Daniil Klimov
780f6f0084
- Add training/ pipeline (step_1..step_5) and own-samples flow
- record_wav.py with single-shot and long-record modes, RMS-based silence filter
- remove_silent.py to drop silent samples and renumber
- modes.py: openwakeword inference with reset() and quiet predictions; commented Lusya block for later
- stt.py: drop local faster-whisper fallback, Groq-only
- config.py: remove unused STT_PROVIDER/WHISPER_*
- llm.py: replace __import__("os") hack with proper import
- tts.py: remove debug traceback in play_error_sound
- requirements.txt: add openwakeword/sounddevice/scipy, drop faster-whisper
- deploy/setup.sh: validate ELEVENLABS_API_KEY and WAKE_WORD_COSMO presence
- README.md, CLAUDE.md, project_roadmap memory updated to reflect new architecture
146 lines
4.9 KiB
Python
146 lines
4.9 KiB
Python
import json
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import os
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import re
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import requests
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from datetime import date
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from .config import AGENTS, log
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from .text import clean_for_speech, find_sentence_end
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from .tts import speak, play_error_sound
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SYSTEM_PROMPT = "Отвечай кратко, 1-2 предложения, без markdown, без эмодзи."
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MAX_HISTORY = int(os.getenv("MAX_HISTORY", "20"))
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RESET_PATTERNS = re.compile(
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r"(начни|начать|создай|открой|давай).{0,10}(новую|новый|чистую|чистый).{0,10}(сессию|сессия|диалог|разговор|чат)"
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r"|"
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r"(сбрось|очисти|обнови).{0,10}(сессию|диалог|разговор|чат|историю|контекст)",
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re.IGNORECASE,
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)
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class Conversation:
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"""Хранит историю сообщений — одна сессия на день"""
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def __init__(self, agent_id: str = "cosmo"):
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self.agent_id = agent_id
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self.created_date = date.today()
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self.messages = [{"role": "system", "content": SYSTEM_PROMPT}]
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def is_expired(self) -> bool:
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return date.today() != self.created_date
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def reset(self):
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self.created_date = date.today()
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self.messages = [{"role": "system", "content": SYSTEM_PROMPT}]
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def add_user(self, text: str):
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self.messages.append({"role": "user", "content": text})
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self._trim()
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def add_assistant(self, text: str):
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self.messages.append({"role": "assistant", "content": text})
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self._trim()
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def _trim(self):
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if len(self.messages) > MAX_HISTORY + 1:
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self.messages = [self.messages[0]] + self.messages[-(MAX_HISTORY):]
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def is_reset_command(text: str) -> bool:
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return bool(RESET_PATTERNS.search(text))
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def ask_agent_stream(text: str, conv: "Conversation | None" = None, agent_id: str = "cosmo") -> str:
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if conv is None:
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conv = Conversation(agent_id)
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conv.add_user(text)
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cfg = AGENTS.get(agent_id, AGENTS["cosmo"])
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gateway_url = cfg["gateway_url"]
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session = cfg["session"]
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agent = cfg["agent"]
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try:
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resp = session.post(
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f"{gateway_url}/v1/chat/completions",
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headers={"x-openclaw-model": cfg["voice_model"]},
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json={
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"model": agent,
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"stream": True,
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"messages": conv.messages,
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"max_tokens": 150,
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},
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stream=True,
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timeout=60,
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)
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resp.raise_for_status()
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except requests.ConnectionError:
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log.exception("Gateway недоступен")
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msg = "Не могу связаться с сервером, попробуй ещё раз."
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print(f"⚠️ {msg}")
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play_error_sound()
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speak(msg, agent_id)
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return msg
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except requests.Timeout:
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log.exception("Gateway таймаут")
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msg = "Сервер не ответил вовремя, попробуй ещё раз."
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print(f"⚠️ {msg}")
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play_error_sound()
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speak(msg, agent_id)
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return msg
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except requests.HTTPError:
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log.exception(f"Gateway HTTP ошибка {resp.status_code}")
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msg = "Ошибка сервера, попробуй ещё раз."
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print(f"⚠️ Gateway {resp.status_code}: {resp.text}")
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play_error_sound()
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speak(msg, agent_id)
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return msg
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full_text = ""
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buffer = ""
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try:
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for line in resp.iter_lines():
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if not line or line == b"data: [DONE]":
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continue
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if line.startswith(b"data: "):
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try:
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chunk = json.loads(line[6:])
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delta = chunk["choices"][0]["delta"].get("content", "")
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if not delta:
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continue
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full_text += delta
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buffer += delta
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last_punct = find_sentence_end(buffer, min_len=60)
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if last_punct > -1:
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sentence = clean_for_speech(buffer[:last_punct + 1])
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if sentence.strip():
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print(f"🔊 Говорю: {sentence}")
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speak(sentence, agent_id)
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buffer = buffer[last_punct + 1:].lstrip()
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except (json.JSONDecodeError, KeyError, IndexError):
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continue
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except Exception as e:
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log.exception("Ошибка при чтении стрима")
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print(f"⚠️ Стрим прервался: {e}")
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# Остаток
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if buffer.strip():
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sentence = clean_for_speech(buffer)
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if sentence:
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speak(sentence, agent_id)
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if not full_text:
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msg = "Не получил ответ, попробуй ещё раз."
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speak(msg, agent_id)
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return msg
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result = clean_for_speech(full_text)
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conv.add_assistant(full_text)
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return result
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