diff --git a/crates/transcription/src/local_engine.rs b/crates/transcription/src/local_engine.rs
index feef65c..66612f1 100644
--- a/crates/transcription/src/local_engine.rs
+++ b/crates/transcription/src/local_engine.rs
@@ -113,6 +113,42 @@ impl LocalEngine {
     }
 }
 
+/// Thin wrapper over `ParakeetModel` that overrides `transcribe_raw` to
+/// request word-granularity segments. `transcribe-rs` 0.3's trait impl for
+/// `ParakeetModel::transcribe_raw` ignores `TranscribeOptions` and uses
+/// `TimestampGranularity::Token` (per-subword) — which surfaces in Kon as
+/// "T Est Ing . One , Two , Three" output. The concrete-type method
+/// `ParakeetModel::transcribe_with` accepts `ParakeetParams` with an
+/// explicit granularity; this wrapper exposes that to the trait object.
+struct ParakeetWordGranularity(transcribe_rs::onnx::parakeet::ParakeetModel);
+
+impl transcribe_rs::SpeechModel for ParakeetWordGranularity {
+    fn capabilities(&self) -> transcribe_rs::ModelCapabilities {
+        self.0.capabilities()
+    }
+
+    fn default_leading_silence_ms(&self) -> u32 {
+        self.0.default_leading_silence_ms()
+    }
+
+    fn default_trailing_silence_ms(&self) -> u32 {
+        self.0.default_trailing_silence_ms()
+    }
+
+    fn transcribe_raw(
+        &mut self,
+        samples: &[f32],
+        options: &TranscribeOptions,
+    ) -> std::result::Result<TranscriptionResult, transcribe_rs::TranscribeError> {
+        use transcribe_rs::onnx::parakeet::{ParakeetParams, TimestampGranularity};
+        let params = ParakeetParams {
+            language: options.language.clone(),
+            timestamp_granularity: Some(TimestampGranularity::Word),
+        };
+        self.0.transcribe_with(samples, &params)
+    }
+}
+
 /// Load a Parakeet model from a directory path.
 pub fn load_parakeet(
     model_dir: &Path,
@@ -127,7 +163,7 @@ pub fn load_parakeet(
             "Failed to load Parakeet: {e}"
         ))
     })?;
-    Ok(Box::new(model))
+    Ok(Box::new(ParakeetWordGranularity(model)))
 }
 
 /// Load a Whisper model from a GGML file path.