Data Engineer Consultant for ASR (Automatic Speech Recognition) at Farm Radio International

Farm Radio International is a Canadian-based, not-for-profit organization working in direct partnership with more than 1,000 radio broadcasters in 41 African countries to fight poverty and food insecurity.

Scope of Work
The consultant will be responsible for the following tasks:

• Data Management: Collect, clean, and preprocess audio and textual data to train and evaluate ASR models.
• Data Pipeline Development: Design and implement scalable data pipelines to automate the extraction, transformation, and loading (ETL) of speech data.
• Model Training: Work closely with the data science team to prepare data for training, validating, and testing ASR models.
• Performance Monitoring: Monitor and analyze the performance of ASR models, including accuracy, latency, and error rates. Identify and address any data-related issues.
• Integration: Collaborate with software developers to integrate ASR models into web applications, ensuring seamless functionality and optimal user experience.
• Documentation: Create and maintain documentation related to data workflows, model performance, and integration processes.
• Collaboration: Work with cross-functional teams including product manager, UX/UI designers, and data scientists to align data engineering efforts with overall product goals.

Deliverables
Data Collection and Management:

• Data collection scripts and tools for gathering speech and text data.
• Clean and labeled datasets for training and evaluation purposes.
• Data storage solutions with appropriate indexing and retrieval mechanisms.

Data Pipeline Documentation:

• Design documents for data pipelines, including flow diagrams and architecture.
• Source code and configurations for ETL processes.

Model Preparation:

• Scripts and processes for preparing and augmenting data for ASR model training.
• Documentation on dataset splits (training, validation, test).

Model Performance Reports:

• Performance metrics reports for ASR models, including accuracy, precision, recall, and F1 scores.
• Analysis and troubleshooting reports on any data-related issues impacting model performance.

Integration Documentation:

• Integration guides and API documentation for incorporating ASR models into web applications.
• Code samples and best practices for interacting with ASR services from web clients.

User Feedback Analysis:

• Reports and insights from user feedback related to ASR accuracy and usability.
• Recommendations for improvements based on user feedback and performance data.

Codebase and Version Control:

• Version-controlled code repository for data processing scripts and pipeline implementations.
• Regular updates and commits to the codebase reflecting ongoing improvements and bug fixes.

Collaboration Reports:

• Documentation of cross-functional team meetings and decisions.
• Progress reports on collaborative projects involving ASR integration.

Testing and Quality Assurance:

• Test cases and results for ensuring data quality and model accuracy.
• Documentation of QA processes and issues encountered during testing.

Training and Support Materials:

• Training materials and documentation for team members on data handling, model integration, and troubleshooting.

Duration and Timeline:

• The consultancy will commence in October 2024 and conclude in August 2025 with a maximum level of effect (LoE) of 80 days.
• Specific deliverable deadlines will be agreed upon at the start of the consultancy.

Reporting:

• The consultant will report to the Project Manager Nathaniel Ofori and provide regular updates on progress.
• Weekly meetings will be held to discuss challenges, milestones, and next steps.

Method of Application

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