Advanced Audio Intelligence Module – Transcription & Contextual Analysis (FastAPI Integration)
Budget: $250 – $750 USD
Title:
Advanced Audio Intelligence Module – Transcription & Contextual Analysis (FastAPI Integration)
Description:
We’re looking for a highly experienced backend developer or AI engineer to build and integrate a modular audio intelligence component into our existing system. Users will upload audio files, which will be transcribed and analyzed for contextual meaning — including summarization, intent, topic extraction, and structured insight generation.
This module will be integrated into a production-ready FastAPI backend and must operate with high performance, scalability, and accuracy. You'll also be expected to enhance or extend parts of the backend where necessary.
Responsibilities:
Build an end-to-end audio pipeline: upload, preprocess, transcribe, analyze
Generate transcriptions using state-of-the-art ASR models (e.g., Whisper)
Analyze transcribed content for structure, relevance, and key signals
Integrate the module with an existing FastAPI backend using clean API endpoints
Optimize audio handling and backend logic for performance and scalability
Ensure modular design that can support future extensions (e.g., speaker diarization, multilingual support)
Deliverables:
Fully functional and tested transcription + analysis module
Integration with FastAPI backend (including necessary enhancements)
API documentation and brief developer handover guide
Efficient handling of large audio files and concurrent requests
Skills Required:
Python (expert-level)
FastAPI (production-grade experience)
Jurassic-2 (prompt design, text summarization, structured output)
Automatic Speech Recognition (ASR) tools (Whisper, DeepSpeech, Vosk)
Audio preprocessing: ffmpeg, librosa, pydub
Natural Language Processing (NLP): summarization, topic modeling, entity/intent extraction
Hugging Face Transformers or equivalent NLP frameworks
Async programming in Python (concurrent requests, non-blocking I/O)
REST API development and documentation
Docker, Git, and CI/CD pipelines for deployment
PostgreSQL / NoSQL for storing structured outputs
Bonus Experience (Nice to Have):
Speaker diarization or segmentation
Real-time streaming audio processing
Previous work on voice-based assistants or analytics dashboards
To Apply:
Please include:
A brief description of similar work you've done
Relevant GitHub or project links
Your approach to integrating transcription and analysis within FastAPI
Skills Required:
Python (expert-level)
FastAPI (advanced, production experience)
Jurassic-2 (strong prompt engineering and text analysis)
Audio transcription frameworks (Whisper, DeepSpeech, Vosk, etc.)
Audio preprocessing tools (ffmpeg, librosa, pydub)
Natural Language Processing (NLP)
Text summarization, entity extraction, sentiment/intent analysis
Hugging Face Transformers, spaCy, or equivalent
REST API design and integration
Asynchronous programming in Python (async/await, concurrent processing)
Docker and container-based development
Git and version control best practices
CI/CD pipelines (for backend service deployment and updates)
PostgreSQL or NoSQL experience (for storing transcription and analysis results)
Advanced Audio Intelligence Module – Transcription & Contextual Analysis (FastAPI Integration)
Description:
We’re looking for a highly experienced backend developer or AI engineer to build and integrate a modular audio intelligence component into our existing system. Users will upload audio files, which will be transcribed and analyzed for contextual meaning — including summarization, intent, topic extraction, and structured insight generation.
This module will be integrated into a production-ready FastAPI backend and must operate with high performance, scalability, and accuracy. You'll also be expected to enhance or extend parts of the backend where necessary.
Responsibilities:
Build an end-to-end audio pipeline: upload, preprocess, transcribe, analyze
Generate transcriptions using state-of-the-art ASR models (e.g., Whisper)
Analyze transcribed content for structure, relevance, and key signals
Integrate the module with an existing FastAPI backend using clean API endpoints
Optimize audio handling and backend logic for performance and scalability
Ensure modular design that can support future extensions (e.g., speaker diarization, multilingual support)
Deliverables:
Fully functional and tested transcription + analysis module
Integration with FastAPI backend (including necessary enhancements)
API documentation and brief developer handover guide
Efficient handling of large audio files and concurrent requests
Skills Required:
Python (expert-level)
FastAPI (production-grade experience)
Jurassic-2 (prompt design, text summarization, structured output)
Automatic Speech Recognition (ASR) tools (Whisper, DeepSpeech, Vosk)
Audio preprocessing: ffmpeg, librosa, pydub
Natural Language Processing (NLP): summarization, topic modeling, entity/intent extraction
Hugging Face Transformers or equivalent NLP frameworks
Async programming in Python (concurrent requests, non-blocking I/O)
REST API development and documentation
Docker, Git, and CI/CD pipelines for deployment
PostgreSQL / NoSQL for storing structured outputs
Bonus Experience (Nice to Have):
Speaker diarization or segmentation
Real-time streaming audio processing
Previous work on voice-based assistants or analytics dashboards
To Apply:
Please include:
A brief description of similar work you've done
Relevant GitHub or project links
Your approach to integrating transcription and analysis within FastAPI
Skills Required:
Python (expert-level)
FastAPI (advanced, production experience)
Jurassic-2 (strong prompt engineering and text analysis)
Audio transcription frameworks (Whisper, DeepSpeech, Vosk, etc.)
Audio preprocessing tools (ffmpeg, librosa, pydub)
Natural Language Processing (NLP)
Text summarization, entity extraction, sentiment/intent analysis
Hugging Face Transformers, spaCy, or equivalent
REST API design and integration
Asynchronous programming in Python (async/await, concurrent processing)
Docker and container-based development
Git and version control best practices
CI/CD pipelines (for backend service deployment and updates)
PostgreSQL or NoSQL experience (for storing transcription and analysis results)