145 lines
4.5 KiB
Python
145 lines
4.5 KiB
Python
#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""Camb.ai MARS-8 TTS example with local audio (microphone/speakers).
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This example demonstrates:
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- Basic TTS synthesis with Camb.ai MARS-8
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- Local audio input/output (no WebRTC or Daily needed)
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- Handling interruptions
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Requirements:
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- CAMB_API_KEY environment variable
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- OPENAI_API_KEY environment variable (for LLM)
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- DEEPGRAM_API_KEY environment variable (for STT)
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Usage:
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export CAMB_API_KEY=your_camb_api_key
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export OPENAI_API_KEY=your_openai_api_key
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export DEEPGRAM_API_KEY=your_deepgram_api_key
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python 07zb-interruptible-camb-local.py
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"""
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import asyncio
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import os
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import sys
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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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.frames.frames import LLMRunFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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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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)
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from pipecat.services.camb.tts import CambTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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async def main():
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# Local audio transport - uses your microphone and speakers
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transport = LocalAudioTransport(
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LocalAudioTransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
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)
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)
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# Deepgram STT for speech recognition
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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# Create HTTP session for Camb.ai TTS
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async with aiohttp.ClientSession() as session:
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# Camb.ai TTS with MARS-8-flash model
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tts = CambTTSService(
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api_key=os.getenv("CAMB_API_KEY"),
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aiohttp_session=session,
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voice_id=2681, # Attic voice
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model="mars-8-flash",
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params=CambTTSService.InputParams(
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speed=1.0,
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),
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)
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# OpenAI LLM
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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# System prompt
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messages = [
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{
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"role": "system",
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"content": """You are a helpful voice assistant powered by Camb.ai's MARS-8
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text-to-speech technology. Keep your responses concise and conversational since
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they will be spoken aloud. Avoid special characters, emojis, or bullet points.""",
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},
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]
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# Context management
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context = LLMContext(messages)
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context_aggregator = LLMContextAggregatorPair(context)
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# Build the pipeline
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pipeline = Pipeline(
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[
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transport.input(), # Microphone input
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stt, # Speech-to-text
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context_aggregator.user(), # User context
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llm, # Language model
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tts, # Camb.ai TTS
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transport.output(), # Speaker output
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context_aggregator.assistant(), # Assistant context
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]
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)
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# Create pipeline task
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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)
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# Run the pipeline
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runner = PipelineRunner()
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logger.info("Starting Camb.ai TTS bot with local audio...")
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logger.info("Speak into your microphone to interact with the bot.")
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# Start the conversation with a greeting after a short delay
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async def start_greeting():
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await asyncio.sleep(1) # Wait for pipeline to start
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messages.append(
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{
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"role": "system",
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"content": "Please introduce yourself briefly and ask how you can help.",
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}
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)
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await task.queue_frames([LLMRunFrame()])
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# Run greeting and pipeline concurrently
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await asyncio.gather(
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runner.run(task),
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start_greeting(),
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)
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if __name__ == "__main__":
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asyncio.run(main())
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