You will get Real-Time AI Voice Agent with Low Latency
Project details
Struggling with slow or robotic AI voice systems?
I build real-time Speech-to-Speech AI pipelines that are
fast, natural, and production-ready — based on a real
delivered project.
What you get:
• Full pipeline: STT → LLM → TTS with low latency
• Speech Emotion Recognition for natural responses
• Clean Python/FastAPI code with streaming support
• Tested, documented, and ready to deploy
Tools: Whisper, ElevenLabs, Deepgram, PyTorch, FastAPI
I build real-time Speech-to-Speech AI pipelines that are
fast, natural, and production-ready — based on a real
delivered project.
What you get:
• Full pipeline: STT → LLM → TTS with low latency
• Speech Emotion Recognition for natural responses
• Clean Python/FastAPI code with streaming support
• Tested, documented, and ready to deploy
Tools: Whisper, ElevenLabs, Deepgram, PyTorch, FastAPI
AI Algorithms
Multimodal Large Language ModelAI Applications
AI Text-to-SpeechAI Development Language
PythonAI Models
ChatGPT, OpenAI CodexWhat's included
| Service Tiers |
Starter
$15
|
Standard
$35
|
Advanced
$60
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | - |
Model Documentation | - | - | - |
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | - | - |
Setup File | - | - | - |
Source Code |
3 reviews
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LP
Liza P.
Apr 3, 2026
Audio Replay Data Collection Participants Needed (One-Time Task) - O-0250042
Great work!
JC
Jeremy C.
Dec 22, 2025
AI Integration Engineer for ChatGPT with Python knowledge (No Agencies) (Intermediate / Intern)
Yassmen was generally polite to work with and worked hard to solve the assigned tasks. This was a junior role so we expected there would be learning involved. Language (english) was a challenge however this role was a recommendation from another team member so we expected this.
Pros
- Friendly
- Worked hard to get a solution
- Followed task instructions
Cons
- English Skills - Unable to have conversations in English resulting in only text. Relied heavily on teammate who spoke the same language.
- Communication - Lacking in clear communication regarding current work / deadlines. Constantly required us to chase for updates on project items and often to chase the teammate to get an answer due to lack of answer.
- Handover was incomplete
- Flexibility - on a few occasions was upset because the priorities changed due to shifting business priorities. Unfortunately this is the nature of business.
Pros
- Friendly
- Worked hard to get a solution
- Followed task instructions
Cons
- English Skills - Unable to have conversations in English resulting in only text. Relied heavily on teammate who spoke the same language.
- Communication - Lacking in clear communication regarding current work / deadlines. Constantly required us to chase for updates on project items and often to chase the teammate to get an answer due to lack of answer.
- Handover was incomplete
- Flexibility - on a few occasions was upset because the priorities changed due to shifting business priorities. Unfortunately this is the nature of business.
MH
Mohamed H.
Jun 11, 2025
Real time speech to speech pipeline MVP
About Yassmen
AI Engineer | LLM & Agentic Workflows Specialist
55%
Job Success
Cairo, Egypt - 10:21 am local time
With 3 years of experience, I've built systems that go beyond the demo straight to real users, real load, and real edge cases. My clients come to me when they need agent workflows that route and recover intelligently, knowledge systems that retrieve the right answer fast, and conversation flows that hold context across complex multi-turn interactions.
I don't just integrate an API and call it a system. I think about concurrency, state persistence, error handling, and what happens when things go wrong at 2am because that's what production actually looks like.
My expertise includes:
1. Multi-agent orchestration: LangChain, LangGraph, OpenAI Assistants, custom routing logic
2. RAG systems: hybrid search, pgvector, document ingestion and embedding pipelines
3. Conversational AI: GPT-4, Azure OpenAI, Claude, multi-turn context management
4. Speech AI: real-time speech-to-speech pipelines, speech emotion recognition, audio processing
5. Computer Vision: image processing pipelines, deep learning models, OpenCV
6. OCR & Document AI: text extraction, document parsing, intelligent data capture
7. Backend systems: Python, FastAPI, PostgreSQL, async workflows, Docker
8. Platform integrations: WhatsApp Business API, web chat, messaging platforms
9 Reliability: Langfuse, logging, fallback handling, human-in-the-loop workflows
If you need it built properly, let's talk.
Steps for completing your project
After purchasing the project, send requirements so Yassmen can start the project.
Delivery time starts when Yassmen receives requirements from you.
Yassmen works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Analysis
We align on your use case, target latency, language, and preferred tools
Architecture Design
I select the best STT, LLM, and TTS models and design the full pipeline