api has llm response event

core/duplex_pipeline.py

@@ -426,6 +426,15 @@ class DuplexPipeline:
                 sentence_buffer += text_chunk
                 await self.conversation.update_assistant_text(text_chunk)
                 
+                # Send LLM response streaming event to client
+                await self.transport.send_event({
+                    "event": "llmResponse",
+                    "trackId": self.session_id,
+                    "text": text_chunk,
+                    "isFinal": False,
+                    "timestamp": self._get_timestamp_ms()
+                })
+                
                 # Check for sentence completion - synthesize immediately for low latency
                 while any(end in sentence_buffer for end in sentence_ends):
                     # Find first sentence end
@@ -454,6 +463,16 @@ class DuplexPipeline:
                     else:
                         break
             
+            # Send final LLM response event
+            if full_response and not self._interrupt_event.is_set():
+                await self.transport.send_event({
+                    "event": "llmResponse",
+                    "trackId": self.session_id,
+                    "text": full_response,
+                    "isFinal": True,
+                    "timestamp": self._get_timestamp_ms()
+                })
+            
             # Speak any remaining text
             if sentence_buffer.strip() and not self._interrupt_event.is_set():
                 if not first_audio_sent:

examples/mic_client.py

@@ -4,10 +4,12 @@ Microphone client for testing duplex voice conversation.
 
 This client captures audio from the microphone, sends it to the server,
 and plays back the AI's voice response through the speakers.
+It also displays the LLM's text responses in the console.
 
 Usage:
     python examples/mic_client.py --url ws://localhost:8000/ws
     python examples/mic_client.py --url ws://localhost:8000/ws --chat "Hello!"
+    python examples/mic_client.py --url ws://localhost:8000/ws --verbose
 
 Requirements:
     pip install sounddevice soundfile websockets numpy
@@ -102,6 +104,9 @@ class MicrophoneClient:
         self._discard_audio = False
         self._audio_sequence = 0  # Track audio sequence to detect stale chunks
         
+        # Verbose mode for streaming LLM responses
+        self.verbose = False
+    
     async def connect(self) -> None:
         """Connect to WebSocket server."""
         print(f"Connecting to {self.url}...")
@@ -314,6 +319,17 @@ class MicrophoneClient:
             # Server-side TTFB event
             latency_ms = event.get("latencyMs", 0)
             print(f"← [TTFB] Server reported latency: {latency_ms}ms")
+        elif event_type == "llmResponse":
+            # LLM text response
+            text = event.get("text", "")
+            is_final = event.get("isFinal", False)
+            if is_final:
+                # Print final LLM response
+                print(f"← AI: {text}")
+            elif self.verbose:
+                # Show streaming chunks only in verbose mode
+                display_text = text[:60] + "..." if len(text) > 60 else text
+                print(f"  [streaming] {display_text}")
         elif event_type == "trackStart":
             print("← Bot started speaking")
             # IMPORTANT: Accept audio again after trackStart
@@ -552,6 +568,11 @@ async def main():
         action="store_true",
         help="Disable interactive mode"
     )
+    parser.add_argument(
+        "--verbose", "-v",
+        action="store_true",
+        help="Show streaming LLM response chunks"
+    )
     
     args = parser.parse_args()
     
@@ -565,6 +586,7 @@ async def main():
         input_device=args.input_device,
         output_device=args.output_device
     )
+    client.verbose = args.verbose
     
     await client.run(
         chat_message=args.chat,

examples/wav_client.py

@@ -115,7 +115,13 @@ class WavFileClient:
             "direction": direction,
             "message": message
         })
-        print(f"{direction} {message}")
+        # Handle encoding errors on Windows
+        try:
+            print(f"{direction} {message}")
+        except UnicodeEncodeError:
+            # Replace problematic characters for console output
+            safe_message = message.encode('ascii', errors='replace').decode('ascii')
+            print(f"{direction} {safe_message}")
     
     async def connect(self) -> None:
         """Connect to WebSocket server."""
@@ -285,6 +291,14 @@ class WavFileClient:
         elif event_type == "ttfb":
             latency_ms = event.get("latencyMs", 0)
             self.log_event("←", f"[TTFB] Server latency: {latency_ms}ms")
+        elif event_type == "llmResponse":
+            text = event.get("text", "")
+            is_final = event.get("isFinal", False)
+            if is_final:
+                self.log_event("←", f"LLM Response (final): {text[:100]}{'...' if len(text) > 100 else ''}")
+            elif self.verbose:
+                # Show streaming chunks only in verbose mode
+                self.log_event("←", f"LLM: {text}")
         elif event_type == "trackStart":
             self.track_started = True
             self.log_event("←", "Bot started speaking")

scripts/generate_test_audio/generate_test_audio.py

@@ -6,7 +6,7 @@ Creates a 16kHz mono WAV file with real speech segments separated by
 configurable silence (for VAD/testing).
 
 Usage:
-  python scripts/generate_test_audio.py [OPTIONS]
+  python generate_test_audio.py [OPTIONS]
 
 Options:
   -o, --output PATH       Output WAV path (default: data/audio_examples/two_utterances_16k.wav)
@@ -18,19 +18,18 @@ Options:
 
 Examples:
   # Default utterances and output
-  python scripts/generate_test_audio.py
+  python generate_test_audio.py
 
   # Custom output path
-  python scripts/generate_test_audio.py -o out.wav
+  python generate_test_audio.py -o out.wav
 
   # Utterances from command line
-  python scripts/generate_test_audio.py -u "Hello" -u "World" -o test.wav
+  python generate_test_audio.py -u "Hello" -u "World" -o test.wav
 
-  # Utterances from JSON file
+  # Utterancgenerate_test_audio.py -j utterances.json -o test.wav
-  python scripts/generate_test_audio.py -j utterances.json -o test.wav
 
   # Custom silence (1s between utterances)
-  python scripts/generate_test_audio.py -u "One" -u "Two" --silence-ms 1000 -o test.wav
+  python generate_test_audio.py -u "One" -u "Two" --silence-ms 1000 -o test.wav
 
 Requires SILICONFLOW_API_KEY in .env.
 """
@@ -47,7 +46,7 @@ from dotenv import load_dotenv
 
 
 # Load .env file from project root
-project_root = Path(__file__).parent.parent
+project_root = Path(__file__).parent.parent.parent
 load_dotenv(project_root / ".env")