Datasets:

ukeje71
/

Dengevoice

DengeVoice is a Next.js (TypeScript) application with a voice-first complaint flow: Record → Review/Confirm → Track. The frontend is built with Zustand for state management and Framer Motion for interaction feedback; Tailwind CSS handles styling.

ASR Integration

Voice input is transcribed via Sahara's ASR API (Intron), called from a server-side Next.js API route (app/api/transcribe/route.ts). The integration explicitly passes a use_language_asr_input parameter on every request — required both by Sahara's platform update (effective 14 Sept 2026) and by DengeVoice's own multilingual design, since the app supports four Nigerian languages (Igbo, Yoruba, Hausa, Pidgin) code-switched with English, and silently defaulting to English would misrepresent what the user actually said.

Upload and transcription follow an async poll pattern: the audio file is uploaded, a file_id is returned, and the client polls a status endpoint until FILE_TRANSCRIBED, then reads the resulting transcript.

AI-Assisted Structuring

Once a transcript is captured, a second step (/api/structure) uses an LLM to turn free-form spoken complaint text into structured fields — category, location, urgency, desired outcome — which the user then reviews and can edit before submission. This is deliberately a human-in-the-loop design: the structuring step never submits on the user's behalf without their confirmation, and every AI-populated field remains directly editable.

Engineering Decisions Worth Noting

Edit-over-rerecord: if ASR mis-transcribes, the user corrects the text directly on the confirmation screen rather than being forced to re-record — reduces friction and respects that voice input, especially across code-switched speech, will not always be perfect.
Text fallback everywhere: a "type instead" option is available on every voice-input screen, so users are never blocked by connectivity, noise, or a personal preference for typing.
Optional anonymity: a toggle lets users submit without contact details, addressing a real barrier — fear of retaliation — for civic reporting in some contexts.

Benchmarking Methodology

Choosing Sahara as the production ASR engine was validated, not assumed. A separate benchmark pipeline (Python, run in Google Colab) tested Sahara against two open alternatives — Meta's MMS and NCAIR/NITDA's language-specific Whisper fine-tunes — across 30 code-switched audio clips per language, spanning all four of DengeVoice's core languages. Scoring used Word Error Rate and Character Error Rate via jiwer, with identical text normalization applied to every engine's output for a fair comparison.

Result: Sahara achieved the lowest WER in all four languages tested. Full methodology, per-language error analysis, and reproducible scripts are published at the Hugging Face link above.

Stack Summary

Layer	Technology
Frontend	Next.js, TypeScript, Tailwind CSS, Framer Motion, Zustand
ASR	Sahara API (Intron)
Structuring	LLM-based field extraction
Benchmarking	Python, Hugging Face `transformers`, `jiwer`, Google Colab
Deployment	Vercel