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Merge pull request #355 from pipecat-ai/aleix/usage-metrics-update

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processors(base): add start_llm_usage_metrics and start_tts_usage_met…
This commit is contained in:
Aleix Conchillo Flaqué
2024-08-09 09:35:36 -07:00
committed by GitHub
parent 85efc30145 22e176e329
commit 91de68aab3
12 changed files with 65 additions and 60 deletions
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5
CHANGELOG.md
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@@ -9,6 +9,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
### Added
- Added `DailyRESTHelper.delete_room_by_name()`.
- Added LLM and TTS usage metrics. Those will be enabled by when
`enable_usage_metrics` is True.
- `AudioRawFrame`s are not pushed downstream from the base output
transport. This allows capturing the exact words the bot says by adding an STT
service at the end of the pipeline.

1
examples/foundational/07-interruptible.py
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@@ -79,6 +79,7 @@ async def main():
task = PipelineTask(pipeline, PipelineParams(
allow_interruptions=True,
enable_metrics=True,
enable_usage_metrics=True,
report_only_initial_ttfb=True,
))

1
src/pipecat/frames/frames.py
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@@ -294,6 +294,7 @@ class StartFrame(ControlFrame):
"""This is the first frame that should be pushed down a pipeline."""
allow_interruptions: bool = False
enable_metrics: bool = False
enable_usage_metrics: bool = False
report_only_initial_ttfb: bool = False

2
src/pipecat/pipeline/task.py
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@@ -21,6 +21,7 @@ from loguru import logger
class PipelineParams(BaseModel):
allow_interruptions: bool = False
enable_metrics: bool = False
enable_usage_metrics: bool = False
send_initial_empty_metrics: bool = True
report_only_initial_ttfb: bool = False
@@ -104,6 +105,7 @@ class PipelineTask:
start_frame = StartFrame(
allow_interruptions=self._params.allow_interruptions,
enable_metrics=self._params.enable_metrics,
enable_usage_metrics=self._params.enable_metrics,
report_only_initial_ttfb=self._params.report_only_initial_ttfb
)
await self._source.process_frame(start_frame, FrameDirection.DOWNSTREAM)

31
src/pipecat/processors/frame_processor.py
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@@ -61,6 +61,19 @@ class FrameProcessorMetrics:
self._start_processing_time = 0
return MetricsFrame(processing=[processing])
async def start_llm_usage_metrics(self, tokens: dict):
logger.debug(
f"{self._name} prompt tokens: {tokens['prompt_tokens']}, completion tokens: {tokens['completion_tokens']}")
return MetricsFrame(tokens=[tokens])
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])
class FrameProcessor:
@@ -80,6 +93,7 @@ class FrameProcessor:
# Properties
self._allow_interruptions = False
self._enable_metrics = False
self._enable_usage_metrics = False
self._report_only_initial_ttfb = False
# Metrics
@@ -93,6 +107,10 @@ class FrameProcessor:
def metrics_enabled(self):
return self._enable_metrics
@property
def usage_metrics_enabled(self):
return self._enable_usage_metrics
@property
def report_only_initial_ttfb(self):
return self._report_only_initial_ttfb
@@ -120,6 +138,18 @@ class FrameProcessor:
if frame:
await self.push_frame(frame)
async def start_llm_usage_metrics(self, tokens: dict):
if self.can_generate_metrics() and self.usage_metrics_enabled:
frame = await self._metrics.start_llm_usage_metrics(tokens)
if frame:
await self.push_frame(frame)
async def start_tts_usage_metrics(self, text: str):
if self.can_generate_metrics() and self.usage_metrics_enabled:
frame = await self._metrics.start_tts_usage_metrics(text)
if frame:
await self.push_frame(frame)
async def stop_all_metrics(self):
await self.stop_ttfb_metrics()
await self.stop_processing_metrics()
@@ -145,6 +175,7 @@ class FrameProcessor:
if isinstance(frame, StartFrame):
self._allow_interruptions = frame.allow_interruptions
self._enable_metrics = frame.enable_metrics
self._enable_usage_metrics = frame.enable_usage_metrics
self._report_only_initial_ttfb = frame.report_only_initial_ttfb
elif isinstance(frame, StartInterruptionFrame):
await self.stop_all_metrics()

9
src/pipecat/services/azure.py
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@@ -88,13 +88,7 @@ class AzureTTSService(TTSService):
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
logger.debug(f"Generating TTS: [{text}]")
if self.can_generate_metrics() and self.metrics_enabled:
characters = {
"processor": self.name,
"value": len(text),
}
logger.debug(f"{self.name} Characters: {characters['value']}")
await self.push_frame(MetricsFrame(characters=[characters]))
await self.start_ttfb_metrics()
ssml = (
@@ -110,6 +104,7 @@ class AzureTTSService(TTSService):
result = await asyncio.to_thread(self._speech_synthesizer.speak_ssml, (ssml))
if result.reason == ResultReason.SynthesizingAudioCompleted:
await self.start_tts_usage_metrics(text)
await self.stop_ttfb_metrics()
# Azure always sends a 44-byte header. Strip it off.
yield AudioRawFrame(audio=result.audio_data[44:], sample_rate=16000, num_channels=1)

8
src/pipecat/services/cartesia.py
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@@ -201,13 +201,6 @@ class CartesiaTTSService(TTSService):
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
logger.debug(f"Generating TTS: [{text}]")
if self.can_generate_metrics() and self.metrics_enabled:
characters = {
"processor": self.name,
"value": len(text),
}
logger.debug(f"{self.name} Characters: {characters['value']}")
await self.push_frame(MetricsFrame(characters=[characters]))
try:
if not self._websocket:
@@ -232,6 +225,7 @@ class CartesiaTTSService(TTSService):
}
try:
await self._websocket.send(json.dumps(msg))
await self.start_tts_usage_metrics(text)
except Exception as e:
logger.exception(f"{self} error sending message: {e}")
await self._disconnect()

10
src/pipecat/services/deepgram.py
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@@ -71,13 +71,7 @@ class DeepgramTTSService(TTSService):
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
logger.debug(f"Generating TTS: [{text}]")
if self.can_generate_metrics() and self.metrics_enabled:
characters = {
"processor": self.name,
"value": len(text),
}
logger.debug(f"{self.name} Characters: {characters['value']}")
await self.push_frame(MetricsFrame(characters=[characters]))
base_url = self._base_url
request_url = f"{base_url}?model={self._voice}&encoding={self._encoding}&container=none&sample_rate={self._sample_rate}"
headers = {"authorization": f"token {self._api_key}"}
@@ -100,6 +94,8 @@ class DeepgramTTSService(TTSService):
yield ErrorFrame(f"Error getting audio (status: {r.status}, error: {response_text})")
return
await self.start_tts_usage_metrics(text)
async for data in r.content:
await self.stop_ttfb_metrics()
frame = AudioRawFrame(audio=data, sample_rate=self._sample_rate, num_channels=1)

10
src/pipecat/services/elevenlabs.py
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@@ -40,13 +40,7 @@ class ElevenLabsTTSService(TTSService):
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
logger.debug(f"Generating TTS: [{text}]")
if self.can_generate_metrics() and self.metrics_enabled:
characters = {
"processor": self.name,
"value": len(text),
}
logger.debug(f"{self.name} Characters: {characters['value']}")
await self.push_frame(MetricsFrame(characters=[characters]))
url = f"https://api.elevenlabs.io/v1/text-to-speech/{self._voice_id}/stream"
payload = {"text": text, "model_id": self._model}
@@ -69,6 +63,8 @@ class ElevenLabsTTSService(TTSService):
yield ErrorFrame(f"Error getting audio (status: {r.status}, error: {text})")
return
await self.start_tts_usage_metrics(text)
async for chunk in r.content:
if len(chunk) > 0:
await self.stop_ttfb_metrics()

28
src/pipecat/services/openai.py
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@@ -135,17 +135,13 @@ class BaseOpenAILLMService(LLMService):
async for chunk in chunk_stream:
if chunk.usage:
if self.can_generate_metrics() and self.metrics_enabled:
tokens = {
"processor": self.name,
"prompt_tokens": chunk.usage.prompt_tokens,
"completion_tokens": chunk.usage.completion_tokens,
"total_tokens": chunk.usage.total_tokens
}
logger.debug(
f"{self.name} prompt tokens: {tokens['prompt_tokens']}, completion tokens: {tokens['completion_tokens']}")
await self.push_frame(MetricsFrame(tokens=[tokens]))
tokens = {
"processor": self.name,
"prompt_tokens": chunk.usage.prompt_tokens,
"completion_tokens": chunk.usage.completion_tokens,
"total_tokens": chunk.usage.total_tokens
}
await self.start_llm_usage_metrics(tokens)
if len(chunk.choices) == 0:
continue
@@ -338,13 +334,6 @@ class OpenAITTSService(TTSService):
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
logger.debug(f"Generating TTS: [{text}]")
if self.can_generate_metrics() and self.metrics_enabled:
characters = {
"processor": self.name,
"value": len(text),
}
logger.debug(f"{self.name} Characters: {characters['value']}")
await self.push_frame(MetricsFrame(characters=[characters]))
try:
await self.start_ttfb_metrics()
@@ -360,6 +349,9 @@ class OpenAITTSService(TTSService):
f"{self} error getting audio (status: {r.status_code}, error: {error})")
yield ErrorFrame(f"Error getting audio (status: {r.status_code}, error: {error})")
return
await self.start_tts_usage_metrics(text)
async for chunk in r.iter_bytes(8192):
if len(chunk) > 0:
await self.stop_ttfb_metrics()

10
src/pipecat/services/playht.py
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@@ -48,13 +48,7 @@ class PlayHTTTSService(TTSService):
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
logger.debug(f"Generating TTS: [{text}]")
if self.can_generate_metrics() and self.metrics_enabled:
characters = {
"processor": self.name,
"value": len(text),
}
logger.debug(f"{self.name} Characters: {characters['value']}")
await self.push_frame(MetricsFrame(characters=[characters]))
try:
b = bytearray()
in_header = True
@@ -66,6 +60,8 @@ class PlayHTTTSService(TTSService):
voice_engine="PlayHT2.0-turbo",
options=self._options)
await self.start_tts_usage_metrics(text)
async for chunk in playht_gen:
# skip the RIFF header.
if in_header:

10
src/pipecat/services/xtts.py
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@@ -70,13 +70,7 @@ class XTTSService(TTSService):
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
logger.debug(f"Generating TTS: [{text}]")
if self.can_generate_metrics() and self.metrics_enabled:
characters = {
"processor": self.name,
"value": len(text),
}
logger.debug(f"{self.name} Characters: {characters['value']}")
await self.push_frame(MetricsFrame(characters=[characters]))
if not self._studio_speakers:
logger.error(f"{self} no studio speakers available")
return
@@ -103,6 +97,8 @@ class XTTSService(TTSService):
yield ErrorFrame(f"Error getting audio (status: {r.status}, error: {text})")
return
await self.start_tts_usage_metrics(text)
buffer = bytearray()
async for chunk in r.content.iter_chunked(1024):