Couch Fashion is a pioneering force in the fashion and styling industry, leading the way with its groundbreaking AI Styling Assistant. The distinguished B2B Fashion Tech Company, has partnered with leading eCommerce giants like Myntra and Flipkart, revolutionizing sales and enhancing customer retention. With a vision to innovate further, Couch Fashion embarked on a journey to develop a cutting-edge recommendation system and a virtual fashion assistant, poised to redefine the fashion industry landscape. Their mission is clear: to empower individuals with effortless style and unshakable confidence. The innovative platform caters to both fashion enthusiasts and trendsetters, offering an array of invaluable features. Couch Fashion's primary objectives include providing users with expert styling advice by leveraging AI technology, personalized outfit recommendations, and an extensive source of fashion inspiration. They've seamlessly integrated shopping into their platform, making the fashion discovery and purchase process remarkably convenient. Moreover, Couch Fashion aims to keep users in the know about the latest fashion trends, ensuring that they consistently project a stylish and contemporary image.
Our challenges encompassed the integration of audio input for voice interaction with Dialog Flow, which involved enabling devices like computers and phones to process and comprehend spoken commands. This was akin to implementing a virtual conversational partner. Subsequently, we tackled the task of text-to-speech conversion to produce responses in a human-like voice, ensuring that the interactions with the device were devoid of a robotic tone. This was a crucial aspect of delivering a seamless and user-friendly experience.
Enabling voice interaction with the system demanded seamless integration of AI-driven audio input capabilities into the Meteor JS platform. This technical feat involved not only facilitating natural language processing but also ensuring precise communication with Dialog Flow.
To deliver an engaging user experience, we tackled the challenge of converting textual responses into natural and human-like voice output using AI-driven voice synthesis technologies. This endeavor guaranteed that conversations with the system felt authentic and conversational.
Within the domain of audio processing optimization, two closely linked challenges come to light. The first entails the conversion of stereo audio streams into a mono format, while the second focuses on the creation of lifelike text-to-speech output. These carefully devised solutions serve as key drivers in streamlining audio processing and enhancing the interaction between users and the system, ushering in a more immersive and engaging experience.
To address the issue of converting stereo audio streams into a mono format suitable for Dialog Flow processing, we explored various strategies. One option involved considering "Recorder.js," a prebuilt recording library capable of automating recording and conversion tasks. However, it encountered compatibility issues with the MeteorJS system. Another approach was to transmit input from a single audio stream, either left or right, but this had the drawback of potentially delivering less clear audio for language processing. Ultimately, our solution combined both audio streams through a series of transformations, resulting in a mono audio format ideal for seamless integration with Dialog Flow.
Our goal was to make text sound natural for audio output, enhancing user interaction. Leveraging AI-powered voice synthesis technologies, such as the Google Text-to-Speech API, we converted text into lifelike audio files and delivered them to clients for playback. This innovative approach resulted in authentic voice interactions, enriching the overall user experience.
