searchi/docs/SPEECH-ALIGNMENT.md

# Local spoken-passage alignment

Status: researched implementation proposal, 2026-10-13. No transcription worker,
download integration, or ASR quality result is implemented by this document.
Google EmbeddingGemma 2 remains the production semantic model.

## Why add a transcript lane

[The frozen media evaluation](MEDIA-QUALITY.md) found the correct spoken-topic
file but achieved **0/8 precise timestamp hits** with production 30-second audio
windows; shorter windows achieved 1/8. A window's beginning is not the beginning
of the relevant utterance. Preserve raw Google audio/frame retrieval for sounds,
music and visual scenes; supplement it with locally recognized speech, timestamped
passages and real Google text vectors. Transcripts are fallible recognition output,
not verified quotations or confidence scores.

## Runtime decision and first proof

Use a **separate native whisper.cpp worker**, with a small bounded protocol and
Rust orchestration. Keep ASR outside the desktop process and outside the existing
Google worker: the two upstream ggml revisions must not share loaded libraries or
global symbols accidentally. Package ASR libraries in their own directory. The
first proof should compare multilingual **base** and **small** on the existing NASA
speech fixture, then held-out English/German recordings. Base is a download/cost
candidate, not an already accepted quality default; do not use English-only `.en`
weights for a bilingual product.

Upstream whisper.cpp supports Windows/Linux/macOS, CPU and Metal/Vulkan backends;
its API accepts 16 kHz float PCM and returns segment times. Word/token alignment
and DTW are explicitly experimental. Start with sentence/segment timing, measure
the actual labelled point hits, and enable DTW only if a measured refinement is
needed. [Runtime documentation](https://github.com/ggml-org/whisper.cpp/blob/master/README.md),
[C API](https://github.com/ggml-org/whisper.cpp/blob/master/include/whisper.h).

whisper-rs exposes the same timestamp controls, but embedding its native library
in the UI gives poorer crash isolation. It is a valid worker implementation option
after checking its bundled upstream revision and backend build flags; avoid mixing
it blindly with the pinned Google native stack.
[Rust API](https://docs.rs/whisper-rs/0.16.0/whisper_rs/struct.FullParams.html).

No runtime build or genuine inference proof was run during this research. Obtain
the coordinator's CPU slot before building or transcribing in the shared VM.

## Pinned assets and distribution

Research resolved whisper.cpp v1.9.5 to commit
`d1be6fde11ac6e0407606b4e42fe72d34add8037` using the official GitHub tag API.
Model metadata was read from the Hugging Face blob API at repository revision
`5359861c739e955e79d9a303bcbc70fb988958b1`:

| Candidate | Exact bytes | SHA-256 |
| --- | ---: | --- |
| `ggml-base.bin` | 147951465 | `60ed5bc3dd14eea856493d334349b405782ddcaf0028d4b5df4088345fba2efe` |
| `ggml-small.bin` | 487601967 | `1be3a9b2063867b937e64e2ec7483364a79917e157fa98c5d94b5c1fffea987b` |

Use immutable download URLs of the form
`https://huggingface.co/ggerganov/whisper.cpp/resolve/5359861c739e955e79d9a303bcbc70fb988958b1/ggml-base.bin`.
These are **publisher metadata**, not hashes verified from downloaded bytes yet.
Commit a manifest only after a genuine download verifies exact size/SHA, model
loading and provenance. Never accept upstream `for-tests-*` empty models.
[Upstream model provenance](https://github.com/ggml-org/whisper.cpp/blob/master/models/README.md),
[blob metadata](https://huggingface.co/api/models/ggerganov/whisper.cpp?blobs=true).

Ship the pinned worker in the normal package, automatically fetch verified weights
when speech indexing is enabled, and reuse the owned model cache. Downloads need
progress, cancellation, retries, partial-file recovery, quota checks, atomic rename
and offline status. No package-manager/compiler/Python requirement for users.
Whisper and whisper.cpp use MIT licenses; include both notices and converted-model
provenance in third-party attribution. Packaging review must inspect every bundled
dependency's license independently.
[Whisper license](https://github.com/openai/whisper/blob/main/LICENSE),
[whisper.cpp license](https://github.com/ggml-org/whisper.cpp/blob/master/LICENSE).

Production worker builds include Metal on macOS or Vulkan on Windows/Linux and
CPU in the same package, with initialization failure retried on CPU. Backend DLLs
must load optionally, so an absent Vulkan loader cannot prevent CPU startup.
Record the backend actually used. All Windows VM and WSL proofs force CPU; physical
GPU speed/fallback and native macOS remain separate open gates.

## Concrete integration contract

1. Add `crates/searchi-speech` with `SpeechPolicy`, `SpeechModelManifest`,
   `SpeechWorker`, `TranscriptSegment` and typed readiness/errors. Worker requests
   carry bounded 16 kHz mono PCM, not arbitrary source paths. Reuse
   `searchi-media::decode_audio`, which already produces that format and limits
   each decode to 30 seconds. Keep any temporary files under the owned scratch
   parent; never use a globally indexed temp folder.
2. Initial worker contract: one request at a time, 30-second maximum input,
   finite PCM validation, bounded JSON output, explicit request id and worker/model
   fingerprint. Return segment `start_ms`, `end_ms`, text and detected language;
   reject out-of-range/nonmonotonic timestamps and oversized text. Parent timeout,
   cancellation and owned process termination are mandatory. Do not log PCM/text.
3. `crates/searchi-service/src/media.rs` currently loops `audio_windows`, encodes
   raw audio and inserts an `audio` row at `window.start_ms`. Add transcript work
   beside that loop, retaining raw vectors. One ASR session is serialized; acquire
   the shared background inference budget, use one CPU thread, and yield between
   windows. ASR cannot preempt an already executing call. Decoder/extraction CPU
   remains a separate explicitly measured budget.
4. Decode continuous windows with a bounded overlap. Convert local ASR times to
   absolute media times once. Deduplicate only overlapping recognized segments
   using time and normalized text; keep original text separately. Never concatenate
   whole movies in RAM. Proposed limits (to validate): maximum 3-hour source,
   1800 windows, 10000 segments/file, 4096 UTF-8 bytes/segment and 16 MiB total
   recognized text/file. Exceeding a limit reports partial/unready status rather
   than claiming complete indexing. Empty/non-speech windows are valid results.
5. Add versioned transcript persistence in `searchi-service/src/store`: file id,
   original file SHA-256, `file_change_revision`, stream index, absolute start/end,
   recognized text, language, model/runtime/policy fingerprint and completion state.
   Add a transcript FTS table with bounded candidates. Do not put unverified
   transcript text into the existing original-document provenance fields.
6. Embed accepted timestamped passages with **Google text embeddings**, stored
   as `transcript` chunks with recognized excerpt and timestamp source location.
   Existing `SourceLocation { kind: "timestamp_ms", number: start_ms }` covers
   opening/seek; an optional end bound can be added compatibly for previews.
   Agent excerpts must distinguish recognized speech from source-document text.
7. Use a distinct completed-source key (`speech`, source SHA, ASR model SHA,
   upstream runtime commit, stream selection, decoding/resampling version,
   language/decoding/alignment/window/dedup policy, Google fingerprint). Same bytes
   may reuse complete recognized segments/vector bytes after rename with current
   citations. Changed policy cannot reuse stale results. Resume per-window staging
   without falsely marking an incomplete source complete.
8. Revalidate SHA and captured change revision before publication. Native media
   hints immediately invalidate old transcripts even with unchanged size/mtime;
   deletion/missing sources suppress transcript FTS and semantic candidates before
   limits. Guard writes with the captured revision, mirroring existing probe guards.
   Prepare ANN before atomic generation activation; preserve the active generation
   on ASR failure and keep other files' keyword/raw media search usable.

## Retrieval and UI behavior

Fuse bounded transcript FTS, transcript Google vectors, document/code vectors and
raw media candidates by ranked retrieval, preserving exact filename/metadata
priority and strict filters before candidate limits. Do not compare heterogeneous
raw scores as confidence. Do not route every query containing “speech” exclusively
to audio: code about speech detection and related documents remain candidates.
Avoid adding extra general-query embedding calls or repeated full eligibility
scans. Segment-level top hits should survive file deduplication with their matching
excerpt and precise timestamp.

Show the recognized passage and timestamp in the detail preview, with a small
“Recognized speech” annotation; keep why-matched collapsed. Speech model setup and
indexing have honest progress/error states. Ordinary search stays local; optional
external Q&A requires the existing explicit endpoint/excerpt consent. Files,
transcripts, worker errors and inferred content never become executable instructions.

## Acceptance sequence

- [ ] Build isolated pinned CPU worker and verify downloaded base/small bytes.
- [ ] Decode the real NASA fixture, produce actual ASR JSON and manually inspect
  recognized text against independent captions; record cost, peak private memory,
  segment errors and timestamps. No invented transcript enters the proof.
- [ ] Compare the original eight frozen speech queries: correct file, ranking,
  segment overlap, strict point hits and absolute-time conversion, separately from
  overall media recall. Existing score stays 4/10 until a real rerun justifies change.
- [ ] Add held-out bilingual accents, long speech, leading silence, music-only,
  non-speech, corrupt streams and absent topics; freeze labels before inference.
  Hallucinated speech on silence/music is a measured failure, not silently accepted.
- [ ] Verify lifecycle: overwrite with same stats, rename, deletion, crash/resume,
  changed model/policy, cancellation, stale publication and corrupted downloads.
- [ ] Measure foreground Google search during ASR background work, first input,
  cold download/model load, warm p95, decoder/ASR/Google/storage stages separately.
- [ ] Verify UI, CLI/MCP recognized citations and filters through shared APIs.
- [ ] Validate normal package dependency closure and missing-driver CPU fallback
  on each native OS; execute physical GPU tests outside this Windows VM.

Next implementation slice: isolated worker + manifest verification + genuine NASA
proof. Keep the initial runtime experiment separate from schema/ranking changes
until it establishes that recognized segments actually fix the measured problem.