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English Speak Reviewer
A microphone-first practice platform designed to help users improve spoken English through structured recording, analysis, and feedback.
Hono HTMX Python Whisper Wav2Vec2 BullMQ Redis MinIO Docker
Overview
English Speak Reviewer is a practice platform built to simulate interview-style speaking scenarios and provide structured feedback on spoken responses. Users record audio directly in a browser-based studio, after which the audio is stored in object storage. A separate Python worker consumes jobs from a Redis-backed queue to perform speech processing tasks such as transcription and alignment using Whisper-based models. The system then derives fluency-related metrics, such as speaking rate and filler usage, and optionally generates narrative feedback to help users understand areas for improvement.
Key Challenges
- — Designing a system where latency-sensitive user interactions remain responsive while speech processing tasks are computationally heavy.
- — Coordinating asynchronous job execution and tracking processing states across API and worker boundaries.
- — Managing storage and retrieval of large audio artifacts reliably in a containerized environment.
Outcomes
- — Delivered a stable practice workflow that provides structured, actionable feedback on spoken responses.
- — Successfully separated resource-intensive speech processing from the user-facing application layer.
- — Established a modular, container-based architecture that can be extended with additional analysis steps.
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