155 lines
5.3 KiB
Python
155 lines
5.3 KiB
Python
#
|
||
# Copyright (c) 2024–2025, Daily
|
||
#
|
||
# SPDX-License-Identifier: BSD 2-Clause License
|
||
#
|
||
|
||
import os
|
||
|
||
from deepgram import LiveOptions
|
||
from dotenv import load_dotenv
|
||
from loguru import logger
|
||
from openai.types.chat import ChatCompletionToolParam
|
||
|
||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||
from pipecat.pipeline.parallel_pipeline import ParallelPipeline
|
||
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.filters.function_filter import FunctionFilter
|
||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||
from pipecat.services.openai.llm import OpenAILLMService
|
||
from pipecat.transports.base_transport import TransportParams
|
||
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
|
||
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||
|
||
load_dotenv(override=True)
|
||
|
||
|
||
current_language = "English"
|
||
|
||
|
||
async def switch_language(function_name, tool_call_id, args, llm, context, result_callback):
|
||
global current_language
|
||
current_language = args["language"]
|
||
await result_callback({"voice": f"Your answers from now on should be in {current_language}."})
|
||
|
||
|
||
async def english_filter(frame) -> bool:
|
||
return current_language == "English"
|
||
|
||
|
||
async def spanish_filter(frame) -> bool:
|
||
return current_language == "Spanish"
|
||
|
||
|
||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||
logger.info(f"Starting bot")
|
||
|
||
transport = SmallWebRTCTransport(
|
||
webrtc_connection=webrtc_connection,
|
||
params=TransportParams(
|
||
audio_in_enabled=True,
|
||
audio_out_enabled=True,
|
||
vad_enabled=True,
|
||
vad_analyzer=SileroVADAnalyzer(),
|
||
vad_audio_passthrough=True,
|
||
),
|
||
)
|
||
|
||
stt = DeepgramSTTService(
|
||
api_key=os.getenv("DEEPGRAM_API_KEY"), live_options=LiveOptions(language="multi")
|
||
)
|
||
|
||
english_tts = CartesiaTTSService(
|
||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||
)
|
||
|
||
spanish_tts = CartesiaTTSService(
|
||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||
voice_id="d4db5fb9-f44b-4bd1-85fa-192e0f0d75f9", # Spanish-speaking Lady
|
||
)
|
||
|
||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||
llm.register_function("switch_language", switch_language)
|
||
|
||
tools = [
|
||
ChatCompletionToolParam(
|
||
type="function",
|
||
function={
|
||
"name": "switch_language",
|
||
"description": "Switch to another language when the user asks you to",
|
||
"parameters": {
|
||
"type": "object",
|
||
"properties": {
|
||
"language": {
|
||
"type": "string",
|
||
"description": "The language the user wants you to speak",
|
||
},
|
||
},
|
||
"required": ["language"],
|
||
},
|
||
},
|
||
)
|
||
]
|
||
messages = [
|
||
{
|
||
"role": "system",
|
||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities. Respond to what the user said in a creative and helpful way. Your output should not include non-alphanumeric characters. You can speak the following languages: 'English' and 'Spanish'.",
|
||
},
|
||
]
|
||
|
||
context = OpenAILLMContext(messages, tools)
|
||
context_aggregator = llm.create_context_aggregator(context)
|
||
|
||
pipeline = Pipeline(
|
||
[
|
||
transport.input(), # Transport user input
|
||
stt, # STT
|
||
context_aggregator.user(), # User responses
|
||
llm, # LLM
|
||
ParallelPipeline( # TTS (bot will speak the chosen language)
|
||
[FunctionFilter(english_filter), english_tts], # English
|
||
[FunctionFilter(spanish_filter), spanish_tts], # Spanish
|
||
),
|
||
transport.output(), # Transport bot output
|
||
context_aggregator.assistant(), # Assistant spoken responses
|
||
]
|
||
)
|
||
|
||
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
|
||
|
||
@transport.event_handler("on_client_connected")
|
||
async def on_client_connected(transport, client):
|
||
logger.info(f"Client connected")
|
||
# Kick off the conversation.
|
||
messages.append(
|
||
{
|
||
"role": "system",
|
||
"content": f"Please introduce yourself to the user and let them know the languages you speak. Your initial responses should be in {current_language}.",
|
||
}
|
||
)
|
||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||
|
||
@transport.event_handler("on_client_disconnected")
|
||
async def on_client_disconnected(transport, client):
|
||
logger.info(f"Client disconnected")
|
||
|
||
@transport.event_handler("on_client_closed")
|
||
async def on_client_closed(transport, client):
|
||
logger.info(f"Client closed connection")
|
||
await task.cancel()
|
||
|
||
runner = PipelineRunner(handle_sigint=False)
|
||
|
||
await runner.run(task)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
from run import main
|
||
|
||
main()
|