacy and security

AI Speech, part of Azure AI Services, is  certified  by SOC, FedRAMP, PCI DSS, HIPAA, HITECH, and ISO.

View and delete your custom speech data and models at any time. Your data is encrypted while it's in storage.

Your data remains yours. Your audio input and transcription data aren't logged during audio processing.

Backed by Azure infrastructure, AI Speech offers enterprise-grade security, availability, compliance, and manageability.

Comprehensive security and compliance, built in

Microsoft invests more than $1 billion annually on cybersecurity research and development.

We employ more than 3,500 security experts who are dedicated to data security and privacy.

Azure has more certifications than any other cloud provider. View the comprehensive list .

Flexible pricing gives you the control you need

With Speech to Text, pay as you go based on the number of hours of audio you transcribe, with no upfront costs.

Get started with an Azure free account

Start free . Get $200 credit to use within 30 days. While you have your credit, get free amounts of many of our most popular services, plus free amounts of 55+ other services that are always free.

After your credit, move to  pay as you go  to keep building with the same free services. Pay only if you use more than your free monthly amounts.

Documentation and resources

Get started.

Browse the  documentation

Create an AI Speech service with the  Microsoft Learn course

Explore code samples

Check out our  sample code

See customization resources

Explore and customize your voice-to-text solution with  Speech Studio . No code required.

Frequently asked questions about Speech to Text

What is speech to text.

It is a feature within the Speech service that accurately and quickly transcribes audio to text.

What are Azure AI Services?

AI Services  are a collection of customizable, prebuilt AI models that can be used to add AI to applications. There are a variety of domains, including Speech, Decision, Language, and Vision. Speech to Text is one feature within the Speech service. Other Speech related features include  Text to Speech ,  Speech Translation , and  Speaker Recognition . An example of a Decision service is  Personalizer , which allows you to deliver personalized, relevant experiences. Examples of AI Languages include  Language Understanding ,  Text Analytics  for natural language processing,  QnA Maker  for FAQ experiences, and  Translator  for language translation.

Start building with AI Services

DEV Community

Posted on Jan 2

Building a Real-time Speech-to-text Web App with Web Speech API

Happy New Year, everyone! In this short tutorial, we will build a simple yet useful real-time speech-to-text web app using the Web Speech API. Feature-wise, it will be straightforward: click a button to start recording, and your speech will be converted to text, displayed in real-time on the screen. We'll also play with voice commands; saying "stop recording" will halt the recording. Sounds fun? Okay, let's get into it. 😊

Web Speech API Overview

The Web Speech API is a browser technology that enables developers to integrate speech recognition and synthesis capabilities into web applications. It opens up possibilities for creating hands-free and voice-controlled features, enhancing accessibility and user experience.

Some use cases for the Web Speech API include voice commands, voice-driven interfaces, transcription services, and more.

Let's Get Started

Now, let's dive into building our real-time speech-to-text web app. I'm going to use vite.js to initiate the project, but feel free to use any build tool of your choice or none at all for this mini demo project.

• Create a new vite project:
• Choose "Vanilla" on the next screen and "JavaScript" on the following one. Use arrow keys on your keyboard to navigate up and down.

HTML Structure

CSS Styling

JavaScript Implementation

This simple web app utilizes the Web Speech API to convert spoken words into text in real-time. Users can start and stop recording with the provided buttons. Customize the design and functionalities further based on your project requirements.

Final demo: https://stt.nixx.dev

Feel free to explore the complete code on the GitHub repository .

Now, you have a basic understanding of how to create a real-time speech-to-text web app using the Web Speech API. Experiment with additional features and enhancements to make it even more versatile and user-friendly. 😊 🙏

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Create Your Own Speech-To-Text Custom Language Model

Steps to data collection, preparation & training your model.

Cobus Greyling

With the creation of a text-based chatbot, much attention is given to the Natural Language Understanding (NLU) portion of the chatbot. Adding user utterances, linking those to intents and making sure entities are annotated within those user utterances.

Often the next step is to voice-enable the chatbot, allowing users to speak to the bot, effectively creating a voicebot.

Initially this seems like a straightforward process, but there are a few elements to consider…

Read This Before Converting Your Chatbot To A Voicebot

There are telling differences between text and voice interfaces.

cobusgreyling.medium.com

Design Different For Voicebots Versus Chatbots

…and why you cannot just voice enable your chatbot.

The first element is conversation design considerations. These are covered in a previous article listed here above.

Another element to keep in mind is converting the user speech into text. Traditionally this was referred to as ASR ( Automated Speech Recognition ), currently the term STT ( Speech-To-Text ) is commonly used.

Hence a voicebot has more moving parts, and more complex from an architectural and implementation point of view.

For demo purposes the out-of-the-box STT usually works well, but in reality the STT engine will require training. In this story I want to look at a few considerations on how this problem may be approached.

For this example I will be using IBM’s STT service. There are two models which can be used to train the STT service.

The first being a language model, the second an acoustic model.

The acoustic model involves the collection of audio files to perform the training of the model. Considerations for when recording is user setting (busy street, office noise, studio recordings. Different genders, ages, accents, ethnicities etc. Different mediums; landline, mobile call etc.

The language model is a text based training approach, described below.

Domain Specific Language Models

But why train in the first place?

The out-of-the-box versions of STT has many words for general and every day conversations. However, most implementations are in industry specific organizations. These words are not used in everyday conversations, most often.

Hence the chatbot we are trying to convert to a voicebot is domain specific and these domain specific utterances need to be added to the STT vocabulary for accurate translation from voice to text.

Making use of a language model customization the vocabulary of the STT engine can be set to include domain-specific utterances.

With IBM Watson STT a corpus file can be used. This file contain words and sentences regularly used in customer conversations. This is a text file, and a more lightweight approach, as apposed to a acoustic model where recordings are used.

Training Data For The Language Model

For this article we will add domain specific data to the language model pertaining to mobile networks. First we create the data which will be used to train with.

Here is an extract from the file:

The full corpus file can be accessed here…

cobusgreyling/IBM_STT_Languge_Model

What is a mobile network many people believe that when you say ''mobile network'' you are referring to a wireless…, sequence of events, create a model.

With IBM STT the whole process can be managed via CURL commands. There are other ways also to interface with the STT environment.

To create a language model with a customization id, run this curl command.

The apikey and url are displayed when you create the service in the IBM Cloud console.

The customization ID is returned. Save this value, as it will be used as a reference going forward.

List Customization Models

If you have multiple models, you can list them and view the meta data.

With each model shown as:

Upload Corpus File

Make sure you are in the directory where your corpus text file is located, and run this command. The customization ID is added to the URL as the reference.

View The Status

Once the corpus file is uploaded, you can view the status of your upload:

Again the customization ID we gleaned earlier in the story is part of the URL, marked by x’s.

Training The Language Model

The language model can now be trained and the out-of-vocabulary words will be added.

View Training Results

The training results can be viewed…

And below you can see result from the training.

Adding Individual Words

A JSON file can be defined within the CURL command and data can be added to the language model in this way, without making use of a corpus file. With this option a word can be added, with tags for:

• Sounds Like (multiple options can be added)

Using the same command as previously used, the result can be viewed:

Should the recordings based acoustic model or the text based language model be used?

You can improve speech recognition accuracy by using the custom language and custom acoustic models in parallel. You can use both types of model during training of your acoustic model, during speech recognition, or both.

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Nlp/nlu, chatbots, voice, conversational ui/ux, cx designer, developer, ubiquitous user interfaces, ambient….

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Cobus Greyling - Medium

There are currently various avenues to introduce structure to entities in order to detected patterns. microsoft luis…, ibm/speech-customization-ui, this code is a user interface for ibm watson speech-to-text and text-to-speech. this will allow users to use the speech…, data collection and training for speech projects, many ai projects have a speech recognition component such as voice assistants. speech recognition models including ibm…, watson speech-to-text: how to train your own speech “dragon” — part 1: data collection and…, over the past years, we’ve seen a lot of ai chatbots deployed in across many organizations. they typically handle….

Written by Cobus Greyling

I explore and write about all things at the intersection of AI & language; LLMs/NLP/NLU, Chat/Voicebots, CCAI. www.cobusgreyling.com

Text to speech

Crafting conversational AI: Speech-to-text (STT)

Speech-to-text (STT) technology is the engine that makes conversational AI chatbots run.

By Kelsie Anderson

This blog post is Part Three of a four-part series where we’ll discuss conversational AI and the developments making waves in the space. Check out Parts One (on HD voice codecs ) and Two (on noise suppression ) to learn more about the features that can help you create high-performance conversational AI tools.

Imagine a world where machines understand and respond to human speech as naturally as another person would. This isn't a distant dream but a reality that's unfolding right now, thanks to the advancements in speech-to-text (STT) and conversational AI technologies.

As a developer, you're at the forefront of this revolution, building chatbots that are becoming increasingly sophisticated. But the landscape is changing rapidly. New tools, techniques, and emerging trends can help you take your chatbots to the next level.

What if you could build chatbots that understand multiple languages, accents, and dialects? What if your chatbots could maintain the context of a conversation, handle multiple topics simultaneously, and even exhibit empathy? With the latest developments in STT and conversational AI, these are realities waiting to be explored.

In this blog post, we'll dive into the intricacies of STT and conversational AI, explore the latest trends, and provide insights into how you can leverage these technologies to enhance your chatbots.

What is speech-to-text (STT), and how does it intersect with conversational AI?

Speech-to-text technology is a critical component of conversational AI systems. It serves as the initial step in the process of understanding and responding to spoken language.

In the context of conversational AI, STT works by converting spoken language into written text. This conversion is crucial because it allows the AI to process and understand the user's spoken input.

Once the spoken words are converted into text, other components of the conversational AI system, such as natural language processing (NLP) and natural language understanding (NLU) , can analyze the text to understand its meaning, context, and intent.

After the AI system understands the user's intent, it can generate an appropriate response. This response is typically in text form, which can then be converted back into speech using text-to-speech (TTS) technology, allowing the AI to respond verbally to the user.

Top benefits of using STT to build conversational AI tools

STT enhances user experience, promotes inclusivity, and drives efficiency, making it an indispensable asset in every conversational AI developer’s toolkit. Below, we’ll explore these benefits in detail and discuss the transformative potential of STT in crafting sophisticated conversational AI tools.

It can help you create an enhanced user experience

STT allows users to interact with AI tools using their natural speech, making the interaction more intuitive and user-friendly. This intuitiveness is particularly beneficial for users who struggle with typing or prefer speaking over writing.

It makes your tools more accessible

STT technology makes conversational AI tools more accessible to people with disabilities. For instance, individuals with visual impairments or motor challenges can use their voices to interact with these tools, making them more inclusive.

Conversations at real-time paces improve efficiency

Speaking is often faster than typing, especially for lengthy or complex instructions. STT technology can process spoken commands quickly, improving the efficiency of user interactions.

STT technology can also transcribe speech in real time, enabling immediate responses from the AI system. Real-time responses help you leverage the efficiency of automation and improve the user experience by providing responses at a more “natural” pace.

Multilingual support helps you reach a larger audience

Modern STT systems can understand and transcribe multiple languages, dialects, and accents. Understanding multiple languages allows you to use your conversational AI tools to cater to a global audience.

Contextual understanding gives users the answers they need

Advanced STT systems use machine learning (ML) and AI to understand the context of the conversation, improving the accuracy of transcriptions and the overall effectiveness of the conversational AI tool.

By integrating STT technology, developers can build more effective, efficient, and accessible conversational AI tools that offer a more natural, engaging user experience.

Advancements in STT technology and conversational AI

STT technology has come a long way since its inception. Modern STT systems leverage deep learning algorithms to achieve high accuracy rates, even in noisy environments . They can understand multiple languages, accents, and dialects, making them more versatile and user-friendly.

One of the latest trends in STT technology is the use of transformer-based models like BERT (Bidirectional Encoder Representations from Transformers). These models can understand the context of a conversation, leading to more accurate transcriptions.

Conversational AI has also seen significant advancements. Traditional rule-based chatbots have given way to AI-powered bots that can understand and respond to complex queries. They can maintain the context of a conversation, handle multiple topics simultaneously, and even exhibit empathy.

The advent of tools like OpenAI’s GPT-4 and Whisper has taken conversational AI to new heights. These language models and neural networks can generate human-like text and responses, making interactions with chatbots more engaging and natural.

Leveraging STT and conversational AI for chatbots

As developers, integrating STT and conversational AI into your chatbots can significantly enhance their functionality. Here are some ways you can take your conversational AI tools to the next level:

1. Use pre-trained models

Training models from scratch requires a significant amount of time, computational resources, and a large, diverse dataset. Pre-trained models have already been trained on extensive datasets, saving developers from the time-consuming and resource-intensive training process.

These datasets are often vast and diverse, enabling them to handle a wide range of tasks and scenarios. They can understand multiple languages, accents, and dialects, making them more versatile and user-friendly.

Especially if models have been trained on deep learning, they should be able to handle complex tasks, such as understanding the context of a conversation, which is crucial for effective conversational AI.

2. Customize your models

The diversity of pre-trained models makes them extremely versatile, but it doesn’t always mean they’ll be suited to your specific needs. Suppose you need to design your chatbot for a specific use case or domain (like healthcare, finance, or customer support). In that case, customizing these models allows them to better understand and respond to domain-specific language, jargon, or user intents.

Outside of domain-specific training, your user base might have unique characteristics, such as a specific accent, dialect, or language that’s not well-represented in the training data of the pre-trained model. By fine-tuning the model on your own data, you can improve its performance for your specific user base.

Finally, customization allows the model to better understand the context and nuances of the conversations it will handle. A better understanding of context leads to more accurate transcriptions in the case of STT and more appropriate responses in the case of conversational AI, thereby improving the overall performance of the chatbot.

3. Expect to handle multiple languages and accents

Especially if you plan to cater to a global audience, multilingual support allows your chatbots to serve users from different regions and linguistic backgrounds, thereby expanding their reach and usability.

Since users prefer interacting with AI tools in their native languages , multilingual chatbots can provide a better user experience, leading to higher user engagement and satisfaction. Language nuances, slang, and colloquialisms can vary greatly between languages. Multilingual support ensures the chatbot can accurately understand and respond to these variations, leading to more accurate and effective interactions.

By supporting multiple languages, chatbots also become more accessible and inclusive, giving them a competitive edge over monolingual bots. Multilingual support can be a key differentiator that sets a chatbot apart from others that only support one or a few languages.

4. Maintain context

Context plays a vital role in understanding the meaning of words and phrases. The same word can have different meanings in different contexts. By maintaining context, chatbots can accurately understand user inputs and provide appropriate responses.

Furthermore, in human conversations, we often refer back to previous statements or rely on the overall context of the conversation. For a chatbot to mimic this natural flow, it needs to maintain and understand the context of the conversation.

A chatbot that maintains context can handle complex conversations, switch between topics seamlessly, and remember past interactions. This ability leads to a more engaging and satisfying user experience.

5. Test and iterate

Testing allows you to evaluate the performance of your chatbot in real-world scenarios. It helps identify areas where the chatbot excels and falls short, providing valuable insights for improvement.

Through testing, you can identify and fix bugs, errors, or unexpected behavior in the chatbot, ensuring it functions as intended and provides a smooth user experience. Testing also provides an opportunity to gather user feedback, which provides a user's perspective on the chatbot's performance, usability, and functionality.

And the testing doesn’t stop once you’ve released your chatbot into the wild. Language and the way people use it are constantly evolving. Regular testing and iteration allow the chatbot to adapt to these changes and stay relevant.

Following these best practices will involve some effort on the part of your development team. But the good news is, you can adhere to many of them by partnering with a platform that incorporates them into their STT engines.

Choose a next-gen platform to build next-level conversational AI

STT and conversational AI are transforming the way we interact with machines. As developers, staying abreast of the latest trends and advancements in these fields can help you build more effective and engaging chatbots. By leveraging these technologies, you can create chatbots that understand and respond to voice commands and provide a more natural and intuitive user experience.

But end-users aren’t the only ones who should have access to an intuitive UX. The platforms we use to create conversational AI tools should also be easy to use, offer natural workflows, and leverage the latest advancements in conversational AI.

Telnyx’s Voice API offers an intuitive, advanced platform for developers interested in building and managing next-level voice-activated chatbots. Our STT engine leverages next-gen technology like OpenAI Whisper , which has been trained on 680,000 hours of multilingual and multi-task supervised data and can support nearly 60 languages . Paired with high-quality voice calls running on our global private network , using the Telnyx platform ensures your conversational AI tools can leverage high-quality inputs for equally high-quality interactions. Want to see Telnyx STT in action? Watch our demo to see how to start transcribing calls in real-time in just a few minutes.

Contact our team of experts to learn how you can leverage Telnyx’s Voice API to build next-level conversational AI solutions.

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Build Speech-To-Text API Into Your Applications: Easy How to Guide

Rev › Blog › Speech to Text Technology › Build Speech-To-Text API Into Your Applications: Easy How to Guide

What is a Speech-to-Text API?

Speech-to-text APIs let you integrate  speech-to-text into your application . This gives you direct, automated access to transcription and captioning services. Such APIs enable new use cases for pre-recorded audio data and  real-time transcription .

AI vs Human Transcription and Captioning

AI transcription accuracy has come a long way. We can now achieve rates above 80% or even in the low 90s in some cases. Yet, it still can’t match the 99% or better accuracy rate possible with human transcribers. 

Accuracy is not the only important factor in captioning and transcription services. Speech-to-text AI shines in cases where speed and cost matter more than accuracy. If accuracy is more important, though, human transcription is still the better option. This is often the case in legal and medical use cases.

Building With a Speech-to-Text API

Using a speech-to-text API makes implementation easy. You just need to add API calls to your application using a software development kit (SDKs). After deployment, you will then be able to send a range of supported audio file types to the API.

Depending on your needs, you will want to pick one or both of our APIs:

• An  asynchronous API  that is perfect for pre-recorded audio and video files. It offers transcription or captioning of hour-long files in under a minute.
• Real-time captioning of live audio and video events
• Keyword monitoring
• Implementing actions based on specified trigger words.

Let’s take a look at some common use cases for these APIs.

How Call Centers Can Use Speech-to-Text APIs

Transcribing call center conversations enhances the ability to:

• Provide targeted coaching to representatives based on specific call behavior.
• Create a searchable archive of call behavior. This enables reference, auditing, or identification of call patterns.
• Utilize voice assistants to aid agents.  
• Train interactive voice response (IVR) system. These IVRs can act in cases where an agent is unavailable or unnecessary. 

How Automated Virtual Assistants Can Use Speech-to-Text APIs

Voice commands are a key feature of many Virtual Assistant systems such as Amazon’s Alexa and Apple’s Siri. Integrating speech-to-text software allows the real-time transcription of voice commands. These transcriptions enable search and comparison to a pre-defined menu of trigger options.

Real-time responses are not the only use case here. Speech-to-text lets you create a searchable user inquiry history for your virtual assistant. This can enable gap analysis and the discovery of problematic trigger words. 

How Conference and Event Venues Can Use Speech-to-Text APIs

Real-time captioning of live speeches at an event improves accessibility. This improves the experience for the hearing impaired, but that’s not the only benefit. Venues can often be noisier than expected, and captions can overcome this concern.

For online events, captions allow speeches to be viewed from any screen. If they can’t access the audio stream, participants can still follow the talks. Even for in-person events, this allows people outside the speaker’s room to follow the speech.

After the event, transcribed speeches can be offered on the event website. This is an excellent way for participants to easily refer back to important points. It also enhances the discoverability of relevant talks that the participant may have missed.

How Academic Institutions Can Use Speech-to-Text APIs

There is no longer a need to manually prepare lecture notes. Recorded lectures can, instead, be transcribed to create automated lecture notes.

These automated notes are just as searchable as manual lecture notes. But, they can be prepared without taking valuable time from a professor’s or teaching assistant’s schedule. They can also be timestamped to make it easy for students to refer to any visuals in the lecture video or slides.

Captioning lecture videos can improve accessibility for hearing-impaired students. Subtitling can even allow translation options for English as a second language (ESL) students. 

How Content Creators and Distributors Can Use Speech-to-Text APIs 

Speech-to-text APIs can power automated captioning of content for creators of platforms. This enhances the user experience and can increase the reach and accessibility of audio and video content.

Transcribing video and audio content (e.g., podcasts) to text offers several advantages:

• A significant boost in discoverability through organic search.
• The creation of a skimmable and searchable directory of episodes. This enables listeners to find their favorite episodes or discover relevant content.
• Enhanced accessibility for hearing-impaired listeners.
• Easy reference when reviewing content or trying to reference previous episodes
• Allows media outlets and bloggers to readily pull quotes and publicize your content.

How Medical Offices Can Use Speech-to-Text APIs

Doctors spend a significant amount of time taking notes and creating electronic health records (EHRs). In a typical visit, doctors can spend  16 minutes on EHRs . They often spend up to 11% of their after-hour time on EHRs as well.

Switching from written to transcribed audio note-taking offers significant time savings. This allows more attention to be given to the patient and allows doctors to see more patients in their day.

These transcribed notes can also be timestamped. Doctors thus gain a way of tracking particular events during a visit. This can lead to valuable insights such as the time interval between symptoms and the time between a treatment and the onset of a side effect.

How Speech-to-Text Can Aid in Regulatory Compliance

Regulatory and Compliance standards are constantly changing. This is especially true in heavily regulated fields like finance and healthcare. To keep up, organizations need better ways to capture, store, and analyze important communications data. 

Converting audio recordings to text is a great start. This way, communications can be made readily indexable and searchable. When needed, text files can be more easily identified and retrieved than can audio files.

How to Get Started With Rev.ai or Rev.com

You can try the  Rev.ai speech-to-text API  right now for free with no credit card required. We offer convenient SDKs and extensive documentation. Our expert support is also ready to help you get started quickly and painlessly.

Our automatic speech recognition (ASR) engine is built with accuracy, security, and reliability in mind. Best of all, it can convert audio to text from within your existing applications.

Of course, some applications need human-level accuracy. If that’s the case, you can also check out  Rev.com’s services  including transcriptions, captioning, and subtitling.

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An opensource text-to-speech (TTS) voice building tool

google/voice-builder

Folders and files.

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Disclaimer: This is not an official Google product.

Voice Builder

Voice Builder is an opensource text-to-speech (TTS) voice building tool that focuses on simplicity, flexibility, and collaboration. Our tool allows anyone with basic computer skills to run voice training experiments and listen to the resulting synthesized voice.

We hope that this tool will reduce the barrier for creating new voices and accelerate TTS research, by making experimentation faster and interdisciplinary collaboration easier. We believe that our tool can help improve TTS research, especially for low-resourced languages, where more experimentations are often needed to get the most out of the limited data.

Publication - https://ai.google/research/pubs/pub46977

Prerequisites

Create an example voice, (optional) using custom data exporter, voice builder specification, additional information, installation.

Create a project on Google Cloud Platform (GCP) .

If you don't have an account yet, please create one for yourself.

Enable billing and request more quota for your project

Install Docker

Go to firebase.com and import the project to firebase platform

Install gcloud cmd line tool by installing Cloud SDK

Install Node.js

Install firebase cmd line tool

Enable all the following GCP services:

• Appengine API
• Firebase Cloud Function
• Genomics Pipeline API

Use this url to enable them all at once.

Usually, it would take a few minutes to enable APIs and GCP will bring you to another page to set credentials for these. Please just skip and close the page as we don't need any new credential setting.

[Optional] Setup your own custom data exporter

If you have not completed all prerequisites, please do so before going further in the following steps.

Clone this project to your current directory by:

If you haven't logged in to your account via gcloud yet, please log in by:

Also, if you haven't logged in to your account via firebase, please log in by:

Open deploy.sh and edit the following variables:

• PROJECT_NAME: your created GCP project's name from Prerequisite 1) e.g. vb-test-project
• PROJECT_ID: your created GCP project's id from Prerequisite 1) e.g. vb-test-project
• GCP_SERVICE_ACCOUNT_EMAIL: Use Compute Engine service account (you can find one by clicking on top left menu under "IAM & admin > Service accounts") e.g. [email protected]

Create GCS buckets for Voice Builder to store each job data

Deploy cloud functions component

Deploy ui component

After the deployment, you should get an IP that you can access from command line's result (EXTERNAL_IP). You can access your instance of Voice Builder by visiting http://EXTERNAL_IP:3389 in your browser.

At this step, you should have all components in place and can access the UI at http://EXTERNAL_IP:3389. VoiceBuilder initially provides you with two example TTS engines ( Festival and Merlin ) and public data from language resources repo .

You can test if everything is now working correctly by creating a new voice yourself using our provided Festival engine by:

• Access http://EXTERNAL_IP:3389 and go to a create-voice form by clicking "CREATE VOICE" tab on top.
• You will see a form where you can choose different TTS engines and input data for your voice. Just skim through as we will use this initial config for building a new voice. Try clicking "Create Voice" button at the bottom. After a short moment, you should get a notification on the top right saying "successfully created a job".
• Click on "JOBS" tab. Now, you should see a new job that you have just created. It usually takes 30mins to 1 hour to run. You can check the status of the job by clicking on the job id to see the job status page.
• After an hour, you should see "Completed Voice Model Deployment" under the job status. This means the successfully built model has been deployed to a voice synthesis server. Try putting in "hello" in the text input box at the bottom of the job status page and click "Synthesize" button. Voice Builder should generate a spectrogram and have a play button for you to listen to the voice!

Data Exporter is another additional component you can add to the system. Normally, Voice Builder can work without Data Exporter. Without it, Voice Builder would just use the input files as they are.

However, in some cases you want to apply some conversion to your input files before feeding them into TTS algorithms. For example:

• You have lexicon file that is in a different format from the one accepted by your chosen TTS algorithm.
• You want to filter out some bad data before using it in your chosen TTS algorithm.

Voice Builder gives you the flexibility to add your own data exporter which you can use to manipulate data before running the actual TTS algorithm. Your custom data exporter will get a Voice Specification containing file location, chosen TTS algorithm, tuning parameters, etc. You can use these information to manipulate/convert your data. In the end, your data exporter should put all necessary files into the designated job folder to trigger the actual TTS algorithm to run.

Firstly, you need to give your data exporter access to GCS buckets.

Open /deploy.sh and edit the following variables:

• DATA_EXPORTER_SERVICE_ACCOUNT: getting it by creating a new service account for your data exporter to access GCS buckets.

Run command to give DATA_EXPORTER_SERVICE_ACCOUNT an ACL access to GCS buckets

Secondly, you need to set your data exporter's url in config.js so that Voice Builder knows where to send Voice Specification information to.

Open /config.js and add DATA_EXPORTER_API to the config as follows:

where BASE_URL is your data exporter url and API_KEY is the api key of your data exporter.

Redeploy Voice Builder UI instance so that it now has a new config and knows where to send Voice Specification info. to your data exporter

Try to create a new job! Voice Builder should now send a request to your DATA_EXPORTER_URL with the created job's Voice Specification.

VoiceBuildingSpecification is a JSON definition of the voice specification. This specification is created by the Voice Builder backend when a user triggers a voice building request from the UI. It can be used by the data exporter (passed to the data exporter via its API) to convert files and by the TTS engine for its training parameters.

EngineParam

EngineParam contains a parameter for TTS Backend engine.

Path contains information about the file path.

For example, if you set up your data exporter, when you create a voice using our predefined Festival engine, Voice Builder will send the request body similar to below to your data exporter. Your data exporter then have to pre-process data and put them in job_folder location (which is gs://your-voice-builder-jobs/1 in this example). After all necessary files are placed in the folder, the actual voice building process will begin automatically as expected.

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Text to Speech in Python [With Code Examples]

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

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

How can you make Python speak?

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

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

The Libraries to Make Python Speak

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

• gTTS (Google text to Speech API)

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

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

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

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

Let’s see how PyTTSx3 works first…

Text-To-Speech With the PyTTSx3 Module

Before using this module remember to install it using pip:

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

You can use pip for that module too:

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

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

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

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

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

With just four lines of code! (excluding comments)

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

Can you see (hear) the difference?

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

could be written also as:

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

Change Voice with PyTTSx3

What else can we do with PyTTSx?

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

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

You will see an output similar to the one below:

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

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

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

Here are the voice IDs printed by the program:

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

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

How does it sound? 🙂

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

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

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

Text to Speech with gTTS

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

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

As usual, we install gTTS using pip:

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

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

To see all the language available you can use:

That’s an impressive list!

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

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

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

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

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

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

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

If you run the program you will hear the message.

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

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

Give it a try!

Change Voice with gTTS

How easy is it to change the voice with gTTS?

Is it even possible to customize the voice?

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

The language parameter uses IETF language tags.

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

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

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

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

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

Makes sense?

Work With Text to Speech Offline

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

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

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

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

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

Let’s try gTTS now…

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

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

If you want to make Python speak offline use PyTTSx3.

We have covered a lot!

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

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

Are you planning to use this for a specific project?

Let me know in the comments below 🙂

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

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

Related posts:

• Search for YouTube Videos Using Python [6 Lines of Code]
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1 thought on “Text to Speech in Python [With Code Examples]”

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

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How to create custom text-to-speech engine

As I know, TTS needs TTS engine to speak one language. In Android emulator 2.2, Pico TTS engine is default. It has only some popular languages. I can see some engines on Market which must be purchased to install. My question: is there any way to create a custom engine which support other languages?(by programming or using software)

(I don't know if I should post this question in StackOverflow or SuperUser. If wrong place, please migrate it)

• text-to-speech

• Please specify for which language you want to enable TTS functionality. Is your requirement for limited vocabulary (e.g TTS functionality for just digits 0 to 9) or for arbitrary text input ? –  Samyak Bhuta Commented Nov 1, 2011 at 13:07
• Any language if possible, I mean I want to create a new TTS engine by coding. –  emeraldhieu Commented Nov 3, 2011 at 8:18

3 Answers 3

I am also interested in making my tts engine. Here are some information I've found. On this link you can find a brief description what you have to do to make your tts engine for android. Since API level 14 there is abstract class for tts engine implementation. More on link .

But making conversion from text to speech isn't so easy. Some basic information what tts engine should implement can be found on wikipedia .

As far as my research goes the best architecture for making a TTS engine currently is Tacotron 2[ Paper here ], a neural network architecture for speech synthesis directly from text (can easily capture via OCR ). It has achieved a MOS(mean opinion score) of 4.53 comparable to a MOS of 4.58 for professionally recorded speech. The official implementation of Tacotron 2 is not public but there is a tensorflow implementation made using tensorflow 1.15.0 here . There is also a pytorch implementation by nvidia here which is more currently maintained. Both implementations can be retrained using dataset for a new language(language with no TTS implementation yet) for easy implementation of a TTS engine. You can also use the architectures above as a stepping stone to build your own architecture.

Use mic recording software to record every sound in IPA or the Internation Phonetic Alphabet. Then create a JSON file that has a pronunciation value for every word key. Finally, tell your program to speak each of the sounds in the IPA pronunciation to form an entire word. Depending on whether there is a question mark or a period, adjust the tone. If the sentence is happy sounding, increase the pitch. If the sentence is sad sounding decrease pitch. Analyze the sentiments of the sentences to determine the pitch.

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The Best Speech-to-Text Apps and Tools for Every Type of User

You don't need to use your fingers when you can type by talking with the best dictation software we've tested. It's fast, easy, and helps people who otherwise can't type.

• Best Text-to-Speech Tools
• Best Transcription Services

Table of Contents

Typing isn't easy or even possible for everyone, which is why you might prefer to talk. Speech-to-text software, also sometimes called dictation software, makes it possible, by turning what you say into typed text.

Speech-to-text software is different from voice control software, although some apps do both. Voice control is the accessibility feature that lets you open programs, select on-screen options, and otherwise control your device using only your voice. Both macOS and Windows have voice control included. It's called VoiceOver on macOS and Speech Recognition in Windows.

Don't confuse speech-to-text software with transcription software , either, even if the categories overlap. Transcription software is typically for transcribing meetings or recordings, sometimes of multiple people, and generally after the fact. Dictation software, meanwhile, is a way to use your voice to type in real time. You talk to your computer or mobile device and immediately see the words on the screen. You can add punctuation by saying the name of the punctuation out loud—for example, "period," "comma," or "open quote" and "end quote."

Speech-to-text features or apps also should not be confused with text-to-speech tools , sometimes known as screen readers, which read text on the screen to you aloud.

Recommended by Our Editors

Most people don't need to install software to dictate text to their computer or phone. That's because every major operating system has a speech-to-text feature built in, and they work about as well as anything else on the market. Here we point out where to find these features on your device, and talk about a powerful commercial product with more features, should you need to do more with a speech-to-text tool than the built-in options offer.

Best Speech-to-Text Tool for Windows

Windows Speech, often referred to as voice typing, was among the most accurate tools I tested for this article. Both Windows 10 and Windows 11 come with Speech, which you can try out using the keyboard shortcut Windows Key-H anywhere you can type. Up pops a window with a microphone icon. Tap the microphone and start talking. Text shows up more or less in real time. 

You can add punctuation manually using commands , or you can try the experimental auto-punctuation feature. As a writer, I prefer adding punctuation manually—I'm pretty particular about my punctuation—but the automated feature worked fairly well and I could imagine it being good enough for some people. See our complete guide to learn more about using speech recognition and dictation in Windows .

Best Speech-to-Text Tool for Microsoft Office

You can dictate text in Microsoft Office by clicking the prominent Dictate button in all versions of Word, Powerpoint, OneNote, and Outlook. This brings the excellent engine Microsoft offers all Windows users, complete with the auto-punctuation feature, to just about every major operating system—the web, Android, iOS, and macOS versions of Office all include this dictation feature. It's great news if you use one of those systems and don't love the built-in speech-to-text engine.

Best Speech-to-Text Tool for macOS

Apple has included Dictation in macOS since 2012. To enable the feature, head to System Settings > Keyboard and scroll down to Dictation, where you can also set a keyboard shortcut. Newer Macs have a dedicated function key that looks like a microphone (F5) to enable and disable dictation in the top row of the keyboard. The speech detection is very accurate and shows up in near real time. You can add punctuation with spoken commands . Potentially incorrect words are underlined in blue after you're done with dictation, and you can right-click or Command-click on them to see other potential options, similar to how spellcheck works. Note that Apple silicon Macs can do dictation for the most common languages offline, whereas Intel Macs send audio to Apple servers for processing.

Best Speech-to-Text Tool for Apple Mobile Devices

Dictation (Mobile)

If you use the default keyboard on the iPhone and iPad, there's a microphone icon to the left of the space bar (as shown in the image) or sometimes below the space bar on the right side, that you can tap to use dictation. It works almost exactly the same as on macOS. Tap that microphone key and a microphone icon will show up next to your cursor. Start talking and your text will appear. You can add punctuation and formatting using spoken commands , just like on the Mac. The text recognition is accurate, the same as on the Mac.

Best Speech-to-Text App for Android

Android's default keyboard, Gboard, also has a built-in dictation feature . Tap the microphone in the top-right corner of the keyboard and start talking. It works in any Android app where you can type text, and the recognition is quite accurate. You can add punctuation with spoken commands, like saying "comma" and "period," just like on other systems.

Best Speech-to-Text Tool for Google Docs

Google Docs Voice Typing

Google Docs has a built-in dictation feature called Voice Typing . Google says it only works if you're using the Chrome browser, but by observation it works in Microsoft Edge and perhaps other Chromium-based browsers. Click Tools > Start voice typing and a large microphone icon appears, which you can click to start talking. Punctuation and formatting is handled by voice commands . Recognition works about as well as Gboard, which makes sense—they're likely using the exact same engine.

Most Powerful Speech-to-Text App

Dragon Professional

Dragon is one of the most sophisticated speech-to-text tools. You use it not only to type using your voice but also to operate your computer with voice control. Dragon Professional, the most general version, isn't cheap at $699. A mobile-only version, Dragon Professional Anywhere, is a $15-per-month subscription with a one-week free trial. Additional versions of the software are available for use by legal, health care, and law enforcement professionals, with a focus on understanding the specialized language in those sectors. If you need a business-grade speech-to-text tool that's more powerful than the default software that comes with your operating system, Dragon is worth looking into.

The Best Text-to-Speech Apps

If you're interested in learning more accessibility and productivity uses for your tech, see our overview of the best text-to-speech tools , also called screen readers.

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About justin pot, contributor.

Justin Pot believes technology is a tool, not a way of life. He writes tutorials and essays that inform and entertain. He loves beer, technology, nature, and people, not necessarily in that order. Learn more at  JustinPot.com .

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Build a Customizable Text-to-Speech System from Scratch

Updated on Feb 12,2024

Table of Contents:

• Introduction

Understanding the Source Code

Changing the text-to-speech language, creating the text-to-speech engine.

• testing the Text-to-Speech System

Fine-tuning the Speed and Language

Setting up the environment, writing the java code, configuring the text-to-speech engine, adding speech functionality to buttons, checking language support, handling text-to-speech errors, enhancing speech application performance, optimizing speed and resource usage, building a text-to-speech system.

In today's digital age, text-to-speech (TTS) systems have become an essential component of many applications. These systems convert written text into spoken words, providing accessibility and convenience to users. If you're interested in building your own TTS system, this article will guide you through the process. Let's dive into the details!

The first step in building a TTS system is to understand the source code. This code serves as the foundation for the entire system. By comprehending the logic and structure of the code, you'll gain insights into how the TTS functionality is implemented.

One of the key features of a TTS system is its ability to speak in different languages. In this section, we'll explore how to change the language settings of the TTS engine. By selecting the desired language, you can ensure that the system Speaks in the language of your choice.

A crucial component of any TTS system is the text-to-speech engine. This engine processes the provided text and generates the corresponding speech output. In this section, we'll learn how to create an efficient and accurate text-to-speech engine that delivers high-quality speech.

Testing the Text-to-Speech System

To ensure the reliability and effectiveness of your TTS system, it's important to thoroughly test it. This involves verifying the system's performance in different scenarios and assessing its ability to accurately convert text into speech. We'll discuss various testing techniques and best practices in this section.

The speed and language of the TTS system play a crucial role in user experience. In this section, we'll explore how to fine-tune the speed of the speech output, allowing users to control the pace at which the text is spoken. Additionally, we'll discuss techniques for improving the TTS system's language support, including accents and dialects.

Implementing the Text-to-Speech System

Now that we have a solid understanding of the TTS system, let's move on to its implementation. In this section, we'll dive into the technical details and guide you through the steps required to build your own TTS system.

Before we begin coding, we need to set up the development environment. This involves installing the necessary software and libraries required for building the TTS system. We'll provide detailed instructions on how to get started with the setup process.

Java is a widely used programming language for building TTS systems. In this section, we'll write the necessary Java code to create the TTS functionality. We'll cover topics such as text processing, Speech Synthesis , and integrating the TTS engine into the application.

Configuring the TTS engine is an important step in customizing the TTS system to meet your requirements. From adjusting the voice settings to selecting the desired speech rate, we'll explore various configuration options and provide guidance on how to optimize the TTS engine for your application.

To make the TTS system user-friendly, we can add speech functionality to buttons in the application's user interface. This enables users to easily convert selected text into speech by simply clicking a button. We'll walk you through the process of integrating speech functionality into buttons and handling user interactions.

Improving the Text-to-Speech System

Building a functional TTS system is just the beginning. To ensure its effectiveness and user satisfaction, it's crucial to continuously improve and optimize the system. In this section, we'll discuss various strategies and techniques for enhancing the performance and capabilities of your TTS system.

Language support is a critical aspect of any TTS system. It's important to regularly check and update the language support to accommodate new languages or dialects. We'll explore methods for checking language support and expanding the TTS system's capabilities to reach a broader audience.

While developing a TTS system, it's inevitable to encounter errors or inconsistencies in the speech output. It's crucial to handle these errors gracefully and provide Meaningful feedback to the users. We'll discuss error handling strategies and best practices for delivering a seamless and error-free TTS experience.

The performance of a TTS system greatly impacts the user experience. In this section, we'll delve into techniques for optimizing the speech synthesis process, reducing latency, and achieving real-time speech generation. By enhancing the performance of your TTS system, you can deliver smooth and responsive speech output.

Efficient resource usage is essential for any software application. In the context of a TTS system, optimizing speed and resource usage can lead to significant improvements in performance and user satisfaction. We'll explore strategies for minimizing resource consumption and maximizing the speed of the TTS engine.

In conclusion, building a text-to-speech system allows you to provide a valuable and accessible feature to your applications. By following the steps outlined in this article, you can create a robust and customizable TTS system that meets the needs of your users. Keep exploring and refining your TTS system to deliver an exceptional speech experience!

Highlights:

• Learn how to build a text-to-speech system from scratch
• Understand the source code and logic behind a TTS system
• Customize the TTS system by changing languages and speech speed
• Implement the TTS system using Java and integrate it into your application
• Improve the TTS system by optimizing performance and handling errors
• Enhance user experience by adding speech functionality to buttons

Q: Can I build a TTS system without programming knowledge? A: Building a TTS system does require some programming knowledge. However, there are user-friendly TTS tools available that allow you to create basic systems without coding.

Q: Is it possible to integrate the TTS system into mobile applications? A: Yes, the TTS system can be integrated into mobile applications by utilizing the appropriate mobile development frameworks and APIs.

Q: Can I add multiple languages to my TTS system? A: Yes, you can add support for multiple languages in your TTS system. Most TTS engines provide language packs that can be installed and used as needed.

Q: Are there any licensing or copyright restrictions for using TTS engines? A: Some TTS engines may have specific licensing requirements or restrictions. It's important to review and comply with the terms and conditions of the chosen TTS engine before integrating it into your application.

Q: How can I improve the naturalness of the speech output in my TTS system? A: Improving the naturalness of the speech output can be done by fine-tuning the TTS engine's parameters, such as prosody, intonation, and pronunciation. Additionally, using high-quality speech synthesis models can greatly enhance the naturalness of the generated speech.

• Example TTS Engine
• Java TTS Library

The above is a brief introduction to Build a Customizable Text-to-Speech System from Scratch

Let's move on to the first section of Build a Customizable Text-to-Speech System from Scratch

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The White House 1600 Pennsylvania Ave NW Washington, DC 20500

Remarks by President   Biden on the Supreme Court’s Immunity   Ruling

7:45 P.M. EDT

THE PRESIDENT:  Good evening. 

The presidency is the most powerful office in the world.  It’s an office that not only tests your judgment, perhaps even more importantly it’s an office that can test your character because you not only face moments where you need the courage to exercise the full power of the presidency, you also face moments where you need the wisdom to respect the limits of the power of the office of the presidency.

This nation was founded on the principle that there are no kings in America.  Each — each of us is equal before the law.  No one — no one is above the law, not even the president of the United States. 

With today’s Supreme Court decision on presidential immunity, that fundamentally changed.  For all — for all practical purposes, today’s decision almost certainly means that there are virtually no limits on what a president can do. 

This is a fundamentally new principle, and it’s a dangerous precedent because the power of the office will no longer be constrained by the law, even including the Supreme Court of the United States.  The only limits will be self-imposed by the president alone.

This decision today has continued the Court’s attack in recent years on a wide range of long-established legal principles in our nation, from gutting voting rights and civil rights to taking away a woman’s right to choose to today’s decision that undermines the rule of law of this nation.

Nearly four years ago, my predecessor sent a violent mob to the U.S. Capitol to stop the peaceful transfer of power.  We all saw it with our own eyes.  We sat there and watched it happen that day.  Attack on the police.  The ransacking of the Capitol.  A mob literally hunting down the House Speaker, Nancy Pelosi.  Gallows erected to hang the vice president, Mike Pence.  I think it’s fair to say it was one of the darkest days in the history of America.

Now the man who sent that mob to the U.S. Capitol is facing potential criminal conviction for what happened that day.  And the American people deserve to have an answer in the courts before the upcoming election.  The public has a right to know the answer about what happened on January 6th before they ask to vote again this year.

Now, because of today’s decision, that is highly, highly unlikely.  It’s a terrible disservice to the people of this nation.

So, now — now the American people have to do what the Court should have been willing to do but would not.  The America people have to render a judgment about Donald Trump’s behavior.  The American people must decide whether Donald Trump’s assault on our democracy on January 6th makes him unfit for public office in the highest office in the land.  The American people must decide if Trump’s embrace of violence to preserve his power is acceptable. 

Perhaps most importantly, the American people must decide if they want to entrust the president — once again, the presidency to Donald Trump, now knowing he’ll be even more emboldened to do whatever he pleases whenever he wants to do it.

You know, at the outset of our nation, it was the character of George Washington, our first president, that defined the presidency.  He believed power was limited, not absolute, and that power would always reside with the people — always.

Now, over 200 years later, with today’s Supreme Court decision, once again it will depend on the character of the men and women who hold that presidency that are going to define the limits of the power of the presidency, because the law will no longer do it.

I know I will respect the limits of the presidential power, as I have for three and a half years.  But any president, including Donald Trump, will now be free to ignore the law. 

I concur with Justice Sotomayor’s dissent today.  She — here’s what she said.  She said, “In every use of official power, the president is now a king above the law.  With fear for our democracy, I dissent,” end of quote.

So should the American people dissent.  I dissent. 

May God bless you all.  And may God help preserve our democracy.  Thank you.  And may God protect our troops.

7:49 P.M. EDT

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Biden assails Project 2025, a plan to transform government, and Trump’s claim to be unaware of it

FILE - Republican presidential candidate former President Donald Trump speaks at a campaign rally, June 22, 2024, in Philadelphia. Trump is seeking to distance himself from a plan for a massive overhaul of the federal government drafted by some of his administration officials. Some of these men are expected to take high-level roles if the Republican presumptive nominee is elected back into the White House. Trump is saying on Truth Social that he “knew nothing about Project 2025.” (AP Photo/Chris Szagola, File)

FILE - Kevin Roberts, president of The Heritage Foundation, speaks in Washington, April 12, 2023. Former President Donald Trump is seeking to distance himself from a plan for a massive overhaul of the federal government drafted by some of his administration officials. Some of these men are expected to take high-level roles if Trump is elected back into the White House. Roberts said that Republicans are “in the process of taking this country back” when he spoke Tuesday, July 2, 2024, on Steve Bannon’s “War Room” podcast. (AP Photo/J. Scott Applewhite)

FILE - Paul Dans, director of Project 2025 at the Heritage Foundation, speaks at the National Religious Broadcasters convention, Feb. 22, 2024, in Nashville, Tenn. Former President Donald Trump is seeking to distance himself from a plan for a massive overhaul of the federal government drafted by some of his administration officials, including Dans. Some of these men are expected to take high-level roles if the Republican presumptive nominee is elected back into the White House. Trump is saying on Truth Social that he “knew nothing about Project 2025.” (AP Photo/George Walker IV, File)

FILE - Acting director of Office of Management and Budget Russ Vought listens during a television interview at the White House, Oct. 21, 2019, in Washington. Former President Donald Trump is seeking to distance himself from a plan for a massive overhaul of the federal government drafted by some of his administration officials, including Vought. Some of these men are expected to take high-level roles if the Republican presumptive nominee is elected back into the White House. Trump is saying on Truth Social that he “knew nothing about Project 2025.” (AP Photo/Alex Brandon, File)

FILE - Director of the Presidential Personnel Office John McEntee is seen on the South Lawn of the White House, Aug. 27, 2020, in Washington. Former President Donald Trump is seeking to distance himself from a plan for a massive overhaul of the federal government drafted by some of his administration officials, including McEntee. Some of these men are expected to take high-level roles if the Republican presumptive nominee is elected back into the White House. Trump is saying on Truth Social that he “knew nothing about Project 2025.” (AP Photo/Evan Vucci, File)

• Copy Link copied

MIAMI (AP) — Donald Trump has distanced himself from Project 2025, a massive proposed overhaul of the federal government drafted by longtime allies and former officials in his administration, days after the head of the think tank responsible for the program suggested there would be a second American Revolution.

“I know nothing about Project 2025,” Trump posted on his social media website. “I have no idea who is behind it. I disagree with some of the things they’re saying and some of the things they’re saying are absolutely ridiculous and abysmal. Anything they do, I wish them luck, but I have nothing to do with them.”

The 922-page plan outlines a dramatic expansion of presidential power and a plan to fire as many as 50,000 government workers to replace them with Trump loyalists. President Joe Biden’s reelection campaign has worked to draw more attention to the agenda, particularly as Biden tries to keep fellow Democrats on board after his disastrous debate.

“He’s trying to hide his connections to his allies’ extreme Project 2025 agenda,” Biden said of Trump in a statement released by his campaign Saturday. “The only problem? It was written for him, by those closest to him. Project 2025 should scare every single American.”

Trump has outlined his own plans to remake the government if he wins a second term, including staging the largest deportation operation in U.S. history and imposing tariffs on potentially all imports. His campaign has previously warned outside allies not to presume to speak for the former president and suggested their transition-in-waiting efforts were unhelpful.

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Heritage Foundation President Kevin Roberts said on Steve Bannon’s “War Room” podcast Tuesday that Republicans are “in the process of taking this country back.” Former U.S. Rep. Dave Brat of Virginia hosted the show for Bannon, who is serving a four-month prison term.

“We are in the process of the second American Revolution, which will remain bloodless if the left allows it to be,” Roberts said.

Those comments were widely circulated online and assailed by Biden’s campaign, which accused Trump and his allies of “dreaming of a violent revolution to destroy the very idea of America.”

Some of the people involved in Project 2025 are former senior administration officials. The project’s director is Paul Dans, who served as chief of staff at the U.S. Office of Personnel Management under Trump. Trump’s campaign spokeswoman Karoline Leavitt was featured in one of Project 2025’s videos.

John McEntee, a former director of the White House Presidential Personnel Office in the Trump administration, is a senior adviser. McEntee told the conservative news site The Daily Wire earlier this year that Project 2025’s team would integrate a lot of its work with the campaign after the summer when Trump would announce his transition team.

Trump’s comments on Project 2025 come before the Republican Party’s meetings this coming week to begin to draft its party platform.

Project 2025 has been preparing its own 180-day agenda for the next administration that it plans to share privately, rather than as part of its public-facing book of priorities for a Republican president. A key Trump ally, Russ Vought, who contributed to Project 2025 and is drafting this final pillar, is also on the Republican National Committee’s platform writing committee.

Project 2025 said in a statement it not tied to a specific candidate or campaign.

“We are a coalition of more than 110 conservative groups advocating policy and personnel recommendations for the next conservative president,” it said. “But it is ultimately up to that president, who we believe will be President Trump, to decide which recommendations to implement.”

A Biden campaign spokesperson said Project 2025 staff members are also leading the Republican policy platform. “Project 2025 is the extreme policy and personnel playbook for Trump’s second term that should scare the hell out of the American people,” said Ammar Moussa.

On Thursday, as the country celebrated Independence Day and Biden prepared for his television interview after his halting debate performance, the president’s campaign posted on X a shot from the dystopian TV drama “The Handmaid’s Tale” showing a group of women in the show’s red dresses and white hats standing in formation by a reflecting pool with a cross at the far end where the Washington Monument should be. The story revolves around women who are stripped of their identities and forced to give birth to children for other couples in a totalitarian regime.

“Fourth of July under Trump’s Project 2025,” the post said.

Associated Press writers Jill Colvin in New York and Lisa Mascaro in Washington contributed to this report.

Biden warns Supreme Court presidential immunity ruling is 'dangerous precedent'

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