Review feedback (MINOR): char::is_whitespace returns false for
zero-width format codepoints (U+200B ZWSP, U+200C ZWNJ, U+200D ZWJ,
U+2060 WORD JOINER, U+FEFF ZWNBSP / BOM). The original normalise
pass let them through to the LLM where they waste tokens without
contributing any natural-language content.
Makes the decision explicit: these chars STRIP entirely rather than
collapse to a space. Collapsing would silently insert a word break
where the source had none ("hello<FEFF>world" → "hello world"
would merge two words into a space-separated pair that the original
author did not intend). Stripping preserves the original token
boundaries and drops the invisible noise.
Three new tests:
- zero_width_format_chars_strip_entirely — exhaustive coverage of
all five handled codepoints.
- zero_width_chars_do_not_break_adjacent_whitespace_collapsing —
"hello <FEFF> world" still collapses to "hello world" (the
strip does not leave behind an artefact that breaks the whitespace
collapse pass).
- leading_bom_is_stripped — a BOM at segment start, the common
artefact pattern when Whisper consumes an encoded file.
New crates/ai-formatting/src/to_plain_text.rs module with one public
function: to_plain_text(&[Segment]) -> String.
Rules the function enforces:
- each segment's text is whitespace-normalised (any run of unicode
whitespace collapses to a single ASCII space, so tabs, newlines,
and NBSPs never reach the LLM),
- empty and whitespace-only segments are dropped,
- remaining segments are joined with a single ASCII space,
- the joined string is normalised again (so a segment ending in a
space followed by one starting in a space does not produce a double
space) and trimmed end-to-end.
pipeline.rs's inline join is replaced with this call. Whisper's
timestamp fields (Segment.start / .end) are carried separately and
never reach the LLM by construction — the "timestamps stripped"
half of brief item #29's acceptance falls out of using Segment.text
alone. The work the module actually adds is whitespace discipline
and the tested boundary (empty input, empty-only input, NBSPs,
pathological whitespace runs, idempotence, double-space at join
boundaries).
Source: Scriberr PR #288 — feeding raw Whisper JSON (with timestamps
and per-segment structure) degraded cleanup quality; plain-text
input raised it back.
Two new Settings → AI knobs that compose cleanly with what already
shipped (aiTier, LLM model, translator prompt framing).
**B.1 #15 — Named cleanup presets.** LlmPromptPreset enum
(Default / Email / Notes / Code) appends a short context hint onto
the CLEANUP_PROMPT just before generation. Presets shape tone and
structure ("email paragraph", "bulleted meeting notes", "preserve
technical terms") without licensing the content-editing the
translator-not-editor framing forbids. cleanup_transcript_text_cmd
now takes `preset: Option<String>` which runs through the new
LlmPromptPreset::parse (normalises aliases like "meeting-notes",
collapses unknown values to Default).
**A.1 #28 — Sequential-GPU guard.** New LocalEngine::unload drops
the backend + model_id so a subsequent load actually reclaims VRAM.
load_llm_model, load_model, and load_parakeet_model Tauri commands
grow an optional `concurrent: bool` argument. When concurrent is
Some(false), loading LLM first unloads whisper+parakeet, and vice
versa — prevents VRAM OOM on tight-VRAM setups. Default is the
previous parallel behaviour so nothing changes for multi-GB cards.
Transcribe-in-progress paths (transcribe_pcm, transcribe_file, live)
pass None, so mid-dictation model loads don't accidentally tear
down the LLM.
Settings UI (AI section):
- Cleanup preset segmented button + descriptive copy for each option.
- GPU concurrency segmented button with explicit trade-off text
("faster transitions vs fits in tight VRAM").
Frontend wiring:
- settings.llmPromptPreset flows from DictationPage's
cleanupTranscriptIfEnabled into the Tauri command.
- settings.aiGpuConcurrency flows from both DictationPage (auto-load
on record) and SettingsPage (manual load/unload buttons) as
`concurrent: "parallel" === true` to the load commands.
Tests: three new preset cases in crates/ai-formatting/src/llm_client.rs
(parse aliases, suffix non-empty for non-default, default suffix
empty). All 139 existing lib tests still pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ufal/whisper_streaming #161 documents the classic Whisper streaming
failure: on ambiguous audio the model falls into a prompt loop,
cascading a single token for 10+ words ("I I I I I I I I I I I…").
The chunk-boundary duplicate detector in live.rs doesn't catch
this — the repeat is within a single chunk, and the text is
technically novel so FTS is happy to keep it.
Fold the detection into is_hallucination as a third pass (after
HALLUCINATION_MARKERS substring-match and HALLUCINATION_TRAIL_PHRASES
exact-match). has_consecutive_repetition walks the token stream
(whitespace-split, lowercased) and returns true when any run of
≥REPETITION_RUN_THRESHOLD (4) identical tokens is found.
Threshold chosen deliberately: three consecutive matches appear in
normal speech ("no no no, that's wrong"), four almost never does.
Tests pin both sides — "I I I I I" detected, "no no no" allowed,
alternating patterns ("I am I am I am I am") allowed regardless of
length.
Phrase-level repetition ("thank you thank you thank you thank you")
is a documented companion failure mode but needs a sliding n-gram
matcher — deferred with a code comment flagging it.
No caller changes: post_process_segments already drops
is_hallucination hits when anti_hallucination is enabled.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Whisper was trained on subtitle corpora, so silence and room tone
trigger caption-style artefacts that the previous three-marker
blocklist ("[blank_audio]", "[music]", "[silence]") didn't catch:
"Thanks for watching!", "Please subscribe.", "ご視聴ありがとうござ
いました", "♪♪♪", etc. Documented in WhisperLive #185 / #246 and
ufal/whisper_streaming #121 as the top streaming-transcript-quality
issue after chunk-boundary repeats.
HALLUCINATION_MARKERS widens from 3 to 16 entries: all common
bracketed non-speech tags (applause / laughter / inaudible /
background noise / sounds), parens variants, and musical notation
(♪ / ♫). Still contains-match so the marker triggers even when
Whisper wraps it in other noise.
HALLUCINATION_TRAIL_PHRASES (renamed from AUTO_THANKS_PHRASES) jumps
from 4 to ~30 entries: YouTube sign-offs, subtitle-credit leakage,
and the two most common non-English variants (Japanese "thanks for
watching" + MBC Korean news sign-off). Stays exact-match so
legitimate dialogue containing "thanks" or "subscribe" mid-sentence
never gets dropped — a new regression test pins that invariant.
The <15-char length gate on trail phrases is removed; some of the
new entries (e.g. "please subscribe to our channel.") are longer.
Exact-match against a known list is safety enough.
No caller changes: post_process_segments already drops segments for
which is_hallucination returns true when anti_hallucination is on.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The previous prompt led with "You are a transcript cleanup assistant"
and listed cleanup rules. That framing quietly licenses the LLM to
treat cleanup as content editing — rephrasing for clarity, summarising
long sentences, "improving" phrasing. That's precisely the failure
mode OpenWhispr / Scriberr / Whispering users complain about ("the
LLM changed my meaning").
New framing lifts Whispering's published baseline: "translator from
spoken to written form — not an editor trying to improve the content."
Adds an explicit rule: do NOT improve, summarise, expand, or rephrase;
faithful written-form translation only, never content editing.
Both load-bearing concerns are now regression-tested — the existing
prompt-injection hardening assertions stay, and a new test pins the
translator framing + explicit no-editing rule against drift during
future refactors.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
kon-llm now owns a real LlamaBackend + LlamaModel, with three Qwen3 tiers
(1.7B Q4, 4B-Instruct-2507 Q4, 14B Q5) selectable per hardware. Downloads
are resumable with SHA-256 verification and stored under ~/.kon/models/llm.
Engine exposes three high-level surfaces — all greedy/temp-0, GBNF-constrained
where output shape matters:
- cleanup_text (prompt-injection-hardened system prompt; profile terms
appended as "preserve these spellings" suffix)
- decompose_task (3–7 micro-steps, constrained JSON array)
- extract_tasks (optional-array; empty when no explicit commitments)
post_process_segments now takes an Option<&LlmEngine> and, when loaded and
format_mode != Raw, joins segments → cleanup → replaces segments with the
cleaned text (first segment span). Rule-based path still runs first; LLM
errors log and keep rule-based output.
Tauri commands: recommend_llm_tier, check_llm_model, download_llm_model,
load_llm_model, unload_llm_model, delete_llm_model, get_llm_status,
cleanup_transcript_text_cmd, extract_tasks_from_transcript_cmd,
decompose_and_store (LLM-backed subtasks).
Settings: AI tier toggle (off / cleanup / tasks), model picker with
downloaded/loaded status, download progress events via
kon:llm-download-progress.
Dictation: ensureLlmModelLoaded on mount, cleanupTranscriptIfEnabled after
stop when tier != off and format_mode != Raw, LLM task extraction when
tier=tasks (regex fallback on failure).
Interim: both llama-cpp-sys-2 and whisper-rs-sys statically link their own
ggml, so src-tauri/build.rs emits -Wl,--allow-multiple-definition on Linux.
Replace with a system-ggml shared-lib setup as a follow-up.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Major quality pass on top of Phase 2. Five substantive changes plus
cross-cutting touches across audio, hotkey, transcription, and Tauri
command layers.
Transcription quality
- Long-audio chunking in commands/transcription.rs: Parakeet and large
file transcription now chunk-and-recompose with overlap trimming, so
the live-path chunking advantage extends to file-based workflows.
- Stateful live speech gate in commands/live.rs on top of the earlier
duplicate-boundary filtering — distinguishes start-of-speech from
mid-speech and holds state across chunks.
Auto-learning corrections
- New crates/ai-formatting/src/correction_learning.rs: extracts user
text corrections from viewer edits and proposes additions to the
active profile's vocabulary.
- src-tauri/src/commands/profiles.rs bridge for frontend-driven
confirmation of learned terms.
- src/routes/viewer/+page.svelte hooks the learning path into the
segment-edit flow so corrections feed profile_terms without a
separate 'train this profile' UX.
Transcript profile provenance
- Migration v8 (crates/storage/src/migrations.rs) adds profile_id to
transcripts, defaulting to DEFAULT_PROFILE_ID so existing rows stay
valid.
- crates/storage/src/database.rs: TranscriptRow + CRUD carry profile_id.
- src-tauri/src/commands/transcripts.rs: add_transcript accepts and
persists profile_id.
- DictationPage.svelte + FilesPage.svelte send activeProfileId on
capture so learned corrections are attributed to the right profile.
Cleanup prompt contract
- crates/ai-formatting/src/llm_client.rs hardened: the CLEANUP_PROMPT
now specifies concrete do/do-not rules, ready for a real model-backed
cleanup pass. The llm_client is still a stub — kon-llm remains unwired
— but the prompt shape is final.
Cross-cutting polish
- Minor touches in audio (capture/decode/resample), hotkey (lib/linux/stub),
core, transcription (concurrency/model_manager/local_engine/whisper_rs),
and the rest of src-tauri/src/commands/*: error-path tightening, log
clarity, TS-migration follow-ups (@ts-nocheck additions for incremental
typing).
Verified locally: npm run check, cargo test -p kon-ai-formatting,
cargo test -p kon-storage, cargo test -p kon --lib commands::live::tests,
cargo check — all green.
Scope boundary: kon-llm crate is still a stub; task extraction remains
rule-based. Bundled local-LLM runtime is the next clean step and is not
in this commit.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ai-formatting:
- rule_based.rs: collapse_repetitions() merges adjacent duplicate
tokens like 'I I can' -> 'I can' and 'think think that' -> 'think
that'. Normalises case and punctuation before comparison.
- pipeline.rs: post_process now calls collapse_repetitions when
format_mode is Clean or Smart. Added unit coverage.
audio:
- capture.rs: replace the seven deprecated cpal DeviceTrait::name()
call sites with a device_display_name() helper that uses the
non-deprecated description() path. Keeps identical behaviour,
silences compile warnings, ready for cpal upgrade.
Addresses the 'Christ. Christ.' live-transcription boundary duplicate
Jake saw during Group 1 dogfooding. Does not fix all cross-chunk
overlap cases (see live.rs OVERLAP_SAMPLES for the root cause) but
catches the common stutter pattern at post-processing.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Normalise BRITISH_REPLACEMENTS: remove baked-in \b from entries so all
entries are plain base words; the function adds boundaries uniformly
- Replace O(n*m) while-loop double-space removal with single-pass collapse
- Add debug_assert! documenting ASCII assumption for byte-indexed suffix slicing
- Expand llm_client.rs module-level doc comment
- Run cargo fmt on ai-formatting crate
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Security fixes from code audit:
- CSP re-enabled in tauri.conf.json with strict directives
(was null — critical vulnerability)
- XSS fix in viewer highlightText(): HTML entities escaped before
inserting <mark> tags via {@html}
- Removed 3 unwrap() calls in rule_based.rs British English conversion
— replaced with safe let-else guards
- Removed unwrap() on main window lookup in lib.rs setup — now uses
if-let for graceful handling
- Wrapped JSON.parse in DictationPage transcription-result listener
with try/catch
Rebrand cleanup:
- Renamed all localStorage keys from ramble_* to kon_* across
7 files (stores, viewer, float, history)
12 tests passing, clippy clean.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
