Abstract

In today’s digital landscape, voice messaging has become an essential mode of communication, offering speed and convenience. However, background noise, misinterpretations, and accessibility challenges often hinder its effectiveness. WhatsWhisper is an AI-powered solution that transcribes WhatsApp voice messages with high accuracy, enhances audio quality, and integrates intelligent task scheduling. By leveraging OpenAI’s Whisper for transcription, Alibaba’s ZipEnhancer for noise reduction, and Microsoft’s Phi-3.5 for task extraction, WhatsWhisper ensures seamless communication. Additionally, Google Calendar integration enables efficient scheduling through voice commands. This paper presents the system architecture, methodology, and experimental results demonstrating the efficiency and accuracy of WhatsWhisper in transforming voice messages into structured, actionable content.

Introduction

Voice messages have become a preferred means of communication due to their efficiency and convenience. However, users often face challenges such as poor audio quality, difficulties in understanding lengthy messages, and the inability to convert spoken content into structured information. WhatsWhisper addresses these limitations by introducing an AI-driven solution that not only transcribes voice messages but also enhances audio clarity and extracts actionable tasks. By integrating state-of-the-art models and automation tools, WhatsWhisper transforms voice communication into a more accessible and organized process. This paper explores the technological foundation of WhatsWhisper, detailing its components and the benefits it offers to users.

Methodology

WhatsWhisper consists of multiple AI-driven modules that work together to process voice messages efficiently. The core methodology follows these key steps:

1. Voice Message Reception: Users send voice messages via WhatsApp, which are received using the WhatsApp Web API through venom-bot.

2. Audio Enhancement: The audio file is optionally processed through Alibaba’s ZipEnhancer for noise suppression and quality improvement.

3. Speech-to-Text Conversion: OpenAI’s Whisper ASR transcribes the enhanced audio into text with high accuracy.

4. Task Extraction & Command Parsing: If a scheduling command is detected, Microsoft’s Phi-3.5 analyzes the transcription to extract task details.

5. Event Scheduling: Extracted details are used to create Google Calendar events automatically.

6. User Response: The transcribed text or task confirmation is sent back to the user.

Experiments

To evaluate the performance of WhatsWhisper, we conducted a series of experiments:

Transcription Accuracy: We tested the Whisper model with various voice messages containing background noise, different accents, and varying speech speeds.

Audio Enhancement Effectiveness: We measured the improvement in speech clarity after applying ZipEnhancer.

Task Extraction Efficiency: We assessed Phi-3.5’s ability to correctly extract scheduling details from natural language commands.

System Latency: We analyzed the response time of each module to ensure real-time processing capabilities.

Conclusion

WhatsWhisper redefines voice messaging by integrating AI-driven transcription, audio enhancement, and task automation. By leveraging cutting-edge models such as Whisper, ZipEnhancer, and Phi-3.5, it ensures seamless communication and improved productivity. Experimental results confirm the system’s high accuracy, efficiency, and reliability in handling voice messages in real-world scenarios. Future work may include expanding language support, enhancing contextual understanding, and integrating additional productivity tools. WhatsWhisper represents a significant step toward making voice communication more accessible and efficient in the digital age.