my_voice_assistant_v3_jamulix/tests/test_realtime.py
Dieter Schlüter 878bf785dd feat: Geräte-TTS, native Stimmen, Favicon, Auth-Gate, UI-Fixes
Web-UI / TTS:
- Geräte-TTS ("📱 Gerät"): Antwort wird on-device vorgelesen (Web Speech
  API), Server sendet nur Text (text_only) -> spart Bandbreite/Kosten.
  Mobil-Default, geräte-lokale Speicherung, iOS-Autoplay-Freischaltung.
- Vorlese-Symbol (🔊) je Bubble: Hybrid-Replay (Assistent-PCM gecacht,
  Eingabe via /api/speak); SVG-Icon mit kontrastreicher Farbe.
- Kombiniertes Sprachmenü (Flex + feste Sprachen) statt separatem Modus-Menü.
- "Neues Gespräch"-Button (frische Session gegen Sprach-Trägheit).
- Dark-Mode: lesbare <option>-Popups (Kontrast-Fix).
- Favicon (SVG + PNG-Fallbacks) aus mund.png.

TTS-Backend:
- Sprache wird an alle TTS-Provider durchgereicht; Piper-Stimme folgt der
  Sprache; Chatterbox mehrsprachig + cross-lingual.
- Native Referenz-Stimmen je Sprache (config/voices/<lang>.wav, FLEURS CC-BY),
  loudness-normalisiert.

LLM-Sprache:
- Antwort folgt zuverlässig der gewählten Sprache (verstärkte Anweisung +
  Erinnerung an der letzten Nutzer-Nachricht gegen History-Trägheit).

Admin / Auth:
- Wörterbuch: alle 8 Sprachen, Zeilen editierbar, alphabetische Sortierung.
- Web-UI hinter Auth-Gate (Redirect auf SSO_LOGIN_URL / 401); Favicons offen.
- Log-Tab: Hinweis, wenn der systemd-Dienst nicht aktiv ist.
- Einstellungen: Hinweis "pro Nutzer überschreibbar" bei Sprache/Modus/Qualität.

Doku (BEDIENUNGSANLEITUNG.md): Geräte-TTS §6.5.0, Fix/Flex §6.6, native
Stimmen §6.5.3, llama.cpp<->Ollama-Wechsel §4.7, Auth/SSO §7.4.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-20 13:12:04 +02:00

129 lines
4.2 KiB
Python

import array
import asyncio
from fastapi.testclient import TestClient
import app.dependencies as deps
from app.main import app
from app.audio.vad import rms, EnergyVAD
client = TestClient(app)
def _pcm(amplitude: int, n_samples: int) -> bytes:
return array.array("h", [amplitude] * n_samples).tobytes()
# --- VAD-Unit-Tests --------------------------------------------------------
def test_rms_silence_vs_loud():
assert rms(b"") == 0.0
assert rms(_pcm(0, 100)) == 0.0
assert rms(_pcm(3000, 100)) > 2000
def test_energy_vad_ends_after_speech_then_silence():
vad = EnergyVAD(sample_rate=16000, threshold=500, silence_ms=300)
loud = _pcm(3000, 1600) # 100 ms Sprache
silent = _pcm(0, 1600) # 100 ms Stille
assert vad.feed(loud) is False
assert vad.feed(silent) is False # 100 ms
assert vad.feed(silent) is False # 200 ms
assert vad.feed(silent) is True # 300 ms -> Ende
def test_energy_vad_ignores_silence_without_speech():
vad = EnergyVAD(sample_rate=16000, threshold=500, silence_ms=100)
for _ in range(10):
assert vad.feed(_pcm(0, 1600)) is False
# --- Barge-in --------------------------------------------------------------
def _install_slow_stream(monkeypatch):
class SlowLLM:
async def complete(self, text, history=None, session_id=None, language=None):
return "fertig"
async def stream(self, text, history=None, session_id=None, language=None):
for i in range(200):
await asyncio.sleep(0.005)
yield f"t{i} "
class StubTTS:
async def synthesize(self, text, voice=None, audio_format="pcm", language=None):
return b"A"
monkeypatch.setitem(deps.LLM_REGISTRY, "slow", lambda s: SlowLLM())
monkeypatch.setitem(deps.TTS_REGISTRY, "stub", lambda s: StubTTS())
return {
"llm_provider": "slow",
"tts_provider": "stub",
"output_endpoint": "loopback",
"stream": True,
}
def test_ws_chat_interrupt_cancels_response(monkeypatch):
base = _install_slow_stream(monkeypatch)
with client.websocket_connect("/ws/chat") as ws:
ws.send_json({"text": "Hallo", **base})
assert ws.receive_json()["type"] == "ack"
assert ws.receive_json()["type"] == "token" # Antwort laeuft
ws.send_json({"type": "interrupt"})
event = None
for _ in range(500):
event = ws.receive_json()
if event["type"] in ("interrupted", "done"):
break
assert event["type"] == "interrupted" # abgebrochen, nicht fertig
# --- VAD im WebSocket ------------------------------------------------------
def _install_voice_stubs(monkeypatch):
class STT:
async def transcribe(self, audio_bytes, fmt, language=None):
return "ok"
class LLM:
async def complete(self, text, history=None, session_id=None, language=None):
return "antwort"
class TTS:
async def synthesize(self, text, voice=None, audio_format="pcm", language=None):
return b"A"
monkeypatch.setitem(deps.STT_REGISTRY, "s", lambda x: STT())
monkeypatch.setitem(deps.LLM_REGISTRY, "l", lambda x: LLM())
monkeypatch.setitem(deps.TTS_REGISTRY, "t", lambda x: TTS())
return {"stt_provider": "s", "llm_provider": "l", "tts_provider": "t", "output_endpoint": "loopback"}
def test_ws_voice_vad_auto_segments_utterance(monkeypatch):
opts = _install_voice_stubs(monkeypatch)
start = {
"type": "start",
"vad": True,
"sample_rate": 16000,
"vad_silence_ms": 200,
"format": "pcm",
**opts,
}
loud = _pcm(3000, 1600) # 100 ms Sprache
silent = _pcm(0, 1600) # je 100 ms Stille
with client.websocket_connect("/ws/voice") as ws:
ws.send_json(start)
ws.send_bytes(loud)
ws.send_bytes(silent) # 100 ms
ws.send_bytes(silent) # 200 ms -> VAD-Ende, Turn startet automatisch
transcript = ws.receive_json()
assert transcript["type"] == "transcript" and transcript["text"] == "ok"
assert ws.receive_json()["type"] == "ack"
assert ws.receive_json()["type"] == "semantic"
assert ws.receive_bytes() == b"A"
assert ws.receive_json()["type"] == "done"