Cleanup on aisle METRICS. Note: See below, this is a breaking change

1. Fleshed out MetricsFrames and broke it into a proper set of types
2. Add model_name as a property to the AIService so that it can be
   automatically included in metrics and also remove that
   overhead from all the various services themselves

Breaking change!

Because of the types improvements, the MetricsFrame type has
changed. Each frame will have a list of metrics simlilar to before
except each item in the list will only contain one type of metric:
"ttfb", "tokens", "characters", or "processing". Previously these
fields would be in every entry but set to None if they didn't apply.

While this changes internal handling of the MetricsFrame, it does NOT
break the RTVI/daily messaging of metrics. That format remains the same.

Also. Remember to use model_name for accessing a service's current
model and set_model_name for setting it.

src/pipecat/processors/frame_processor.py

@@ -19,6 +19,13 @@ from pipecat.frames.frames import (
     StartInterruptionFrame,
     StopInterruptionFrame,
     SystemFrame)
+from pipecat.metrics.metrics import (
+    LLMTokenUsage,
+    LLMUsageMetricsData,
+    MetricsData,
+    ProcessingMetricsData,
+    TTFBMetricsData,
+    TTSUsageMetricsData)
 from pipecat.utils.utils import obj_count, obj_id
 
 from loguru import logger
@@ -31,11 +38,20 @@ class FrameDirection(Enum):
 
 class FrameProcessorMetrics:
     def __init__(self, name: str):
-        self._name = name
+        self._core_metrics_data = MetricsData(processor=name)
         self._start_ttfb_time = 0
         self._start_processing_time = 0
         self._should_report_ttfb = True
 
+    def _processor_name(self):
+        return self._core_metrics_data.processor
+
+    def _model_name(self):
+        return self._core_metrics_data.model
+
+    def set_core_metrics_data(self, data: MetricsData):
+        self._core_metrics_data = data
+
     async def start_ttfb_metrics(self, report_only_initial_ttfb):
         if self._should_report_ttfb:
             self._start_ttfb_time = time.time()
@@ -46,13 +62,13 @@ class FrameProcessorMetrics:
             return None
 
         value = time.time() - self._start_ttfb_time
-        logger.debug(f"{self._name} TTFB: {value}")
-        ttfb = {
-            "processor": self._name,
-            "value": value
-        }
+        logger.debug(f"{self._processor_name()} TTFB: {value}")
+        ttfb = TTFBMetricsData(
+            processor=self._processor_name(),
+            value=value,
+            model=self._model_name())
         self._start_ttfb_time = 0
-        return MetricsFrame(ttfb=[ttfb])
+        return MetricsFrame(data=[ttfb])
 
     async def start_processing_metrics(self):
         self._start_processing_time = time.time()
@@ -62,26 +78,28 @@ class FrameProcessorMetrics:
             return None
 
         value = time.time() - self._start_processing_time
-        logger.debug(f"{self._name} processing time: {value}")
-        processing = {
-            "processor": self._name,
-            "value": value
-        }
+        logger.debug(f"{self._processor_name()} processing time: {value}")
+        processing = ProcessingMetricsData(
+            processor=self._processor_name(), value=value, model=self._model_name())
         self._start_processing_time = 0
-        return MetricsFrame(processing=[processing])
+        return MetricsFrame(data=[processing])
 
-    async def start_llm_usage_metrics(self, tokens: dict):
+    async def start_llm_usage_metrics(self, tokens: LLMTokenUsage):
         logger.debug(
-            f"{self._name} prompt tokens: {tokens['prompt_tokens']}, completion tokens: {tokens['completion_tokens']}")
-        return MetricsFrame(tokens=[tokens])
+            f"{self._processor_name()} prompt tokens: {tokens.prompt_tokens}, completion tokens: {tokens.completion_tokens}")
+        value = LLMUsageMetricsData(
+            processor=self._processor_name(),
+            model=self._model_name(),
+            value=tokens)
+        return MetricsFrame(data=[value])
 
     async def start_tts_usage_metrics(self, text: str):
-        characters = {
-            "processor": self._name,
-            "value": len(text),
-        }
-        logger.debug(f"{self._name} usage characters: {characters['value']}")
-        return MetricsFrame(characters=[characters])
+        characters = TTSUsageMetricsData(
+            processor=self._processor_name(),
+            model=self._model_name(),
+            value=len(text))
+        logger.debug(f"{self._processor_name()} usage characters: {characters.value}")
+        return MetricsFrame(data=[characters])
 
 
 class FrameProcessor:
@@ -140,6 +158,9 @@ class FrameProcessor:
     def can_generate_metrics(self) -> bool:
         return False
 
+    def set_core_metrics_data(self, data: MetricsData):
+        self._metrics.set_core_metrics_data(data)
+
     async def start_ttfb_metrics(self):
         if self.can_generate_metrics() and self.metrics_enabled:
             await self._metrics.start_ttfb_metrics(self._report_only_initial_ttfb)
@@ -160,7 +181,7 @@ class FrameProcessor:
             if frame:
                 await self.push_frame(frame)
 
-    async def start_llm_usage_metrics(self, tokens: dict):
+    async def start_llm_usage_metrics(self, tokens: LLMTokenUsage):
         if self.can_generate_metrics() and self.usage_metrics_enabled:
             frame = await self._metrics.start_llm_usage_metrics(tokens)
             if frame: