match python starter defaults and enable expressive mode

README.md

@@ -11,8 +11,10 @@ The starter project includes:
 - A simple voice AI assistant, ready for extension and customization
 - A voice AI pipeline built on [LiveKit Inference](https://docs.livekit.io/agents/models/inference), providing zero-configuration access to [models](https://docs.livekit.io/agents/models) from top labs
   - Uses the fast, open-weight Gemma 4 31B model, [hosted by LiveKit](https://docs.livekit.io/agents/models/llm/livekit/) and tuned for optimal performance in voice AI, as the default LLM
+  - Uses Fish Audio S2.1 Pro for TTS, which renders the inline delivery markup that expressive mode relies on
   - Supports more than 50 models from OpenAI, Cartesia, Deepgram, and other providers
   - Access to a wide range of other models, including [Realtime models](https://docs.livekit.io/agents/models/realtime), through extensive plugin ecosystem
+- Expressive mode, enabled by default: the framework injects the TTS provider's markup guide into the LLM prompt, so the model emits inline delivery tags (emotion, pacing, non-verbal sounds) that the TTS renders and the transcript never shows
 - Eval suite based on the LiveKit Agents [testing & evaluation framework](https://docs.livekit.io/agents/start/testing)
 - [LiveKit Turn Detector](https://docs.livekit.io/agents/logic/turns/turn-detector/), an end-of-turn model that listens to the user's audio directly, combining semantic understanding with acoustic cues for state-of-the-art accuracy across 14 languages
 - [Background voice cancellation](https://docs.livekit.io/transport/media/noise-cancellation/)

package.json

@@ -30,7 +30,7 @@
     "vitest": "^4.1.4"
   },
   "dependencies": {
-    "@livekit/agents": "^1.5.0",
+    "@livekit/agents": "^1.6.3",
     "@livekit/plugins-ai-coustics": "^0.2.14",
     "@livekit/rtc-node": "^0.13.31",
     "dotenv": "^17.4.1",

src/main.ts

@@ -11,32 +11,41 @@ dotenv.config({ path: '.env.local' });
 
 export default defineAgent({
   entry: async (ctx) => {
-    // Set up a voice AI pipeline using OpenAI, Cartesia, Deepgram, and the LiveKit turn detector
+    // Set up a voice AI pipeline using AssemblyAI, Fish Audio, and the LiveKit turn detector
     const session = new voice.AgentSession({
       // Speech-to-text (STT) is your agent's ears, turning the user's speech into text that the LLM can understand
       // See all available models at https://docs.livekit.io/agents/models/stt/
       stt: new inference.STT({
-        model: 'deepgram/nova-3',
-        language: 'multi',
+        model: 'assemblyai/universal-3-5-pro',
+        language: 'en',
       }),
 
       // Text-to-speech (TTS) is your agent's voice, turning the LLM's text into speech that the user can hear
       // See all available models as well as voice selections at https://docs.livekit.io/agents/models/tts/
       tts: new inference.TTS({
-        model: 'cartesia/sonic-3',
-        voice: '9626c31c-bec5-4cca-baa8-f8ba9e84c8bc',
+        model: 'fishaudio/s2.1-pro',
+        voice: 'fa4c9eb3dccc4806b382b40d61c6b10a',
       }),
 
-      // Turn detection determines when the user is speaking and when the agent should respond.
-      // The LiveKit audio turn detector is a multimodal model that encodes the user's audio
-      // directly to predict end of turn. It's built into the SDK (no extra plugin) and
-      // AgentSession supplies the required VAD automatically.
-      // See more at https://docs.livekit.io/agents/logic/turns/turn-detector/
       turnHandling: {
+        // Turn detection determines when the user is speaking and when the agent should respond.
+        // The LiveKit audio turn detector is a multimodal model that encodes the user's audio
+        // directly to predict end of turn. It's built into the SDK (no extra plugin) and
+        // AgentSession supplies the required VAD automatically.
+        // See more at https://docs.livekit.io/agents/logic/turns/turn-detector/
         turnDetection: new inference.TurnDetector(),
+        // Adaptive interruptions use the turn detector to tell a real interruption from a
+        // backchannel like "mhm" or "right", so the agent keeps talking through the latter.
+        interruption: { mode: 'adaptive' },
         // Allow the LLM to generate a response while waiting for the end of turn
         preemptiveGeneration: { enabled: true },
       },
+
+      // Expressive mode injects the TTS provider's markup guide into the LLM prompt, so the model
+      // emits inline delivery tags (emotion, pacing, non-verbal sounds) that the TTS renders and
+      // the transcript never shows. Requires a TTS model that supports markup, such as the Fish
+      // Audio model above.
+      expressive: true,
     });
 
     // Start the session, which initializes the voice pipeline and warms up the models