e8234b7fb1
The transcript channel only showed successful transcripts, so dropped utterances (the 47/50 misses) were invisible. Now every captured utterance is mirrored with its outcome and per-stage timing: - too-short blip (<300ms), VAD "음성 아님(VAD 차단)", "인식 실패", "답변 없음", or "ok" - transcript + reply (or "(무응답)") - ⏱️ stt/llm seconds The bridge meta now carries note + stt_sec + think_sec; voice.ts fires onTurn for every turn (not only non-empty transcripts) and for the too-short drop; userbot formats the diagnostic line. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
511 lines
21 KiB
Python
511 lines
21 KiB
Python
"""
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Jarvis Brain Bridge
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===================
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A thin local HTTP service that exposes the existing Jarvis "brain"
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(speech-to-text + reply engine + text-to-speech) to the Node/bun Discord bot.
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The Discord layer (``bot/``) is responsible for everything Discord-specific:
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joining voice channels, capturing user audio, playing audio back, slash
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commands, and streaming the VNC screen. It does NOT contain any AI logic.
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Instead it calls this bridge:
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POST /converse (multipart wav) -> { transcript, reply, audio_b64 }
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POST /text (json {text}) -> { reply, audio_b64 }
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POST /stt (multipart wav) -> { text, language }
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POST /tts (json {text}) -> { audio_b64 }
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GET /health -> { ok, brain, stt, tts }
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This keeps the mature ~39k-line Python brain intact while letting Node own the
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Discord/voice/video integration (which is only feasible in the Node ecosystem).
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Run:
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python -m bridge.server # from repo root
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# or
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BRIDGE_HOST=127.0.0.1 BRIDGE_PORT=8765 python bridge/server.py
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"""
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from __future__ import annotations
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import base64
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import io
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import json
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import os
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import sys
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import threading
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import wave
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from pathlib import Path
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from typing import Optional
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# Ensure repo-root/src is importable (jarvis package lives in src/jarvis) and
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# the repo root itself (so ``bridge.text_utils`` resolves whether this module is
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# launched as ``python -m bridge.server`` or ``python bridge/server.py``).
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_REPO_ROOT = Path(__file__).resolve().parent.parent
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_SRC = _REPO_ROOT / "src"
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if str(_SRC) not in sys.path:
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sys.path.insert(0, str(_SRC))
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if str(_REPO_ROOT) not in sys.path:
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sys.path.insert(0, str(_REPO_ROOT))
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from flask import Flask, request, jsonify, Response, stream_with_context
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try: # package-relative when imported as ``bridge.server``
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from bridge.text_utils import split_sentences
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from bridge.stt_filter import filter_speech_segments, has_speech
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except ImportError: # script-relative when run as ``bridge/server.py``
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from text_utils import split_sentences
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from stt_filter import filter_speech_segments, has_speech
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app = Flask(__name__)
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# ---------------------------------------------------------------------------
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# Configuration (env-driven; see .env.example)
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# ---------------------------------------------------------------------------
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BRIDGE_HOST = os.environ.get("BRIDGE_HOST", "127.0.0.1")
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BRIDGE_PORT = int(os.environ.get("BRIDGE_PORT", "8765"))
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BRAIN_ENABLED = os.environ.get("JARVIS_BRAIN_ENABLED", "1") not in ("0", "false", "False")
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TTS_ENABLED = os.environ.get("JARVIS_TTS_ENABLED", "1") not in ("0", "false", "False")
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# Pre-STT speech gate (Silero VAD). Tunable for the Discord mic without a code
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# change: raise VAD_THRESHOLD to reject more noise, lower it to catch quieter
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# speech. VAD_MIN_SPEECH_MS is the shortest run of speech that counts (a brief
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# loud blip shorter than this never reaches Whisper). Set VAD_ENABLED=0 to fall
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# back to the old behaviour (always transcribe, rely on the post-filter only).
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VAD_ENABLED = os.environ.get("VAD_ENABLED", "1") not in ("0", "false", "False")
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VAD_THRESHOLD = float(os.environ.get("VAD_THRESHOLD", "0.4"))
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VAD_MIN_SPEECH_MS = int(os.environ.get("VAD_MIN_SPEECH_MS", "200"))
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# Lock STT to a single language (this deployment is Korean-only). Skipping
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# Whisper's language auto-detect both fixes occasional mis-detection (e.g. a
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# Korean phrase decoded as Chinese) and shaves a little latency. Empty = auto.
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STT_LANGUAGE = os.environ.get("STT_LANGUAGE", "ko").strip() or None
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# TTS engine: "melo" (MeloTTS Korean speaker, the warm worker) is the primary
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# voice; Piper is kept as a fallback if the worker is unreachable. Set
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# TTS_ENGINE=piper to disable MeloTTS entirely.
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TTS_ENGINE = os.environ.get("TTS_ENGINE", "melo").strip().lower()
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MELO_WORKER_URL = os.environ.get("MELO_WORKER_URL", "http://127.0.0.1:8770")
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MELO_TIMEOUT = float(os.environ.get("MELO_TIMEOUT", "30"))
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# When MeloTTS is the engine, do NOT silently fall back to the English Piper
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# voice on failure: speaking Korean text through an English voice produces
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# mangled audio. Default is melo-only (return no audio on failure); set
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# MELO_FALLBACK_PIPER=1 to opt into the Piper fallback.
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MELO_FALLBACK_PIPER = os.environ.get("MELO_FALLBACK_PIPER", "0") in ("1", "true", "True", "yes", "on")
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# ---------------------------------------------------------------------------
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# Lazy singletons. The first request pays the model-load cost; afterwards the
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# brain stays warm. A lock guards initialization so concurrent Discord events
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# don't double-load Whisper.
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# ---------------------------------------------------------------------------
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_init_lock = threading.Lock()
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_cfg = None
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_db = None
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_dialogue_memory = None
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_whisper = None
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_piper_voice = None
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_brain_error: Optional[str] = None
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def _ensure_brain():
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"""Initialize cfg, db, dialogue memory, and Whisper once."""
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global _cfg, _db, _dialogue_memory, _whisper, _brain_error
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if _cfg is not None or _brain_error is not None:
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return
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with _init_lock:
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if _cfg is not None or _brain_error is not None:
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return
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try:
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from jarvis.config import load_settings
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from jarvis.memory.db import Database
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from jarvis.memory.conversation import DialogueMemory
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from faster_whisper import WhisperModel
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cfg = load_settings()
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db = Database(cfg.db_path, cfg.sqlite_vss_path)
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dialogue_memory = DialogueMemory(
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inactivity_timeout=getattr(cfg, "dialogue_memory_timeout", 300.0),
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max_interactions=20,
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)
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device = os.environ.get("WHISPER_DEVICE", "auto")
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compute = os.environ.get("WHISPER_COMPUTE_TYPE", "auto")
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try:
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whisper = WhisperModel(cfg.whisper_model, device=device, compute_type=compute)
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except Exception as ge:
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# GPU not available / unsupported -> fall back to CPU so the
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# bridge still works without a GPU passed to the container.
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if device != "cpu":
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print(f"[bridge] whisper device='{device}' failed ({ge}); falling back to CPU", flush=True)
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whisper = WhisperModel(cfg.whisper_model, device="cpu", compute_type="int8")
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else:
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raise
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_cfg, _db, _dialogue_memory, _whisper = cfg, db, dialogue_memory, whisper
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print(f"[bridge] brain ready (chat={cfg.ollama_chat_model}, whisper={cfg.whisper_model})", flush=True)
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except Exception as e: # pragma: no cover - depends on local models
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_brain_error = f"{type(e).__name__}: {e}"
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print(f"[bridge] brain init FAILED: {_brain_error}", flush=True)
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def _ensure_piper():
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"""Initialize the Piper TTS voice once (independent of the brain)."""
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global _piper_voice
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if _piper_voice is not None or not TTS_ENABLED:
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return
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with _init_lock:
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if _piper_voice is not None:
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return
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try:
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from piper import PiperVoice # piper-tts package
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model_path = os.environ.get("TTS_PIPER_MODEL_PATH")
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if not model_path:
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# Fall back to jarvis' default piper model location.
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from jarvis.output.tts import _get_default_piper_model_path # type: ignore
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model_path = _get_default_piper_model_path()
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if not model_path or not Path(model_path).exists():
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raise FileNotFoundError(
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f"Piper voice model not found at '{model_path}'. "
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f"Set TTS_PIPER_MODEL_PATH in .env or run scripts/setup_models.sh"
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)
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_piper_voice = PiperVoice.load(model_path)
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print(f"[bridge] piper TTS ready ({model_path})", flush=True)
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except Exception as e: # pragma: no cover
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print(f"[bridge] piper init failed (TTS disabled): {e}", flush=True)
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# ---------------------------------------------------------------------------
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# Core operations
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# ---------------------------------------------------------------------------
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def _read_wav_pcm(raw: bytes) -> tuple[bytes, int]:
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"""Decode an incoming WAV blob to mono 16-bit PCM @ its sample rate."""
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with wave.open(io.BytesIO(raw), "rb") as wf:
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sr = wf.getframerate()
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frames = wf.readframes(wf.getnframes())
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return frames, sr
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def transcribe(wav_bytes: bytes) -> dict:
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_ensure_brain()
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if _whisper is None:
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return {"text": "", "language": None, "error": _brain_error or "stt unavailable"}
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import numpy as np
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pcm, sr = _read_wav_pcm(wav_bytes)
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audio = np.frombuffer(pcm, dtype=np.int16).astype(np.float32) / 32768.0
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# faster-whisper expects 16kHz mono float32; linearly resample if needed.
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if sr != 16000 and audio.size:
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n_out = int(round(audio.size * 16000 / sr))
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if n_out > 0:
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x_old = np.linspace(0.0, 1.0, num=audio.size, endpoint=False)
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x_new = np.linspace(0.0, 1.0, num=n_out, endpoint=False)
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audio = np.interp(x_new, x_old, audio).astype(np.float32)
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# Pre-STT speech gate: don't even invoke Whisper unless there is real speech
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# in the clip. Noise or a brief loud blip (no actual speech) is dropped here,
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# before transcription, so the model never gets a chance to hallucinate a
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# phrase from it. Fail-open inside has_speech() keeps a real utterance from
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# being swallowed if the VAD is unavailable.
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if VAD_ENABLED and not has_speech(
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audio,
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16000,
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threshold=VAD_THRESHOLD,
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min_speech_duration_ms=VAD_MIN_SPEECH_MS,
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log=lambda m: print(f"[bridge] {m}", flush=True),
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):
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print("[bridge] no speech detected (VAD) — skipping STT", flush=True)
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return {"text": "", "language": None, "note": "음성 아님(VAD 차단)"}
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segments, info = _whisper.transcribe(audio, beam_size=1, language=STT_LANGUAGE)
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# Second line of defence: drop non-speech / hallucinated segments by
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# Whisper's own no_speech_prob. The no_speech_prob hard cutoff (plus the VAD
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# pre-gate above) is what rejects noise/hallucinations. The avg_logprob
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# CONFIDENCE floor is deliberately OFF by default (STT_MIN_CONFIDENCE=0):
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# short, accented, or quiet real speech over a Discord mic scores very low
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# avg_logprob (e.g. the wake word "자비스" at 0.0-0.3) and a confidence floor
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# silently eats it, making the bot need many tries to hear one utterance.
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# Raise STT_MIN_CONFIDENCE only if hallucinations slip past the no_speech gate.
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no_speech_threshold = float(os.environ.get("STT_NO_SPEECH_THRESHOLD", str(getattr(_cfg, "whisper_no_speech_threshold", 0.5))))
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min_confidence = float(os.environ.get("STT_MIN_CONFIDENCE", "0.0"))
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kept = filter_speech_segments(
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segments,
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no_speech_threshold=no_speech_threshold,
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min_confidence=min_confidence,
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log=lambda m: print(f"[bridge] {m}", flush=True),
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)
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text = "".join(seg.text for seg in kept).strip()
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note = "ok" if text else "인식 실패(빈 결과/필터)"
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return {"text": text, "language": getattr(info, "language", None), "note": note}
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def think(text: str, language: Optional[str] = None, broadcasting: Optional[bool] = None) -> dict:
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"""Run the Jarvis reply engine on a piece of text.
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``broadcasting`` is the bot's live screen-share state for this turn; it is
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stashed in request-scoped state so the search routing can pick Chrome vs
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Gemini. If the reply engine calls the setBroadcast tool, the recorded
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directive is returned as ``broadcast_action`` for the bot to act on.
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"""
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if not BRAIN_ENABLED:
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return {"reply": text, "error": "brain disabled (JARVIS_BRAIN_ENABLED=0)"}
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_ensure_brain()
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if _cfg is None:
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return {"reply": "", "error": _brain_error or "brain unavailable"}
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try:
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from jarvis.reply.engine import run_reply_engine
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from jarvis.reply import turn_state
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turn_state.reset()
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turn_state.set_broadcasting(broadcasting)
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# tts=None: we do our own Discord-side synthesis, the engine must not
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# try to speak to a local speaker that doesn't exist in this process.
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reply = run_reply_engine(
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_db, _cfg, None, text, _dialogue_memory, language=language
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)
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reply = (reply or "").strip()
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if reply:
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_dialogue_memory.add_interaction(text, reply)
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return {"reply": reply, "broadcast_action": turn_state.get_broadcast_action()}
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except Exception as e: # pragma: no cover
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return {"reply": "", "error": f"{type(e).__name__}: {e}"}
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def _coerce_bool(value) -> Optional[bool]:
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"""Parse a broadcasting flag from JSON (bool) or a query string."""
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if value is None:
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return None
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if isinstance(value, bool):
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return value
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return str(value).strip().lower() in ("1", "true", "yes", "on")
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def _melo_synthesize(text: str) -> Optional[bytes]:
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"""Synthesise via the warm MeloTTS worker (separate /opt/melo venv, Korean
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speaker @ speed 1.5). Returns a 16-bit PCM WAV, or None on any failure so
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the caller can fall back to Piper."""
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import urllib.request
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try:
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req = urllib.request.Request(
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f"{MELO_WORKER_URL}/synth",
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data=json.dumps({"text": text}).encode("utf-8"),
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headers={"Content-Type": "application/json"},
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)
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with urllib.request.urlopen(req, timeout=MELO_TIMEOUT) as resp:
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if resp.status == 200:
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return resp.read()
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print(f"[bridge] melo worker HTTP {resp.status}", flush=True)
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except Exception as e: # pragma: no cover - worker may be down
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print(f"[bridge] melo worker unreachable: {e}", flush=True)
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return None
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def _piper_synthesize(text: str) -> Optional[bytes]:
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"""Fallback: synthesise with Piper (English voice). Returns WAV bytes."""
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_ensure_piper()
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if _piper_voice is None:
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return None
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buf = io.BytesIO()
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with wave.open(buf, "wb") as wf:
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# piper-tts API: synthesize_wav(text, wav_file) writes a full WAV;
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# plain synthesize() returns AudioChunks and takes a SynthesisConfig
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# (NOT a wav file) as its 2nd arg.
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_piper_voice.synthesize_wav(text, wf)
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return buf.getvalue()
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def _tts_ready() -> bool:
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"""Whether the configured TTS voice can synthesise right now.
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The bot polls this before logging in so the very first spoken reply is not
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silently dropped while the voice is still warming up. For MeloTTS the worker
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only binds its HTTP port AFTER the model is loaded (``main()`` warms the
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model before ``serve_forever()``), so a successful /health ping is a precise
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"voice is warm" signal. Piper loads on first synth and was never gated, so
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it reports ready. TTS disabled means there is nothing to wait for.
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"""
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if not TTS_ENABLED:
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return True
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if TTS_ENGINE == "melo":
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import urllib.request
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try:
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with urllib.request.urlopen(f"{MELO_WORKER_URL}/health", timeout=2) as resp:
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return resp.status == 200
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except Exception:
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return False
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return True
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def synthesize(text: str) -> Optional[bytes]:
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"""Synthesize text to a 16-bit PCM WAV. The primary voice is MeloTTS
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(Korean speaker, speed 1.5) served by the warm melo worker; Piper is a
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fallback if the worker is unavailable. Returns None if TTS is off."""
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if not TTS_ENABLED or not text.strip():
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return None
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if TTS_ENGINE == "melo":
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audio = _melo_synthesize(text)
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if audio:
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return audio
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if not MELO_FALLBACK_PIPER:
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# Melo-only: better silent than mangled English for Korean text.
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print("[bridge] melo synth failed; no audio (Piper fallback disabled)", flush=True)
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return None
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print("[bridge] melo synth failed; falling back to Piper", flush=True)
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return _piper_synthesize(text)
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# ---------------------------------------------------------------------------
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# HTTP endpoints
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# ---------------------------------------------------------------------------
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@app.get("/health")
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def health():
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return jsonify(
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{
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"ok": True,
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"brain_enabled": BRAIN_ENABLED,
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"brain_ready": _cfg is not None,
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"brain_error": _brain_error,
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"tts_enabled": TTS_ENABLED,
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"tts_engine": TTS_ENGINE,
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"tts_ready": _tts_ready(),
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}
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)
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@app.post("/stt")
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def http_stt():
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raw = request.get_data()
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if not raw:
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return jsonify({"error": "empty body; send a WAV blob"}), 400
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return jsonify(transcribe(raw))
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@app.post("/text")
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def http_text():
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data = request.get_json(silent=True) or {}
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text = (data.get("text") or "").strip()
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if not text:
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return jsonify({"error": "missing 'text'"}), 400
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result = think(text, data.get("language"), _coerce_bool(data.get("broadcasting")))
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audio = synthesize(result.get("reply", ""))
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if audio:
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result["audio_b64"] = base64.b64encode(audio).decode("ascii")
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return jsonify(result)
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@app.post("/tts")
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def http_tts():
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data = request.get_json(silent=True) or {}
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text = (data.get("text") or "").strip()
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if not text:
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return jsonify({"error": "missing 'text'"}), 400
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audio = synthesize(text)
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if not audio:
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return jsonify({"error": "tts unavailable"}), 503
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return jsonify({"audio_b64": base64.b64encode(audio).decode("ascii")})
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@app.post("/converse")
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def http_converse():
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"""Full turn: speech in -> transcript -> reply -> speech out."""
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raw = request.get_data()
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if not raw:
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return jsonify({"error": "empty body; send a WAV blob"}), 400
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stt = transcribe(raw)
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transcript = stt.get("text", "")
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if not transcript:
|
|
return jsonify({"transcript": "", "reply": "", "audio_b64": None})
|
|
broadcasting = _coerce_bool(request.args.get("broadcasting"))
|
|
result = think(transcript, stt.get("language"), broadcasting)
|
|
audio = synthesize(result.get("reply", ""))
|
|
return jsonify(
|
|
{
|
|
"transcript": transcript,
|
|
"language": stt.get("language"),
|
|
"reply": result.get("reply", ""),
|
|
"error": result.get("error"),
|
|
"broadcast_action": result.get("broadcast_action"),
|
|
"audio_b64": base64.b64encode(audio).decode("ascii") if audio else None,
|
|
}
|
|
)
|
|
|
|
|
|
@app.post("/converse_stream")
|
|
def http_converse_stream():
|
|
"""Streaming full turn: speech in -> transcript -> reply -> speech out.
|
|
|
|
Reduces perceived latency by synthesising the reply one sentence at a time
|
|
and emitting each clip as soon as it is ready, so the Discord layer can play
|
|
the first sentence while the rest are still being spoken. The response is
|
|
newline-delimited JSON (NDJSON):
|
|
|
|
{"type":"meta","transcript":..,"language":..,"reply":..,"error":..,"broadcast_action":..}
|
|
{"type":"audio","seq":0,"audio_b64":..}
|
|
{"type":"audio","seq":1,"audio_b64":..}
|
|
{"type":"end"}
|
|
|
|
STT and the reply engine still run to completion before the meta line; only
|
|
TTS is pipelined. The non-streaming /converse endpoint is unchanged.
|
|
"""
|
|
raw = request.get_data()
|
|
if not raw:
|
|
return jsonify({"error": "empty body; send a WAV blob"}), 400
|
|
broadcasting = _coerce_bool(request.args.get("broadcasting"))
|
|
|
|
def gen():
|
|
import time
|
|
t0 = time.monotonic()
|
|
stt = transcribe(raw)
|
|
t_stt = time.monotonic()
|
|
transcript = stt.get("text", "")
|
|
if not transcript:
|
|
yield json.dumps({"type": "meta", "transcript": "", "language": stt.get("language"),
|
|
"reply": "", "error": stt.get("error"),
|
|
"note": stt.get("note", "빈 결과"),
|
|
"stt_sec": round(t_stt - t0, 1), "broadcast_action": None}) + "\n"
|
|
yield json.dumps({"type": "end"}) + "\n"
|
|
return
|
|
result = think(transcript, stt.get("language"), broadcasting)
|
|
t_think = time.monotonic()
|
|
reply = result.get("reply", "")
|
|
yield json.dumps({
|
|
"type": "meta",
|
|
"transcript": transcript,
|
|
"language": stt.get("language"),
|
|
"reply": reply,
|
|
"error": result.get("error"),
|
|
"note": "ok" if reply.strip() else "답변 없음",
|
|
"stt_sec": round(t_stt - t0, 1),
|
|
"think_sec": round(t_think - t_stt, 1),
|
|
"broadcast_action": result.get("broadcast_action"),
|
|
}) + "\n"
|
|
tts_total = 0.0
|
|
for seq, sentence in enumerate(split_sentences(reply)):
|
|
ts = time.monotonic()
|
|
audio = synthesize(sentence)
|
|
tts_total += time.monotonic() - ts
|
|
if audio:
|
|
yield json.dumps({
|
|
"type": "audio",
|
|
"seq": seq,
|
|
"audio_b64": base64.b64encode(audio).decode("ascii"),
|
|
}) + "\n"
|
|
yield json.dumps({"type": "end"}) + "\n"
|
|
print(
|
|
f"[bridge] ⏱️ turn stt={t_stt - t0:.1f}s think(LLM)={t_think - t_stt:.1f}s "
|
|
f"tts={tts_total:.1f}s total={time.monotonic() - t0:.1f}s replylen={len(reply)} "
|
|
f"transcript={transcript[:40]!r}",
|
|
flush=True,
|
|
)
|
|
|
|
return Response(stream_with_context(gen()), mimetype="application/x-ndjson")
|
|
|
|
|
|
def main():
|
|
print(f"[bridge] listening on http://{BRIDGE_HOST}:{BRIDGE_PORT}", flush=True)
|
|
# threaded=True so STT (slow) on one request doesn't block /health, etc.
|
|
app.run(host=BRIDGE_HOST, port=BRIDGE_PORT, threaded=True)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|