tune: faster endpointing and rebrand to DigiOps

.env.example

@@ -9,7 +9,7 @@ LLM_PROVIDER=openai
 OPENAI_API_KEY=
 OPENAI_BASE_URL=
 OPENAI_LLM_MODEL=gpt-4o-mini
-OPENAI_LLM_SYSTEM_PROMPT=You are a concise voice assistant for a telecom call center. Answer clearly and briefly.
+OPENAI_LLM_SYSTEM_PROMPT=You are a concise voice assistant for the DigiOps contact center. Answer clearly and briefly.
 OPENAI_TIMEOUT_SECONDS=30
 OPENAI_STT_MODEL=whisper-1
 OPENAI_STT_LANGUAGE=ru
@@ -54,8 +54,9 @@ YANDEX_STT_TOPIC=general
 
 VAD_THRESHOLD=0.5
 VAD_NEGATIVE_THRESHOLD=
-VAD_SILENCE_TIMEOUT_MS=550
+VAD_SILENCE_TIMEOUT_MS=350
 VAD_SPEECH_PAD_MS=64
 VAD_MIN_SPEECH_DURATION_MS=0
 VAD_USE_ONNX=false
 REALTIME_VOICE_INITIAL_GREETING_TEXT=Здравствуйте! Меня зовут Айнур, я эй-ай-ассистент компании ДиджиОпс. Как я могу к вам обращаться?
+SEMANTIC_ENDPOINTING_HOLD_MS=600

core/session.py

@@ -21,7 +21,16 @@ from realtime_voice_service.transports.base import BaseMediaTransport
 
 
 LOGGER = logging.getLogger("uvicorn.error")
-SEMANTIC_ENDPOINTING_HOLD_MS = 1000
+def _semantic_hold_ms() -> int:
+    raw = str(os.getenv("SEMANTIC_ENDPOINTING_HOLD_MS", "700")).strip()
+    try:
+        value = int(float(raw))
+    except Exception:
+        value = 700
+    return max(200, min(value, 2000))
+
+
+SEMANTIC_ENDPOINTING_HOLD_MS = _semantic_hold_ms()
 SEMANTIC_CONTINUATION_TOKENS = {
     "а",
     "в",