Real-Time Multilingual Speech Transcription Platform

Title: Real‑Time Multilingual (Code‑Switch) Speech Transcription + Speaker Diarization + Optional Translation

Goal: Build from scratch a low‑latency streaming platform that:

Captures live audio (web / mobile).
Produces real‑time partial + final transcripts in multiple languages (auto language & code‑switch detection).
Performs online speaker diarization + optional enrolled speaker recognition.
(Optional) Outputs live translation alongside original text.
Supports domain vocabulary boosting and proper noun retention.
Exposes WebSocket + REST APIs, scalable from single machine to GPU server.
Key Features:

Streaming ASR tiered: fast model for partials; high‑accuracy escalation for finalized segments.
Code‑switch & language auto‑detect; per-word or per-segment language tagging.
Online diarization (embedding + incremental clustering) + profile matching.
Multi-pass decoding (temperature/beam) with quality & domain coverage scoring.
Word timestamps + alignment validation to reduce hallucinations.
Glossary/domain term boosting + phonetic hint injection.
Optional translation layer (MT model) with dual text output.
Structured JSON events (partial, final, speaker_update, translation).
Metrics: latency, confidence, language mix, speaker turn stats.
Privacy: in‑memory processing, optional PII redaction, configurable retention.
Non-Functional:

First partial <1.2 s, final segment <0.8 s additional.
WER competitive with Whisper medium (target improvements via rescoring).
DER (diarization error rate) within acceptable baseline (<12% on test set).
Horizontal scalability (stateless ASR workers + shared embedding/profile store).
Deliverables:

Clean repository (Docker: CPU & GPU images).
Modular services (ingest, ASR, diarization, translation) or single service with clear modules.
Config system (YAML/ENV) for all thresholds & feature flags.
Test suite: unit + integration + benchmark scripts (WER/DER measurement).
Documentation: architecture, setup, API spec, tuning guide, benchmarking steps.
Sample client (minimal web page or CLI) demonstrating real-time updates.
Optional: regression harness with reference audio set.
Suggested Milestones & Time (adjust as bidder sees fit):

M1 (2–3 days): Core streaming ingest + fast ASR partials + API skeleton.
M2 (2–3 days): Diarization + speaker clustering + profile enrollment.
M3 (2–3 days): Multi-pass decoding + escalation + scoring logic.
M4 (2 days): Translation & code‑switch detection integration.
M5 (2 days): Domain boosting, glossary, phonetic hints; alignment validation.
M6 (2 days): Metrics, logging, privacy / redaction, hardening.
M7 (1–2 days): Tests, benchmarks, docs, packaging. (Adjustable: 14–18 working days total baseline.)
Bidder Proposal Should Include:

Chosen ASR & MT models (sizes, memory, GPU needs).
Diarization approach (library or custom).
Rescoring strategy (features + weights).
Expected WER/DER on public benchmarks or similar past results.
Hardware assumptions (CPU vs GPU).
Risk mitigation (latency spikes, hallucinations, code‑switch edge cases).
Config Highlights (example keys): ACCUM_MIN_SEC, FALLBACK_TEMPS, ESCALATION_THRESHOLD ENABLE_CODE_SWITCH, LANG_CONF_MIN GLOSSARY_TERMS[], REQUIRED_TERMS_MIN SPEAKER_MATCH_THRESHOLD, CLUSTER_EPS ENABLE_TRANSLATION, TARGET_LANG WORD_TIMESTAMP_ALIGNMENT, HALLUCINATION_GUARDS PII_REDACTION, RETENTION_POLICY

Acceptance Criteria:

Live demo: multilingual sentence with code‑switch + correct diarization.
Domain terms recognized (sample glossary provided).
Translation output aligned with final segments.
Benchmarks report with latency & accuracy metrics.
All tests pass; documentation complete.
Optional Add‑Ons:

Keyword spotting.
Real-time sentiment/emotion tags.
Summary generation per speaker.
Use this condensed version in your posting. If you want a one-paragraph ultra‑short pitch or a checklist-only version, say so.