You will get Audio to Text Transcription Pipeline with Whisper, FastAPI and SRT Export
Top Rated
Project details
I will build you a complete audio transcription pipeline that converts any audio format into accurate, timestamped text and delivers it as JSON, SRT subtitles, or plain text, ready for subtitles, meeting notes, or feeding into your own systems. Unlike generic scripts, my pipeline handles recordings of any length through chunked processing, skips silence automatically with voice activity detection, and runs on ordinary CPU servers with no GPU required, keeping your running costs low. You will receive clean, documented source code tested on your own audio files before delivery.
AI Algorithms
Convolutional Neural Network, Generative Adversarial Network, Multimodal Large Language Model, Transformer ModelAI Applications
AI Content Creation, AI-Generated Code, Natural Language Generation, Natural Language Understanding, Speech Synthesis, Synthetic Data Generation, Text RecognitionAI Development Language
PythonAI Tools
Copy.ai, GitHub Copilot, Hugging Face, PyTorch, Replit, TensorFlow, Word2vecAI Models
LaMDA, LLaMA, Midjourney AI, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$75
|
Standard
$200
|
Advanced
$450
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 days |
Number of Revisions | 2 | 3 | 4 |
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 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$50 - $150
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About Faizan
AI Engineer | LLM, RAG & LangChain | Healthcare Full-Stack Developer
100%
Job Success
Rawalpindi, Pakistan - 3:25 pm local time
I build retrieval augmented generation systems for searching clinical documents in plain language, machine learning prediction models, clinical NLP pipelines, and analytics dashboards. My core stack is GPT-4, Claude, LangChain, and LangGraph for the AI layer, and Python, FastAPI, React, Node.js, and PostgreSQL for the application layer. I handle the full project myself, from data pipeline through deployment, including web application, API, and payment integration with Stripe when needed.
Recent Work:
HealthDataVitals: A live healthcare analytics platform I built and launched that tracks cost, quality, and performance data across providers, with the dashboard, data pipeline, and payment integration handled end to end.
Agentic Medical Document RAG System: Lets clinical teams upload medical documents and ask questions in plain language, built on LangChain with Pinecone and ChromaDB for vector search. It automatically detects and removes protected health information before storage, so documents stay searchable without exposing patient data.
AssistMedica, AI Clinic Scheduling Agent: A clinic administration dashboard with an agentic scheduling assistant powered by the Claude API, using tool calling for live schedule reads and updates, plus voice input and speech output for hands free appointment management.
Diabetes Prediction System: A machine learning prediction model built with Random Forest, XGBoost, and LightGBM in Python, with SHAP explainability so users can see which factors drove each prediction. Deployed through a web app, a REST API, and a Discord bot.
Multi-Label Disease Prediction from Nutrition Data: A full stack machine learning web app predicting risk across seven conditions, including diabetes, hypertension, and heart disease, from nutrition and lifestyle data. The production Random Forest model reached 99.71 percent accuracy, served through a live web interface with a model transparency dashboard.
Voice Controlled AI Assistant: Takes spoken questions, analyzes uploaded medical images, and replies out loud, useful for hands free clinical workflows like dictating notes. Supports OpenAI, Anthropic, and local open source models.
Tools I Use Most Often:
LangChain and LangGraph for RAG and AI agent development, OpenAI GPT-4, Claude, and Hugging Face for LLM and machine learning models, TensorFlow and PyTorch for deep learning, Pinecone and ChromaDB for vector search and document retrieval, Docker and AWS Lambda for hosting and deployment. For the application layer, React, Node.js, Python, FastAPI, and PostgreSQL for dashboards, APIs, and connecting everything together. For analytics and reporting, Tableau, Streamlit, and Gradio.
Handling Healthcare Data:
Healthcare and clinical data need careful handling. On my RAG project, I built automatic PHI detection and removal before any data entered the system, so documents could be searched without exposing patient details. On every project, I also set up role based access and encrypt data both in storage and in transit, following HIPAA aligned practices.
If you have a healthcare AI, EHR integration, or clinical data project in mind, send me a message with the type of data you are working with and the main question you want your system to answer. I will reply within a day with some initial thoughts.
Steps for completing your project
After purchasing the project, send requirements so Faizan can start the project.
Delivery time starts when Faizan receives requirements from you.
Faizan works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements review and audio assessment
I will review your sample audio files and requirements, confirm the expected transcription quality with your actual recordings, and align on the output format and delivery details before starting.
Pipeline setup and configuration
I will set up the transcription pipeline with the Whisper model, configure audio normalization for your file formats, and tune chunking and voice activity detection based on your typical audio length.