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feat(voice): mode VEILLE/CONVERSATION + webrtcvad
Réécriture complète pour une expérience chat naturelle : - Mode VEILLE : écoute "Ok Hermès" → active la conversation - Mode CONVERSATION : 60s de parole libre sans répéter le wake word - webrtcvad mode=3 (remplace détection énergie) — fini les faux déclenchements - Single large-v3 (supprime la détection 2 niveaux peu fiable) - VOICE_INJECT prefix pour forcer des réponses courtes - Mots de sortie : "au revoir", "bonne nuit", "stop hermes", "fin" Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
alkatrazz
parent
69ea82c0e7
commit
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2 changed files with 156 additions and 160 deletions
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@ -1,11 +1,11 @@
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#!/usr/bin/env python3
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"""
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Hermes Voice Client
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VAD → Whisper STT (2 niveaux) → keyword "hermes" → hermes -z SSH → piper TTS
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Deux modes : VEILLE (wake word "Ok Hermes") → CONVERSATION (parole libre 60s)
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Usage:
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python hermes-voice.py # dites "Ok Hermes, ..." — écoute en permanence
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python hermes-voice.py --ptt # Entrée pour commencer/terminer
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python hermes-voice.py # écoute en permanence
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python hermes-voice.py --ptt # Entrée pour parler
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python hermes-voice.py --no-tts # réponses texte seulement (debug)
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"""
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@ -13,6 +13,8 @@ import argparse
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import queue
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import subprocess
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import tempfile
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import time
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import unicodedata
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from collections import deque
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from pathlib import Path
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@ -21,44 +23,36 @@ import requests
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import sounddevice as sd
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# ── Configuration ──────────────────────────────────────────────────────────────
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# Si port 4000 inaccessible : ouvrir un tunnel SSH avant de lancer :
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# ssh -L 4000:localhost:4000 user@192.168.1.200
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# puis changer LITELLM_URL → http://localhost:4000/...
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LITELLM_URL = "http://192.168.1.200:4000/v1/chat/completions"
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LITELLM_KEY = "lm-studio"
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HERMES_MODEL = "hermes-default"
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SYSTEM_PROMPT = (
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LITELLM_URL = "http://192.168.1.200:4000/v1/chat/completions"
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LITELLM_KEY = "lm-studio"
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HERMES_MODEL = "hermes-default"
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SYSTEM_PROMPT = (
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"Tu es Hermes, l'assistant vocal du homelab Funk. "
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"Réponds toujours en français, de façon concise (2-3 phrases maximum)."
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)
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SAMPLE_RATE = 16000
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BLOCK_SIZE = 512 # 32ms par bloc
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PRE_ROLL = 10 # blocs à conserver avant la détection (début de phrase)
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VOICE_THRESH = 0.012 # seuil RMS normalisé pour détecter la parole
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SILENCE_SEC = 1.5 # silence pour terminer l'enregistrement
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MIN_SPEECH = 0.8 # durée minimale traitée (filtre les bruits courts)
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SAMPLE_RATE = 16000
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SILENCE_SEC = 1.2 # secondes de silence pour terminer l'enregistrement
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MIN_SPEECH = 0.6 # durée minimale (filtre les bruits courts)
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PRE_ROLL = 15 # frames à conserver avant la détection
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WHISPER_SIZE = "large-v3" # large-v3 : meilleure reconnaissance FR (distil-large-v3 dérive vers l'anglais)
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KEYWORD = "hermes"
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KEYWORD_POS = 5 # le mot-clé doit être dans les N premiers mots
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WHISPER_SIZE = "large-v3"
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KEYWORD = "hermes"
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CHAT_TIMEOUT = 60 # secondes avant retour en veille (sans interaction)
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CHAT_EXIT = {"au revoir", "bonne nuit", "stop hermes", "fin"}
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# Piper TTS — binaire : https://github.com/rhasspy/piper/releases
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# Voix FR : wget .../fr_FR-upmc-medium.onnx → ~/.local/share/piper/
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PIPER_BIN = Path.home() / ".local/share/piper-runtime/piper"
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PIPER_VOICE = Path.home() / ".local/share/piper/fr_FR-upmc-medium.onnx"
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PIPER_BIN = Path.home() / ".local/share/piper-runtime/piper"
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PIPER_VOICE = Path.home() / ".local/share/piper/fr_FR-upmc-medium.onnx"
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MAX_HISTORY = 10 # nombre d'échanges (user+assistant) conservés en mémoire
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MAX_HISTORY = 10
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HERMES_MODE = "ssh"
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HERMES_SSH = "s01"
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# Mode "full Hermes" via SSH (soul + skills + RAG + mémoire long-terme)
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# Passer HERMES_MODE = "ssh" pour router par `hermes -z` sur storage-01
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# Passer HERMES_MODE = "litellm" pour appel direct LiteLLM (session history uniquement)
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HERMES_MODE = "ssh" # "litellm" | "ssh"
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HERMES_SSH = "s01" # alias SSH défini dans ~/.ssh/config
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VOICE_INJECT = "[Réponse vocale : courte, 1-2 phrases max, sans markdown ni listes] "
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# ──────────────────────────────────────────────────────────────────────────────
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_history: list[dict] = [] # historique session (mode litellm uniquement)
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_history: list[dict] = []
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def beep(freq: int = 880, duration: float = 0.12):
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@ -68,23 +62,18 @@ def beep(freq: int = 880, duration: float = 0.12):
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sd.wait()
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def rms(chunk: np.ndarray) -> float:
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return float(np.sqrt(np.mean(chunk.astype(np.float32) ** 2)) / 32768.0)
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def _deaccent(s: str) -> str:
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return unicodedata.normalize("NFD", s).encode("ascii", "ignore").decode("ascii").lower()
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def detect_keyword(text: str) -> tuple[bool, str]:
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"""Vérifie si KEYWORD est dans les premiers mots et retourne la commande sans le mot-clé."""
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words = text.strip().split()
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stripped = [w.lower().strip(",.!?«»") for w in words]
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found = any(KEYWORD in w for w in stripped[:KEYWORD_POS])
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if not found:
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return False, ""
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words = text.strip().split()
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low = [_deaccent(w).strip(",.!?«»") for w in words]
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try:
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idx = next(i for i, w in enumerate(stripped) if KEYWORD in w)
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command = " ".join(words[idx + 1:]).strip()
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idx = next(i for i, w in enumerate(low) if KEYWORD in w)
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return True, " ".join(words[idx + 1:]).strip()
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except StopIteration:
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command = text
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return True, command or text
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return False, ""
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def transcribe(whisper_model, audio: np.ndarray) -> str:
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@ -93,7 +82,7 @@ def transcribe(whisper_model, audio: np.ndarray) -> str:
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audio_f32,
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language="fr",
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task="transcribe",
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beam_size=5,
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beam_size=3,
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vad_filter=True,
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initial_prompt="Transcription en français.",
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)
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@ -107,12 +96,9 @@ def ask_hermes(text: str) -> str:
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def _ask_via_ssh(text: str) -> str:
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"""Route par le vrai Hermes sur storage-01 (soul + skills + RAG + mémoire)."""
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import subprocess
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# Forcer le français + échapper les guillemets simples pour bash
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query = f"Réponds en français. {text}"
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query = VOICE_INJECT + text
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safe = query.replace("'", "'\\''")
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cmd = [
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cmd = [
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"ssh", HERMES_SSH,
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f"sudo -i -u hermes bash -c 'HERMES_HOME=/srv/data/hermes hermes --profile funk-ai -z \"{safe}\"'"
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]
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@ -124,23 +110,19 @@ def _ask_via_ssh(text: str) -> str:
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def _ask_via_litellm(text: str) -> str:
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"""Appel direct LiteLLM avec historique de session."""
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global _history
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lower = text.lower().strip()
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lower = _deaccent(text)
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if any(w in lower for w in ("reset", "nouveau contexte", "oublie tout", "efface")):
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_history.clear()
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return "Contexte effacé, on repart de zéro."
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return "Contexte effacé."
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_history.append({"role": "user", "content": text})
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window = _history[-(MAX_HISTORY * 2):]
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resp = requests.post(
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LITELLM_URL,
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json={
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"model": HERMES_MODEL,
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"messages": [{"role": "system", "content": SYSTEM_PROMPT}] + window,
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"max_tokens": 300,
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"max_tokens": 200,
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"temperature": 0.7,
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},
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headers={"Authorization": f"Bearer {LITELLM_KEY}"},
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@ -153,134 +135,149 @@ def _ask_via_litellm(text: str) -> str:
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def speak(text: str):
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voice = Path(PIPER_VOICE).expanduser()
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voice = Path(PIPER_VOICE).expanduser()
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bin_path = Path(PIPER_BIN).expanduser()
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if not voice.exists():
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print(f" [TTS] voix introuvable : {voice}")
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return
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if not bin_path.exists():
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print(f" [TTS] piper introuvable : {bin_path}")
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if not voice.exists() or not bin_path.exists():
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return
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
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out = Path(f.name)
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# Les .so de piper doivent être dans le même répertoire que le binaire
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lib_dir = str(bin_path.parent)
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env = {"LD_LIBRARY_PATH": lib_dir}
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try:
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subprocess.run(
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[str(bin_path), "--model", str(voice), "--output_file", str(out)],
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input=text.encode(),
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capture_output=True,
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check=True,
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env=env,
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env={"LD_LIBRARY_PATH": str(bin_path.parent)},
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)
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subprocess.run(["aplay", "-q", str(out)], check=False)
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except FileNotFoundError:
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print(" [TTS] piper non trouvé — voir https://github.com/rhasspy/piper/releases")
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pass
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finally:
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out.unlink(missing_ok=True)
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def handle_vad(whisper_fast, whisper_full, audio: np.ndarray, no_tts: bool):
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"""VAD : détection rapide (small) puis transcription précise (large-v3)."""
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if len(audio) / SAMPLE_RATE < MIN_SPEECH:
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return
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# Étape 1 — vérification rapide du mot-clé avec le petit modèle
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quick = transcribe(whisper_fast, audio)
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found, _ = detect_keyword(quick)
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if not found:
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print(f"(ignoré : \"{quick[:60]}\")")
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return
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# Étape 2 — transcription précise uniquement si mot-clé trouvé
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print(" ⏳ Transcription...", end=" ", flush=True)
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text = transcribe(whisper_full, audio)
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if not text:
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print("(rien compris)")
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return
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_, command = detect_keyword(text)
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query = command or text
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print(f"\n 🗣️ Vous : {text}")
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_respond(query, no_tts)
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def handle_ptt(whisper_full, audio: np.ndarray, no_tts: bool):
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"""PTT : transcription directe sans filtre mot-clé."""
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if len(audio) / SAMPLE_RATE < MIN_SPEECH:
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return
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print(" ⏳ Transcription...", end=" ", flush=True)
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text = transcribe(whisper_full, audio)
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if not text:
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print("(rien compris)")
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return
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print(f"\n 🗣️ Vous : {text}")
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_respond(text, no_tts)
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def _respond(query: str, no_tts: bool):
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print(" ⏳ Hermes...", end=" ", flush=True)
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try:
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response = ask_hermes(query)
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except (requests.RequestException, RuntimeError) as e:
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print(f"\n ❌ Erreur : {e}")
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print(f"\n ❌ {e}")
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return
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print(f"\n 🤖 Hermes : {response}\n")
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if not no_tts:
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speak(response)
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def run_vad(whisper_fast, whisper_full, no_tts: bool):
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"""Écoute continue — dites 'Ok Hermes, ...' pour déclencher."""
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print(f"🎙️ En écoute — dites \"Ok {KEYWORD.capitalize()}, ...\" (Ctrl+C pour quitter)\n")
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def record_until_silence(audio_q: queue.Queue, vad, frame_samples: int, frame_ms: int) -> np.ndarray | None:
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"""Enregistre depuis la queue jusqu'à SILENCE_SEC de silence webrtcvad."""
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required_silent = int(SILENCE_SEC * 1000 / frame_ms)
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captured = []
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silent_frames = 0
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while True:
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try:
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frame = audio_q.get(timeout=3.0)
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except queue.Empty:
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break
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captured.append(frame)
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try:
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is_speech = vad.is_speech(frame.tobytes(), SAMPLE_RATE)
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except Exception:
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is_speech = True
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if not is_speech:
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silent_frames += 1
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else:
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silent_frames = 0
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if silent_frames >= required_silent:
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break
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if not captured:
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return None
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audio = np.concatenate(captured)
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return audio if len(audio) / SAMPLE_RATE >= MIN_SPEECH else None
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def run_vad(whisper_model, no_tts: bool):
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import webrtcvad
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vad = webrtcvad.Vad(mode=3)
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FRAME_MS = 20
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FRAME_SAMPLES = int(SAMPLE_RATE * FRAME_MS / 1000) # 320 samples
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chat_mode = False
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last_activity = 0.0
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print("💤 Veille — dites \"Ok Hermès\" pour commencer (Ctrl+C pour quitter)\n")
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audio_q: queue.Queue = queue.Queue()
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pre_roll: deque = deque(maxlen=PRE_ROLL)
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pre_roll: deque = deque(maxlen=PRE_ROLL)
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def callback(indata, frames, time_info, status):
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chunk = (indata[:, 0] if indata.ndim > 1 else indata.flatten()).copy()
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audio_q.put(chunk)
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with sd.InputStream(samplerate=SAMPLE_RATE, channels=1, dtype="int16",
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blocksize=BLOCK_SIZE, callback=callback):
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blocksize=FRAME_SAMPLES, callback=callback):
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while True:
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chunk = audio_q.get()
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pre_roll.append(chunk)
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# Timeout conversation
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if chat_mode and time.time() - last_activity > CHAT_TIMEOUT:
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chat_mode = False
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print("💤 Retour en veille — dites \"Ok Hermès\" pour reprendre\n")
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if rms(chunk) < VOICE_THRESH:
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frame = audio_q.get()
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pre_roll.append(frame)
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try:
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is_speech = vad.is_speech(frame.tobytes(), SAMPLE_RATE)
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except Exception:
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continue
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# Voix détectée — enregistrer jusqu'au silence
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print(" 🔴 ", end="", flush=True)
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if not is_speech:
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continue
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# Parole détectée — vider le pre-roll dans captured et continuer
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captured = list(pre_roll)
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silent_blocks = 0
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required_silent = int(SILENCE_SEC * SAMPLE_RATE / BLOCK_SIZE)
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# Drainer la queue le temps de silence
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audio = record_until_silence(audio_q, vad, FRAME_SAMPLES, FRAME_MS)
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if audio is None:
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continue
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captured_audio = np.concatenate([np.concatenate(captured), audio])
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while True:
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try:
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chunk = audio_q.get(timeout=3.0)
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except queue.Empty:
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break
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captured.append(chunk)
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if rms(chunk) < VOICE_THRESH:
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silent_blocks += 1
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else:
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silent_blocks = 0
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print(".", end="", flush=True)
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if silent_blocks >= required_silent:
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break
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text = transcribe(whisper_model, captured_audio)
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if not text:
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continue
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print()
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beep(440)
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handle_vad(whisper_fast, whisper_full, np.concatenate(captured), no_tts)
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if chat_mode:
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# En conversation — mots de fin ?
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if any(w in _deaccent(text) for w in CHAT_EXIT):
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chat_mode = False
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print(f"\n 🗣️ Vous : {text}")
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beep(440)
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speak("À bientôt !")
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print("💤 Retour en veille\n")
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continue
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# Envoyer directement
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print(f"\n 🗣️ Vous : {text}")
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last_activity = time.time()
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_respond(text, no_tts)
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else:
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# En veille — chercher le wake word
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found, command = detect_keyword(text)
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if not found:
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continue
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# Wake word confirmé
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chat_mode = True
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last_activity = time.time()
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beep(880)
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print(f"\n 🗣️ Vous : {text}")
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print(f"💬 Mode conversation ({CHAT_TIMEOUT}s) — parlez librement\n")
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if command:
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_respond(command, no_tts)
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def run_ptt(whisper_full, no_tts: bool):
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"""Push-to-talk : Entrée pour commencer, Entrée pour terminer."""
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def run_ptt(whisper_model, no_tts: bool):
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print("🎙️ Mode push-to-talk — Entrée pour parler, Entrée pour terminer (Ctrl+C pour quitter)\n")
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while True:
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input("[ ↵ Entrée pour parler ]")
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audio_q: queue.Queue = queue.Queue()
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@ -291,7 +288,7 @@ def run_ptt(whisper_full, no_tts: bool):
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print(" 🔴 Enregistrement... (Entrée pour terminer)")
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with sd.InputStream(samplerate=SAMPLE_RATE, channels=1, dtype="int16",
|
||||
blocksize=BLOCK_SIZE, callback=callback):
|
||||
blocksize=512, callback=callback):
|
||||
input()
|
||||
chunks = []
|
||||
while not audio_q.empty():
|
||||
|
|
@ -300,7 +297,16 @@ def run_ptt(whisper_full, no_tts: bool):
|
|||
if not chunks:
|
||||
continue
|
||||
beep(440)
|
||||
handle_ptt(whisper_full, np.concatenate(chunks), no_tts)
|
||||
audio = np.concatenate(chunks)
|
||||
if len(audio) / SAMPLE_RATE < MIN_SPEECH:
|
||||
continue
|
||||
print(" ⏳ Transcription...", end=" ", flush=True)
|
||||
text = transcribe(whisper_model, audio)
|
||||
if not text:
|
||||
print("(rien compris)")
|
||||
continue
|
||||
print(f"\n 🗣️ Vous : {text}")
|
||||
_respond(text, no_tts)
|
||||
|
||||
|
||||
def main():
|
||||
|
|
@ -311,29 +317,18 @@ def main():
|
|||
|
||||
print("Hermes Voice Client")
|
||||
print("=" * 40)
|
||||
|
||||
print(f" ⏳ Chargement Whisper ({WHISPER_SIZE})...", end=" ", flush=True)
|
||||
from faster_whisper import WhisperModel
|
||||
whisper = WhisperModel(WHISPER_SIZE, device="cpu", compute_type="int8")
|
||||
print("✓\n")
|
||||
|
||||
if args.ptt:
|
||||
print(f" ⏳ Chargement Whisper ({WHISPER_SIZE})...", end=" ", flush=True)
|
||||
whisper_full = WhisperModel(WHISPER_SIZE, device="cpu", compute_type="int8")
|
||||
print("✓\n")
|
||||
try:
|
||||
run_ptt(whisper_full, args.no_tts)
|
||||
except KeyboardInterrupt:
|
||||
print("\n👋 Au revoir")
|
||||
else:
|
||||
# Mode VAD : deux modèles — small pour détection rapide, large pour transcription
|
||||
print(" ⏳ Chargement Whisper rapide (small)...", end=" ", flush=True)
|
||||
whisper_fast = WhisperModel("small", device="cpu", compute_type="int8")
|
||||
print("✓")
|
||||
print(f" ⏳ Chargement Whisper précis ({WHISPER_SIZE})...", end=" ", flush=True)
|
||||
whisper_full = WhisperModel(WHISPER_SIZE, device="cpu", compute_type="int8")
|
||||
print("✓\n")
|
||||
try:
|
||||
run_vad(whisper_fast, whisper_full, args.no_tts)
|
||||
except KeyboardInterrupt:
|
||||
print("\n👋 Au revoir")
|
||||
try:
|
||||
if args.ptt:
|
||||
run_ptt(whisper, args.no_tts)
|
||||
else:
|
||||
run_vad(whisper, args.no_tts)
|
||||
except KeyboardInterrupt:
|
||||
print("\n👋 Au revoir")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
|
|
@ -2,3 +2,4 @@ faster-whisper>=1.0.0
|
|||
sounddevice>=0.4.6
|
||||
numpy>=1.24.0
|
||||
requests>=2.31.0
|
||||
webrtcvad-wheels>=2.0.10
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue