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

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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.
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javis-bot
2026-06-24 17:55:20 +09:00
parent 680f5a656a
commit 7da2fcb5e5
3 changed files with 21 additions and 1 deletions
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18
bridge/server.py
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@@ -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