Multilingual AI Voice Rewriter

@xcybernetic

Hello, I can deliver Phase 1 as a low-latency Hindi/English voice pipeline: streaming speech-to-text, language detection, transcript cleanup, and an LLM rephrase/grammar layer exposed through a clean API. I would first benchmark Whisper, Google STT, or Azure on your own accent/noise samples, then select the provider using measured word-error rate and latency rather than assumptions. The MVP will include: • real-time Hindi/English transcription • tap/voice “rephrase” flow • confidence, retry, and failure handling • Dockerized API, tests, logging, and setup guide • a small benchmark report covering accuracy and response time Milestones: (1) architecture and sample benchmark, (2) testable MVP, (3) production hardening and handover. I can provide the MVP in 5 days and complete hardening within 8 days. Before starting, please confirm whether Phase 1 needs a web demo, mobile SDK, or API-only delivery.

@eternitech

Hi, New on Freelancer — 20 years of development experience behind us. We're taking our first few projects here at a fraction of our normal rate purely to build our review history. You get senior agency work at junior pricing; we get a review. Straight trade. detecting nuances in multilingual speech often requires fine-tuning the model to handle language switching smoothly. I'd start by ensuring the AI can differentiate between Hindi and English phonetics accurately. Can you share any existing datasets or models you're currently using?

@shubhamraj3

I can build your Hindi/English real-time speech-to-text and GPT rephrasing module using Python, APIs and WebSockets.

@Z205698

Hello, I fully understand your multilingual AI voice rewriter needs and can deliver a stable production-grade solution for this first phase. 1. **Tech Stack** I use OpenAI Whisper for high-accuracy English & Hindi transcription, with solid accent and informal speech support. I can also switch to Google Speech-to-Text or Azure Speech Service seamlessly. For AI rewriting, I integrate GPT-class LLMs for grammar correction, restatement and polishing, meeting your GPT-level quality standard. The modular design supports your future e-commerce, payment and encryption modules. 2. **Timeline** - Testable MVP: 5 working days (full core flow: real-time transcription, one-click rewrite, final output) - Production optimization: 2 extra working days (edge case tuning, latency reduction, exception handling, stability check) Full delivery within 7 working days. 3. **Cost Breakdown** Total: 20 hours at ₹150 INR/hour, total ₹3,000 INR. - Milestone 1: Speech recognition module & bilingual tuning – 6h | ₹900 - Milestone 2: AI rewriting layer & prompt optimization – 6h | ₹900 - Milestone 3: Workflow build & end-to-end testing – 5h | ₹750 - Milestone 4: Acceptance, fixes & 1 free minor adjustment – 3h | ₹450 I have solid speech AI & LLM development experience, focusing on on-time, maintainable delivery with clean code and full documentation. Looking forward to long-term cooperation on this and future modules.

@pereval

I will design and deliver a real-time, low-latency Speech-to-Text and AI-driven grammar/rephrasing system. As a Senior Systems Architect and Voice AI integrations expert, I recently designed a production-ready Voice AI customer support agent (utilizing Whisper, ElevenLabs, and GPT-4o) handling 20,000+ support calls monthly with real-time text transcription, tool calling, and session memory context. Here is the technical design I propose for your MVP: 1. Real-time Speech-to-Text (STT): • Integrate OpenAI's Whisper API or a local Whisper-large-v3-turbo instance for high-accuracy translation of spoken Hindi and English. • Handle streaming audio input with chunk-based transcription for real-time visual response. 2. AI-driven Rephraser Engine: • Configure GPT-4o-mini / GPT-3.5 prompt templates for low-latency grammatical polishing and contextual rephrasing on demand. • Implement structured JSON schema outputs to ensure reliable parsing in the front-end application. 3. Phase 1 Architecture: • Deliver a clean Python/Node.js backend module with WebSocket endpoints for real-time audio ingress and event emissions. • Outline clear integration points for the e-commerce, payment, and crypto modules that follow. Ready to discuss latency optimization strategies and share my Voice AI repository layout.

@harshu384

Hello, I can develop a production-ready, real-time Hindi & English speech-to-text and AI rephrasing module with the following: Bilingual Speech Recognition: Accurate Hindi/English detection and transcription using Whisper or Google Speech-to-Text. Real-Time Processing: Streaming transcription using FastAPI + WebSockets for low-latency output. AI Rephrasing: GPT-class LLM for grammar correction, rewriting, and polishing on demand. Interactive Workflow: Users can speak, view the live transcript, and trigger rephrasing by voice or button. Scalable Architecture: Modular backend designed for production deployment and future feature expansion. Optimization: Focus on transcription accuracy, response latency, reliability, and API cost. I can deliver a testable MVP quickly and then harden it for production with proper testing, error handling, and monitoring.

@guptakishan805

With my 5+ years of experience as a Full Stack Developer specializing in AI Development and Natural Language Processing, I am the perfect fit to deliver your desired Multilingual AI Voice Rewriter module. I understand the complexity involved in seamlessly detecting and converting spoken language into clean, accurate text, especially for languages like Hindi and English. My extensive experience with technologies such as Google Speech-to-Text and Azure has proven their capability to support real-time translation at scale. Moreover, my track record in developing AI-powered solutions will be invaluable for implementing an AI layer that can rephrase or correct grammar on demand. I have worked extensively with GPT-class models, ensuring quality output aligned with your expectations. I also have strong proficiency in building multichannel platforms and deploying apps on the cloud - strengths that are relevant for this project. In terms of the timeline, my efficiency and thoroughness will ensure that a testable Minimum Viable Product can be delivered within a reasonable timeframe. I estimate the time it will take to move to production at around X weeks. As for costing, my rates are fair and competitive, and I'm happy to provide a clear milestone breakdown upon further discussion. Let's start Phase 1 together, and if you find satisfaction in my work ethic and output, we can proceed confidently to the remaining aspects of your project!