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>
105 lines
3.4 KiB
Rust
105 lines
3.4 KiB
Rust
//! Direct whisper-rs backend. Owns a WhisperContext; each call builds a
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//! fresh WhisperState (state can be reused, but fresh-per-call is simpler
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//! and matches the transcribe-rs call style we are replacing).
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//!
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//! Exists because transcribe-rs does not expose set_initial_prompt; this
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//! wrapper is the only path that can pipe per-capture vocabulary context
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//! into Whisper.
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use std::path::Path;
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use whisper_rs::{FullParams, SamplingStrategy, WhisperContext, WhisperContextParameters};
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use kon_core::types::{Segment, TranscriptionOptions};
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#[derive(Debug, thiserror::Error)]
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pub enum WhisperBackendError {
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#[error("whisper-rs load failed: {0}")]
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Load(String),
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#[error("whisper-rs state creation failed: {0}")]
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State(String),
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#[error("whisper-rs transcribe failed: {0}")]
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Transcribe(String),
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}
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pub struct WhisperRsBackend {
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ctx: WhisperContext,
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}
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impl WhisperRsBackend {
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pub fn load(model_path: &Path) -> Result<Self, WhisperBackendError> {
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let ctx = WhisperContext::new_with_params(model_path, WhisperContextParameters::default())
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.map_err(|e| WhisperBackendError::Load(e.to_string()))?;
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Ok(Self { ctx })
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}
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/// Synchronously transcribe 16 kHz mono f32 PCM.
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///
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/// `options.initial_prompt` is piped directly to whisper-rs.
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pub fn transcribe_sync(
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&self,
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samples: &[f32],
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options: &TranscriptionOptions,
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) -> Result<Vec<Segment>, WhisperBackendError> {
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tracing::info!(
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language = ?options.language,
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has_initial_prompt = options.initial_prompt.as_deref().map(|p| !p.is_empty()).unwrap_or(false),
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"WhisperRsBackend::transcribe_sync entering"
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);
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let mut state = self
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.ctx
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.create_state()
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.map_err(|e| WhisperBackendError::State(e.to_string()))?;
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let mut params = FullParams::new(SamplingStrategy::Greedy { best_of: 1 });
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if let Some(lang) = options.language.as_deref() {
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if !lang.is_empty() {
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params.set_language(Some(lang));
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}
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}
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if let Some(prompt) = options.initial_prompt.as_deref() {
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if !prompt.is_empty() {
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params.set_initial_prompt(prompt);
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}
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}
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params.set_n_threads(num_cpus::get() as i32);
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params.set_print_special(false);
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params.set_print_progress(false);
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params.set_print_realtime(false);
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state
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.full(params, samples)
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.map_err(|e| WhisperBackendError::Transcribe(e.to_string()))?;
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let n = state.full_n_segments();
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let mut out = Vec::with_capacity(n.max(0) as usize);
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for i in 0..n {
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let Some(seg) = state.get_segment(i) else {
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continue;
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};
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let text = seg
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.to_str()
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.map_err(|e| WhisperBackendError::Transcribe(e.to_string()))?
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.to_string();
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// whisper-rs timestamps are centiseconds (10ms units). Convert to seconds (f64).
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let start = seg.start_timestamp() as f64 * 0.01;
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let end = seg.end_timestamp() as f64 * 0.01;
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out.push(Segment { start, end, text });
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}
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Ok(out)
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn backend_error_displays() {
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let e = WhisperBackendError::Load("oops".into());
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assert!(e.to_string().contains("oops"));
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}
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}
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