Add Streamlit meeting assistant MVP

ROADMAP.md

@@ -5,8 +5,10 @@
 Build the user-facing application over the validated Meeting Lab Python API.
 
 - select an existing audio file
+- reliably import WAV, FLAC and M4A through canonical FFmpeg preparation
+- offer optional audio normalization, enabled by default
 - create and edit Meeting Context through structured fields
-- add and manage participants
+- add and manage present and `mentioned_only` people
 - optionally map `SPEAKER_XX` labels to participants with explicit confirmation
 - configure and start `run_mvp_meeting(...)`
 - display stage-based status and real progress when available
@@ -18,6 +20,9 @@ The milestone must keep diarization, GPU acceleration and fixed-duration audio
 chunking optional. It must not launch the Meeting Lab CLI as a subprocess or
 duplicate Meeting Lab processing logic.
 
+Complete real-meeting validation of this vertical slice before expanding the
+post-run workflow.
+
 ## Following Milestone: Distribution Protocol
 
 - derive or generate a shorter participant-facing version
@@ -28,7 +33,12 @@ duplicate Meeting Lab processing logic.
 
 ## Later Product Work
 
-- transcript viewing and correction workflows
+- speaker/name review with explicit human confirmation
+- deterministic correction of suitable derived artifacts, beginning with a
+  simple search-and-replace path
+- protocol-only regeneration from existing transcription and diarization plus
+  corrected mappings and Meeting Context
+- transcript viewing and broader correction workflows
 - recording and artifact lifecycle management
 - search, tags, projects and meeting history
 - richer Markdown, PDF and DOCX export
@@ -42,6 +52,8 @@ duplicate Meeting Lab processing logic.
 - live transcription and real-time summaries
 - company glossary and custom vocabulary
 - voice identification with explicit consent and confirmation
+- automatic name and speaker suggestions only as non-authoritative candidates
+  for later human review
 - audio cleanup
 - OCR for shared screens
 - RAG and knowledge-graph integration