Move metrics from transport to rtvi

src/pipecat/processors/frameworks/rtvi.py

@@ -6,7 +6,17 @@
 
 import asyncio
 from dataclasses import dataclass
-from typing import Any, Awaitable, Callable, Dict, List, Literal, Optional, Union
+from typing import (
+    Any,
+    Awaitable,
+    Callable,
+    Dict,
+    List,
+    Literal,
+    Mapping,
+    Optional,
+    Union,
+)
 
 from loguru import logger
 from pydantic import BaseModel, Field, PrivateAttr, ValidationError
@@ -24,6 +34,7 @@ from pipecat.frames.frames import (
     InterimTranscriptionFrame,
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
+    MetricsFrame,
     StartFrame,
     SystemFrame,
     TextFrame,
@@ -35,6 +46,12 @@ from pipecat.frames.frames import (
     UserStartedSpeakingFrame,
     UserStoppedSpeakingFrame,
 )
+from pipecat.metrics.metrics import (
+    LLMUsageMetricsData,
+    ProcessingMetricsData,
+    TTFBMetricsData,
+    TTSUsageMetricsData,
+)
 from pipecat.processors.aggregators.openai_llm_context import (
     OpenAILLMContext,
     OpenAILLMContextFrame,
@@ -343,6 +360,12 @@ class RTVIBotStoppedSpeakingMessage(BaseModel):
     type: Literal["bot-stopped-speaking"] = "bot-stopped-speaking"
 
 
+class RTVIMetricsMessage(BaseModel):
+    label: Literal["rtvi-ai"] = "rtvi-ai"
+    type: Literal["metrics"] = "metrics"
+    data: Mapping[str, Any]
+
+
 class RTVIProcessorParams(BaseModel):
     send_bot_ready: bool = True
 
@@ -509,6 +532,42 @@ class RTVIBotTTSProcessor(RTVIFrameProcessor):
             await self._push_transport_message_urgent(message)
 
 
+class RTVIMetricsProcessor(RTVIFrameProcessor):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        await self.push_frame(frame, direction)
+
+        if isinstance(frame, MetricsFrame):
+            await self._handle_metrics(frame)
+
+    async def _handle_metrics(self, frame: MetricsFrame):
+        metrics = {}
+        for d in frame.data:
+            if isinstance(d, TTFBMetricsData):
+                if "ttfb" not in metrics:
+                    metrics["ttfb"] = []
+                metrics["ttfb"].append(d.model_dump(exclude_none=True))
+            elif isinstance(d, ProcessingMetricsData):
+                if "processing" not in metrics:
+                    metrics["processing"] = []
+                metrics["processing"].append(d.model_dump(exclude_none=True))
+            elif isinstance(d, LLMUsageMetricsData):
+                if "tokens" not in metrics:
+                    metrics["tokens"] = []
+                metrics["tokens"].append(d.value.model_dump(exclude_none=True))
+            elif isinstance(d, TTSUsageMetricsData):
+                if "characters" not in metrics:
+                    metrics["characters"] = []
+                metrics["characters"].append(d.model_dump(exclude_none=True))
+
+        message = RTVIMetricsMessage(data=metrics)
+        await self._push_transport_message_urgent(message)
+
+
 class RTVIProcessor(FrameProcessor):
     def __init__(
         self,

src/pipecat/transports/base_output.py

@@ -6,37 +6,34 @@
 
 import asyncio
 import itertools
-import time
 import sys
-
+import time
-from PIL import Image
 from typing import List
 
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from loguru import logger
+from PIL import Image
+
 from pipecat.frames.frames import (
     BotSpeakingFrame,
     BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     CancelFrame,
-    MetricsFrame,
+    EndFrame,
+    Frame,
     OutputAudioRawFrame,
     OutputImageRawFrame,
     SpriteFrame,
     StartFrame,
-    EndFrame,
-    Frame,
     StartInterruptionFrame,
     StopInterruptionFrame,
     SystemFrame,
-    TTSStartedFrame,
-    TTSStoppedFrame,
     TransportMessageFrame,
     TransportMessageUrgentFrame,
+    TTSStartedFrame,
+    TTSStoppedFrame,
 )
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.transports.base_transport import TransportParams
-
-from loguru import logger
-
 from pipecat.utils.time import nanoseconds_to_seconds
 
 
@@ -141,9 +138,6 @@ class BaseOutputTransport(FrameProcessor):
     async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
         pass
 
-    async def send_metrics(self, frame: MetricsFrame):
-        pass
-
     async def write_frame_to_camera(self, frame: OutputImageRawFrame):
         pass
 
@@ -173,9 +167,6 @@ class BaseOutputTransport(FrameProcessor):
         elif isinstance(frame, (StartInterruptionFrame, StopInterruptionFrame)):
             await self.push_frame(frame, direction)
             await self._handle_interruptions(frame)
-        elif isinstance(frame, MetricsFrame):
-            await self.push_frame(frame, direction)
-            await self.send_metrics(frame)
         elif isinstance(frame, TransportMessageUrgentFrame):
             await self.send_message(frame)
         elif isinstance(frame, SystemFrame):

src/pipecat/transports/services/daily.py

@@ -28,7 +28,6 @@ from pipecat.frames.frames import (
     Frame,
     InputAudioRawFrame,
     InterimTranscriptionFrame,
-    MetricsFrame,
     OutputAudioRawFrame,
     OutputImageRawFrame,
     SpriteFrame,
@@ -39,12 +38,6 @@ from pipecat.frames.frames import (
     UserImageRawFrame,
     UserImageRequestFrame,
 )
-from pipecat.metrics.metrics import (
-    LLMUsageMetricsData,
-    ProcessingMetricsData,
-    TTFBMetricsData,
-    TTSUsageMetricsData,
-)
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_input import BaseInputTransport
@@ -759,31 +752,6 @@ class DailyOutputTransport(BaseOutputTransport):
     async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame):
         await self._messages_queue.put(frame)
 
-    async def send_metrics(self, frame: MetricsFrame):
-        metrics = {}
-        for d in frame.data:
-            if isinstance(d, TTFBMetricsData):
-                if "ttfb" not in metrics:
-                    metrics["ttfb"] = []
-                metrics["ttfb"].append(d.model_dump(exclude_none=True))
-            elif isinstance(d, ProcessingMetricsData):
-                if "processing" not in metrics:
-                    metrics["processing"] = []
-                metrics["processing"].append(d.model_dump(exclude_none=True))
-            elif isinstance(d, LLMUsageMetricsData):
-                if "tokens" not in metrics:
-                    metrics["tokens"] = []
-                metrics["tokens"].append(d.value.model_dump(exclude_none=True))
-            elif isinstance(d, TTSUsageMetricsData):
-                if "characters" not in metrics:
-                    metrics["characters"] = []
-                metrics["characters"].append(d.model_dump(exclude_none=True))
-
-        message = DailyTransportMessageFrame(
-            message={"label": "rtvi-ai", "type": "metrics", "data": metrics}
-        )
-        await self._messages_queue.put(message)
-
     async def write_raw_audio_frames(self, frames: bytes):
         await self._client.write_raw_audio_frames(frames)
 

src/pipecat/transports/services/livekit.py

@@ -10,31 +10,25 @@ from typing import Any, Awaitable, Callable, List
 
 import numpy as np
 from loguru import logger
+from pydantic import BaseModel
+from scipy import signal
+
 from pipecat.frames.frames import (
     AudioRawFrame,
     CancelFrame,
     EndFrame,
     Frame,
     InputAudioRawFrame,
-    MetricsFrame,
     OutputAudioRawFrame,
     StartFrame,
     TransportMessageFrame,
     TransportMessageUrgentFrame,
 )
-from pipecat.metrics.metrics import (
-    LLMUsageMetricsData,
-    ProcessingMetricsData,
-    TTFBMetricsData,
-    TTSUsageMetricsData,
-)
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.transports.base_input import BaseInputTransport
 from pipecat.transports.base_output import BaseOutputTransport
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.vad.vad_analyzer import VADAnalyzer
-from pydantic import BaseModel
-from scipy import signal
 
 try:
     from livekit import rtc
@@ -450,31 +444,6 @@ class LiveKitOutputTransport(BaseOutputTransport):
         else:
             await self._client.send_data(frame.message.encode())
 
-    async def send_metrics(self, frame: MetricsFrame):
-        metrics = {}
-        for d in frame.data:
-            if isinstance(d, TTFBMetricsData):
-                if "ttfb" not in metrics:
-                    metrics["ttfb"] = []
-                metrics["ttfb"].append(d.model_dump(exclude_none=True))
-            elif isinstance(d, ProcessingMetricsData):
-                if "processing" not in metrics:
-                    metrics["processing"] = []
-                metrics["processing"].append(d.model_dump(exclude_none=True))
-            elif isinstance(d, LLMUsageMetricsData):
-                if "tokens" not in metrics:
-                    metrics["tokens"] = []
-                metrics["tokens"].append(d.value.model_dump(exclude_none=True))
-            elif isinstance(d, TTSUsageMetricsData):
-                if "characters" not in metrics:
-                    metrics["characters"] = []
-                metrics["characters"].append(d.model_dump(exclude_none=True))
-
-        message = LiveKitTransportMessageFrame(
-            message={"type": "pipecat-metrics", "metrics": metrics}
-        )
-        await self._client.send_data(str(message.message).encode())
-
     async def write_raw_audio_frames(self, frames: bytes):
         livekit_audio = self._convert_pipecat_audio_to_livekit(frames)
         await self._client.publish_audio(livekit_audio)