removed space from event handler

examples/fast-bot-metrics/bot.py

@@ -1 +0,0 @@
-classic-pipeline.py

examples/fast-bot-metrics/classic-pipeline.py

@@ -1,199 +0,0 @@
-#
-# Copyright (c) 2024, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-from loguru import logger
-from runner import configure
-import argparse
-import asyncio
-import aiohttp
-import os
-import sys
-from typing import List, Optional
-
-from pydantic import BaseModel, ValidationError
-
-from pipecat.vad.vad_analyzer import VADParams
-from pipecat.vad.silero import SileroVADAnalyzer
-from pipecat.transports.services.daily import DailyParams, DailyTransport, DailyTransportMessageFrame
-from pipecat.services.openai import OpenAILLMService, OpenAILLMContext
-from pipecat.services.deepgram import DeepgramSTTService
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.processors.logger import FrameLogger
-from pipecat.frames.frames import LLMMessagesFrame, EndFrame
-
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator
-)
-
-
-from fastbothelpers import (
-    GreedyLLMAggregator,
-    ClearableDeepgramTTSService,
-    VADGate,
-    AudioVolumeTimer,
-    TranscriptionTimingLogger
-)
-
-
-from dotenv import load_dotenv
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-class BotSettings(BaseModel):
-    room_url: str
-    room_token: str
-    bot_name: str = "Pipecat"
-    prompt: Optional[str] = "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Respond to what the user said in a creative and helpful way in a few short sentences."
-    deepgram_api_key: Optional[str] = None
-    deepgram_voice: Optional[str] = "aura-asteria-en"
-    deepgram_base_url: Optional[str] = "https://api.deepgram.com/v1/speak"
-    openai_api_key: Optional[str] = None
-    openai_model: Optional[str] = "gpt-4o"
-    openai_base_url: Optional[str] = None
-
-
-async def main(settings: BotSettings):
-    async with aiohttp.ClientSession() as session:
-        transport = DailyTransport(
-            settings.room_url,
-            settings.room_token,
-            settings.bot_name,
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=False,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.200)),
-                vad_audio_passthrough=True
-            )
-        )
-
-        stt = DeepgramSTTService(
-            api_key=os.getenv("DEEPGRAM_API_KEY"),
-            **({'url': url} if (url := os.getenv("DEEPGRAM_STT_URL")) else {})
-
-        )
-
-        tts = ClearableDeepgramTTSService(
-            name="Voice",
-            aiohttp_session=session,
-            api_key=os.getenv("DEEPGRAM_API_KEY"),
-            voice="aura-asteria-en",
-            **({'base_url': url} if (url := os.getenv("DEEPGRAM_TTS_BASE_URL")) else {})
-        )
-
-        llm = OpenAILLMService(
-            name="LLM",
-            # To use OpenAI
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model=os.getenv("OPENAI_MODEL"),
-            base_url=os.getenv("OPENAI_BASE_URL")
-        )
-
-        messages = [
-            {
-                "role": "system",
-                "content": """You are a helpful assistant in an audio conversation.
-
-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 and helpful way. Be concise in your answers to basic questions. If you are asked to elaborate or tell a story, provide a longer response.
-""",
-            },
-        ]
-
-        avt = AudioVolumeTimer()
-        tl = TranscriptionTimingLogger(avt)
-
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
-
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            avt,
-            stt,
-            tl,
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out,             # Assistant spoken responses
-        ])
-
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                report_only_initial_ttfb=True
-            ))
-
-        # When a participant joins, start transcription for that participant so the
-        # bot can "hear" and respond to them.
-        # @ transport.event_handler("on_participant_joined")
-        # async def on_participant_joined(transport, participant):
-        #    transport.capture_participant_transcription(participant["id"])
-
-        # When the participant leaves, we exit the bot.
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
-            
-        # When the first participant joins, the bot should introduce itself.
-        @ transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            messages.append(
-                {"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMMessagesFrame(messages)])
-
-        # Handle "latency-ping" messages. The client will send app messages that look like
-        # this:
-        #   { "latency-ping": { ts: <client-side timestamp> }}
-        #
-        # We want to send an immediate pong back to the client from this handler function.
-        # Also, we will push a frame into the top of the pipeline and send it after the
-        #
-        @ transport.event_handler("on_app_message")
-        async def on_app_message(transport, message, sender):
-            try:
-                if "latency-ping" in message:
-                    logger.debug(f"Received latency ping app message: {message}")
-                    ts = message["latency-ping"]["ts"]
-                    # Send immediately
-                    transport.output().send_message(DailyTransportMessageFrame(
-                        message={"latency-pong-msg-handler": {"ts": ts}},
-                        participant_id=sender))
-                    # And push to the pipeline for the Daily transport.output to send
-                    await tma_in.push_frame(
-                        DailyTransportMessageFrame(
-                            message={"latency-pong-pipeline-delivery": {"ts": ts}},
-                            participant_id=sender))
-            except Exception as e:
-                logger.debug(f"message handling error: {e} - {message}")
-
-        runner = PipelineRunner()
-        await runner.run(task)
-
-
-# if __name__ == "__main__":
-#     (url, token) = configure()
-#     asyncio.run(main(url, token))
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Pipecat Bot")
-    parser.add_argument("-s", "--settings", type=str, required=True, help="Pipecat bot settings")
-
-    args, unknown = parser.parse_known_args()
-
-    try:
-        settings = BotSettings.model_validate_json(args.settings)
-        asyncio.run(main(settings))
-    except ValidationError as e:
-        print(e)

examples/fast-bot-metrics/env-example

@@ -1,15 +0,0 @@
-DAILY_SAMPLE_ROOM_URL=...
-DAILY_API_KEY=...
-
-DEEPGRAM_API_KEY=...
-DEEPGRAM_VOICE=alpha-asteria-en-v2
-# DEEPGRAM_STT_URL=ws://localhost:8080
-# DEEPGRAM_TTS_BASE_URL=http://0.0.0.0:8080/v1/speak
-
-OPENAI_API_KEY=...
-# OPENAI_API_KEY=
-OPENAI_MODEL=gpt-4o
-# OPENAI_MODEL=meta-llama/Meta-Llama-3-70B-Instruct
-# OPENAI_MODEL=meta-llama/Meta-Llama-3-8B-Instruct
-# OPENAI_MODEL=neuralmagic/Meta-Llama-3-70B-Instruct-FP8
-# OPENAI_BASE_URL=http://0.0.0.0:8000/v1

examples/fast-bot-metrics/example-bot-runner-bot.py

@@ -1,158 +0,0 @@
-#
-# Copyright (c) 2024, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import argparse
-import asyncio
-import aiohttp
-import sys
-
-from pydantic import BaseModel, ValidationError
-from typing import Optional
-
-from pipecat.frames.frames import EndFrame, LLMMessagesFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
-from pipecat.services.deepgram import DeepgramTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport, DailyTransportMessageFrame
-from pipecat.vad.silero import SileroVADAnalyzer
-
-from loguru import logger
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-class BotSettings(BaseModel):
-    room_url: str
-    room_token: str
-    bot_name: str = "Pipecat"
-    prompt: Optional[str] = "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Respond to what the user said in a creative and helpful way in a few short sentences."
-    deepgram_api_key: Optional[str] = None
-    deepgram_voice: Optional[str] = "aura-asteria-en"
-    deepgram_base_url: Optional[str] = "https://api.deepgram.com/v1/speak"
-    openai_api_key: Optional[str] = None
-    openai_model: Optional[str] = "gpt-4o"
-    openai_base_url: Optional[str] = None
-
-
-async def main(settings: BotSettings):
-    async with aiohttp.ClientSession() as session:
-        transport = DailyTransport(
-            settings.room_url,
-            settings.room_token,
-            settings.bot_name,
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
-        )
-
-        tts = DeepgramTTSService(
-            aiohttp_session=session,
-            api_key=settings.deepgram_api_key,
-            voice=settings.deepgram_voice,
-            base_url=settings.deepgram_base_url
-        )
-
-        llm = OpenAILLMService(
-            api_key=settings.openai_api_key,
-            model=settings.openai_model,
-            base_url=settings.openai_base_url
-        )
-
-        messages = [
-            {
-                "role": "system",
-                "content": settings.prompt,
-            },
-        ]
-
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
-
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out,             # Assistant spoken responses
-        ])
-
-        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))
-
-        # When the first participant joins, the bot should introduce itself.
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            # Kick off the conversation.
-            messages.append(
-                {"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frame(LLMMessagesFrame(messages))
-
-        # When a participant joins, start transcription for that participant so the
-        # bot can "hear" and respond to them.
-        @transport.event_handler("on_participant_joined")
-        async def on_participant_joined(transport, participant):
-            transport.capture_participant_transcription(participant["id"])
-
-        # When the participant leaves, we exit the bot.
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
-
-        # If the call is ended make sure we quit as well.
-        @transport.event_handler("on_call_state_updated")
-        async def on_call_state_updated(transport, state):
-            if state == "left":
-                await task.queue_frame(EndFrame())
-
-        # Handle "latency-ping" messages. The client will send app messages that look like
-        # this:
-        #   { "latency-ping": { ts: <client-side timestamp> }}
-        #
-        # We want to send an immediate pong back to the client from this handler function.
-        # Also, we will push a frame into the top of the pipeline and send it after the
-        #
-        @transport.event_handler("on_app_message")
-        async def on_app_message(transport, message, sender):
-            try:
-                if "latency-ping" in message:
-                    logger.debug(f"Received latency ping app message: {message}")
-                    ts = message["latency-ping"]["ts"]
-                    # Send immediately
-                    transport.output().send_message(DailyTransportMessageFrame(
-                        message={"latency-pong-msg-handler": {"ts": ts}},
-                        participant_id=sender))
-                    # And push to the pipeline for the Daily transport.output to send
-                    await tma_in.push_frame(
-                        DailyTransportMessageFrame(
-                            message={"latency-pong-pipeline-delivery": {"ts": ts}},
-                            participant_id=sender))
-            except Exception as e:
-                logger.debug(f"message handling error: {e} - {message}")
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Pipecat Bot")
-    parser.add_argument("-s", "--settings", type=str, required=True, help="Pipecat bot settings")
-
-    args, unknown = parser.parse_known_args()
-
-    try:
-        settings = BotSettings.model_validate_json(args.settings)
-        asyncio.run(main(settings))
-    except ValidationError as e:
-        print(e)

examples/fast-bot-metrics/runner.py

@@ -1,58 +0,0 @@
-import argparse
-import os
-import time
-import urllib
-import requests
-
-
-def configure():
-    parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
-    parser.add_argument(
-        "-u",
-        "--url",
-        type=str,
-        required=False,
-        help="URL of the Daily room to join")
-    parser.add_argument(
-        "-k",
-        "--apikey",
-        type=str,
-        required=False,
-        help="Daily API Key (needed to create an owner token for the room)",
-    )
-
-    args, unknown = parser.parse_known_args()
-
-    url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
-    key = args.apikey or os.getenv("DAILY_API_KEY")
-
-    if not url:
-        raise Exception(
-            "No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL.")
-
-    if not key:
-        raise Exception("No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers.")
-
-    # Create a meeting token for the given room with an expiration 1 hour in
-    # the future.
-    room_name: str = urllib.parse.urlparse(url).path[1:]
-    expiration: float = time.time() + 60 * 60
-
-    res: requests.Response = requests.post(
-        f"https://api.daily.co/v1/meeting-tokens",
-        headers={
-            "Authorization": f"Bearer {key}"},
-        json={
-            "properties": {
-                "room_name": room_name,
-                "is_owner": True,
-                "exp": expiration}},
-    )
-
-    if res.status_code != 200:
-        raise Exception(
-            f"Failed to create meeting token: {res.status_code} {res.text}")
-
-    token: str = res.json()["token"]
-
-    return (url, token)

examples/fast-bot-metrics/vad-gated-pipeline.py

@@ -1,165 +0,0 @@
-#
-# Copyright (c) 2024, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-from loguru import logger
-from runner import configure
-import asyncio
-import aiohttp
-import os
-import sys
-from typing import List
-
-
-from pipecat.vad.vad_analyzer import VADParams
-from pipecat.vad.silero import SileroVADAnalyzer
-from pipecat.transports.services.daily import DailyParams, DailyTransport, DailyTransportMessageFrame
-from pipecat.services.openai import OpenAILLMService, OpenAILLMContext
-from pipecat.services.deepgram import DeepgramSTTService
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.processors.logger import FrameLogger
-from pipecat.frames.frames import LLMMessagesFrame
-
-
-from fastbothelpers import (
-    GreedyLLMAggregator,
-    ClearableDeepgramTTSService,
-    VADGate,
-    AudioVolumeTimer,
-    TranscriptionTimingLogger
-)
-
-
-from dotenv import load_dotenv
-load_dotenv(override=True)
-
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main(room_url: str, token):
-    async with aiohttp.ClientSession() as session:
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=False,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.400)),
-                vad_audio_passthrough=True
-            )
-        )
-
-        stt = DeepgramSTTService(
-            api_key=os.getenv("DEEPGRAM_API_KEY"),
-            **({'url': url} if (url := os.getenv("DEEPGRAM_STT_URL")) else {})
-
-        )
-
-        tts = ClearableDeepgramTTSService(
-            name="Voice",
-            aiohttp_session=session,
-            api_key=os.getenv("DEEPGRAM_API_KEY"),
-            voice="aura-asteria-en",
-            **({'base_url': url} if (url := os.getenv("DEEPGRAM_TTS_BASE_URL")) else {})
-        )
-
-        llm = OpenAILLMService(
-            name="LLM",
-            # To use OpenAI
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model=os.getenv("OPENAI_MODEL"),
-            base_url=os.getenv("OPENAI_BASE_URL")
-        )
-
-        messages = [
-            {
-                "role": "system",
-                "content": """You are a helpful assistant in an audio conversation.
-
-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 and helpful way. Be concise in your answers to basic questions. If you are asked to elaborate or tell a story, provide a longer response.
-""",
-            },
-        ]
-
-        ctx = OpenAILLMContext()
-        greedy = GreedyLLMAggregator(name="greedy", context=ctx)
-        gate = VADGate(name="gate", vad_analyzer=transport.input().vad_analyzer(), context=ctx)
-        avt = AudioVolumeTimer()
-        tl = TranscriptionTimingLogger(avt)
-
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            avt,
-            stt,
-            tl,
-            greedy,
-            llm,                 # LLM
-            tts,                 # TTS
-            gate,
-            transport.output(),  # Transport bot output
-            # FrameLogger()
-        ])
-
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                report_only_initial_ttfb=True
-            ))
-
-        # When a participant joins, start transcription for that participant so the
-        # bot can "hear" and respond to them.
-        # @ transport.event_handler("on_participant_joined")
-        # async def on_participant_joined(transport, participant):
-        #    transport.capture_participant_transcription(participant["id"])
-
-        # When the first participant joins, the bot should introduce itself.
-        @ transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            messages.append(
-                {"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMMessagesFrame(messages)])
-
-        # Handle "latency-ping" messages. The client will send app messages that look like
-        # this:
-        #   { "latency-ping": { ts: <client-side timestamp> }}
-        #
-        # We want to send an immediate pong back to the client from this handler function.
-        # Also, we will push a frame into the top of the pipeline and send it after the
-        #
-        @ transport.event_handler("on_app_message")
-        async def on_app_message(transport, message, sender):
-            try:
-                if "latency-ping" in message:
-                    logger.debug(f"Received latency ping app message: {message}")
-                    ts = message["latency-ping"]["ts"]
-                    # Send immediately
-                    transport.output().send_message(DailyTransportMessageFrame(
-                        message={"latency-pong-msg-handler": {"ts": ts}},
-                        participant_id=sender))
-                    # And push to the pipeline for the Daily transport.output to send
-                    await tma_in.push_frame(
-                        DailyTransportMessageFrame(
-                            message={"latency-pong-pipeline-delivery": {"ts": ts}},
-                            participant_id=sender))
-            except Exception as e:
-                logger.debug(f"message handling error: {e} - {message}")
-
-        runner = PipelineRunner()
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))

examples/fast-chatbot/.gitignore

@@ -0,0 +1,165 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+runpod.toml
+
+# custom script to recursively upgrade items in requirements.py
+upgrade_requirements.py
+.DS_Store

examples/fast-chatbot/bot.py

@@ -0,0 +1,164 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+from loguru import logger
+import argparse
+import asyncio
+import aiohttp
+import os
+import sys
+import time
+from typing import Optional
+
+from pydantic import BaseModel, ValidationError
+
+from pipecat.vad.vad_analyzer import VADParams
+from pipecat.vad.silero import SileroVADAnalyzer
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.services.openai import OpenAILLMService
+from pipecat.services.deepgram import DeepgramSTTService
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.frames.frames import LLMMessagesFrame, EndFrame
+
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator
+)
+
+from helpers import (
+    ClearableDeepgramTTSService,
+    AudioVolumeTimer,
+    TranscriptionTimingLogger
+)
+
+
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level=os.getenv("LOG_LEVEL", "DEBUG"))
+
+
+class BotSettings(BaseModel):
+    room_url: str
+    room_token: str
+    bot_name: str = "Pipecat"
+    prompt: Optional[str] = "You are a helpful assistant."
+    deepgram_api_key: Optional[str] = os.getenv("DEEPGRAM_API_KEY", None)
+    deepgram_voice: Optional[str] = os.getenv("DEEPGRAM_VOICE", "aura-asteria-en")
+    deepgram_tts_base_url: Optional[str] = os.getenv(
+        "DEEPGRAM_TTS_BASE_URL", "https://api.deepgram.com/v1/speak")
+    deepgram_stt_base_url: Optional[str] = os.getenv(
+        "DEEPGRAM_STT_BASE_URL", "https://api.deepgram.com/v1/speak")
+    openai_api_key: Optional[str] = os.getenv("OPENAI_API_KEY", None),
+    openai_model: Optional[str] = os.getenv("OPENAI_MODEL", None),
+    openai_base_url: Optional[str] = os.getenv("OPENAI_BASE_URL", None)
+    vad_stop_secs: Optional[float] = os.getenv("VAD_STOP_SECS", 0.200)
+
+
+async def main(settings: BotSettings):
+    async with aiohttp.ClientSession() as session:
+        transport = DailyTransport(
+            settings.room_url,
+            settings.room_token,
+            settings.bot_name,
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=False,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(params=VADParams(
+                    stop_secs=settings.vad_stop_secs
+                )),
+                vad_audio_passthrough=True
+            )
+        )
+
+        stt = DeepgramSTTService(
+            name="STT",
+            api_key=settings.deepgram_api_key,
+            url=settings.deepgram_stt_base_url
+        )
+
+        tts = ClearableDeepgramTTSService(
+            name="Voice",
+            aiohttp_session=session,
+            api_key=settings.deepgram_api_key,
+            voice=settings.deepgram_voice,
+            **({'base_url': url} if (url := settings.deepgram_tts_base_url) else {})
+        )
+
+        llm = OpenAILLMService(
+            name="LLM",
+            api_key=settings.openai_api_key,
+            model=settings.openai_model,
+            base_url=settings.openai_base_url,
+        )
+
+        messages = [
+            {
+                "role": "system",
+                "content": settings.prompt,
+            },
+        ]
+
+        avt = AudioVolumeTimer()
+        tl = TranscriptionTimingLogger(avt)
+
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
+
+        pipeline = Pipeline([
+            transport.input(),   # Transport user input
+            avt,                 # Audio volume timer
+            stt,                 # Speech-to-text
+            tl,                  # Transcription timing logger
+            tma_in,              # User responses
+            llm,                 # LLM
+            tts,                 # TTS
+            transport.output(),  # Transport bot output
+            tma_out,             # Assistant spoken responses
+        ])
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                report_only_initial_ttfb=True
+            ))
+
+        # When the participant leaves, we exit the bot.
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.queue_frame(EndFrame())
+
+        # When the first participant joins, the bot should introduce itself.
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            # Provide some air whilst tracks subscribe
+            time.sleep(2)
+            messages.append(
+                {
+                    "role": "system",
+                    "content": "Briefly introduce yourself by saying 'hello, I'm FastBot, how can I help you today?'"})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Pipecat Bot")
+    parser.add_argument("-s", "--settings", type=str, required=True, help="Pipecat bot settings")
+
+    args, unknown = parser.parse_known_args()
+
+    try:
+        settings = BotSettings.model_validate_json(args.settings)
+        asyncio.run(main(settings))
+    except ValidationError as e:
+        print(e)

examples/fast-chatbot/bot_runner.py

@@ -1,11 +1,9 @@
-"""
-bot_runner.py
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
 
-HTTP service that listens for incoming calls from either Daily or Twilio,
-provisioning a room and starting a Pipecat bot in response.
-
-Refer to README for more information.
-"""
 import os
 import argparse
 import subprocess
@@ -13,12 +11,11 @@ import subprocess
 from pydantic import BaseModel, ValidationError
 from typing import Optional
 
-from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomObject, DailyRoomProperties, DailyRoomSipParams, DailyRoomParams
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomObject, DailyRoomProperties, DailyRoomParams
 
 from fastapi import FastAPI, Request, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse, PlainTextResponse
-from twilio.twiml.voice_response import VoiceResponse
+from fastapi.responses import JSONResponse
 
 from bot import BotSettings
 
@@ -37,9 +34,10 @@ daily_rest_helper = DailyRESTHelper(
 
 
 class RunnerSettings(BaseModel):
-    prompt: Optional[str] = "You are a helpful assistant."
+    prompt: Optional[
+        str] = "You are a fast, low-latency chatbot. Your goal is to demonstrate voice-driven AI capabilities at human-like speeds. When introducing yourself briefly mention your goal is to showcase speed and conversational flow. The technology powering you is Daily for transport, Cerebrium for GPU hosting, Llama 3 (8-B version) LLM, and Deepgram for speech-to-text and text-to-speech. You are hosted on the east coast of the United States. Respond to what the user said in a creative and helpful way, but keep responses short and legible. Ensure responses contain only words. Check again that you have not included special characters other than '?' or '!'."
     deepgram_voice: Optional[str] = os.getenv("DEEPGRAM_VOICE")
-    openai_model: Optional[str] = os.getenv("OPENAI_MODEL")
+    openai_model: Optional[str] = os.getenv("OPENAI_MODEL", "gpt-4o")
     openai_api_key: Optional[str] = os.getenv("OPENAI_API_KEY")
     test: Optional[bool] = None
 
@@ -107,18 +105,14 @@ async def start_bot(request: Request) -> JSONResponse:
             status_code=500, detail=f"Failed to get token for room: {room_url}")
 
     # Spawn a new agent, and join the user session
-    # Note: this is mostly for demonstration purposes (refer to 'deployment' in README)
     try:
         bot_settings = BotSettings(
             room_url=room.url,
             room_token=token,
             prompt=runner_settings.prompt,
             deepgram_voice=runner_settings.deepgram_voice,
-            deepgram_api_key=os.getenv("DEEPGRAM_API_KEY"),
-            # deepgram_base_url="http://0.0.0.0:8080/v1/speak",
             openai_model=runner_settings.openai_model,
             openai_api_key=runner_settings.openai_api_key,
-            # openai_base_url="http://0.0.0.0:8000/v1",
         )
         bot_settings_str = bot_settings.model_dump_json(exclude_none=True)
 

examples/fast-chatbot/env.example

@@ -0,0 +1,12 @@
+DAILY_SAMPLE_ROOM_URL= #optional: use the same room each time, or create a new one if unset
+DAILY_API_KEY=
+DAILY_API_URL=
+
+DEEPGRAM_API_KEY=
+DEEPGRAM_VOICE=
+DEEPGRAM_STT_URL=
+DEEPGRAM_TTS_BASE_URL=
+
+OPENAI_API_KEY=
+OPENAI_MODEL=
+OPENAI_BASE_URL=

examples/fast-chatbot/helpers.py

@@ -15,14 +15,13 @@ from pipecat.frames.frames import (
     TranscriptionFrame,
     TextFrame,
     StartInterruptionFrame,
-    StopInterruptionFrame,
     LLMFullResponseStartFrame,
     TTSStoppedFrame,
     MetricsFrame
 )
 
 from pipecat.vad.vad_analyzer import VADAnalyzer, VADState
-from pipecat.services.deepgram import DeepgramTTSService, DeepgramSTTService
+from pipecat.services.deepgram import DeepgramTTSService
 from pipecat.services.openai import OpenAILLMContext, OpenAILLMContextFrame
 
 
@@ -251,7 +250,7 @@ class AudioVolumeTimer(FrameProcessor):
             raise ValueError(f"Expected 1 channel, got {frame.num_channels}")
 
         # Unpack audio data into 16-bit integers
-        fmt = f"{len(frame.audio)//2}h"
+        fmt = f"{len(frame.audio) // 2}h"
         audio_samples = struct.unpack(fmt, frame.audio)
 
         # Calculate RMS

examples/fast-chatbot/requirements.txt

@@ -0,0 +1,6 @@
+pipecat-ai[daily,openai,silero,deepgram]
+fastapi
+uvicorn
+requests
+python-dotenv
+loguru