refactor(transcription): LocalEngine dispatches SpeechBackend enum — Whisper now on whisper-rs

crates/transcription/src/lib.rs

@@ -5,7 +5,7 @@ pub mod whisper_rs_backend;
 
 pub use concurrency::run_inference;
 pub use local_engine::{
-    load_parakeet, load_whisper, LocalEngine, TimedTranscript,
+    load_parakeet, load_whisper, LocalEngine, SpeechBackend, TimedTranscript,
 };
 pub use transcribe_rs::SpeechModel;
 pub use model_manager::{

crates/transcription/src/local_engine.rs

@@ -10,17 +10,30 @@ use kon_core::types::{
     TranscriptionOptions,
 };
 
+use crate::whisper_rs_backend::WhisperRsBackend;
+
 /// Result of a timed transcription: transcript + inference duration.
 pub struct TimedTranscript {
     pub transcript: Transcript,
     pub inference_ms: u64,
 }
 
+/// Public discriminator selected by the loaders (`load_parakeet`, `load_whisper`)
+/// and passed to `LocalEngine::load`. `src-tauri::commands::models` names this
+/// type as the return of `load_model_from_disk`, so it must be `pub`.
+pub enum SpeechBackend {
+    /// transcribe-rs-owned model. Used for Parakeet ONNX (wrapped in
+    /// ParakeetWordGranularity for word-level timestamps).
+    Adapter(Box<dyn SpeechModel + Send>),
+    /// Direct whisper-rs. The only path that actually forwards `initial_prompt`.
+    WhisperRs(WhisperRsBackend),
+}
+
 /// Wraps any transcribe-rs engine in Kon's SpeechToText trait.
 /// Encapsulates threading: inference always runs on a blocking thread.
 /// The rest of the app never imports transcribe-rs directly.
 pub struct LocalEngine {
-    engine: Mutex<Option<Box<dyn SpeechModel + Send>>>,
+    engine: Mutex<Option<SpeechBackend>>,
     engine_name: EngineName,
     loaded_model_id: Mutex<Option<ModelId>>,
 }
@@ -34,10 +47,10 @@ impl LocalEngine {
         }
     }
 
-    pub fn load(&self, model: Box<dyn SpeechModel + Send>, model_id: ModelId) {
+    pub fn load(&self, backend: SpeechBackend, model_id: ModelId) {
         let mut guard =
             self.engine.lock().unwrap_or_else(|e| e.into_inner());
-        *guard = Some(model);
+        *guard = Some(backend);
         let mut id_guard = self
             .loaded_model_id
             .lock()
@@ -72,33 +85,37 @@ impl LocalEngine {
     ) -> Result<TimedTranscript> {
         let mut guard =
             self.engine.lock().unwrap_or_else(|e| e.into_inner());
-        let engine =
-            guard.as_mut().ok_or(KonError::EngineNotLoaded)?;
-
-        let opts = TranscribeOptions {
-            language: options.language.clone(),
-            translate: false,
-            leading_silence_ms: None,
-            trailing_silence_ms: None,
-        };
+        let backend = guard.as_mut().ok_or(KonError::EngineNotLoaded)?;
 
         let start = Instant::now();
-        let result: TranscriptionResult = engine
-            .transcribe(audio.samples(), &opts)
-            .map_err(|e| KonError::TranscriptionFailed(e.to_string()))?;
+        let segments: Vec<Segment> = match backend {
+            SpeechBackend::Adapter(model) => {
+                let opts = TranscribeOptions {
+                    language: options.language.clone(),
+                    translate: false,
+                    leading_silence_ms: None,
+                    trailing_silence_ms: None,
+                };
+                let result: TranscriptionResult = model
+                    .transcribe(audio.samples(), &opts)
+                    .map_err(|e| KonError::TranscriptionFailed(e.to_string()))?;
+                result
+                    .segments
+                    .unwrap_or_default()
+                    .into_iter()
+                    .map(|s| Segment {
+                        start: s.start as f64,
+                        end: s.end as f64,
+                        text: s.text,
+                    })
+                    .collect()
+            }
+            SpeechBackend::WhisperRs(w) => w
+                .transcribe_sync(audio.samples(), options)
+                .map_err(|e| KonError::TranscriptionFailed(e.to_string()))?,
+        };
         let inference_ms = start.elapsed().as_millis() as u64;
 
-        let segments = result
-            .segments
-            .unwrap_or_default()
-            .into_iter()
-            .map(|s| Segment {
-                start: s.start as f64,
-                end: s.end as f64,
-                text: s.text,
-            })
-            .collect();
-
         Ok(TimedTranscript {
             transcript: Transcript::new(
                 segments,
@@ -150,9 +167,7 @@ impl transcribe_rs::SpeechModel for ParakeetWordGranularity {
 }
 
 /// Load a Parakeet model from a directory path.
-pub fn load_parakeet(
-    model_dir: &Path,
-) -> Result<Box<dyn SpeechModel + Send>> {
+pub fn load_parakeet(model_dir: &Path) -> Result<SpeechBackend> {
     use transcribe_rs::onnx::Quantization;
     let model = transcribe_rs::onnx::parakeet::ParakeetModel::load(
         model_dir,
@@ -163,21 +178,15 @@ pub fn load_parakeet(
             "Failed to load Parakeet: {e}"
         ))
     })?;
-    Ok(Box::new(ParakeetWordGranularity(model)))
+    Ok(SpeechBackend::Adapter(Box::new(ParakeetWordGranularity(model))))
 }
 
-/// Load a Whisper model from a GGML file path.
-pub fn load_whisper(
-    model_path: &Path,
-) -> Result<Box<dyn SpeechModel + Send>> {
-    let engine =
-        transcribe_rs::whisper_cpp::WhisperEngine::load(model_path)
-            .map_err(|e| {
-                KonError::TranscriptionFailed(format!(
-                    "Failed to load Whisper: {e}"
-                ))
-            })?;
-    Ok(Box::new(engine))
+/// Load a Whisper model from a GGML file path via whisper-rs.
+pub fn load_whisper(model_path: &Path) -> Result<SpeechBackend> {
+    let backend = WhisperRsBackend::load(model_path).map_err(|e| {
+        KonError::TranscriptionFailed(format!("Failed to load Whisper: {e}"))
+    })?;
+    Ok(SpeechBackend::WhisperRs(backend))
 }
 
 #[cfg(test)]

src-tauri/src/commands/models.rs

@@ -72,7 +72,7 @@ fn model_capability(
     }
 }
 
-pub fn load_model_from_disk(model_id: &ModelId) -> Result<Box<dyn kon_transcription::SpeechModel + Send>, String> {
+pub fn load_model_from_disk(model_id: &ModelId) -> Result<kon_transcription::SpeechBackend, String> {
     let entry = model_registry::find_model(model_id)
         .ok_or_else(|| format!("Unknown model: {model_id}"))?;