diff --git a/frontend/src/components/InputAndControlsPanel.tsx b/frontend/src/components/InputAndControlsPanel.tsx
index a6cbbcb1..84d096bc 100644
--- a/frontend/src/components/InputAndControlsPanel.tsx
+++ b/frontend/src/components/InputAndControlsPanel.tsx
@@ -116,6 +116,7 @@ export function InputAndControlsPanel({
 
   // Check if this pipeline supports multiple input modes
   const isMultiMode = pipelineIsMultiMode(pipelineId);
+  const promptLabel = pipelineId === "vibevoice" ? "Text input" : "Prompts";
 
   useEffect(() => {
     if (videoRef.current && localStream) {
@@ -288,7 +289,7 @@ export function InputAndControlsPanel({
             return (
               <div>
                 <div className="flex items-center justify-between mb-2">
-                  <h3 className="text-sm font-medium">Prompts</h3>
+                  <h3 className="text-sm font-medium">{promptLabel}</h3>
                   {isEditMode && (
                     <Badge variant="secondary" className="text-xs">
                       Editing
diff --git a/frontend/src/components/VideoOutput.tsx b/frontend/src/components/VideoOutput.tsx
index 4ed6bb61..e60564aa 100644
--- a/frontend/src/components/VideoOutput.tsx
+++ b/frontend/src/components/VideoOutput.tsx
@@ -14,6 +14,7 @@ interface VideoOutputProps {
   onPlayPauseToggle?: () => void;
   onStartStream?: () => void;
   onVideoPlaying?: () => void;
+  isAudioOnly?: boolean;
 }
 
 export function VideoOutput({
@@ -27,17 +28,60 @@ export function VideoOutput({
   onPlayPauseToggle,
   onStartStream,
   onVideoPlaying,
+  isAudioOnly = false,
 }: VideoOutputProps) {
   const videoRef = useRef<HTMLVideoElement>(null);
+  const audioRef = useRef<HTMLAudioElement>(null);
   const [showOverlay, setShowOverlay] = useState(false);
   const [isFadingOut, setIsFadingOut] = useState(false);
+  const [isAudioPlaying, setIsAudioPlaying] = useState(false);
   const overlayTimeoutRef = useRef<number | null>(null);
 
   useEffect(() => {
-    if (videoRef.current && remoteStream) {
+    if (!isAudioOnly && videoRef.current && remoteStream) {
       videoRef.current.srcObject = remoteStream;
     }
-  }, [remoteStream]);
+  }, [remoteStream, isAudioOnly]);
+
+  useEffect(() => {
+    if (isAudioOnly && audioRef.current && remoteStream) {
+      audioRef.current.srcObject = remoteStream;
+    }
+  }, [remoteStream, isAudioOnly]);
+
+  // Notify when audio starts playing (parity with video)
+  useEffect(() => {
+    if (!isAudioOnly) return;
+    const audio = audioRef.current;
+    if (!audio || !remoteStream) return;
+
+    const handlePlaying = () => {
+      setIsAudioPlaying(true);
+      onVideoPlaying?.();
+    };
+
+    const handlePause = () => {
+      setIsAudioPlaying(false);
+    };
+
+    const handleEnded = () => {
+      setIsAudioPlaying(false);
+    };
+
+    if (!audio.paused && audio.currentTime > 0 && !audio.ended) {
+      setIsAudioPlaying(true);
+      setTimeout(() => onVideoPlaying?.(), 0);
+    }
+
+    audio.addEventListener("playing", handlePlaying);
+    audio.addEventListener("pause", handlePause);
+    audio.addEventListener("ended", handleEnded);
+    return () => {
+      audio.removeEventListener("playing", handlePlaying);
+      audio.removeEventListener("pause", handlePause);
+      audio.removeEventListener("ended", handleEnded);
+    };
+  }, [isAudioOnly, onVideoPlaying, remoteStream]);
 
   // Listen for video playing event to notify parent
   useEffect(() => {
@@ -132,7 +176,50 @@ export function VideoOutput({
         <CardTitle className="text-base font-medium">Video Output</CardTitle>
       </CardHeader>
       <CardContent className="flex-1 flex items-center justify-center min-h-0 p-4">
-        {remoteStream ? (
+        {remoteStream && isAudioOnly ? (
+          <div className="w-full h-full flex flex-col items-center justify-center">
+            {/* Hidden audio element for actual playback */}
+            <audio
+              ref={audioRef}
+              className="hidden"
+              autoPlay
+              playsInline
+            />
+            {/* Audio visualization */}
+            <div className="relative flex items-center justify-center mb-6">
+              {/* Animated concentric circles */}
+              {isAudioPlaying && (
+                <>
+                  <div className="absolute w-32 h-32 rounded-full border-2 border-purple-500/30 animate-ping" />
+                  <div className="absolute w-40 h-40 rounded-full border-2 border-blue-500/20 animate-ping" style={{ animationDelay: "0.2s" }} />
+                  <div className="absolute w-48 h-48 rounded-full border-2 border-purple-500/10 animate-ping" style={{ animationDelay: "0.4s" }} />
+                </>
+              )}
+              {/* Speaker icon */}
+              <div className="relative z-10 w-24 h-24 flex items-center justify-center">
+                <svg
+                  className="w-full h-full text-white"
+                  fill="none"
+                  viewBox="0 0 24 24"
+                  stroke="currentColor"
+                  strokeWidth={2}
+                >
+                  <path
+                    strokeLinecap="round"
+                    strokeLinejoin="round"
+                    d="M19.114 5.636a9 9 0 010 12.728M16.463 8.289a5 5 0 010 7.072M12 3v18M8 8l-4-4v12l4-4"
+                  />
+                </svg>
+              </div>
+            </div>
+            {/* Status text */}
+            <div className="text-center">
+              <p className="text-lg font-medium text-foreground">
+                {isAudioPlaying ? "Playing audio..." : "Audio ready"}
+              </p>
+            </div>
+          </div>
+        ) : remoteStream ? (
           <div
             className="relative w-full h-full cursor-pointer flex items-center justify-center"
             onClick={handleVideoClick}
diff --git a/frontend/src/data/pipelines.ts b/frontend/src/data/pipelines.ts
index 8ab68cde..bea6cf40 100644
--- a/frontend/src/data/pipelines.ts
+++ b/frontend/src/data/pipelines.ts
@@ -98,6 +98,13 @@ export const PIPELINES: Record<string, PipelineInfo> = {
     supportedModes: ["video"],
     defaultMode: "video",
   },
+  vibevoice: {
+    name: "VibeVoice",
+    about: "Streaming text-to-speech pipeline that outputs audio chunks.",
+    requiresModels: false,
+    supportedModes: ["text"],
+    defaultMode: "text",
+  },
 };
 
 export function pipelineSupportsLoRA(pipelineId: string): boolean {
diff --git a/frontend/src/hooks/useWebRTC.ts b/frontend/src/hooks/useWebRTC.ts
index faefe0b3..7584ca53 100644
--- a/frontend/src/hooks/useWebRTC.ts
+++ b/frontend/src/hooks/useWebRTC.ts
@@ -44,7 +44,11 @@ export function useWebRTC(options?: UseWebRTCOptions) {
   const queuedCandidatesRef = useRef<RTCIceCandidate[]>([]);
 
   const startStream = useCallback(
-    async (initialParameters?: InitialParameters, stream?: MediaStream) => {
+    async (
+      initialParameters?: InitialParameters,
+      stream?: MediaStream,
+      pipelineId?: string
+    ) => {
       if (isConnecting || peerConnectionRef.current) return;
 
       setIsConnecting(true);
@@ -127,7 +131,7 @@ export function useWebRTC(options?: UseWebRTCOptions) {
           console.error("Data channel error:", error);
         };
 
-        // Add video track for sending to server only if stream is provided
+        // Add media tracks based on pipeline type
         if (stream) {
           stream.getTracks().forEach(track => {
             if (track.kind === "video") {
@@ -137,10 +141,11 @@ export function useWebRTC(options?: UseWebRTCOptions) {
           });
         } else {
           console.log(
-            "No video stream provided - adding video transceiver for no-input pipelines"
+            "No media stream provided - adding transceiver for no-input pipelines"
           );
-          // For no-video-input pipelines, add a video transceiver to establish proper WebRTC connection
-          pc.addTransceiver("video");
+          const mediaKind = pipelineId === "vibevoice" ? "audio" : "video";
+          // For no-input pipelines, add a transceiver to establish proper WebRTC connection
+          pc.addTransceiver(mediaKind);
         }
 
         // Named event handlers
diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index 9e657d44..47e14afb 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -23,6 +23,7 @@ export interface WebRTCOfferRequest {
     noise_controller?: boolean;
     manage_cache?: boolean;
     kv_cache_attention_bias?: number;
+    text?: string;
   };
 }
 
@@ -63,6 +64,11 @@ export interface KreaRealtimeVideoLoadParams extends PipelineLoadParams {
   lora_merge_mode?: "permanent_merge" | "runtime_peft";
 }
 
+export interface VibeVoiceLoadParams extends PipelineLoadParams {
+  audio_path?: string;
+  chunk_size?: number;
+}
+
 export interface PipelineLoadRequest {
   pipeline_id?: string;
   load_params?:
@@ -70,6 +76,7 @@ export interface PipelineLoadRequest {
     | StreamDiffusionV2LoadParams
     | LongLiveLoadParams
     | KreaRealtimeVideoLoadParams
+    | VibeVoiceLoadParams
     | null;
 }
 
diff --git a/frontend/src/pages/StreamPage.tsx b/frontend/src/pages/StreamPage.tsx
index 212c07b3..a89aa6e6 100644
--- a/frontend/src/pages/StreamPage.tsx
+++ b/frontend/src/pages/StreamPage.tsx
@@ -722,6 +722,7 @@ export function StreamPage() {
         kv_cache_attention_bias?: number;
         spout_sender?: { enabled: boolean; name: string };
         spout_receiver?: { enabled: boolean; name: string };
+        text?: string;
       } = {
         // Signal the intended input mode to the backend so it doesn't
         // briefly fall back to text mode before video frames arrive
@@ -766,11 +767,15 @@ export function StreamPage() {
         initialParameters.spout_receiver = settings.spoutReceiver;
       }
 
+      if (pipelineIdToUse === "vibevoice") {
+        initialParameters.text = promptItems[0]?.text ?? "";
+      }
+
       // Reset paused state when starting a fresh stream
       updateSettings({ paused: false });
 
       // Pipeline is loaded, now start WebRTC stream
-      startStream(initialParameters, streamToSend);
+      startStream(initialParameters, streamToSend, pipelineIdToUse);
 
       return true; // Stream started successfully
     } catch (error) {
@@ -849,6 +854,7 @@ export function StreamPage() {
               pipelineError={pipelineError}
               isPlaying={!settings.paused}
               isDownloading={isDownloading}
+              isAudioOnly={settings.pipelineId === "vibevoice"}
               onPlayPauseToggle={() => {
                 // Use timeline's play/pause handler instead of direct video toggle
                 if (timelinePlayPauseRef.current) {
diff --git a/frontend/src/types/index.ts b/frontend/src/types/index.ts
index 95bf3e1a..e0be03eb 100644
--- a/frontend/src/types/index.ts
+++ b/frontend/src/types/index.ts
@@ -3,7 +3,8 @@ export type PipelineId =
   | "passthrough"
   | "longlive"
   | "krea-realtime-video"
-  | "reward-forcing";
+  | "reward-forcing"
+  | "vibevoice";
 
 // Input mode for pipeline operation
 export type InputMode = "text" | "video";
diff --git a/pyproject.toml b/pyproject.toml
index 307642f9..ab66a42d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,7 +38,8 @@ dependencies = [
     "easydict>=1.13",
     "diffusers>=0.31.0",
     "ftfy>=6.3.1",
-    "transformers>=4.49.0",
+    "transformers==4.51.3",  # Pinned for VibeVoice compatibility
+    "vibevoice @ git+https://github.com/microsoft/VibeVoice.git",
     "einops>=0.8.1",
     "lmdb>=1.7.3",
     "omegaconf>=2.3.0",
@@ -149,6 +150,9 @@ known-first-party = ["src"]
 requires = ["hatchling", "editables"]
 build-backend = "hatchling.build"
 
+[tool.hatch.metadata]
+allow-direct-references = true
+
 [tool.hatch.build.targets.wheel]
 packages = ["src/scope"]
 
diff --git a/src/scope/core/pipelines/__init__.py b/src/scope/core/pipelines/__init__.py
index 9ea63303..b0d8674d 100644
--- a/src/scope/core/pipelines/__init__.py
+++ b/src/scope/core/pipelines/__init__.py
@@ -24,6 +24,10 @@ def __getattr__(name):
         from .passthrough.pipeline import PassthroughPipeline
 
         return PassthroughPipeline
+    elif name == "VibeVoicePipeline":
+        from .vibevoice.pipeline import VibeVoicePipeline
+
+        return VibeVoicePipeline
     # Config classes
     elif name == "BasePipelineConfig":
         from .schema import BasePipelineConfig
@@ -45,6 +49,10 @@ def __getattr__(name):
         from .schema import PassthroughConfig
 
         return PassthroughConfig
+    elif name == "VibeVoiceConfig":
+        from .schema import VibeVoiceConfig
+
+        return VibeVoiceConfig
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
 
 
@@ -55,10 +63,12 @@ def __getattr__(name):
     "RewardForcingPipeline",
     "StreamDiffusionV2Pipeline",
     "PassthroughPipeline",
+    "VibeVoicePipeline",
     # Config classes
     "BasePipelineConfig",
     "LongLiveConfig",
     "StreamDiffusionV2Config",
     "KreaRealtimeVideoConfig",
     "PassthroughConfig",
+    "VibeVoiceConfig",
 ]
diff --git a/src/scope/core/pipelines/registry.py b/src/scope/core/pipelines/registry.py
index 493804be..77e16dd3 100644
--- a/src/scope/core/pipelines/registry.py
+++ b/src/scope/core/pipelines/registry.py
@@ -73,6 +73,7 @@ def _register_pipelines():
     from .passthrough.pipeline import PassthroughPipeline
     from .reward_forcing.pipeline import RewardForcingPipeline
     from .streamdiffusionv2.pipeline import StreamDiffusionV2Pipeline
+    from .vibevoice.pipeline import VibeVoicePipeline
 
     # Register each pipeline with its ID from its config class
     for pipeline_class in [
@@ -81,6 +82,7 @@ def _register_pipelines():
         StreamDiffusionV2Pipeline,
         PassthroughPipeline,
         RewardForcingPipeline,
+        VibeVoicePipeline,
     ]:
         config_class = pipeline_class.get_config_class()
         PipelineRegistry.register(config_class.pipeline_id, pipeline_class)
diff --git a/src/scope/core/pipelines/schema.py b/src/scope/core/pipelines/schema.py
index db11474e..88210262 100644
--- a/src/scope/core/pipelines/schema.py
+++ b/src/scope/core/pipelines/schema.py
@@ -418,6 +418,22 @@ class PassthroughConfig(BasePipelineConfig):
     )
 
 
+class VibeVoiceConfig(BasePipelineConfig):
+    """Configuration for VibeVoice text-to-speech pipeline."""
+
+    pipeline_id: ClassVar[str] = "vibevoice"
+    pipeline_name: ClassVar[str] = "VibeVoice"
+    pipeline_description: ClassVar[str] = "Streamed text-to-speech generation"
+
+    # Text-only pipeline
+    supported_modes: ClassVar[list[InputMode]] = ["text"]
+    default_mode: ClassVar[InputMode] = "text"
+
+    # Keep minimal defaults (not used for audio generation but required by base model)
+    height: int = Field(default=256, ge=1, description="Placeholder height")
+    width: int = Field(default=256, ge=1, description="Placeholder width")
+
+
 # Registry of pipeline config classes
 PIPELINE_CONFIGS: dict[str, type[BasePipelineConfig]] = {
     "longlive": LongLiveConfig,
@@ -425,6 +441,7 @@ class PassthroughConfig(BasePipelineConfig):
     "krea-realtime-video": KreaRealtimeVideoConfig,
     "reward-forcing": RewardForcingConfig,
     "passthrough": PassthroughConfig,
+    "vibevoice": VibeVoiceConfig,
 }
 
 
diff --git a/src/scope/core/pipelines/vibevoice/__init__.py b/src/scope/core/pipelines/vibevoice/__init__.py
new file mode 100644
index 00000000..bc2bdeea
--- /dev/null
+++ b/src/scope/core/pipelines/vibevoice/__init__.py
@@ -0,0 +1,3 @@
+from .pipeline import VibeVoicePipeline
+
+__all__ = ["VibeVoicePipeline"]
diff --git a/src/scope/core/pipelines/vibevoice/pipeline.py b/src/scope/core/pipelines/vibevoice/pipeline.py
new file mode 100644
index 00000000..a4a64943
--- /dev/null
+++ b/src/scope/core/pipelines/vibevoice/pipeline.py
@@ -0,0 +1,419 @@
+import copy
+import logging
+import os
+import threading
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+import numpy as np
+import torch
+
+from ..interface import Pipeline, Requirements
+from ..schema import VibeVoiceConfig
+
+if TYPE_CHECKING:
+    from ..schema import BasePipelineConfig
+
+
+logger = logging.getLogger(__name__)
+
+
+def _resample_audio(audio: np.ndarray, source_rate: int, target_rate: int) -> np.ndarray:
+    """Naive linear resampling to match WebRTC's expected sample rate."""
+    if source_rate == target_rate or audio.size == 0:
+        return audio
+
+    # Generate target indices and interpolate
+    duration = audio.shape[0] / source_rate
+    target_length = int(duration * target_rate)
+    target_positions = np.linspace(0, audio.shape[0] - 1, num=target_length)
+    resampled = np.interp(target_positions, np.arange(audio.shape[0]), audio)
+    return resampled.astype(audio.dtype)
+
+
+def _generate_fallback_tone(duration_seconds: float, sample_rate: int) -> np.ndarray:
+    """Generate a short fallback tone so the player has audible output."""
+    t = np.linspace(0, duration_seconds, int(sample_rate * duration_seconds), endpoint=False)
+    # 440Hz sine wave scaled to int16
+    tone = 0.2 * np.sin(2 * np.pi * 440 * t)
+    return (tone * 32767).astype(np.int16)
+
+
+class VibeVoicePipeline(Pipeline):
+    """Real-time text-to-speech pipeline using Microsoft VibeVoice."""
+
+    # VibeVoice outputs at 24kHz, we upsample to 48kHz for WebRTC
+    vibevoice_sample_rate = 24_000
+    target_sample_rate = 48_000
+    default_chunk_size = 960  # 20ms @ 48k, aligns with common WebRTC audio pacing
+
+    @classmethod
+    def get_config_class(cls) -> type["BasePipelineConfig"]:
+        return VibeVoiceConfig
+
+    def __init__(
+        self,
+        model_path: str = "microsoft/VibeVoice-Realtime-0.5B",
+        speaker_name: str = "Emma",
+        device: str | None = None,
+        chunk_size: int | None = None,
+        cfg_scale: float = 1.5,
+    ):
+        """Initialize VibeVoice pipeline.
+
+        Args:
+            model_path: Path to the HuggingFace model
+            speaker_name: Name of the speaker voice to use
+            device: Device for inference (cuda/mps/cpu), auto-detected if None
+            chunk_size: Size of audio chunks to stream
+            cfg_scale: Classifier-free guidance scale for generation
+        """
+        self.model_path = model_path
+        self.speaker_name = speaker_name
+        self.chunk_size = chunk_size or self.default_chunk_size
+        self.cfg_scale = cfg_scale
+
+        # Auto-detect device if not specified
+        if device is None:
+            if torch.cuda.is_available():
+                device = "cuda"
+            elif torch.backends.mps.is_available():
+                device = "mps"
+            else:
+                device = "cpu"
+
+        # Normalize 'mpx' typo to 'mps'
+        if device.lower() == "mpx":
+            device = "mps"
+
+        # Validate mps availability
+        if device == "mps" and not torch.backends.mps.is_available():
+            logger.warning("MPS not available, falling back to CPU")
+            device = "cpu"
+
+        self.device = device
+        logger.info(f"VibeVoice using device: {self.device}")
+
+        # Lazy initialization of model components
+        self._model = None
+        self._processor = None
+        self._voice_mapper = None
+        self._voice_sample = None
+
+        # Audio buffer for streaming
+        self._audio_buffer: np.ndarray | None = None
+        self._position = 0
+        self._lock = threading.Lock()
+        self._last_text: str | None = None
+
+    def _ensure_initialized(self):
+        """Lazy load model and processor on first use."""
+        if self._model is not None:
+            return
+
+        logger.info(f"Loading VibeVoice model from {self.model_path}")
+
+        try:
+            # Import VibeVoice modules
+            from vibevoice.modular.modeling_vibevoice_streaming_inference import (
+                VibeVoiceStreamingForConditionalGenerationInference,
+            )
+            from vibevoice.processor.vibevoice_streaming_processor import (
+                VibeVoiceStreamingProcessor,
+            )
+        except ImportError as e:
+            logger.error("Failed to import VibeVoice modules. Make sure VibeVoice is installed.")
+            logger.error("Install from: /home/user/VibeVoice")
+            raise RuntimeError(
+                "VibeVoice not available. Please install it from ~/VibeVoice"
+            ) from e
+
+        # Load processor
+        self._processor = VibeVoiceStreamingProcessor.from_pretrained(self.model_path)
+
+        # Determine dtype and attention implementation based on device
+        if self.device == "mps":
+            load_dtype = torch.float32
+            attn_impl = "sdpa"
+        elif self.device == "cuda":
+            load_dtype = torch.bfloat16
+            attn_impl = "flash_attention_2"
+        else:  # cpu
+            load_dtype = torch.float32
+            attn_impl = "sdpa"
+
+        logger.info(f"Loading model with dtype={load_dtype}, attn_implementation={attn_impl}")
+
+        # Load model with device-specific settings
+        try:
+            if self.device == "mps":
+                self._model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
+                    self.model_path,
+                    torch_dtype=load_dtype,
+                    attn_implementation=attn_impl,
+                    device_map=None,
+                )
+                self._model.to("mps")
+            elif self.device == "cuda":
+                self._model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
+                    self.model_path,
+                    torch_dtype=load_dtype,
+                    device_map="cuda",
+                    attn_implementation=attn_impl,
+                )
+            else:  # cpu
+                self._model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
+                    self.model_path,
+                    torch_dtype=load_dtype,
+                    device_map="cpu",
+                    attn_implementation=attn_impl,
+                )
+        except Exception as e:
+            if attn_impl == "flash_attention_2":
+                logger.warning(f"Failed with flash_attention_2: {e}")
+                logger.warning("Retrying with SDPA (may result in lower audio quality)")
+                self._model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
+                    self.model_path,
+                    torch_dtype=load_dtype,
+                    device_map=(self.device if self.device in ("cuda", "cpu") else None),
+                    attn_implementation="sdpa",
+                )
+                if self.device == "mps":
+                    self._model.to("mps")
+            else:
+                raise
+
+        self._model.eval()
+        self._model.set_ddpm_inference_steps(num_steps=5)
+
+        # Load voice sample
+        self._load_voice_sample()
+
+        logger.info("VibeVoice model loaded successfully")
+
+    def _load_voice_sample(self):
+        """Load the voice sample for the specified speaker."""
+        # Try to find voices directory in multiple locations:
+        # 1. From VIBEVOICE_VOICES_DIR environment variable
+        # 2. In a local VibeVoice git clone at ~/VibeVoice
+        # 3. In the installed vibevoice package (if demo files were included)
+
+        voices_dir = None
+
+        # Try environment variable first
+        env_voices_dir = os.environ.get("VIBEVOICE_VOICES_DIR")
+        if env_voices_dir:
+            voices_dir = Path(env_voices_dir)
+            if voices_dir.exists():
+                logger.info(f"Using voices from VIBEVOICE_VOICES_DIR: {voices_dir}")
+
+        # Try local VibeVoice clone
+        if not voices_dir or not voices_dir.exists():
+            local_voices = Path.home() / "VibeVoice" / "demo" / "voices" / "streaming_model"
+            if local_voices.exists():
+                voices_dir = local_voices
+                logger.info(f"Using voices from local VibeVoice clone: {voices_dir}")
+
+        # Try installed package
+        if not voices_dir or not voices_dir.exists():
+            try:
+                import vibevoice
+                pkg_voices = Path(vibevoice.__file__).parent.parent / "demo" / "voices" / "streaming_model"
+                if pkg_voices.exists():
+                    voices_dir = pkg_voices
+                    logger.info(f"Using voices from installed package: {voices_dir}")
+            except (ImportError, AttributeError):
+                pass
+
+        if not voices_dir or not voices_dir.exists():
+            logger.error(
+                "Voices directory not found. Tried:\n"
+                f"  - VIBEVOICE_VOICES_DIR environment variable\n"
+                f"  - ~/VibeVoice/demo/voices/streaming_model\n"
+                f"  - Installed vibevoice package\n"
+            )
+            raise RuntimeError(
+                "Voice files not found. Please either:\n"
+                "  1. Clone VibeVoice to ~/VibeVoice: git clone https://github.com/microsoft/VibeVoice.git ~/VibeVoice\n"
+                "  2. Set VIBEVOICE_VOICES_DIR to point to the voices/streaming_model directory"
+            )
+
+        # Find matching voice file
+        voice_files = list(voices_dir.glob("*.pt"))
+        voice_map = {f.stem: f for f in voice_files}
+
+        # Try exact match or partial match
+        voice_path = None
+        if self.speaker_name in voice_map:
+            voice_path = voice_map[self.speaker_name]
+        else:
+            # Try partial matching (case insensitive)
+            speaker_lower = self.speaker_name.lower()
+            for name, path in voice_map.items():
+                if speaker_lower in name.lower() or name.lower() in speaker_lower:
+                    voice_path = path
+                    break
+
+        # Default to en-Emma_woman if no match
+        if voice_path is None:
+            default_name = "en-Emma_woman"
+            voice_path = voice_map.get(default_name)
+            if voice_path is None and voice_files:
+                voice_path = voice_files[0]
+            logger.warning(
+                f"Speaker '{self.speaker_name}' not found, using {voice_path.stem if voice_path else 'default'}"
+            )
+
+        if voice_path is None:
+            raise RuntimeError("No voice files found in voices directory")
+
+        logger.info(f"Loading voice sample: {voice_path}")
+        target_device = self.device if self.device != "cpu" else "cpu"
+        self._voice_sample = torch.load(voice_path, map_location=target_device, weights_only=False)
+
+    # Interface compatibility -------------------------------------------------
+    def prepare(self, **kwargs) -> Requirements:
+        """Generate audio from text and prepare for streaming."""
+        # Ensure model is loaded
+        self._ensure_initialized()
+
+        # Extract text from kwargs
+        text = kwargs.get("text")
+
+        # Check for transition.target_prompts first (takes precedence over old prompts)
+        transition = kwargs.get("transition")
+        prompts = None
+        if transition and isinstance(transition, dict):
+            prompts = transition.get("target_prompts")
+
+        # Fall back to direct prompts if no transition
+        if not prompts:
+            prompts = kwargs.get("prompts") or []
+
+        # Extract new text
+        new_text = None
+        if text:
+            new_text = text
+        elif prompts:
+            # Use the first prompt's text as the input text
+            first = prompts[0]
+            if isinstance(first, dict):
+                new_text = first.get("text")
+            else:
+                new_text = getattr(first, "text", None)
+
+        # Check if text has changed - if so, reset position
+        text_changed = new_text != self._last_text
+        self._last_text = new_text
+
+        if not self._last_text:
+            logger.warning("No text provided for VibeVoice generation")
+            with self._lock:
+                self._audio_buffer = _generate_fallback_tone(3.0, self.vibevoice_sample_rate)
+                self._position = 0
+            return Requirements(input_size=1)
+
+        # If text changed, reset position to start from beginning
+        if text_changed:
+            with self._lock:
+                self._position = 0
+
+        # Update chunk size if provided
+        chunk_size = kwargs.get("chunk_size")
+        if chunk_size:
+            self.chunk_size = chunk_size
+
+        # Generate audio
+        logger.info(f"Generating audio for text: {self._last_text[:100]}...")
+
+        try:
+            # Prepare inputs
+            inputs = self._processor.process_input_with_cached_prompt(
+                text=self._last_text,
+                cached_prompt=self._voice_sample,
+                padding=True,
+                return_tensors="pt",
+                return_attention_mask=True,
+            )
+
+            # Move tensors to device
+            for k, v in inputs.items():
+                if torch.is_tensor(v):
+                    inputs[k] = v.to(self.device)
+
+            # Generate audio
+            with torch.no_grad():
+                outputs = self._model.generate(
+                    **inputs,
+                    max_new_tokens=None,
+                    cfg_scale=self.cfg_scale,
+                    tokenizer=self._processor.tokenizer,
+                    generation_config={"do_sample": False},
+                    verbose=False,
+                    all_prefilled_outputs=copy.deepcopy(self._voice_sample),
+                )
+
+            # Extract audio (outputs.speech_outputs is a list with one tensor per batch item)
+            if outputs.speech_outputs and outputs.speech_outputs[0] is not None:
+                audio_tensor = outputs.speech_outputs[0]  # Shape: (audio_samples,) or (1, audio_samples)
+
+                # Convert to numpy int16 (handle bfloat16 by converting to float32 first)
+                if audio_tensor.dtype == torch.bfloat16:
+                    audio_tensor = audio_tensor.float()
+                audio_np = audio_tensor.cpu().numpy()
+
+                # Flatten if multi-dimensional
+                if audio_np.ndim > 1:
+                    audio_np = audio_np.flatten()
+
+                # Clamp to [-1, 1] and convert to int16
+                audio_np = np.clip(audio_np, -1.0, 1.0)
+                audio_int16 = (audio_np * 32767).astype(np.int16)
+
+                # Resample from 24kHz to 48kHz
+                audio_48k = _resample_audio(
+                    audio_int16,
+                    self.vibevoice_sample_rate,
+                    self.target_sample_rate,
+                )
+
+                with self._lock:
+                    self._audio_buffer = audio_48k
+                    self._position = 0
+
+                audio_duration = audio_int16.shape[0] / self.vibevoice_sample_rate
+                logger.info(f"Generated {audio_duration:.2f}s of audio ({audio_48k.shape[0]} samples @ 48kHz)")
+            else:
+                logger.error("VibeVoice generation returned no audio")
+                with self._lock:
+                    self._audio_buffer = _generate_fallback_tone(3.0, self.target_sample_rate)
+                    self._position = 0
+
+        except Exception as e:
+            logger.error(f"VibeVoice generation failed: {e}", exc_info=True)
+            with self._lock:
+                self._audio_buffer = _generate_fallback_tone(3.0, self.target_sample_rate)
+                self._position = 0
+
+        return Requirements(input_size=1)
+
+    def __call__(self, **kwargs) -> torch.Tensor | None:
+        """Return the next chunk of audio as a tensor in [-1, 1] range."""
+        reset_requested = kwargs.get("init_cache") or kwargs.get("reset_cache")
+        if reset_requested:
+            self.prepare(**kwargs)
+
+        with self._lock:
+            if self._audio_buffer is None or self._audio_buffer.size == 0:
+                return None
+
+            if self._position >= self._audio_buffer.shape[0]:
+                return None
+
+            end = min(self._position + self.chunk_size, self._audio_buffer.shape[0])
+            chunk = self._audio_buffer[self._position : end]
+            self._position = end
+
+        # Normalize to [-1, 1] float32 for downstream processing
+        chunk_float = np.clip(chunk.astype(np.float32) / 32767.0, -1.0, 1.0)
+        return torch.from_numpy(chunk_float)
diff --git a/src/scope/server/pipeline_manager.py b/src/scope/server/pipeline_manager.py
index 884bd43b..7b8d9500 100644
--- a/src/scope/server/pipeline_manager.py
+++ b/src/scope/server/pipeline_manager.py
@@ -491,6 +491,19 @@ def _load_pipeline_implementation(
             logger.info("RewardForcing pipeline initialized")
             return pipeline
 
+        elif pipeline_id == "vibevoice":
+            from scope.core.pipelines import VibeVoicePipeline
+
+            chunk_size = None
+            if load_params:
+                chunk_size = load_params.get("chunk_size")
+
+            pipeline = VibeVoicePipeline(
+                chunk_size=chunk_size,
+            )
+            logger.info("VibeVoice pipeline initialized")
+            return pipeline
+
         else:
             raise ValueError(f"Invalid pipeline ID: {pipeline_id}")
 
diff --git a/src/scope/server/schema.py b/src/scope/server/schema.py
index 1916dab7..1aca1320 100644
--- a/src/scope/server/schema.py
+++ b/src/scope/server/schema.py
@@ -360,6 +360,21 @@ class KreaRealtimeVideoLoadParams(LoRAEnabledLoadParams):
     )
 
 
+class VibeVoiceLoadParams(PipelineLoadParams):
+    """Load parameters for VibeVoice pipeline."""
+
+    audio_path: str | None = Field(
+        default=None,
+        description="Optional override path for the output audio file (wav).",
+    )
+    chunk_size: int | None = Field(
+        default=None,
+        description="Optional override for audio chunk size (samples per chunk).",
+        ge=160,
+        le=4096,
+    )
+
+
 class PipelineLoadRequest(BaseModel):
     """Pipeline load request schema."""
 
@@ -371,6 +386,7 @@ class PipelineLoadRequest(BaseModel):
         | PassthroughLoadParams
         | LongLiveLoadParams
         | KreaRealtimeVideoLoadParams
+        | VibeVoiceLoadParams
         | None
     ) = Field(default=None, description="Pipeline-specific load parameters")
 
diff --git a/src/scope/server/tracks.py b/src/scope/server/tracks.py
index 6269ba9e..c707e815 100644
--- a/src/scope/server/tracks.py
+++ b/src/scope/server/tracks.py
@@ -3,10 +3,13 @@
 import logging
 import threading
 import time
+from typing import Callable, Optional
 
 from aiortc import MediaStreamTrack
 from aiortc.mediastreams import VIDEO_CLOCK_RATE, VIDEO_TIME_BASE, MediaStreamError
-from av import VideoFrame
+from av import AudioFrame, VideoFrame
+import numpy as np
+import torch
 
 from .frame_processor import FrameProcessor
 from .pipeline_manager import PipelineManager
@@ -22,7 +25,7 @@ def __init__(
         pipeline_manager: PipelineManager,
         fps: int = 30,
         initial_parameters: dict = None,
-        notification_callback: callable = None,
+        notification_callback: Optional[Callable[[dict], None]] = None,
     ):
         super().__init__()
         self.pipeline_manager = pipeline_manager
@@ -173,3 +176,130 @@ async def stop(self):
             self.frame_processor.stop()
 
         await super().stop()
+
+
+class AudioProcessingTrack(MediaStreamTrack):
+    kind = "audio"
+
+    def __init__(
+        self,
+        pipeline_manager: PipelineManager,
+        initial_parameters: dict | None = None,
+        notification_callback: Optional[Callable[[dict], None]] = None,
+        chunk_size: int = 960,
+    ):
+        super().__init__()
+        self.pipeline_manager = pipeline_manager
+        self.parameters = initial_parameters or {}
+        self.notification_callback = notification_callback
+
+        self.chunk_size = chunk_size
+        self.sample_rate = 48_000
+        self.time_base = fractions.Fraction(1, self.sample_rate)
+        self.timestamp = 0
+
+        self._prepared = False
+        self._reset_requested = True
+        self._lock = threading.Lock()
+        self._paused = False
+        self._stop_notified = False
+
+    def _prepare_pipeline(self):
+        pipeline = self.pipeline_manager.get_pipeline()
+        pipeline.prepare(**self.parameters, chunk_size=self.chunk_size)
+        self.sample_rate = getattr(pipeline, "sample_rate", self.sample_rate)
+        self.time_base = fractions.Fraction(1, self.sample_rate)
+        # Reset timestamp to start from beginning when new audio is prepared
+        self.timestamp = 0
+        self._prepared = True
+
+    def update_parameters(self, params: dict):
+        with self._lock:
+            # When a transition is received, clear old prompts so transition.target_prompts takes precedence
+            if "transition" in params and "prompts" in self.parameters:
+                self.parameters.pop("prompts", None)
+            self.parameters.update(params)
+            if "chunk_size" in params and isinstance(params["chunk_size"], int):
+                self.chunk_size = params["chunk_size"]
+            self._reset_requested = True
+
+    def pause(self, paused: bool):
+        self._paused = paused
+
+    async def stop(self):
+        """Stop the audio track and cleanup resources."""
+        await super().stop()
+
+    def _next_chunk(self) -> np.ndarray | None:
+        with self._lock:
+            reset_cache = False
+            if self._reset_requested or not self._prepared:
+                self._prepare_pipeline()
+                reset_cache = True  # Signal pipeline to reset on first call after prepare
+                self._reset_requested = False
+
+            pipeline = self.pipeline_manager.get_pipeline()
+            try:
+                call_params = dict(self.parameters)
+                call_params["chunk_size"] = self.chunk_size
+                if reset_cache:
+                    call_params["reset_cache"] = True
+                chunk_tensor = pipeline(**call_params)
+            except Exception as exc:
+                logger.error("AudioProcessingTrack: pipeline error %s", exc)
+                return None
+
+            if chunk_tensor is None:
+                return None
+
+            chunk_tensor = torch.as_tensor(chunk_tensor)
+            if chunk_tensor.numel() == 0:
+                return None
+
+            chunk_np = chunk_tensor.detach().cpu().numpy()
+            chunk_np = np.squeeze(chunk_np)
+            chunk_np = np.clip(chunk_np, -1.0, 1.0)
+            if chunk_np.dtype != np.int16:
+                chunk_np = (chunk_np * 32767.0).astype(np.int16)
+
+            return chunk_np
+
+    async def recv(self) -> AudioFrame:
+        if self.readyState != "live":
+            raise MediaStreamError
+
+        if self._paused:
+            await asyncio.sleep(self.chunk_size / self.sample_rate)
+            return self._silence_frame()
+
+        loop = asyncio.get_event_loop()
+        chunk = await loop.run_in_executor(None, self._next_chunk)
+
+        if chunk is None or chunk.size == 0:
+            # Audio finished, but keep session open - return silence and wait for new audio
+            # This allows the session to stay open so new prompts can trigger new audio
+            await asyncio.sleep(self.chunk_size / self.sample_rate)
+            return self._silence_frame()
+
+        # AudioFrame expects shape (channels, samples) for packed mono
+        frame = AudioFrame.from_ndarray(
+            chunk.reshape(1, -1), format="s16", layout="mono"
+        )
+        frame.sample_rate = self.sample_rate
+        frame.pts = self.timestamp
+        frame.time_base = self.time_base
+        self.timestamp += chunk.shape[0]
+
+        await asyncio.sleep(chunk.shape[0] / self.sample_rate)
+        return frame
+
+    def _silence_frame(self) -> AudioFrame:
+        silence = np.zeros(self.chunk_size, dtype=np.int16)
+        frame = AudioFrame.from_ndarray(
+            silence.reshape(1, -1), format="s16", layout="mono"
+        )
+        frame.sample_rate = self.sample_rate
+        frame.pts = self.timestamp
+        frame.time_base = self.time_base
+        self.timestamp += silence.shape[0]
+        return frame
diff --git a/src/scope/server/webrtc.py b/src/scope/server/webrtc.py
index d0d2c458..29cf0ac5 100644
--- a/src/scope/server/webrtc.py
+++ b/src/scope/server/webrtc.py
@@ -19,7 +19,7 @@
 from .credentials import get_turn_credentials
 from .pipeline_manager import PipelineManager
 from .schema import WebRTCOfferRequest
-from .tracks import VideoProcessingTrack
+from .tracks import AudioProcessingTrack, VideoProcessingTrack
 
 logger = logging.getLogger(__name__)
 
@@ -45,11 +45,13 @@ def __init__(
         self,
         pc: RTCPeerConnection,
         video_track: MediaStreamTrack | None = None,
+        audio_track: MediaStreamTrack | None = None,
         data_channel: RTCDataChannel | None = None,
     ):
         self.id = str(uuid.uuid4())
         self.pc = pc
         self.video_track = video_track
+        self.audio_track = audio_track
         self.data_channel = data_channel
 
     async def close(self):
@@ -58,6 +60,8 @@ async def close(self):
             # Stop video track first to properly cleanup FrameProcessor
             if self.video_track is not None:
                 await self.video_track.stop()
+            if self.audio_track is not None:
+                await self.audio_track.stop()
 
             if self.pc.connectionState not in ["closed", "failed"]:
                 await self.pc.close()
@@ -167,13 +171,24 @@ async def handle_offer(
             # Create NotificationSender for this session to send notifications to the frontend
             notification_sender = NotificationSender()
 
-            video_track = VideoProcessingTrack(
-                pipeline_manager,
-                initial_parameters=initial_parameters,
-                notification_callback=notification_sender.call,
-            )
-            session.video_track = video_track
-            pc.addTrack(video_track)
+            # Choose media track based on loaded pipeline
+            pipeline_id = pipeline_manager.pipeline_id
+            if pipeline_id == "vibevoice":
+                audio_track = AudioProcessingTrack(
+                    pipeline_manager,
+                    initial_parameters=initial_parameters,
+                    notification_callback=notification_sender.call,
+                )
+                session.audio_track = audio_track
+                pc.addTrack(audio_track)
+            else:
+                video_track = VideoProcessingTrack(
+                    pipeline_manager,
+                    initial_parameters=initial_parameters,
+                    notification_callback=notification_sender.call,
+                )
+                session.video_track = video_track
+                pc.addTrack(video_track)
 
             logger.info(f"Created new session: {session}")
 
@@ -228,18 +243,25 @@ def on_data_channel_message(message):
                         data = json.loads(message)
                         logger.info(f"Received parameter update: {data}")
 
-                        # Check for paused parameter and call pause() method on video track
-                        if "paused" in data and session.video_track:
-                            session.video_track.pause(data["paused"])
+                        # Check for paused parameter and call pause() method on media tracks
+                        target_track = session.video_track or session.audio_track
+                        if "paused" in data and target_track and hasattr(
+                            target_track, "pause"
+                        ):
+                            target_track.pause(data["paused"])
 
-                        # Send parameters to the frame processor
+                        # Send parameters to the frame/audio processor
                         if session.video_track and hasattr(
                             session.video_track, "frame_processor"
                         ):
                             session.video_track.frame_processor.update_parameters(data)
+                        elif session.audio_track and hasattr(
+                            session.audio_track, "update_parameters"
+                        ):
+                            session.audio_track.update_parameters(data)
                         else:
                             logger.warning(
-                                "No frame processor available for parameter update"
+                                "No processor available for parameter update"
                             )
 
                     except json.JSONDecodeError as e:
diff --git a/uv.lock b/uv.lock
index 82d4a89b..c15f4c75 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,25 +2,37 @@ version = 1
 revision = 3
 requires-python = ">=3.10.12"
 resolution-markers = [
-    "python_full_version >= '3.12' and sys_platform == 'linux'",
+    "python_full_version >= '3.13' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and sys_platform == 'linux'",
     "python_full_version == '3.11.*' and sys_platform == 'linux'",
     "python_full_version < '3.11' and sys_platform == 'linux'",
-    "python_full_version >= '3.12' and sys_platform == 'win32'",
+    "python_full_version >= '3.13' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and sys_platform == 'win32'",
     "python_full_version == '3.11.*' and sys_platform == 'win32'",
     "python_full_version < '3.11' and sys_platform == 'win32'",
-    "python_full_version >= '3.12' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "python_full_version >= '3.13' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "python_full_version == '3.12.*' and sys_platform != 'linux' and sys_platform != 'win32'",
     "python_full_version == '3.11.*' and sys_platform != 'linux' and sys_platform != 'win32'",
     "python_full_version < '3.11' and sys_platform != 'linux' and sys_platform != 'win32'",
 ]
 
+[[package]]
+name = "absl-py"
+version = "2.3.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588, upload-time = "2025-07-03T09:31:44.05Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811, upload-time = "2025-07-03T09:31:42.253Z" },
+]
+
 [[package]]
 name = "accelerate"
-version = "1.11.0"
+version = "1.6.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.3.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "packaging" },
     { name = "psutil" },
     { name = "pyyaml" },
@@ -28,9 +40,18 @@ dependencies = [
     { name = "torch", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
     { name = "torch", version = "2.8.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/23/60/2757c4f03a8705dbf80b1268b03881927878dca5ed07d74f733fb6c219e0/accelerate-1.11.0.tar.gz", hash = "sha256:bb1caf2597b4cd632b917b5000c591d10730bb024a79746f1ee205bba80bd229", size = 393715, upload-time = "2025-10-20T14:42:25.025Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/8a/6e/c29a1dcde7db07f47870ed63e5124086b11874ad52ccd533dc1ca2c799da/accelerate-1.6.0.tar.gz", hash = "sha256:28c1ef1846e690944f98b68dc7b8bb6c51d032d45e85dcbb3adb0c8b99dffb32", size = 363804, upload-time = "2025-04-01T11:53:03.037Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/63/b1/8198e3cdd11a426b1df2912e3381018c4a4a55368f6d0857ba3ca418ef93/accelerate-1.6.0-py3-none-any.whl", hash = "sha256:1aee717d3d3735ad6d09710a7c26990ee4652b79b4e93df46551551b5227c2aa", size = 354748, upload-time = "2025-04-01T11:53:01.298Z" },
+]
+
+[[package]]
+name = "aiofiles"
+version = "24.1.0"
+source = { registry = "https://pypi.org/simple" }
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@@ -2831,7 +3509,8 @@ name = "sageattention"
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@@ -2844,7 +3523,8 @@ name = "sageattention"
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 resolution-markers = [
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