55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
"""Classic STT -> LLM -> TTS pipeline builder."""
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from __future__ import annotations
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import os
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from character_prompt import LANGUAGE_LEARNING_PAL_PROMPT
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from loguru import logger
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from models.llm import create_llm_service
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from models.stt import create_stt_service
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from models.tts import create_tts_service
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.vad_analyzer import VADParams
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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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def build_classic_route(input_processor, context: LLMContext):
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stt_provider = os.getenv("CLASSIC_STT_PROVIDER", "deepgram")
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llm_provider = os.getenv("CLASSIC_LLM_PROVIDER", "openai")
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tts_provider = os.getenv("CLASSIC_TTS_PROVIDER", "elevenlabs")
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logger.info(
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"Building classic route with stt={} llm={} tts={}",
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stt_provider,
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llm_provider,
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tts_provider,
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)
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stt = create_stt_service(stt_provider)
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llm = create_llm_service(
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llm_provider,
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system_instruction=LANGUAGE_LEARNING_PAL_PROMPT,
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)
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tts = create_tts_service(tts_provider)
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=1))
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),
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)
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processors = [
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input_processor,
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stt,
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user_aggregator,
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llm,
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tts,
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]
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return processors, assistant_aggregator
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