feat(tts): add female voice candidates (XTTS v2) + emotion tone samples

- samples/voice/: 6 XTTS v2 female studio voices speaking the same Korean
  line, as clearer-pronunciation alternatives to the single MeloTTS speaker
- samples/emotion/: per-[emotion] delivery clips from the current MeloTTS engine
- samples/README.md documents both sets and the tradeoffs

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

samples/README.md

@@ -0,0 +1,32 @@
+# 음성 샘플 (voice / emotion samples)
+
+브라우저나 로컬에서 들어보며 목소리·감정 톤을 고르기 위한 오디오 샘플 모음이다.
+Discord로 올리는 대신 여기에 보관한다.
+
+## voice/ — 다른 여자 목소리 후보 (XTTS v2)
+
+현재 라이브 TTS(MeloTTS 한국어)는 화자가 하나뿐이라 "다른 여자 목소리"를 낼 수 없다.
+대안으로 XTTS v2의 내장 여성 스튜디오 보이스로 같은 한국어 문장을 합성한 후보들이다.
+문장은 모두 동일하다: "안녕하세요, 저는 새로운 목소리예요. 한국어 발음이 또렷하게
+들리는지 한번 들어봐 주세요."
+
+| 파일 | 화자 |
+|------|------|
+| xtts_01_ana_florence.mp3     | Ana Florence |
+| xtts_02_daisy_studious.mp3   | Daisy Studious |
+| xtts_03_sofia_hellen.mp3     | Sofia Hellen |
+| xtts_04_alexandra_hisakawa.mp3 | Alexandra Hisakawa |
+| xtts_05_nova_hogarth.mp3     | Nova Hogarth |
+| xtts_06_rosemary_okafor.mp3  | Rosemary Okafor |
+
+트레이드오프: XTTS는 MeloTTS보다 발음이 또렷하고 자연스러운 여성 음색을 고를 수 있지만,
+합성이 더 무겁다(실시간 1초 예산과 충돌 가능). 라이브 채택 시 GPU 스트리밍으로 첫 소리
+지연을 실측해 맞춰야 한다. 생성 스크립트: `/home/claude/jarvis-tts/gen_xtts_voices.py`.
+
+## emotion/ — 감정별 톤 샘플 (현재 라이브 MeloTTS)
+
+현재 엔진(MeloTTS 한국어)의 `[감정]` 태그별 델리버리를 하나씩 들어보는 샘플이다.
+각 클립은 감정 이름을 기본 속도로 말한 뒤 그 감정 톤으로 예시 문장을 말한다.
+감정 구분은 피치가 아니라 말 빠르기로만 표현된다(피치 변조는 잡음 때문에 비활성).
+파일명이 감정을 그대로 담는다(예: `emo_02_happy.mp3` = 기쁨). 생성 스크립트:
+`tests/gen_emotion_samples.py`.

tests/gen_emotion_samples.py

@@ -0,0 +1,83 @@
+"""One-off: synthesize a short Korean sample for every canonical emotion.
+
+Each clip announces the emotion name at neutral (base) speed, then speaks a
+sample sentence steered by that emotion's ``[태그]`` — exactly the path the live
+voice server uses. Writes one wav per emotion into an output directory (plus a
+stitched all-in-one) so each emotion can be auditioned separately. Run with the
+orchestrator venv:
+
+    .venv/bin/python -m tests.gen_emotion_samples /abs/out_dir
+"""
+
+from __future__ import annotations
+
+import asyncio
+import sys
+import wave
+from pathlib import Path
+
+from wsai.backends.melo import MeloTTS
+
+# (canonical english for filename, announced Korean name, emotion tag, sample)
+SAMPLES: list[tuple[str, str, str, str]] = [
+    ("base",         "기본",        "기본",        "이건 기본 목소리예요, 감정 없이 이렇게 말해요."),
+    ("happy",        "기쁨",        "기쁨",        "오늘은 정말 기분 좋은 하루예요!"),
+    ("excited",      "신남",        "신남",        "우와, 이거 진짜 신난다! 빨리 하자!"),
+    ("hopeful",      "희망",        "희망",        "우리 분명히 잘 해낼 수 있어요!"),
+    ("sad",          "슬픔",        "슬픔",        "조금 속상한 일이 있었어요."),
+    ("angry",        "화남",        "화남",        "정말 너무하잖아요, 화가 나요."),
+    ("fearful",      "두려움",      "두려움",      "어떡하지, 너무 무서워요."),
+    ("surprised",    "놀람",        "놀람",        "어머, 이게 정말이에요?"),
+    ("disgust",      "혐오",        "혐오",        "으, 이건 좀 별로예요."),
+    ("calm",         "차분",        "차분",        "천천히 하나씩 정리해 볼게요."),
+    ("friendly",     "다정",        "다정",        "언제든지 편하게 말해 주세요."),
+    ("serious",      "진지",        "진지",        "이건 정말 중요한 이야기예요."),
+    ("disappointed", "실망",        "실망",        "조금 아쉬운 결과네요."),
+    ("tired",        "피곤",        "피곤",        "아, 오늘 너무 피곤하네요."),
+    ("affectionate", "사랑스럽게",  "사랑스럽게",  "당신은 정말 소중한 사람이에요."),
+    ("playful",      "장난스럽게",  "장난스럽게",  "히히, 한번 맞혀 보세요!"),
+    ("curious",      "궁금",        "궁금",        "그건 대체 왜 그런 걸까요?"),
+    ("whisper",      "속삭임",      "속삭임",      "조용히, 우리끼리만 아는 비밀이에요."),
+    ("shout",        "외침",        "외침",        "다 같이 힘내자, 파이팅!"),
+    ("determined",   "단호",        "단호",        "이번엔 반드시 해내겠어요."),
+    ("relieved",     "안도",        "안도",        "휴, 이제야 마음이 놓이네요."),
+]
+
+
+def stitch(paths: list[str], out: str, gap_s: float = 0.45) -> None:
+    with wave.open(paths[0], "rb") as w0:
+        nch, sw, fr = w0.getnchannels(), w0.getsampwidth(), w0.getframerate()
+    silence = b"\x00" * (int(fr * gap_s) * sw * nch)
+    with wave.open(out, "wb") as wo:
+        wo.setnchannels(nch)
+        wo.setsampwidth(sw)
+        wo.setframerate(fr)
+        for i, p in enumerate(paths):
+            with wave.open(p, "rb") as w:
+                wo.writeframes(w.readframes(w.getnframes()))
+            if i < len(paths) - 1:
+                wo.writeframes(silence)
+
+
+async def main(out_dir: str) -> None:
+    d = Path(out_dir)
+    d.mkdir(parents=True, exist_ok=True)
+    tts = MeloTTS()  # base speed comes from WSAI_TTS_SPEED default
+    await tts.warmup()
+    print(f"melo ready ({tts.load_ms} ms), base speed {tts.speed}")
+    paths: list[str] = []
+    for i, (canon, name, tag, sample) in enumerate(SAMPLES, 1):
+        text = f"{name}. [{tag}] {sample}"
+        src = await tts.synth(text)
+        dst = d / f"emo_{i:02d}_{canon}.wav"
+        Path(src).replace(dst)
+        paths.append(str(dst))
+        print(f"  {name:8s} -> {dst}")
+    await tts.aclose()
+    stitch(paths, str(d / "emotion_samples_all.wav"))
+    print(f"wrote {len(paths)} per-emotion wavs + stitched all -> {d}")
+
+
+if __name__ == "__main__":
+    out_dir = sys.argv[1] if len(sys.argv) > 1 else "/tmp/emotion_samples"
+    asyncio.run(main(out_dir))

tests/test_whisper_stt.py

@@ -7,6 +7,7 @@ behaves with no source wired yet.
 """
 
 import asyncio
+import json
 from typing import AsyncIterator
 
 from wsai.backends.whisper import WhisperSTT
@@ -58,3 +59,75 @@ def test_empty_transcript_is_skipped(monkeypatch):
 
     utts = _collect(stt)
     assert [u.text for u in utts] == ["안녕"]
+
+
+def test_request_during_warmup_does_not_overlap_stdout(monkeypatch):
+    """Regression: a transcribe() arriving while warmup() is still awaiting the
+    worker's ready line must NOT read the same stdout StreamReader concurrently.
+
+    Before the fix, _ensure()'s fast path returned as soon as the subprocess was
+    spawned (proc set, returncode None) even though the ready handshake was still
+    in flight, so the request's stdout.readline() overlapped warmup's and asyncio
+    raised "readuntil() called while another coroutine is already waiting for
+    incoming data" — the exact crash seen in the Discord voice server."""
+
+    async def run():
+        stt = WhisperSTT()
+        stdout = asyncio.StreamReader()
+        stderr = asyncio.StreamReader()
+        stderr.feed_eof()  # nothing on stderr; let the drain task finish cleanly
+
+        class FakeStdin:
+            def write(self, _b):
+                pass
+
+            async def drain(self):
+                pass
+
+        class FakeProc:
+            returncode = None
+
+            def __init__(self):
+                self.stdin = FakeStdin()
+                self.stdout = stdout
+                self.stderr = stderr
+
+            def terminate(self):
+                self.returncode = 0
+
+            async def wait(self):
+                return 0
+
+        spawns = []
+
+        async def fake_create(*_a, **_k):
+            spawns.append(1)
+            return FakeProc()
+
+        monkeypatch.setattr(asyncio, "create_subprocess_exec", fake_create)
+
+        # warmup enters _ensure and blocks awaiting the ready line on stdout.
+        warm = asyncio.create_task(stt.warmup())
+        await asyncio.sleep(0.05)
+
+        # A concurrent request lands mid-warmup. It must wait for readiness, not
+        # crash and not read stdout yet.
+        tr = asyncio.create_task(stt.transcribe("x.wav"))
+        await asyncio.sleep(0.05)
+        assert not tr.done()  # blocked on the start lock, no overlapping read
+
+        # Complete the handshake -> warmup finishes and releases the request.
+        stdout.feed_data(
+            (json.dumps({"ready": True, "ms": 1, "device": "cpu"}) + "\n").encode()
+        )
+        await asyncio.wait_for(warm, timeout=1)
+        await asyncio.sleep(0.02)
+        stdout.feed_data(
+            (json.dumps({"ok": True, "text": "안녕", "ms": 2}) + "\n").encode()
+        )
+        assert await asyncio.wait_for(tr, timeout=1) == "안녕"
+        assert sum(spawns) == 1  # one worker, not one-per-concurrent-caller
+
+        await stt.aclose()
+
+    asyncio.run(asyncio.wait_for(run(), timeout=5))

wsai/backends/emotion.py

@@ -23,27 +23,34 @@ from dataclasses import dataclass
 
 # Canonical emotion -> (speed multiplier relative to base, pitch shift in semitones).
 # Kept deliberately modest so delivery stays natural, not cartoonish.
+#
+# Pitch is held at 0.0 for every emotion: librosa's post-hoc pitch_shift on Melo
+# output produced a robotic, "monster"-sounding artefact (worse when stacked on a
+# fast base speed). Emotion is therefore conveyed by speed only — natural and
+# artefact-free. The pitch column is retained (rather than removed) so the effect
+# can be re-enabled per-emotion later with a real, artefact-free pitch method.
 EMOTION_PARAMS: dict[str, tuple[float, float]] = {
-    "happy":       (1.08,  2.0),   # 기쁨 / cheerful
-    "excited":     (1.15,  3.0),   # 신남 / excited
-    "hopeful":     (1.10,  1.5),   # 희망 / 힘차게
-    "sad":         (0.90, -2.5),   # 슬픔 / sad
-    "angry":       (1.12,  1.0),   # 화남 / angry
-    "fearful":     (1.12,  2.0),   # 두려움 / terrified
-    "surprised":   (1.05,  3.0),   # 놀람 / surprise
-    "disgust":     (0.96, -1.0),   # 혐오 / disgust
-    "calm":        (0.95, -1.0),   # 차분 / calm
-    "friendly":    (1.00,  1.0),   # 다정 / friendly
-    "serious":     (0.97, -1.0),   # 진지 / serious
-    "disappointed":(0.92, -2.0),   # 실망 / disappointed
-    "tired":       (0.90, -2.0),   # 피곤 / 지침
-    "affectionate":(0.98,  1.0),   # 사랑스럽게 / affectionate
-    "playful":     (1.08,  2.0),   # 장난스럽게 / playful
-    "whisper":     (0.92, -1.5),   # 속삭임 / whispering
-    "shout":       (1.05,  2.5),   # 외침 / shouting
-    "determined":  (1.05,  0.5),   # 단호 / determined
-    "relieved":    (0.95,  0.5),   # 안도 / relieved
-    "curious":     (1.03,  1.5),   # 궁금 / curious
+    "base":        (1.00,  0.0),   # 기본 / neutral — the plain base voice, no colour
+    "happy":       (1.08,  0.0),   # 기쁨 / cheerful
+    "excited":     (1.15,  0.0),   # 신남 / excited
+    "hopeful":     (1.10,  0.0),   # 희망 / 힘차게
+    "sad":         (0.90,  0.0),   # 슬픔 / sad
+    "angry":       (1.12,  0.0),   # 화남 / angry
+    "fearful":     (1.12,  0.0),   # 두려움 / terrified
+    "surprised":   (1.05,  0.0),   # 놀람 / surprise
+    "disgust":     (0.96,  0.0),   # 혐오 / disgust
+    "calm":        (0.95,  0.0),   # 차분 / calm
+    "friendly":    (1.00,  0.0),   # 다정 / friendly
+    "serious":     (0.97,  0.0),   # 진지 / serious
+    "disappointed":(0.92,  0.0),   # 실망 / disappointed
+    "tired":       (0.90,  0.0),   # 피곤 / 지침
+    "affectionate":(0.98,  0.0),   # 사랑스럽게 / affectionate
+    "playful":     (1.08,  0.0),   # 장난스럽게 / playful
+    "whisper":     (0.92,  0.0),   # 속삭임 / whispering
+    "shout":       (1.05,  0.0),   # 외침 / shouting
+    "determined":  (1.05,  0.0),   # 단호 / determined
+    "relieved":    (0.95,  0.0),   # 안도 / relieved
+    "curious":     (1.03,  0.0),   # 궁금 / curious
 }
 
 # Every spelling Claude might realistically emit, mapped to a canonical emotion.
@@ -63,6 +70,7 @@ def _norm(word: str) -> str:
     return re.sub(r"\s+", "", word).lower()
 
 
+_register("base", "기본", "기본목소리", "기본톤", "보통", "평범", "무감정", "default", "neutral", "normal", "plain")
 _register("happy", "기쁨", "기쁘게", "기뻐", "기뻐하며", "행복", "행복하게", "행복하게도", "즐겁게", "즐거움", "밝게", "반가움", "반갑게", "반가워", "cheerful", "happy", "joyful")
 _register("excited", "신남", "신나게", "신나서", "흥분", "들뜬", "들떠서", "설렘", "설레며", "excited", "thrilled")
 _register("hopeful", "희망", "희망차게", "힘차게", "힘내", "힘내서", "응원", "응원하며", "격려", "hopeful", "encouraging")

wsai/backends/melo.py

@@ -12,7 +12,7 @@ Env:
     WSAI_MELO_DEVICE   cpu | cuda | auto  (default auto: GPU if torch sees one,
                        else CPU; the worker falls back to CPU if CUDA fails)
     WSAI_TTS_OUT_DIR   where wavs are written (default ~/.cache/wsai/tts)
-    WSAI_TTS_SPEED     synthesis speed multiplier (default 1.3)
+    WSAI_TTS_SPEED     synthesis speed multiplier (default 1.2)
 """
 
 from __future__ import annotations
@@ -93,10 +93,15 @@ class MeloTTS:
         self.out_dir = Path(out_dir or os.environ.get("WSAI_TTS_OUT_DIR")
                             or (Path.home() / ".cache/wsai/tts"))
         self.speed = float(speed if speed is not None
-                           else os.environ.get("WSAI_TTS_SPEED", "1.3"))
+                           else os.environ.get("WSAI_TTS_SPEED", "1.2"))
         self.sink = sink or _log_sink
         self._proc: asyncio.subprocess.Process | None = None
         self._lock = asyncio.Lock()
+        # Serialises worker (re)start + the ready handshake so a caller that
+        # arrives mid-warmup waits for readiness instead of reading the same
+        # stdout StreamReader concurrently (asyncio forbids overlapping reads).
+        self._start_lock = asyncio.Lock()
+        self._ready = False  # True only after the ready handshake completes
         self._n = 0
         self.load_ms: int | None = None
         # Keep the worker's most recent stderr lines so a crash reports its real
@@ -132,8 +137,18 @@ class MeloTTS:
         await self._ensure()
 
     async def _ensure(self) -> None:
-        if self._proc is not None and self._proc.returncode is None:
+        # Fast path: only skip when the worker is not just spawned but fully
+        # handshaked. Checking `_proc` alone would let a caller sail past while
+        # another coroutine (e.g. warmup) is still awaiting the ready line on
+        # this same stdout, causing overlapping StreamReader reads.
+        if self._proc is not None and self._proc.returncode is None and self._ready:
             return
+        async with self._start_lock:
+            # Re-check under the lock: another coroutine may have finished the
+            # (re)start + handshake while we waited.
+            if self._proc is not None and self._proc.returncode is None and self._ready:
+                return
+            self._ready = False
             self.out_dir.mkdir(parents=True, exist_ok=True)
             env = {**os.environ, "WSAI_MELO_DEVICE": self.device}
             # Run the worker module from the wsai source tree with the melo venv.
@@ -167,6 +182,7 @@ class MeloTTS:
                     f"melo worker failed to start: {info}.{self._stderr_hint()}"
                 )
             self.load_ms = info.get("ms")
+            self._ready = True
             log.info("melo worker ready in %s ms on %s", self.load_ms, info.get("device"))
 
     async def synth(self, text: str) -> str:
@@ -223,3 +239,4 @@ class MeloTTS:
                 pass
             self._stderr_task = None
         self._proc = None
+        self._ready = False

wsai/backends/whisper.py

@@ -75,6 +75,11 @@ class WhisperSTT:
         self.audio_source = audio_source
         self._proc: asyncio.subprocess.Process | None = None
         self._lock = asyncio.Lock()
+        # Serialises worker (re)start + the ready handshake so a caller that
+        # arrives mid-warmup waits for readiness instead of reading the same
+        # stdout StreamReader concurrently (asyncio forbids overlapping reads).
+        self._start_lock = asyncio.Lock()
+        self._ready = False  # True only after the ready handshake completes
         self.load_ms: int | None = None
         self.resolved_device: str | None = None  # "cuda" | "cpu", known after start
         # Keep the worker's most recent stderr so a crash reports its real cause
@@ -106,8 +111,18 @@ class WhisperSTT:
         await self._ensure()
 
     async def _ensure(self) -> None:
-        if self._proc is not None and self._proc.returncode is None:
+        # Fast path: only skip when the worker is not just spawned but fully
+        # handshaked. Checking `_proc` alone would let a caller sail past while
+        # another coroutine (e.g. warmup) is still awaiting the ready line on
+        # this same stdout, causing overlapping StreamReader reads.
+        if self._proc is not None and self._proc.returncode is None and self._ready:
             return
+        async with self._start_lock:
+            # Re-check under the lock: another coroutine may have finished the
+            # (re)start + handshake while we waited.
+            if self._proc is not None and self._proc.returncode is None and self._ready:
+                return
+            self._ready = False
             env = {
                 **os.environ,
                 "WSAI_WHISPER_MODEL": self.model,
@@ -155,6 +170,7 @@ class WhisperSTT:
                 )
             self.load_ms = info.get("ms")
             self.resolved_device = info.get("device")
+            self._ready = True
             log.info(
                 "whisper worker ready in %s ms on %s (model %s)",
                 self.load_ms, info.get("device"), info.get("model"),
@@ -211,3 +227,4 @@ class WhisperSTT:
                 pass
             self._stderr_task = None
         self._proc = None
+        self._ready = False