# # Copyright (c) 2024, Daily # # SPDX-License-Identifier: BSD 2-Clause License # import aiohttp import asyncio import os import sys import google.ai.generativelanguage as glm from dataclasses import dataclass from dotenv import load_dotenv from loguru import logger from runner import configure from pipecat.audio.vad.silero import SileroVADAnalyzer from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext from pipecat.services.cartesia import CartesiaTTSService from pipecat.services.google import GoogleLLMService from pipecat.processors.frame_processor import FrameProcessor from pipecat.transports.services.daily import DailyParams, DailyTransport from pipecat.frames.frames import ( LLMFullResponseStartFrame, LLMFullResponseEndFrame, InputAudioRawFrame, Frame, StartInterruptionFrame, TextFrame, TranscriptionFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame, ) load_dotenv(override=True) logger.remove(0) logger.add(sys.stderr, level="DEBUG") marker = "|----|" system_message = f""" You are a helpful LLM in a WebRTC call. Your goals are to be helpful and brief in your responses. You are expert at transcribing audio to text. You will receive a mixture of audio and text input. When asked to transcribe what the user said, output an exact, word-for-word transcription. Your output will be converted to audio so don't include special characters in your answers. Each time you answer, you should respond in three parts. 1. Transcribe exactly what the user said. 2. Output the separator field '{marker}'. 3. Respond to the user's input in a helpful, creative way using only simple text and punctuation. Example: User: How many ounces are in a pound? You: How many ounces are in a pound? {marker} There are 16 ounces in a pound. """ @dataclass class MagicDemoTranscriptionFrame(Frame): text: str class UserAudioCollector(FrameProcessor): def __init__(self, context, user_context_aggregator): super().__init__() self._context = context self._user_context_aggregator = user_context_aggregator self._audio_frames = [] self._start_secs = 0.2 # this should match VAD start_secs (hardcoding for now) self._user_speaking = False async def process_frame(self, frame, direction): await super().process_frame(frame, direction) if isinstance(frame, TranscriptionFrame): # We could gracefully handle both audio input and text/transcription input ... # but let's leave that as an exercise to the reader. :-) return if isinstance(frame, UserStartedSpeakingFrame): self._user_speaking = True elif isinstance(frame, UserStoppedSpeakingFrame): self._user_speaking = False self._context.add_audio_frames_message(audio_frames=self._audio_frames) await self._user_context_aggregator.push_frame( self._user_context_aggregator.get_context_frame() ) elif isinstance(frame, InputAudioRawFrame): if self._user_speaking: self._audio_frames.append(frame) else: # Append the audio frame to our buffer. Treat the buffer as a ring buffer, dropping the oldest # frames as necessary. Assume all audio frames have the same duration. self._audio_frames.append(frame) frame_duration = len(frame.audio) / 16 * frame.num_channels / frame.sample_rate buffer_duration = frame_duration * len(self._audio_frames) while buffer_duration > self._start_secs: self._audio_frames.pop(0) buffer_duration -= frame_duration await self.push_frame(frame, direction) class TranscriptExtractor(FrameProcessor): def __init__(self, context): super().__init__() self._context = context self._accumulator = "" self._processing_llm_response = False self._accumulating_transcript = False def reset(self): self._accumulator = "" self._processing_llm_response = False self._accumulating_transcript = False async def process_frame(self, frame, direction): await super().process_frame(frame, direction) if isinstance(frame, LLMFullResponseStartFrame): self._processing_llm_response = True self._accumulating_transcript = True elif isinstance(frame, TextFrame) and self._processing_llm_response: if self._accumulating_transcript: text = frame.text split_index = text.find(marker) if split_index < 0: self._accumulator += frame.text # do not push this frame return else: self._accumulating_transcript = False self._accumulator += text[:split_index] frame.text = text[split_index + len(marker) :] await self.push_frame(frame) return elif isinstance(frame, LLMFullResponseEndFrame): await self.push_frame(MagicDemoTranscriptionFrame(text=self._accumulator.strip())) self.reset() await self.push_frame(frame, direction) class TanscriptionContextFixup(FrameProcessor): def __init__(self, context): super().__init__() self._context = context self._transcript = "THIS IS A TRANSCRIPT" def swap_user_audio(self): if not self._transcript: return message = self._context.messages[-2] last_part = message.parts[-1] if ( message.role == "user" and last_part.inline_data and last_part.inline_data.mime_type == "audio/wav" ): self._context.messages[-2] = glm.Content( role="user", parts=[glm.Part(text=self._transcript)] ) def add_transcript_back_to_inference_output(self): if not self._transcript: return message = self._context.messages[-1] last_part = message.parts[-1] if message.role == "model" and last_part.text: self._context.messages[-1].parts[-1].text += f"\n\n{marker}\n{self._transcript}\n" async def process_frame(self, frame, direction): await super().process_frame(frame, direction) if isinstance(frame, MagicDemoTranscriptionFrame): self._transcript = frame.text elif isinstance(frame, LLMFullResponseEndFrame) or isinstance( frame, StartInterruptionFrame ): self.swap_user_audio() self.add_transcript_back_to_inference_output() self._transcript = "" await self.push_frame(frame, direction) async def main(): async with aiohttp.ClientSession() as session: (room_url, token) = await configure(session) transport = DailyTransport( room_url, token, "Respond bot", DailyParams( audio_out_enabled=True, # No transcription at all. just audio input to Gemini! # transcription_enabled=True, vad_enabled=True, vad_analyzer=SileroVADAnalyzer(), vad_audio_passthrough=True, ), ) tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY"), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady ) llm = GoogleLLMService(model="gemini-1.5-flash-latest", api_key=os.getenv("GOOGLE_API_KEY")) messages = [ { "role": "system", "content": system_message, }, { "role": "user", "content": "Start by saying hello.", }, ] context = OpenAILLMContext(messages) context_aggregator = llm.create_context_aggregator(context) audio_collector = UserAudioCollector(context, context_aggregator.user()) pull_transcript_out_of_llm_output = TranscriptExtractor(context) fixup_context_messages = TanscriptionContextFixup(context) pipeline = Pipeline( [ transport.input(), # Transport user input audio_collector, context_aggregator.user(), # User responses llm, # LLM pull_transcript_out_of_llm_output, tts, # TTS transport.output(), # Transport bot output context_aggregator.assistant(), # Assistant spoken responses fixup_context_messages, ] ) task = PipelineTask( pipeline, PipelineParams( allow_interruptions=True, enable_metrics=True, enable_usage_metrics=True, ), ) @transport.event_handler("on_first_participant_joined") async def on_first_participant_joined(transport, participant): await transport.capture_participant_transcription(participant["id"]) # Kick off the conversation. await task.queue_frames([context_aggregator.user().get_context_frame()]) runner = PipelineRunner() await runner.run(task) if __name__ == "__main__": asyncio.run(main())