From d591f9e108e7128ab72dfe5478b4eb1645cc6eda Mon Sep 17 00:00:00 2001 From: Mark Backman Date: Tue, 13 Jan 2026 15:20:59 -0500 Subject: [PATCH] Remove 28-transcription-processor.py --- .../28-transcription-processor.py | 209 ------------------ 1 file changed, 209 deletions(-) delete mode 100644 examples/foundational/28-transcription-processor.py diff --git a/examples/foundational/28-transcription-processor.py b/examples/foundational/28-transcription-processor.py deleted file mode 100644 index 0b057a621..000000000 --- a/examples/foundational/28-transcription-processor.py +++ /dev/null @@ -1,209 +0,0 @@ -# -# Copyright (c) 2024-2026, Daily -# -# SPDX-License-Identifier: BSD 2-Clause License -# - -import os -from typing import List, Optional - -from dotenv import load_dotenv -from loguru import logger - -from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3 -from pipecat.audio.vad.silero import SileroVADAnalyzer -from pipecat.audio.vad.vad_analyzer import VADParams -from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage, TranscriptionUpdateFrame -from pipecat.pipeline.pipeline import Pipeline -from pipecat.pipeline.runner import PipelineRunner -from pipecat.pipeline.task import PipelineParams, PipelineTask -from pipecat.processors.aggregators.llm_context import LLMContext -from pipecat.processors.aggregators.llm_response_universal import ( - LLMContextAggregatorPair, - LLMUserAggregatorParams, -) -from pipecat.processors.transcript_processor import TranscriptProcessor -from pipecat.runner.types import RunnerArguments -from pipecat.runner.utils import create_transport -from pipecat.services.cartesia.tts import CartesiaTTSService -from pipecat.services.deepgram.stt import DeepgramSTTService -from pipecat.services.openai.llm import OpenAILLMService -from pipecat.transports.base_transport import BaseTransport, TransportParams -from pipecat.transports.daily.transport import DailyParams -from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams -from pipecat.turns.user_stop import TurnAnalyzerUserTurnStopStrategy -from pipecat.turns.user_turn_strategies import UserTurnStrategies - -load_dotenv(override=True) - - -class TranscriptHandler: - """Handles real-time transcript processing and output. - - Maintains a list of conversation messages and outputs them either to a log - or to a file as they are received. Each message includes its timestamp and role. - - Attributes: - messages: List of all processed transcript messages - output_file: Optional path to file where transcript is saved. If None, outputs to log only. - """ - - def __init__(self, output_file: Optional[str] = None): - """Initialize handler with optional file output. - - Args: - output_file: Path to output file. If None, outputs to log only. - """ - self.messages: List[TranscriptionMessage] = [] - self.output_file: Optional[str] = output_file - logger.debug( - f"TranscriptHandler initialized {'with output_file=' + output_file if output_file else 'with log output only'}" - ) - - async def save_message(self, message: TranscriptionMessage): - """Save a single transcript message. - - Outputs the message to the log and optionally to a file. - - Args: - message: The message to save - """ - timestamp = f"[{message.timestamp}] " if message.timestamp else "" - line = f"{timestamp}{message.role}: {message.content}" - - # Always log the message - logger.info(f"Transcript: {line}") - - # Optionally write to file - if self.output_file: - try: - with open(self.output_file, "a", encoding="utf-8") as f: - f.write(line + "\n") - except Exception as e: - logger.error(f"Error saving transcript message to file: {e}") - - async def on_transcript_update( - self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame - ): - """Handle new transcript messages. - - Args: - processor: The TranscriptProcessor that emitted the update - frame: TranscriptionUpdateFrame containing new messages - """ - logger.debug(f"Received transcript update with {len(frame.messages)} new messages") - - for msg in frame.messages: - self.messages.append(msg) - await self.save_message(msg) - - -# We store functions so objects (e.g. SileroVADAnalyzer) don't get -# instantiated. The function will be called when the desired transport gets -# selected. -transport_params = { - "daily": lambda: DailyParams( - audio_in_enabled=True, - audio_out_enabled=True, - vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)), - ), - "twilio": lambda: FastAPIWebsocketParams( - audio_in_enabled=True, - audio_out_enabled=True, - vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)), - ), - "webrtc": lambda: TransportParams( - audio_in_enabled=True, - audio_out_enabled=True, - vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)), - ), -} - - -async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): - logger.info(f"Starting bot") - - stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY")) - - tts = CartesiaTTSService( - api_key=os.getenv("CARTESIA_API_KEY"), - voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady - ) - - llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY")) - - messages = [ - { - "role": "system", - "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative, helpful, and brief way. Say hello.", - }, - ] - - context = LLMContext(messages) - context_aggregator = LLMContextAggregatorPair( - context, - user_params=LLMUserAggregatorParams( - user_turn_strategies=UserTurnStrategies( - stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())] - ), - ), - ) - - # Create transcript processor and handler - transcript = TranscriptProcessor() - transcript_handler = TranscriptHandler() # Output to log only - # transcript_handler = TranscriptHandler(output_file="transcript.txt") # Output to file and log - - pipeline = Pipeline( - [ - transport.input(), # Transport user input - stt, # STT - transcript.user(), # User transcripts - context_aggregator.user(), # User responses - llm, # LLM - tts, # TTS - transport.output(), # Transport bot output - transcript.assistant(), # Assistant transcripts - context_aggregator.assistant(), # Assistant spoken responses - ] - ) - - task = PipelineTask( - pipeline, - params=PipelineParams( - enable_metrics=True, - enable_usage_metrics=True, - ), - idle_timeout_secs=runner_args.pipeline_idle_timeout_secs, - ) - - @transport.event_handler("on_client_connected") - async def on_client_connected(transport, client): - logger.info(f"Client connected") - # Start conversation - empty prompt to let LLM follow system instructions - await task.queue_frames([LLMRunFrame()]) - - # Register event handler for transcript updates - @transcript.event_handler("on_transcript_update") - async def on_transcript_update(processor, frame): - await transcript_handler.on_transcript_update(processor, frame) - - @transport.event_handler("on_client_disconnected") - async def on_client_disconnected(transport, client): - logger.info(f"Client disconnected") - await task.cancel() - - runner = PipelineRunner(handle_sigint=runner_args.handle_sigint) - await runner.run(task) - - -async def bot(runner_args: RunnerArguments): - """Main bot entry point compatible with Pipecat Cloud.""" - transport = await create_transport(runner_args, transport_params) - await run_bot(transport, runner_args) - - -if __name__ == "__main__": - from pipecat.runner.run import main - - main()