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Alexander
2026-02-20 02:24:44 +03:00
parent 564b407300
commit 721bf3d6d8
8 changed files with 268 additions and 171 deletions
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179
.gitignore vendored
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# ---> Python # Python
# Byte-compiled / optimized / DLL files
__pycache__/ __pycache__/
*.py[cod] *.py[cod]
*$py.class
# C extensions
*.so *.so
# Distribution / packaging
.Python .Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# UV
# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
#uv.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
.pdm.toml
.pdm-python
.pdm-build/
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/ env/
venv/ venv/
ENV/ *.egg-info/
env.bak/ dist/
venv.bak/ build/
# Spyder project settings # Docker
.spyderproject *.log
.spyproject .env
# Rope project settings # Аудио файлы (если будут временные)
.ropeproject *.wav
*.mp3
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
# Ruff stuff:
.ruff_cache/
# PyPI configuration file
.pypirc
# Эмбеддинги (но embeddings.json мы пока храним в репозитории для простоты)
# services/speaker-id/embeddings.json

22
README.md
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# ai1 # Джарвис (ai1)
DS AI Локальный голосовой ассистент с идентификацией по голосу и поддержкой русского языка.
test ## Архитектура
ещё один тест Микросервисы на Docker:
- `vad` – определение голосовой активности (Silero VAD)
- `asr` – распознавание речи и диаризация (WhisperX)
- `speaker-id` – идентификация говорящего (SpeechBrain ECAPA-TDNN)
- `orchestrator` – маршрутизация и инструменты
- `tts` – синтез речи (CosyVoice / Piper)
- `redis` – шина событий
## Запуск
1. Убедитесь, что Docker Desktop с WSL2 работает и NVIDIA Container Toolkit настроен.
2. Склонируйте репозиторий.
3. Выполните:
```bash
docker-compose up --build

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client/client.py Normal file
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import asyncio
import sounddevice as sd
import numpy as np
import websockets
import json
import requests
# Настройки
VAD_URL = "ws://localhost:8001/audio-stream"
TTS_URL = "http://localhost:8005/synthesize"
SAMPLE_RATE = 16000
BLOCK_SIZE = 1600 # 100 мс при 16 кГц
async def audio_stream():
"""Захватывает аудио с микрофона и отправляет в VAD сервис."""
async with websockets.connect(VAD_URL) as websocket:
print("Подключен к VAD сервису. Говорите...")
def callback(indata, frames, time, status):
"""Отправляет аудио-блок в WebSocket."""
audio_bytes = indata.tobytes()
asyncio.run_coroutine_threadsafe(
websocket.send(audio_bytes), loop
)
stream = sd.InputStream(
samplerate=SAMPLE_RATE,
channels=1,
dtype='int16',
blocksize=BLOCK_SIZE,
callback=callback
)
with stream:
while True:
await asyncio.sleep(0.1)
def play_audio(audio_data):
"""Воспроизводит аудио (полученное от TTS)."""
audio_array = np.frombuffer(audio_data, dtype=np.int16)
sd.play(audio_array, samplerate=SAMPLE_RATE)
sd.wait()
async def main():
# Запускаем поток аудио в фоне
asyncio.create_task(audio_stream())
# Основной цикл не нужен, так как всё работает через WebSocket
await asyncio.Future() # бесконечное ожидание
if __name__ == "__main__":
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
loop.run_until_complete(main())

111
docker-compose.yml Normal file
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version: '3.8'
services:
redis:
image: redis:7-alpine
container_name: jarvis-redis
restart: unless-stopped
ports:
- "6379:6379"
networks:
- jarvis-net
vad:
build: ./services/vad
container_name: jarvis-vad
restart: unless-stopped
ports:
- "8001:8000" # API для приема аудио
environment:
- REDIS_HOST=redis
- REDIS_PORT=6379
depends_on:
- redis
networks:
- jarvis-net
# VAD работает на CPU, deploy не нужен
asr:
build: ./services/asr
container_name: jarvis-asr
restart: unless-stopped
ports:
- "8002:8000"
environment:
- REDIS_HOST=redis
- REDIS_PORT=6379
depends_on:
- redis
networks:
- jarvis-net
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
speaker-id:
build: ./services/speaker-id
container_name: jarvis-speaker-id
restart: unless-stopped
ports:
- "8003:8000"
volumes:
- ./services/speaker-id/embeddings.json:/app/embeddings.json
environment:
- REDIS_HOST=redis
- REDIS_PORT=6379
depends_on:
- redis
networks:
- jarvis-net
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu] # может работать и на CPU, но GPU ускорит
orchestrator:
build: ./services/orchestrator
container_name: jarvis-orchestrator
restart: unless-stopped
ports:
- "8004:8000"
environment:
- REDIS_HOST=redis
- REDIS_PORT=6379
depends_on:
- redis
networks:
- jarvis-net
# CPU, GPU не нужен
tts:
build: ./services/tts
container_name: jarvis-tts
restart: unless-stopped
ports:
- "8005:8000"
environment:
- REDIS_HOST=redis
- REDIS_PORT=6379
depends_on:
- redis
networks:
- jarvis-net
# Для iGPU потребуется дополнительная конфигурация, пока оставим на CPU
# deploy:
# resources:
# reservations:
# devices:
# - driver: intel
# count: 1
# capabilities: [gpu]
networks:
jarvis-net:
driver: bridge

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info.txt Normal file
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ai1/
├── .gitignore
├── README.md
├── docker-compose.yml
├── services/
│ ├── vad/
│ │ ├── Dockerfile
│ │ ├── requirements.txt
│ │ └── app.py
│ ├── asr/
│ │ ├── Dockerfile
│ │ ├── requirements.txt
│ │ └── app.py
│ ├── speaker-id/
│ │ ├── Dockerfile
│ │ ├── requirements.txt
│ │ ├── app.py
│ │ └── embeddings.json
│ ├── orchestrator/
│ │ ├── Dockerfile
│ │ ├── requirements.txt
│ │ ├── app.py
│ │ └── tools/
│ │ └── time_tool.py
│ └── tts/
│ ├── Dockerfile
│ ├── requirements.txt
│ └── app.py
├── client/
│ └── client.py
└── redis/
└── (файлы Redis не нужны, образ стандартный)

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services/orchestrator/app.py Normal file
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from fastapi import FastAPI
import redis
import os
import json
app = FastAPI()
redis_host = os.getenv("REDIS_HOST", "localhost")
redis_port = int(os.getenv("REDIS_PORT", 6379))
r = redis.Redis(host=redis_host, port=redis_port, decode_responses=True)
@app.get("/health")
async def health():
return {"status": "ok", "service": "orchestrator"}
@app.post("/process")
async def process_text(request: dict):
# Здесь будет логика обработки текста и вызова инструментов
text = request.get("text", "")
user = request.get("user", "guest")
print(f"Processing from {user}: {text}")
# Простой ответ для теста
response = "Я получил ваше сообщение, но пока не умею отвечать."
return {"response": response}

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services/orchestrator/dockerfile Normal file
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FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

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services/orchestrator/requirements.txt Normal file
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fastapi==0.115.0
uvicorn[standard]==0.30.1
redis==5.0.4
pydantic==2.8.2
python-dotenv==1.0.1