added fastbot example

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 +1,157 @@
+#
+# 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
+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)
+
+
+async def main(settings: BotSettings):
+    print(settings.prompt)
+    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(
+            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):
+            messages.append(
+                {"role": "system", "content": "Please introduce yourself to the user."})
+            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

@@ -0,0 +1,165 @@
+"""
+bot_runner.py
+
+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
+
+from pydantic import BaseModel, ValidationError
+from typing import Optional
+
+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
+
+from bot import BotSettings
+
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+
+# ------------ Configuration ------------ #
+
+MAX_SESSION_TIME = 5 * 60  # 5 minutes
+REQUIRED_ENV_VARS = ['DAILY_API_URL', 'DAILY_API_KEY', 'DEEPGRAM_API_KEY']
+
+daily_rest_helper = DailyRESTHelper(
+    os.getenv("DAILY_API_KEY", ""),
+    os.getenv("DAILY_API_URL", 'https://api.daily.co/v1'))
+
+
+class RunnerSettings(BaseModel):
+    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, Llama3 8B LLM, and Deepgram for speech-to-text and text-to-speech. You are hosted on the west coast of America. 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", "gpt-4o")
+    openai_api_key: Optional[str] = os.getenv("OPENAI_API_KEY")
+    test: Optional[bool] = None
+
+# ----------------- API ----------------- #
+
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"]
+)
+
+# ----------------- Main ----------------- #
+
+
+@app.post("/start_bot")
+async def start_bot(request: Request) -> JSONResponse:
+    runner_settings = RunnerSettings()
+    try:
+        request_body = await request.body()
+        if len(request_body) > 0:
+            runner_settings = RunnerSettings.model_validate_json(request_body)
+    except ValidationError as e:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Invalid request: {e}")
+    except Exception as e:
+        # If no data in request, pass
+        pass
+
+    # Is this a webhook creation request?
+    if runner_settings.test is not None:
+        return JSONResponse({"test": True})
+
+    # Use specified room URL, or create a new one if not specified
+    room_url = os.getenv("DAILY_SAMPLE_ROOM_URL", "")
+
+    if not room_url:
+        params = DailyRoomParams(
+            properties=DailyRoomProperties()
+        )
+        try:
+            room: DailyRoomObject = daily_rest_helper.create_room(params=params)
+        except Exception as e:
+            raise HTTPException(
+                status_code=500,
+                detail=f"Unable to provision room {e}")
+    else:
+        # Check passed room URL exists, we should assume that it already has a sip set up
+        try:
+            room: DailyRoomObject = daily_rest_helper.get_room_from_url(room_url)
+        except Exception:
+            raise HTTPException(
+                status_code=500, detail=f"Room not found: {room_url}")
+
+    # Give the agent a token to join the session
+    token = daily_rest_helper.get_token(room.url, MAX_SESSION_TIME)
+
+    if not room or not token:
+        raise HTTPException(
+            status_code=500, detail=f"Failed to get token for room: {room_url}")
+
+    # Spawn a new agent, and join the user session
+    try:
+        bot_settings = BotSettings(
+            room_url=room.url,
+            room_token=token,
+            prompt=runner_settings.prompt,
+            deepgram_voice=runner_settings.deepgram_voice,
+            openai_model=runner_settings.openai_model,
+            openai_api_key=runner_settings.openai_api_key,
+        )
+        bot_settings_str = bot_settings.model_dump_json(exclude_none=True)
+
+        subprocess.Popen(
+            [f"python3 -m bot -s '{bot_settings_str}'"],
+            shell=True,
+            bufsize=1,
+            cwd=os.path.dirname(os.path.abspath(__file__)))
+    except Exception as e:
+        raise HTTPException(
+            status_code=500, detail=f"Failed to start subprocess: {e}")
+
+    # Grab a token for the user to join with
+    user_token = daily_rest_helper.get_token(room.url, MAX_SESSION_TIME)
+
+    return JSONResponse({
+        "room_url": room.url,
+        "token": user_token,
+    })
+
+
+if __name__ == "__main__":
+    # Check environment variables
+    for env_var in REQUIRED_ENV_VARS:
+        if env_var not in os.environ:
+            raise Exception(f"Missing environment variable: {env_var}.")
+
+    parser = argparse.ArgumentParser(description="Pipecat Bot Runner")
+    parser.add_argument("--host", type=str,
+                        default=os.getenv("HOST", "0.0.0.0"), help="Host address")
+    parser.add_argument("--port", type=int,
+                        default=os.getenv("PORT", 7860), help="Port number")
+    parser.add_argument("--reload", action="store_true",
+                        default=True, help="Reload code on change")
+
+    config = parser.parse_args()
+
+    try:
+        import uvicorn
+
+        uvicorn.run(
+            "bot_runner:app",
+            host=config.host,
+            port=config.port,
+            reload=config.reload
+        )
+
+    except KeyboardInterrupt:
+        print("Pipecat runner shutting down...")

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

@@ -0,0 +1,267 @@
+from loguru import logger
+import asyncio
+import math
+import struct
+import time
+from dataclasses import dataclass, field
+from typing import List
+
+
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.frames.frames import (
+    Frame,
+    AudioRawFrame,
+    InterimTranscriptionFrame,
+    TranscriptionFrame,
+    TextFrame,
+    StartInterruptionFrame,
+    LLMFullResponseStartFrame,
+    TTSStoppedFrame,
+    MetricsFrame
+)
+
+from pipecat.vad.vad_analyzer import VADAnalyzer, VADState
+from pipecat.services.deepgram import DeepgramTTSService
+from pipecat.services.openai import OpenAILLMContext, OpenAILLMContextFrame
+
+
+class GreedyLLMAggregator(FrameProcessor):
+    def __init__(self, context: OpenAILLMContext = None, **kwargs):
+        super().__init__(**kwargs)
+        self.context: OpenAILLMContext = context if context else OpenAILLMContext()
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        logger.debug(f"{frame}")
+
+        try:
+            if isinstance(frame, InterimTranscriptionFrame):
+                return
+
+            if isinstance(frame, TranscriptionFrame):
+                # append transcribed text to last "user" frame
+                if self.context.messages and self.context.messages[-1]["role"] == "user":
+                    last_frame = self.context.messages.pop()
+                else:
+                    last_frame = {"role": "user", "content": ""}
+
+                last_frame["content"] += " " + frame.text
+                self.context.messages.append(last_frame)
+
+                oai_context_frame = OpenAILLMContextFrame(context=self.context)
+                logger.debug(f"pushing frame {oai_context_frame}")
+                await self.push_frame(oai_context_frame)
+                return
+
+            await self.push_frame(frame, direction)
+        except Exception as e:
+            logger.debug(f"error: {e}")
+
+
+class ClearableDeepgramTTSService(DeepgramTTSService):
+    def __init___(self, **kwargs):
+        super().__init(**kwargs)
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, StartInterruptionFrame):
+            self._current_sentence = ""
+
+
+@dataclass
+class BufferedSentence:
+    audio_frames: List[AudioRawFrame] = field(default_factory=list)
+    text_frame: TextFrame = None
+
+
+class VADGate(FrameProcessor):
+
+    def __init__(
+            self,
+            vad_analyzer: VADAnalyzer = None,
+            context: OpenAILLMContext = None,
+            **kwargs):
+        super().__init__(**kwargs)
+        self.vad_analyzer = vad_analyzer
+        self.context = context
+
+        self._audio_pusher_task = None
+        self._expect_text_frame_next = False
+        self._sentences: List[BufferedSentence] = []
+
+    # queue output from tts one sentence at a time. associate a buffer of audio frames with the content of
+    # each text frame.
+    #
+    # start a coroutine to service the queue and send sentences down the pipeline when possible.
+    # 1. do not send anything when we are not in VADState.QUIET
+    # 2. if we are in VADState.QUIET, send a sentence, estimate how long it will take for that sentence
+    #    to output, sleep until it's time to send another sentence
+    # 3. each time we send a sentence, append it to the conversation context
+    # 3. when the sentence buffer becomes empty, cancel the coroutine
+    # 4. if we get a new LLMFullResponse, treat that as a cancellation, too
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        try:
+
+            # A TTSService will emit a series of AudioRawFrame objects, then a TTSStoppedFrame,
+            # then a TextFrame.
+
+            if self._expect_text_frame_next:
+                self._expect_text_frame_next = False
+                if isinstance(frame, TextFrame):
+                    self._sentences[-1].text_frame = frame
+                else:
+                    logger.debug(f"expected a text frame, but received {frame}")
+                    await self.push_frame(frame, direction)
+                return
+            else:
+                if isinstance(frame, TextFrame):
+                    logger.error(f"XXXXXXXXXXXXXXXXXXX received a text frame, wasn't expecting it.")
+
+            if isinstance(frame, AudioRawFrame):
+                # if our buffer is empty or has a "finished" sentence at the end,
+                # then we need to start buffering a new sentence
+                if not self._sentences or self._sentences[-1].text_frame:
+                    self._sentences.append(BufferedSentence())
+                self._sentences[-1].audio_frames.append(frame)
+                await self.maybe_start_audio_pusher_task()
+                return
+
+            if isinstance(frame, TTSStoppedFrame):
+                self._expect_text_frame_next = True
+                await self.push_frame(frame, direction)
+                return
+
+            # There are two ways we can be interrupted. During greedy inference, a new
+            # LLM response can start. Or, during playout, we can get a traditional
+            # user interruption frame.
+            if (isinstance(frame, LLMFullResponseStartFrame) or
+                    isinstance(frame, StartInterruptionFrame)):
+                logger.debug(f"{frame} - Handle interruption in VADGate")
+                self._sentences = []
+                if self._audio_pusher_task:
+                    self._audio_pusher_task.cancel()
+                    self._audio_pusher_task = None
+                await self.push_frame(frame, direction)
+                return
+
+            await self.push_frame(frame, direction)
+        except Exception as e:
+            logger.debug(f"error: {e}")
+
+    async def maybe_start_audio_pusher_task(self):
+        try:
+            if self._audio_pusher_task:
+                return
+            self._audio_pusher_task = self.get_event_loop().create_task(self.push_audio())
+
+        except Exception as e:
+            logger.debug(f"Exception {e}")
+
+    async def push_audio(self):
+        try:
+            while True:
+                if not self._sentences:
+                    await asyncio.sleep(0.01)
+                    continue
+
+                if self.vad_analyzer._vad_state != VADState.QUIET:
+                    await asyncio.sleep(0.01)
+                    continue
+
+                # we only want to push completed sentence buffers
+                if not self._sentences[0].text_frame:
+                    await asyncio.sleep(0.01)
+                    continue
+
+                s = self._sentences.pop(0)
+                if not s.audio_frames:
+                    continue
+                sample_rate = s.audio_frames[0].sample_rate
+                duration = 0
+                logger.debug(f"Pushing {len(s.audio_frames)} audio frames")
+                for frame in s.audio_frames:
+                    await self.push_frame(frame)
+                    # assume linear16 encoding (2 bytes per sample). todo: add some more
+                    # metadata to AudioRawFrame, maybe
+                    duration += (len(frame.audio) / 2 / frame.num_channels) / sample_rate
+                await asyncio.sleep(duration - 20 / 1000)
+                if self.context:
+                    logger.debug(f"Appending assistant message to context: [{s.text_frame.text}]")
+                    self.context.messages.append(
+                        {"role": "assistant", "content": s.text_frame.text}
+                    )
+                await self.push_frame(s.text_frame)
+
+        except Exception as e:
+            logger.debug(f"Exception {e}")
+
+
+class TranscriptionTimingLogger(FrameProcessor):
+    def __init__(self, avt):
+        super().__init__()
+        self.name = "Transcription"
+        self._avt = avt
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        try:
+            await super().process_frame(frame, direction)
+            if isinstance(frame, TranscriptionFrame):
+                elapsed = time.time() - self._avt.last_transition_ts
+                logger.debug(f"Transcription TTF: {elapsed}")
+                await self.push_frame(MetricsFrame(ttfb={self.name: elapsed}))
+
+            await self.push_frame(frame, direction)
+        except Exception as e:
+            logger.debug(f"Exception {e}")
+
+
+class AudioVolumeTimer(FrameProcessor):
+    def __init__(self):
+        super().__init__()
+        self.last_transition_ts = 0
+        self._prev_volume = -80
+        self._speech_volume_threshold = -50
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, AudioRawFrame):
+            volume = self.calculate_volume(frame)
+            # print(f"Audio volume: {volume:.2f} dB")
+            if (volume >= self._speech_volume_threshold and
+                    self._prev_volume < self._speech_volume_threshold):
+                # logger.debug("transition above speech volume threshold")
+                self.last_transition_ts = time.time()
+            elif (volume < self._speech_volume_threshold and
+                    self._prev_volume >= self._speech_volume_threshold):
+                # logger.debug("transition below non-speech volume threshold")
+                self.last_transition_ts = time.time()
+            self._prev_volume = volume
+
+        await self.push_frame(frame, direction)
+
+    def calculate_volume(self, frame: AudioRawFrame) -> float:
+        if frame.num_channels != 1:
+            raise ValueError(f"Expected 1 channel, got {frame.num_channels}")
+
+        # Unpack audio data into 16-bit integers
+        fmt = f"{len(frame.audio) // 2}h"
+        audio_samples = struct.unpack(fmt, frame.audio)
+
+        # Calculate RMS
+        sum_squares = sum(sample**2 for sample in audio_samples)
+        rms = math.sqrt(sum_squares / len(audio_samples))
+
+        # Convert RMS to decibels (dB)
+        # Reference: maximum value for 16-bit audio is 32767
+        if rms > 0:
+            db = 20 * math.log10(rms / 32767)
+        else:
+            db = -96  # Minimum value (almost silent)
+
+        return db

examples/fast-chatbot/requirements.txt

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