feat(stt): beam-search decoding + no prev-text conditioning for accuracy

Whisper was decoding with beam_size=1 (greedy), the least accurate setting,
which hurt recognition on short/accented/noisy Discord-mic speech. Switch the
default to beam search (5, Whisper's own default) and stop conditioning on the
previous clip's transcript (which causes repetition/drift on isolated short
utterances rather than helping). Both are env-tunable (STT_BEAM_SIZE,
STT_CONDITION_ON_PREV) so accuracy/latency can be traded without a code change;
wired into docker-compose and documented in .env.example.

.env.example

@@ -227,6 +227,7 @@ COMPOSE_FILE=docker-compose.yml:docker-compose.gpu-linux.yml
 # OLLAMA_CHAT_MODEL=qwen2.5:7b   # quality (needs ~5GB VRAM + whisper small)
 # OLLAMA_CHAT_MODEL=qwen2.5:3b   # speed (fits easily, faster on 8GB GPUs)
 # WHISPER_MODEL=small            # small frees VRAM for a bigger LLM; medium=more accurate
+# STT_BEAM_SIZE=5                # beam search (5) > greedy (1) for accuracy; lower for speed
 # MELO_DEVICE=cuda               # cpu if no GPU on the bot host
 
 # --- Settings web UI (http://localhost:8765/settings on the bot host) ---

bridge/server.py

@@ -87,6 +87,17 @@ VAD_MIN_SPEECH_MS = int(os.environ.get("VAD_MIN_SPEECH_MS", "200"))
 # Korean phrase decoded as Chinese) and shaves a little latency. Empty = auto.
 STT_LANGUAGE = os.environ.get("STT_LANGUAGE", "ko").strip() or None
 
+# Whisper decoding accuracy knobs. beam_size=1 is greedy decoding — fast but the
+# least accurate; beam search (5 is the Whisper default) explores alternatives
+# and noticeably improves recognition on short, accented, or noisy Discord-mic
+# speech. condition_on_previous_text=False stops Whisper from feeding a previous
+# clip's transcript back in as a prompt, which on isolated short utterances
+# causes repetition loops and drift rather than helping. Both are env-tunable so
+# accuracy/latency can be traded without a code change (lower STT_BEAM_SIZE for
+# speed, raise it for accuracy).
+STT_BEAM_SIZE = max(1, int(os.environ.get("STT_BEAM_SIZE", "5")))
+STT_CONDITION_ON_PREV = os.environ.get("STT_CONDITION_ON_PREV", "0") in ("1", "true", "True", "yes", "on")
+
 # TTS engine: "edge" (Microsoft Edge TTS, natural Korean neural voice) is the
 # primary voice. "melo" (a warm MeloTTS worker) and "piper" remain selectable.
 def _tts_engine_setting() -> str:
@@ -243,7 +254,12 @@ def transcribe(wav_bytes: bytes) -> dict:
         print("[bridge] no speech detected (VAD) — skipping STT", flush=True)
         return {"text": "", "language": None, "note": "음성 아님(VAD 차단)"}
 
-    segments, info = _whisper.transcribe(audio, beam_size=1, language=STT_LANGUAGE)
+    segments, info = _whisper.transcribe(
+        audio,
+        beam_size=STT_BEAM_SIZE,
+        language=STT_LANGUAGE,
+        condition_on_previous_text=STT_CONDITION_ON_PREV,
+    )
     # Second line of defence: drop non-speech / hallucinated segments by
     # Whisper's own no_speech_prob. The no_speech_prob hard cutoff (plus the VAD
     # pre-gate above) is what rejects noise/hallucinations. The avg_logprob

docker-compose.yml

@@ -97,6 +97,9 @@ services:
       PLANNER_ENABLED: ${PLANNER_ENABLED:-0}
       # Lock STT to Korean (skip Whisper auto-detect).
       STT_LANGUAGE: ${STT_LANGUAGE:-ko}
+      # Whisper decode accuracy: beam search (5) over greedy (1) lifts recognition
+      # on short/noisy Discord speech. Lower to 1 for minimum latency.
+      STT_BEAM_SIZE: ${STT_BEAM_SIZE:-5}
       VOICE_SILENCE_MS: ${VOICE_SILENCE_MS:-600}
       BRIDGE_URL: http://127.0.0.1:8765
       # Split-deployment role: full (default, all-in-one), browser (only the