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Remove unnecessary TranscriptProcessor examples

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This commit is contained in:
Mark Backman
2025-03-13 14:34:05 -04:00
parent d5776c27f4
commit 3f002f8ffb
4 changed files with 4 additions and 387 deletions
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4
CHANGELOG.md
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@@ -123,6 +123,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
- Added a Pipecat Cloud deployment example to the `examples` directory. - Added a Pipecat Cloud deployment example to the `examples` directory.
- Removed foundational examples 28b and 28c as the TranscriptProcessor no
longer has an LLM depedency. Renamed foundational example 28a to
`28-transcript-processor.py`.
## [0.0.58] - 2025-02-26 ## [0.0.58] - 2025-02-26
### Added ### Added

0
examples/foundational/28a-transcription-processor-openai.py → examples/foundational/28-transcription-processor.py
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177
examples/foundational/28b-transcript-processor-anthropic.py
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@@ -1,177 +0,0 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sys
from typing import List, Optional
import aiohttp
from dotenv import load_dotenv
from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import 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.openai_llm_context import OpenAILLMContext
from pipecat.processors.transcript_processor import TranscriptProcessor
from pipecat.services.anthropic import AnthropicLLMService
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.deepgram import DeepgramSTTService
from pipecat.transports.services.daily import DailyParams, DailyTransport
load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
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)
async def main():
async with aiohttp.ClientSession() as session:
(room_url, token) = await configure(session)
transport = DailyTransport(
room_url,
None,
"Respond bot",
DailyParams(
audio_out_enabled=True,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
vad_audio_passthrough=True,
),
)
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 = AnthropicLLMService(
api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20241022"
)
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 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.",
},
{"role": "user", "content": "Say hello."},
]
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
# 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(allow_interruptions=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()])
# 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_participant_left")
async def on_participant_left(transport, participant, reason):
# Stop the pipeline immediately when the participant leaves
await task.cancel()
runner = PipelineRunner()
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())

210
examples/foundational/28c-transcription-processor-gemini.py
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@@ -1,210 +0,0 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sqlite3
import sys
from typing import List, Optional
import aiohttp
from dotenv import load_dotenv
from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import 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.openai_llm_context import OpenAILLMContext
from pipecat.processors.transcript_processor import TranscriptProcessor
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.deepgram import DeepgramSTTService
from pipecat.services.google import GoogleLLMService
from pipecat.services.openai import OpenAILLMContext
from pipecat.transports.services.daily import DailyParams, DailyTransport
load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
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, output_db: Optional[str] = None):
"""Initialize handler with optional file or database 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
self.output_db: Optional[str] = output_db
if self.output_db:
self.con = sqlite3.connect("example.db")
self.db = self.con.cursor()
table = self.db.execute("SELECT name FROM sqlite_master WHERE name='messages'")
if not (table.fetchone()):
self.db.execute(
"CREATE TABLE messages(role TEXT, content TEXT, timestamp DATETIME DEFAULT CURRENT_TIMESTAMP )"
)
logger.debug(
f"TranscriptHandler initialized; output file: {output_file}, output DB: {output_db}"
)
async def save_message(self, message: TranscriptionMessage):
"""Save a single transcript message.
Outputs the message to the log and optionally to a SQLite database or 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}")
# and/or to a SQLite database
if self.output_db:
self.db.execute(
"INSERT INTO messages VALUES (?, ?, ?)",
(message.role, message.content, message.timestamp),
)
self.con.commit()
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)
async def main():
async with aiohttp.ClientSession() as session:
(room_url, token) = await configure(session)
transport = DailyTransport(
room_url,
None,
"Respond bot",
DailyParams(
audio_out_enabled=True,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
vad_audio_passthrough=True,
),
)
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 = GoogleLLMService(
model="models/gemini-2.0-flash-exp",
# model="gemini-exp-1114",
api_key=os.getenv("GOOGLE_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 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.",
},
{"role": "user", "content": "Say hello."},
]
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
# Create transcript processor and handler
transcript = TranscriptProcessor()
# Select a TranscriptHandler output method
# Uncomment out only one of the following lines:
transcript_handler = TranscriptHandler() # Output to log only
# transcript_handler = TranscriptHandler(output_file="transcript.txt") # Output to file and log
# transcript_handler = TranscriptHandler(output_db="example.db") # Output to SQLite DB 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(
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()])
# 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_participant_left")
async def on_participant_left(transport, participant, reason):
# Stop the pipeline immediately when the participant leaves
await task.cancel()
runner = PipelineRunner()
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())