Reorganize examples into topic-based subfolders
Move 304 examples from a flat numbered directory into 14 descriptive subfolders: getting-started, services (speech + function-calling), transcription, vision, realtime, persistent-context, context-summarization, update-settings (stt/tts/llm), turn-management, thinking-and-mcp, transports, video-avatar, video-processing, and features. Strip numbered prefixes from filenames (e.g. 07c-interruptible-deepgram.py becomes services/speech/deepgram.py) since the folder context makes them redundant. Keep numbered prefixes only in getting-started/ where ordering matters. Update eval script paths and README to match the new structure.
This commit is contained in:
Mark Backman
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examples/services/speech/speechmatics-vad.py
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examples/services/speech/speechmatics-vad.py
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#
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# Copyright (c) 2024-2026, 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 os
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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 pipecat.frames.frames import LLMRunFrame
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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.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
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from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
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from pipecat.transcriptions.language import Language
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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from pipecat.turns.user_turn_strategies import ExternalUserTurnStrategies
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load_dotenv(override=True)
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# We use lambdas to defer transport parameter creation until the transport
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# type is selected at runtime.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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"""Speechmatics STT and TTS Service Example
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This example demonstrates using Speechmatics Speech-to-Text and Text-to-Speech services
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with speaker diarization and intelligent speaker management. Key features:
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1. Speaker Diarization (STT)
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- Automatically identifies and distinguishes between different speakers
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- First speaker is identified as 'S1', others get subsequent IDs
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- Uses `enable_diarization` parameter to manage speaker detection
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2. Smart Speaker Control (STT)
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- `focus_speakers` parameter lets you target specific speakers (e.g. ["S1"])
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- Other speakers will be wrapped in PASSIVE tags
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- Only processes speech from focused speakers
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- Words from all speakers are wrapped with XML tags for clear speaker identification
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- Other speakers' speech only sent when focused speaker is active
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3. Voice Activity Detection (STT)
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- Built-in VAD using `enable_vad` parameter
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- Remove `vad_analyzer` from `transport` config to use module's VAD
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- Emits speaker started/stopped events
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4. Text-to-Speech (TTS)
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- Low latency streaming audio synthesis
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- Multiple voice options available including `sarah`, `theo`, `megan` and `jack`
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5. Configuration Options
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- `operating_point` parameter defaults to `ENHANCED` for optimal accuracy
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- Configurable `end_of_utterance_silence_trigger` (default 0.5s)
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- Customizable speaker formatting
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- Additional diarization settings available
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For detailed information:
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- STT: https://docs.speechmatics.com/rt-api-ref
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- TTS: https://docs.speechmatics.com/text-to-speech/quickstart
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"""
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logger.info(f"Starting bot")
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async with aiohttp.ClientSession() as session:
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stt = SpeechmaticsSTTService(
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api_key=os.getenv("SPEECHMATICS_API_KEY"),
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settings=SpeechmaticsSTTService.Settings(
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language=Language.EN,
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turn_detection_mode=SpeechmaticsSTTService.TurnDetectionMode.ADAPTIVE,
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# focus_speakers=["S1"],
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speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
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speaker_passive_format="<PASSIVE><{speaker_id}>{text}</{speaker_id}></PASSIVE>",
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),
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)
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tts = SpeechmaticsTTSService(
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api_key=os.getenv("SPEECHMATICS_API_KEY"),
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settings=SpeechmaticsTTSService.Settings(
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voice="sarah",
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),
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aiohttp_session=session,
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)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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settings=OpenAILLMService.Settings(
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temperature=0.75,
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system_instruction="You are a helpful British assistant called Sarah in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Always include punctuation in your responses. Give very short replies - do not give longer replies unless strictly necessary. Respond to what the user said in a concise, funny, creative and helpful way. Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to.",
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),
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)
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context = LLMContext()
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(user_turn_strategies=ExternalUserTurnStrategies()),
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)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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stt,
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user_aggregator, # 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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assistant_aggregator, # 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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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Kick off the conversation.
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context.add_message({"role": "developer", "content": "Say a short hello to the user."})
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await task.queue_frames([LLMRunFrame()])
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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