case study

Text-to-Speech & Speech-to-Text

Client

CodeLink

industry

services

AI ML

Custom Software Development

Overview

The CodeLink text-to-speech converts speech to text and back smoothly in both English and Vietnamese with a natural output available in a range of voices and tones.

Team Model

  • Front-end Developer
  • Back-end Developer
  • DevOps
  • AI & ML Lead
  • Quality Assurance Engineer
  • Technology

    AI/ML

    TensorflowJS

    RabbitMQ

    Kubernetes

    Python

    Firebase Crashlytics

    Google Cloud Platform (GCP)

    ReactJS

    Redis

    NestJS

    NextJS

    Flutter

    AI (Pytorch)

    Tensorflow

    Platform

  • Web
  • Challenge

    How can we leverage Machine Learning to create a fluid and natural text-to-speech and speech-to-text model for Vietnamese?

    Request

    CodeLink tasked the internal product team with creating a model and standards for text-to-speech and speech-to-text between English and Vietnamese.

    Engagement Model

    We worked as an autonomous team to create the text-to-speech and speech-to-text model for Vietnamese.

    Engagement Length and Scale

    CodeLink internal teams worked for 4 months to develop the proprietary text-to-speech and speech-to-text AI model.

    Project Outcome

    To find the best approach, we tested several TTS and STT models. Because there were no quality Vietnamese datasets available in the market, we decided to start with text-to-speech technology in Vietnamese. We created an annotation app using ReactJS, Firebase, and GCP for the annotation process and coordinated with local voice talent and text labelers to create the data. To optimize the core back-end, we built a Kubernetes cluster for inference services. After training the model, we deployed the system.

    Tags

    Autonomous Team

    Artificial Intelligence

    Machine Learning

    Product Development

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