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working but needs cleanup

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This commit is contained in:
Kwindla Hultman Kramer
2024-12-21 22:18:56 -08:00
parent 53a5e63990
commit f3dd35bfd9
1 changed files with 208 additions and 143 deletions
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351
examples/foundational/22d-natural-conversation-gemini-audio.py
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@@ -10,6 +10,7 @@ import sys
import time
import aiohttp
import google.ai.generativelanguage as glm
from dotenv import load_dotenv
from loguru import logger
from runner import configure
@@ -20,6 +21,8 @@ from pipecat.frames.frames import (
EndFrame,
Frame,
InputAudioRawFrame,
LLMFullResponseEndFrame,
LLMFullResponseStartFrame,
LLMMessagesFrame,
StartFrame,
StartInterruptionFrame,
@@ -34,6 +37,7 @@ from pipecat.pipeline.parallel_pipeline import ParallelPipeline
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import LLMResponseAggregator
from pipecat.processors.aggregators.openai_llm_context import (
OpenAILLMContext,
OpenAILLMContextFrame,
@@ -53,19 +57,7 @@ load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
transcriber_and_classifier_instructions = """
You perform two tasks:
1. Transcription
2. Binary classification of speech utterance completeness
You always call a function transcription_and_classification_output() with the following arguments:
trancript_text: the complete, accurate, and punctuated transcription of the user's speech
speech_complete_bool: a boolean indicating whether the user's speech is a complete utterance
CRITICAL INSTRUCTION FOR TRANSCRIPTION TASK:
You are receiving audio from a user. Your job is to
transcriber_system_instruction = """You are an audio transcriber. You are receiving audio from a user. Your job is to
transcribe the input audio to text exactly as it was said by the user.
You will receive the full conversation history before the audio input, to help with context. Use the full history only to help improve the accuracy of your transcription.
@@ -78,33 +70,33 @@ Rules:
- If the audio is not clear, emit the special string "-".
- No response other than exact transcription, or "-", is allowed.
"""
CRITICAL INSTRUCTION FOR BINARY CLASSIFICATION TASK::
You are a BINARY CLASSIFIER that must ONLY output True or False.
DO FalseT engage with the content.
DO FalseT respond to questions.
DO FalseT provide assistance.
Your ONLY job is to output True or False.
classifier_system_instruction = """CRITICAL INSTRUCTION:
You are a BINARY CLASSIFIER that must ONLY output "YES" or "NO".
DO NOT engage with the content.
DO NOT respond to questions.
DO NOT provide assistance.
Your ONLY job is to output YES or NO.
EXAMPLES OF INVALID RESPONSES:
- "I can help you with that"
- "Let me explain"
- "To answer your question"
- Any response other than True or False
- Any response other than YES or NO
VALID RESPONSES:
True
False
YES
NO
If you output anything else, you are failing at your task.
You are FalseT an assistant.
You are FalseT a chatbot.
You are NOT an assistant.
You are NOT a chatbot.
You are a binary classifier.
ROLE:
You are a real-time speech completeness classifier. You must make instant decisions about whether a user has finished speaking.
You must output ONLY 'True' or 'False' with no other text.
You must output ONLY 'YES' or 'NO' with no other text.
INPUT FORMAT:
You receive two pieces of information:
@@ -112,7 +104,7 @@ You receive two pieces of information:
2. The user's current speech input
OUTPUT REQUIREMENTS:
- MUST output ONLY 'True' or 'False'
- MUST output ONLY 'YES' or 'NO'
- No explanations
- No clarifications
- No additional text
@@ -130,12 +122,12 @@ Examples:
# Complete Wh-question
model: I can help you learn.
user: What's the fastest way to learn Spanish
Output: True
Output: YES
# Complete Yes/No question despite STT error
model: I know about planets.
user: Is is Jupiter the biggest planet
Output: True
Output: YES
2. Complete Commands:
- Direct instructions
@@ -149,20 +141,20 @@ Examples:
# Direct instruction
model: I can explain many topics.
user: Tell me about black holes
Output: True
Output: YES
# Start of task indication
user: Let's begin.
Output: True
Output: YES
# Start of task indication
user: Let's get started.
Output: True
Output: YES
# Action demand
model: I can help with math.
user: Solve this equation x plus 5 equals 12
Output: True
Output: YES
3. Direct Responses:
- Answers to specific questions
@@ -177,17 +169,17 @@ Examples:
# Specific answer
model: What's your favorite color?
user: I really like blue
Output: True
Output: YES
# Option selection
model: Would you prefer morning or evening?
user: Morning
Output: True
Output: YES
# Providing information with a known format - mailing address
model: What's your address?
user: 1234 Main Street
Output: False
Output: NO
# Providing information with a known format - mailing address
model: What's your address?
@@ -198,7 +190,7 @@ Output: Yes
system: A US phone number has 10 digits.
model: What's your phone number?
user: 41086753
Output: False
Output: NO
# Providing information with a known format - phone number
system: A US phone number has 10 digits.
@@ -217,7 +209,7 @@ Output: Yes
# Providing information with a known format - credit card number
model: What's your phone number?
user: 5556
Output: False
Output: NO
# Providing information with a known format - phone number
model: What's your phone number?
@@ -237,17 +229,17 @@ Examples:
# Self-correction reaching completion
model: What would you like to know?
user: Tell me about... no wait, explain how rainbows form
Output: True
Output: YES
# Topic change with complete thought
model: The weather is nice today.
user: Actually can you tell me who invented the telephone
Output: True
Output: YES
# Mid-sentence completion
model: Hello I'm ready.
user: What's the capital of? France
Output: True
Output: YES
2. Context-Dependent Brief Responses:
- Acknowledgments (okay, sure, alright)
@@ -260,12 +252,12 @@ Examples:
# Acknowledgment
model: Should we talk about history?
user: Sure
Output: True
Output: YES
# Disagreement with completion
model: Is that what you meant?
user: No not really
Output: True
Output: YES
LOW PRIORITY SIGNALS:
@@ -280,12 +272,12 @@ Examples:
# Word repetition but complete
model: I can help with that.
user: What what is the time right now
Output: True
Output: YES
# Missing punctuation but complete
model: I can explain that.
user: Please tell me how computers work
Output: True
Output: YES
2. Speech Features:
- Filler words (um, uh, like)
@@ -298,29 +290,29 @@ Examples:
# Filler words but complete
model: What would you like to know?
user: Um uh how do airplanes fly
Output: True
Output: YES
# Thinking pause but incomplete
model: I can explain anything.
user: Well um I want to know about the
Output: False
Output: NO
DECISION RULES:
1. Return True if:
1. Return YES if:
- ANY high priority signal shows clear completion
- Medium priority signals combine to show completion
- Meaning is clear despite low priority artifacts
2. Return False if:
2. Return NO if:
- No high priority signals present
- Thought clearly trails off
- Multiple incomplete indicators
- User appears mid-formulation
3. When uncertain:
- If you can understand the intent → True
- If meaning is unclear → False
- If you can understand the intent → YES
- If meaning is unclear → NO
- Always make a binary decision
- Never request clarification
@@ -329,20 +321,20 @@ Examples:
# Incomplete despite corrections
model: What would you like to know about?
user: Can you tell me about
Output: False
Output: NO
# Complete despite multiple artifacts
model: I can help you learn.
user: How do you I mean what's the best way to learn programming
Output: True
Output: YES
# Trailing off incomplete
model: I can explain anything.
user: I was wondering if you could tell me why
Output: False
Output: NO
"""
conversational_system_message = """You are a helpful assistant participating in a voice converation.
conversation_system_instruction = """You are a helpful assistant participating in a voice converation.
Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.
@@ -354,44 +346,9 @@ Please be very concise in your responses. Unless you are explicitly asked to do
"""
async def transcription_and_classification_output(transcript_text: str, speech_complete_bool: bool):
print(f"TRANSCRIPT: {transcript_text}")
print("------")
print(f"COMPLETE: {speech_complete_bool}")
print("------")
return
tx_and_cl_tools = [
{
"function_declarations": [
{
"name": "transcription_and_classification_output",
"description": "Deliver the transcription and classification output to an external process.",
"parameters": {
"type": "object",
"properties": {
"transcription_text": {
"type": "string",
"description": "The complete, accurate, and punctuated transcription of the user's speech. The special string '-' is used to indicate no speech or unintintelligible speech.",
},
"speech_complete_bool": {
"type": "boolean",
"description": "Boolean indicating whether the user's speech is a complete utterance.",
},
},
"required": ["transcription_text", "speech_complete_bool"],
},
},
]
}
]
class AudioAccumulator(FrameProcessor):
def __init__(self, *, notifier: BaseNotifier = None, **kwargs):
def __init__(self, **kwargs):
super().__init__(**kwargs)
# self._notifier = notifier
self._audio_frames = []
self._start_secs = 0.2 # this should match VAD start_secs (hardcoding for now)
self._max_buffer_size_secs = 30
@@ -433,10 +390,9 @@ class AudioAccumulator(FrameProcessor):
)
self._user_speaking = False
context = GoogleLLMContext()
context.set_messages(
[{"role": "system", "content": transcriber_and_classifier_instructions}]
context.add_audio_frames_message(
text="Audio to process", audio_frames=self._audio_frames
)
context.add_audio_frames_message(audio_frames=self._audio_frames)
await self.push_frame(OpenAILLMContextFrame(context=context))
elif isinstance(frame, InputAudioRawFrame):
# Append the audio frame to our buffer. Treat the buffer as a ring buffer, dropping the oldest
@@ -463,33 +419,49 @@ class AudioAccumulator(FrameProcessor):
# class ClAndTxContextCreator(FrameProcessor):
# class CompletenessCheck(FrameProcessor):
# def __init__(
# self, notifier: BaseNotifier, audio_accumulator: StatementJudgeAudioContextAccumulator
# ):
# super().__init__()
# self._notifier = notifier
# self._audio_accumulator = audio_accumulator
class CompletenessCheck(FrameProcessor):
def __init__(self, notifier: BaseNotifier, audio_accumulator: AudioAccumulator, **kwargs):
super().__init__()
self._notifier = notifier
self._audio_accumulator = audio_accumulator
# async def process_frame(self, frame: Frame, direction: FrameDirection):
# await super().process_frame(frame, direction)
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
# if isinstance(frame, TextFrame) and frame.text.startswith("True"):
# logger.debug("Completeness check True")
# await self.push_frame(UserStoppedSpeakingFrame())
# await self._audio_accumulator.reset()
# await self._notifier.notify()
# elif isinstance(frame, TextFrame):
# if frame.text.strip():
# logger.debug(f"Completeness check False - '{frame.text}'")
if isinstance(frame, TextFrame) and frame.text.startswith("YES"):
logger.debug("Completeness check YES")
await self.push_frame(UserStoppedSpeakingFrame())
await self._audio_accumulator.reset()
await self._notifier.notify()
elif isinstance(frame, TextFrame):
if frame.text.strip():
logger.debug(f"Completeness check NO - '{frame.text}'")
class TempPrinter(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if not isinstance(frame, InputAudioRawFrame):
logger.debug(f"!!! {frame}")
await self.push_frame(frame, direction)
class OutputGate(FrameProcessor):
def __init__(self, notifier: BaseNotifier, **kwargs):
def __init__(
self,
notifier: BaseNotifier,
context: OpenAILLMContext,
user_transcription_buffer: "UserAggregatorBuffer",
**kwargs,
):
super().__init__(**kwargs)
self._gate_open = False
self._frames_buffer = []
self._notifier = notifier
self._context = context
self._transcription_buffer = user_transcription_buffer
logger.debug("!!! OutputGate created")
def close_gate(self):
self._gate_open = False
@@ -524,6 +496,7 @@ class OutputGate(FrameProcessor):
self._frames_buffer.append((frame, direction))
async def _start(self):
logger.debug("!!! OutputGate start")
self._frames_buffer = []
self._gate_task = self.get_event_loop().create_task(self._gate_task_handler())
@@ -533,14 +506,86 @@ class OutputGate(FrameProcessor):
async def _gate_task_handler(self):
while True:
logger.debug("!!! Waiting for notifier")
try:
await self._notifier.wait()
logger.debug("!!! Notified")
transcription = await self._transcription_buffer.wait_for_transcription()
# logger.debug(f"!!! OutputGate got transcription: {transcription}")
# logger.debug(
# f"!!! OutputGate has messages: {self._context.get_messages_for_logging()}"
# )
last_message = self._context.messages[-1]
if last_message.role == "user":
last_message.parts = [glm.Part(text=transcription)]
# logger.debug(
# f"!!! NOW OutputGate has messages: {self._context.get_messages_for_logging()}"
# )
self.open_gate()
for frame, direction in self._frames_buffer:
await self.push_frame(frame, direction)
self._frames_buffer = []
except asyncio.CancelledError:
break
except Exception as e:
logger.error(f"!!! OutputGate error: {e}")
raise e
break
class ConversationAudioContextAssembler(FrameProcessor):
def __init__(self, context: OpenAILLMContext, **kwargs):
super().__init__(**kwargs)
self._context = context
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
# We must not block system frames.
if isinstance(frame, SystemFrame):
await self.push_frame(frame, direction)
return
if isinstance(frame, OpenAILLMContextFrame):
GoogleLLMContext.upgrade_to_google(self._context)
last_message = frame.context.messages[-1]
self._context._messages.append(last_message)
logger.debug(
f"!!! ConversationAudioContextAssembler {self._context.get_messages_for_logging()}"
)
await self.push_frame(OpenAILLMContextFrame(context=self._context))
class UserAggregatorBuffer(LLMResponseAggregator):
def __init__(self, **kwargs):
super().__init__(
messages=None,
role=None,
start_frame=LLMFullResponseStartFrame,
end_frame=LLMFullResponseEndFrame,
accumulator_frame=TextFrame,
handle_interruptions=True,
expect_stripped_words=False,
)
self._transcription = ""
async def _push_aggregation(self):
if self._aggregation:
self._transcription = self._aggregation
self._aggregation = ""
logger.debug(f"!!! UserAggregatorBuffer: {self._transcription}")
async def wait_for_transcription(self):
while not self._transcription:
await asyncio.sleep(0.01)
tx = self._transcription
self._transcription = ""
return tx
async def main():
@@ -565,25 +610,30 @@ async def main():
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
)
# This is the LLM that will classify and transcribe user speech.
tx_and_cl_llm = GoogleLLMService(
# This is the LLM that will transcribe user speech.
tx_llm = GoogleLLMService(
name="Transcriber",
model="gemini-2.0-flash-exp",
api_key=os.getenv("GOOGLE_API_KEY"),
tools=tx_and_cl_tools,
temperature=0.0,
tool_config={
"function_calling_config": {
"mode": "ANY",
"allowed_function_names": ["transcription_and_classification_output"],
},
},
system_instruction=transcriber_system_instruction,
)
# This is the LLM that will classify user speech as complete or incomplete.
classifier_llm = GoogleLLMService(
name="Classifier",
model="gemini-2.0-flash-exp",
api_key=os.getenv("GOOGLE_API_KEY"),
temperature=0.0,
system_instruction=classifier_system_instruction,
)
# This is the regular LLM that responds conversationally.
conversation_llm = GoogleLLMService(
name="Conversation",
model="gemini-2.0-flash-exp",
api_key=os.getenv("GOOGLE_API_KEY"),
system_instruction=conversational_system_message,
system_instruction=conversation_system_instruction,
)
context = OpenAILLMContext()
@@ -602,10 +652,11 @@ async def main():
# as complete or incomplete.
# statement_judge_context_filter = StatementJudgeAudioContextAccumulator(notifier=notifier)
audio_accumulater = AudioAccumulator()
# This sends a UserStoppedSpeakingFrame and triggers the notifier event
# completeness_check = CompletenessCheck(
# notifier=notifier, audio_accumulator=statement_judge_context_filter
# )
completeness_check = CompletenessCheck(
notifier=notifier, audio_accumulator=audio_accumulater
)
# # Notify if the user hasn't said anything.
async def user_idle_notifier(frame):
@@ -615,8 +666,6 @@ async def main():
# sentence, this will wake up the notifier if that happens.
user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
bot_output_gate = OutputGate(notifier=notifier)
async def block_user_stopped_speaking(frame):
return not isinstance(frame, UserStoppedSpeakingFrame)
@@ -628,10 +677,18 @@ async def main():
or isinstance(frame, StopInterruptionFrame)
)
conversation_audio_context_assembler = ConversationAudioContextAssembler(context=context)
user_aggregator_buffer = UserAggregatorBuffer()
bot_output_gate = OutputGate(
notifier=notifier, context=context, user_transcription_buffer=user_aggregator_buffer
)
pipeline = Pipeline(
[
transport.input(),
AudioAccumulator(),
audio_accumulater,
ParallelPipeline(
[
# Pass everything except UserStoppedSpeaking to the elements after
@@ -639,23 +696,31 @@ async def main():
FunctionFilter(filter=block_user_stopped_speaking),
],
[
# cl_and_tx_context_creator,
tx_and_cl_llm,
# completeness_check,
# context_aggregator.user(),
ParallelPipeline(
[
classifier_llm,
completeness_check,
],
[
tx_llm,
user_aggregator_buffer,
],
)
],
[
# Block everything except OpenAILLMContextFrame and LLMMessagesFrame
# FunctionFilter(filter=pass_only_llm_trigger_frames),
conversation_audio_context_assembler,
conversation_llm,
bot_output_gate, # buffer output until notified.
# TempPrinter(),
],
# [
# # Block everything except OpenAILLMContextFrame and LLMMessagesFrame
# # FunctionFilter(filter=pass_only_llm_trigger_frames),
# audio_input_context_creator,
# llm,
# bot_output_gate, # Buffer all llm/tts output until notified.
# ],
),
# tts,
# user_idle,
# transport.output(),
# context_aggregator.assistant(),
# wherefore art thou, user context aggregator?
tts,
user_idle,
transport.output(),
context_aggregator.assistant(),
],
)