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import os
from loguru import logger
from pipecat.audio.vad.silero import SileroVADAnalyzer
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 opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from pipecat.services.ollama.llm import OLLamaLLMService
# from pipecat.services.fish.tts import FishAudioTTSService
# from pipecat.services.xtts.tts import XTTSService
from pipecat.transcriptions.language import Language
# from service.Dia.tts import DiaTTSService
from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
from pipecat.serializers.protobuf import ProtobufFrameSerializer
from pipecat.transports.network.fastapi_websocket import (
FastAPIWebsocketParams,
FastAPIWebsocketTransport,
)
from pipecat.services.whisper.stt import WhisperSTTService
from pipecat.transports.network.websocket_server import (
WebsocketServerParams,
WebsocketServerTransport,
)
import aiohttp
from dotenv import load_dotenv
from service.Kokoro.tts import KokoroTTSService
# from service.orpheus.tts import OrpheusTTSService
# from service.chatterbot.tts import ChatterboxTTSService
# from pipecat.utils.tracing.setup import setup_tracing
SYSTEM_INSTRUCTION = f"""
"You are Gemini Chatbot, a friendly, helpful robot.
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. Keep your responses brief. One or two sentences at most.
"""
load_dotenv(override=True)
# IS_TRACING_ENABLED = bool(os.getenv("ENABLE_TRACING"))
# # Initialize tracing if enabled
# if IS_TRACING_ENABLED:
# # Create the exporter
# otlp_exporter = OTLPSpanExporter()
# # Set up tracing with the exporter
# setup_tracing(
# service_name="pipecat-demo",
# exporter=otlp_exporter,
# console_export=bool(os.getenv("OTEL_CONSOLE_EXPORT")),
# )
# logger.info("OpenTelemetry tracing initialized")
async def run_bot_websocket_server(websocket_client):
# ws_transport = WebsocketServerTransport(
# params=WebsocketServerParams(
# serializer=ProtobufFrameSerializer(),
# audio_in_enabled=True,
# audio_out_enabled=True,
# add_wav_header=False,
# vad_analyzer=SileroVADAnalyzer(),
# session_timeout=60 * 3, # 3 minutes
# )
# )
ws_transport = FastAPIWebsocketTransport(
websocket=websocket_client,
params=FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
add_wav_header=False,
vad_analyzer=SileroVADAnalyzer(),
serializer=ProtobufFrameSerializer(),
),
)
stt = WhisperSTTService(
model="smollm",
device="cpu",
compute_type="default",
language="en",
)
llm = OLLamaLLMService(
model="smoll",
# params=OLLamaLLMService.InputParams(temperature=0.7, max_tokens=1000),
)
# TTS = FishAudioTTSService(
# api_key=os.getenv("CARTESIA_API_KEY"),
# voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Reading Lady
# )
# async with aiohttp.ClientSession() as session:
# TTS = XTTSService(
# voice_id="speaker_1",
# language=Language.EN,
# base_url="http://localhost:8000",
# aiohttp_session=session
# )
context = OpenAILLMContext(
[
{
"role": "system",
"content": SYSTEM_INSTRUCTION,
},
{
"role": "user",
"content": "Start by greeting the user warmly and introducing yourself.",
},
],
)
context_aggregator = llm.create_context_aggregator(context)
# RTVI events for Pipecat client UI
rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
TTS = KokoroTTSService(
model_path=os.path.join(
os.path.dirname(__file__), "assets", "kokoro-v1.0.int8.onnx"
),
voices_path=os.path.join(os.path.dirname(__file__), "assets", "voices.json"),
voice_id="af",
sample_rate=16000,
)
# TTS = OrpheusTTSService(
# model_name="canopylabs/orpheus-3b-0.1-ft",
# sample_rate=16000,
# )
# TTS = ChatterboxTTSService(
# model_name="",
# sample_rate=16000,
# )
# TTS = DiaTTSService(
# model_name="nari-labs/Dia-1.6B",
# sample_rate=16000,
# )
pipeline = Pipeline(
[
ws_transport.input(),
rtvi,
stt, # STT
context_aggregator.user(),
llm,
TTS, # TTS
ws_transport.output(),
context_aggregator.assistant(),
]
)
task = PipelineTask(
pipeline,
params=PipelineParams(
enable_metrics=True,
allow_interruptions=True,
enable_usage_metrics=True,
),
# enable_turn_tracking=True,
# enable_tracing=IS_TRACING_ENABLED,
conversation_id="test",
observers=[RTVIObserver(rtvi)],
)
@rtvi.event_handler("on_client_ready")
async def on_client_ready(rtvi):
logger.info("Pipecat client ready.")
await rtvi.set_bot_ready()
# Kick off the conversation.
await task.queue_frames([context_aggregator.user().get_context_frame()])
@ws_transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
logger.info("Pipecat Client connected")
@ws_transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
logger.info("Pipecat Client disconnected")
await task.cancel()
@ws_transport.event_handler("on_session_timeout")
async def on_session_timeout(transport, client):
logger.info(f"Entering in timeout for {client.remote_address}")
await task.cancel()
runner = PipelineRunner()
await runner.run(task)
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