init

2026-07-19 23:52:25 +04:00
commit 99b06b0468
155 changed files with 3808611 additions and 0 deletions
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+"""
+build the piper LJSpeech dataset from the teacher-generated dataset.
+
+  dataset/metadata.csv (file_path,text,mood,emotion_id) + 24kHz wavs
+    → piper/dataset/wav/<id>.wav   (22050 Hz mono)
+    → piper/dataset/metadata.csv   (id|stressed_text)
+
+stress convention: the mood lists / teacher text use "+VOWEL" (plus before the
+stressed vowel). espeak-ng — piper's phonemizer — wants the COMBINING ACUTE
+(U+0301) AFTER the vowel. we convert here. (the uppercase-vowel form in
+generate_synthetic_voice.py's plus_to_acute is for the qwen/OmniVoice teacher,
+NOT espeak — don't reuse it.)
+
+run: python piper/build_dataset.py            # build
+     python piper/build_dataset.py --check    # self-check only
+requires: pip install soundfile librosa
+"""
+
+import csv
+import sys
+import argparse
+from pathlib import Path
+
+ACUTE = "́"                       # combining acute accent
+VOWELS = set("аеёиоуыэюяАЕЁИОУЫЭЮЯ")
+SRC_META = Path("tts/dataset/metadata.csv")
+OUT_DIR = Path("tts/piper/dataset")
+TARGET_SR = 22050
+
+
+def plus_to_espeak(text: str) -> str:
+    """'+VOWEL' -> 'VOWEL' + U+0301 (espeak-ng stress). Leaves other '+' intact."""
+    out = []
+    i = 0
+    while i < len(text):
+        if text[i] == "+" and i + 1 < len(text) and text[i + 1] in VOWELS:
+            out.append(text[i + 1])
+            out.append(ACUTE)
+            i += 2
+        else:
+            out.append(text[i])
+            i += 1
+    return "".join(out)
+
+
+def build():
+    import soundfile as sf
+    import librosa
+
+    wav_out = OUT_DIR / "wav"
+    wav_out.mkdir(parents=True, exist_ok=True)
+
+    rows = list(csv.DictReader(SRC_META.open(encoding="utf-8")))
+    meta_lines, n = [], 0
+    for r in rows:
+        src = Path(r["file_path"])
+        if not src.exists():
+            print(f"  [!] missing wav, skipping: {src}")
+            continue
+        # id: <mood>-<stem> so ids stay unique across per-mood NNNN.wav collisions
+        wid = f"{r['mood']}-{src.stem}"
+        audio, sr = sf.read(str(src))
+        if audio.ndim > 1:
+            audio = audio.mean(axis=1)
+        if sr != TARGET_SR:
+            audio = librosa.resample(audio, orig_sr=sr, target_sr=TARGET_SR)
+        sf.write(str(wav_out / f"{wid}.wav"), audio, TARGET_SR)
+        meta_lines.append(f"{wid}|{plus_to_espeak(r['text'])}")
+        n += 1
+
+    (OUT_DIR / "metadata.csv").write_text("\n".join(meta_lines) + "\n", encoding="utf-8")
+    print(f"[✓] {n} samples → {OUT_DIR} (wav {TARGET_SR}Hz, metadata id|text)")
+
+
+def check():
+    assert plus_to_espeak("з+амок") == "за" + ACUTE + "мок"
+    assert plus_to_espeak("сто+ят") == "стоя" + ACUTE + "т"
+    assert plus_to_espeak("нет плюса") == "нет плюса"      # no stress → untouched
+    assert plus_to_espeak("1 + 2") == "1 + 2"              # '+' not before vowel → kept
+    print("[✓] plus_to_espeak self-check passed")
+
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--check", action="store_true", help="run self-check only")
+    args = ap.parse_args()
+    check()
+    if not args.check:
+        build()