July 26, 2024 • Business, Tips • by Deva Jayantha

A Simple Introduction to Text-to-Speech Techniques in Natural Language Processing

A Simple Introduction to Text-to-Speech Techniques in Natural Language Processing

A simple introduction Text-to-speech and how to implement in project

  1. What is Natural Language Processing
  2. What is Text To Speech
  3. Example code implement

As time goes on, technology keeps getting better and better, especially with the growth of Artificial Intelligence (AI). This term is quite familiar now because many tools have been developed to help us with various tasks.

One part of Artificial Intelligence is Natural Language Processing (NLP). This term might be unfamiliar to many, but we use its applications often without realizing it, such as Apple Siri, Google Translate, Grammarly, Duolingo, and customer service chatbots. NLP is a technique within AI that focuses on interactions between computers and humans through natural language. In other words, it aims to enable machines to understand human language, with the goal of producing results that are useful to people.

A simple example we often encounter is translating text from Indonesian to English. Google uses machines to process text, producing results as either text or audio. Text and audio are representations of human natural language.

There are several subfields within NLP, such as Question Answering Systems (QAS), summarization, machine translation, speech processing, and sentiment analysis.

Natural Language Processing (NLP) covers a wide range of fields, making it impossible to discuss all at once. Currently, we want to focus on one aspect: speech processing. This can be divided into two parts: speech recognition and text-to-speech. We will discuss text-to-speech, as it is something we frequently use in our daily lives.

Text-to-Speech technology is a type of speech synthesis that converts written text into spoken words using computer algorithms. This allows machines to communicate with humans in a natural-sounding voice by processing text into synthesized speech. It’s important to understand what speech synthesis means. Speech synthesis refers to the process of using computers to generate artificial human speech.

This is a generative model commonly used to convert written text into audio information, and it’s utilized in voice-enabled services and mobile applications. The key point is that we use a model that has undergone a learning process, allowing us to generate audio results from text.

Currently, there are many tools with their own models to support text-to-speech implementation, such as Google Cloud Text-to-Speech, IBM Watson Text-to-Speech, and Microsoft Azure Text-to-Speech. These tools offer the advantage of being highly customizable to meet specific needs.

For those interested in learning about text-to-speech, especially Python programmers, libraries like Google Text-to-Speech can be used.

from gtts import gTTS
import os

text = "Hello, world!"
tts = gTTS(text=text, lang='en')
tts.save("output.mp3")
os.system("start output.mp3") # or "open output.mp3" on macOSSe

 

The code snippet above is an example of creating text-to-speech using a Python library. This library provides a function called gTTS(), which processes a text input into audio.

One option for real-world projects is Google Cloud Text-to-Speech. Its advantages include high-quality voices, customization features, and API integration.

Here is an example of implementing text-to-speech in a project using the Google Cloud Text-to-Speech service. The system overview is as follows: text input is provided through a website, processed to generate voice, and then stored as both text and audio. The list of content displayed includes both text and audio, which can be played at any time.

The purposes of text-to-speech are numerous, including accessibility, language learning, customer service, and navigation guidance.

# google text to speech credential
ACCOUNT = {}
text_title = "Hello World"

credentials = service_account.Credentials.from_service_account_info(ACCOUNT)

"""Synthesizes speech from the input string of text."""
clientTextToSpeech = texttospeech.TextToSpeechClient(credentials=credentials)

input_text = texttospeech.SynthesisInput(text=text_title)

voice = texttospeech.VoiceSelectionParams(
language_code="en-gb",
name="en-GB-Standard-A",
ssml_gender=texttospeech.SsmlVoiceGender.FEMALE,
)

audio_config = texttospeech.AudioConfig(
audio_encoding=texttospeech.AudioEncoding.MP3
)

response = clientTextToSpeech.synthesize_speech(
input=input_text, voice=voice, audio_config=audio_config
)

Here is an example code snippet implementing Google Cloud Text-to-Speech. For the complete implementation, you can refer to this link.

This concludes the discussion on the introduction to text-to-speech, which is one of the techniques in natural language processing. Hope this helps you!

Why Choose Timedoor Indonesia?

At Timedoor Indonesia, we specialize in implementing advanced technologies like Text-to-Speech for various applications. Our IT development services include creating applications with TTS features to enhance accessibility, language learning, customer service, and navigation guidance. Contact us for IT solutions tailored to your needs.

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