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speech-recognition
code-switching
nigerian-languages
asr-benchmark
sahara-codeswitch-challenge
License:
| # DengeVoice — Technical Execution | |
| **Live product:** https://dengevoice.vercel.app | |
| **Benchmark repo:** https://huggingface.co/datasets/ukeje71/Dengevoice | |
| ## Architecture Overview | |
| 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 | | |