atom-audio-engine 0.1.6

A pluggable, async-first Python framework for real-time audio-to-audio conversational AI

Audio Engine

A pluggable audio-to-audio conversational engine with real-time streaming support.

Features

• Pluggable Architecture: Swap ASR, LLM, and TTS providers easily
• Real-time Streaming: WebSocket server for low-latency conversations
• GeneFace++ Integration: Optional face animation from audio
• Simple API: Get started with just a few lines of code

Installation

pip install atom-audio-engine

For development with all optional dependencies:

pip install atom-audio-engine[all,dev]

Quick Start

Basic Usage

from audio_engine import Pipeline
from audio_engine.asr import WhisperASR
from audio_engine.llm import AnthropicLLM
from audio_engine.tts import CartesiaTTS

# Create pipeline with your providers
pipeline = Pipeline(
    asr=CartesiaASR(api_key="your-cartesia-key"),
    llm=GroqLLM(api_key="your-groq-key", model="mixtral-8x7b-32768"),
    tts=CartesiaTTS(api_key="your-cartesia-key", voice_id="your-voice-id"),
    system_prompt="You are a helpful assistant.",
)

async with pipeline:
    # Simple: process complete audio
    response_audio = await pipeline.process(input_audio_bytes)

    # Streaming: lower latency
    async for chunk in pipeline.stream(audio_stream):
        play_audio(chunk)

WebSocket Server

from audio_engine import Pipeline
from audio_engine.streaming import WebSocketServer

pipeline = Pipeline(asr=..., llm=..., tts=...)
server = WebSocketServer(pipeline, host="0.0.0.0", port=8765)

await server.start()

With GeneFace++ Face Animation

from audio_engine.integrations.geneface import GeneFacePipelineWrapper, GeneFaceConfig

wrapped = GeneFacePipelineWrapper(
    pipeline=pipeline,
    geneface_config=GeneFaceConfig(
        geneface_path="/path/to/ai-geneface-realtime"
    )
)

audio, video_path = await wrapped.process_with_video(input_audio)

Architecture

User Audio → ASR → LLM → TTS → Response Audio
                           ↓
                    GeneFace++ (optional)
                           ↓
                    Animated Face Video

Directory Structure

audio_engine/
├── core/           # Pipeline and configuration
├── asr/            # Speech-to-Text providers
├── llm/            # LLM providers
├── tts/            # Text-to-Speech providers
├── streaming/      # WebSocket server
├── integrations/   # GeneFace++ integration
├── utils/          # Audio utilities
└── examples/       # Example scripts

Implementing a Provider

Custom ASR

from audio_engine.asr.base import BaseASR

class MyASR(BaseASR):
    @property
    def name(self) -> str:
        return "my-asr"

    async def transcribe(self, audio: bytes, sample_rate: int = 16000) -> str:
        # Your implementation
        pass

    async def transcribe_stream(self, audio_stream):
        # Your streaming implementation
        pass

Custom LLM

from audio_engine.llm.base import BaseLLM

class MyLLM(BaseLLM):
    @property
    def name(self) -> str:
        return "my-llm"

    async def generate(self, prompt: str, context=None) -> str:
        # Your implementation
        pass

    async def generate_stream(self, prompt: str, context=None):
        # Your streaming implementation
        pass

Custom TTS

from audio_engine.tts.base import BaseTTS

class MyTTS(BaseTTS):
    @property
    def name(self) -> str:
        return "my-tts"

    async def synthesize(self, text: str) -> bytes:
        # Your implementation
        pass

    async def synthesize_stream(self, text: str):
        # Your streaming implementation
        pass

WebSocket Protocol

Client → Server

• Binary: Raw audio chunks (PCM 16-bit, 16kHz mono)
• JSON: {"type": "end_of_speech"} or {"type": "reset"}

Server → Client

• Binary: Response audio chunks
• JSON Events:
  • {"type": "connected", "client_id": "..."}
  • {"type": "transcript", "text": "..."}
  • {"type": "response_text", "text": "..."}
  • {"type": "response_start"}
  • {"type": "response_end"}

Environment Variables

# ASR
ASR_PROVIDER=whisper
ASR_API_KEY=your-key

# LLM
LLM_PROVIDER=anthropic
LLM_API_KEY=your-key
LLM_MODEL=claude-sonnet-4-20250514

# TTS
TTS_PROVIDER=cartesia
TTS_API_KEY=your-key
TTS_VOICE_ID=your-voice-id

# Debug
DEBUG=true

License

MIT