Remove unnecessary TranscriptProcessor examples
This commit is contained in:
Mark Backman
@@ -123,6 +123,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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- Added a Pipecat Cloud deployment example to the `examples` directory.
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- Removed foundational examples 28b and 28c as the TranscriptProcessor no
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longer has an LLM depedency. Renamed foundational example 28a to
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`28-transcript-processor.py`.
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## [0.0.58] - 2025-02-26
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### Added
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@@ -1,177 +0,0 @@
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#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import asyncio
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import os
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import sys
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from typing import List, Optional
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from runner import configure
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import TranscriptionMessage, TranscriptionUpdateFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.processors.transcript_processor import TranscriptProcessor
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from pipecat.services.anthropic import AnthropicLLMService
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.services.deepgram import DeepgramSTTService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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class TranscriptHandler:
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"""Handles real-time transcript processing and output.
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Maintains a list of conversation messages and outputs them either to a log
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or to a file as they are received. Each message includes its timestamp and role.
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Attributes:
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messages: List of all processed transcript messages
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output_file: Optional path to file where transcript is saved. If None, outputs to log only.
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"""
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def __init__(self, output_file: Optional[str] = None):
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"""Initialize handler with optional file output.
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Args:
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output_file: Path to output file. If None, outputs to log only.
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"""
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self.messages: List[TranscriptionMessage] = []
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self.output_file: Optional[str] = output_file
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logger.debug(
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f"TranscriptHandler initialized {'with output_file=' + output_file if output_file else 'with log output only'}"
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)
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async def save_message(self, message: TranscriptionMessage):
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"""Save a single transcript message.
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Outputs the message to the log and optionally to a file.
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Args:
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message: The message to save
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"""
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timestamp = f"[{message.timestamp}] " if message.timestamp else ""
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line = f"{timestamp}{message.role}: {message.content}"
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# Always log the message
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logger.info(f"Transcript: {line}")
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# Optionally write to file
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if self.output_file:
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try:
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with open(self.output_file, "a", encoding="utf-8") as f:
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f.write(line + "\n")
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except Exception as e:
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logger.error(f"Error saving transcript message to file: {e}")
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async def on_transcript_update(
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self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
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):
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"""Handle new transcript messages.
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Args:
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processor: The TranscriptProcessor that emitted the update
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frame: TranscriptionUpdateFrame containing new messages
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"""
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logger.debug(f"Received transcript update with {len(frame.messages)} new messages")
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for msg in frame.messages:
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self.messages.append(msg)
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await self.save_message(msg)
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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transport = DailyTransport(
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room_url,
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None,
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"Respond bot",
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DailyParams(
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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vad_audio_passthrough=True,
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),
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)
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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llm = AnthropicLLMService(
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api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20241022"
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)
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messages = [
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{
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"role": "system",
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"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 converted to audio so don't include special characters in your answers. Respond to what the user said in a creative, helpful, and brief way.",
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},
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{"role": "user", "content": "Say hello."},
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]
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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# Create transcript processor and handler
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transcript = TranscriptProcessor()
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transcript_handler = TranscriptHandler() # Output to log only
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# transcript_handler = TranscriptHandler(output_file="transcript.txt") # Output to file and log
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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stt, # STT
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transcript.user(), # User transcripts
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context_aggregator.user(), # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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transcript.assistant(), # Assistant transcripts
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context_aggregator.assistant(), # Assistant spoken responses
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]
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)
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task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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await transport.capture_participant_transcription(participant["id"])
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# Kick off the conversation.
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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# Register event handler for transcript updates
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@transcript.event_handler("on_transcript_update")
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async def on_transcript_update(processor, frame):
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await transcript_handler.on_transcript_update(processor, frame)
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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# Stop the pipeline immediately when the participant leaves
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await task.cancel()
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -1,210 +0,0 @@
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#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import asyncio
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import os
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import sqlite3
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import sys
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from typing import List, Optional
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from runner import configure
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import TranscriptionMessage, TranscriptionUpdateFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.processors.transcript_processor import TranscriptProcessor
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.services.deepgram import DeepgramSTTService
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from pipecat.services.google import GoogleLLMService
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from pipecat.services.openai import OpenAILLMContext
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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class TranscriptHandler:
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"""Handles real-time transcript processing and output.
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Maintains a list of conversation messages and outputs them either to a log
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or to a file as they are received. Each message includes its timestamp and role.
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Attributes:
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messages: List of all processed transcript messages
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output_file: Optional path to file where transcript is saved. If None, outputs to log only.
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"""
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def __init__(self, output_file: Optional[str] = None, output_db: Optional[str] = None):
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"""Initialize handler with optional file or database output.
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Args:
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output_file: Path to output file. If None, outputs to log only.
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"""
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self.messages: List[TranscriptionMessage] = []
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self.output_file: Optional[str] = output_file
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self.output_db: Optional[str] = output_db
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if self.output_db:
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self.con = sqlite3.connect("example.db")
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self.db = self.con.cursor()
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table = self.db.execute("SELECT name FROM sqlite_master WHERE name='messages'")
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if not (table.fetchone()):
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self.db.execute(
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"CREATE TABLE messages(role TEXT, content TEXT, timestamp DATETIME DEFAULT CURRENT_TIMESTAMP )"
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)
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logger.debug(
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f"TranscriptHandler initialized; output file: {output_file}, output DB: {output_db}"
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)
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async def save_message(self, message: TranscriptionMessage):
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"""Save a single transcript message.
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Outputs the message to the log and optionally to a SQLite database or file.
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Args:
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message: The message to save
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"""
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timestamp = f"[{message.timestamp}] " if message.timestamp else ""
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line = f"{timestamp}{message.role}: {message.content}"
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# Always log the message
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logger.info(f"Transcript: {line}")
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# Optionally write to file
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if self.output_file:
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try:
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with open(self.output_file, "a", encoding="utf-8") as f:
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f.write(line + "\n")
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except Exception as e:
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logger.error(f"Error saving transcript message to file: {e}")
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# and/or to a SQLite database
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if self.output_db:
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self.db.execute(
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"INSERT INTO messages VALUES (?, ?, ?)",
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(message.role, message.content, message.timestamp),
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)
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self.con.commit()
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async def on_transcript_update(
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self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
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):
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"""Handle new transcript messages.
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Args:
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processor: The TranscriptProcessor that emitted the update
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frame: TranscriptionUpdateFrame containing new messages
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"""
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logger.debug(f"Received transcript update with {len(frame.messages)} new messages")
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for msg in frame.messages:
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self.messages.append(msg)
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await self.save_message(msg)
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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transport = DailyTransport(
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room_url,
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None,
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"Respond bot",
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DailyParams(
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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vad_audio_passthrough=True,
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),
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)
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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llm = GoogleLLMService(
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model="models/gemini-2.0-flash-exp",
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# model="gemini-exp-1114",
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api_key=os.getenv("GOOGLE_API_KEY"),
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)
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messages = [
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{
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"role": "system",
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"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 converted to audio so don't include special characters in your answers. Respond to what the user said in a creative, helpful, and brief way.",
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},
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{"role": "user", "content": "Say hello."},
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]
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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# Create transcript processor and handler
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transcript = TranscriptProcessor()
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# Select a TranscriptHandler output method
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# Uncomment out only one of the following lines:
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transcript_handler = TranscriptHandler() # Output to log only
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# transcript_handler = TranscriptHandler(output_file="transcript.txt") # Output to file and log
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# transcript_handler = TranscriptHandler(output_db="example.db") # Output to SQLite DB and log
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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stt, # STT
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transcript.user(), # User transcripts
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context_aggregator.user(), # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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transcript.assistant(), # Assistant transcripts
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context_aggregator.assistant(), # Assistant spoken responses
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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)
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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await transport.capture_participant_transcription(participant["id"])
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# Kick off the conversation.
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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# Register event handler for transcript updates
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@transcript.event_handler("on_transcript_update")
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async def on_transcript_update(processor, frame):
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await transcript_handler.on_transcript_update(processor, frame)
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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# Stop the pipeline immediately when the participant leaves
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await task.cancel()
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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Block a user