Añadir mu-plugins y scripts de feadulta

scripts/tts_carta.py

@@ -0,0 +1,99 @@
+#!/usr/bin/env python3
+"""Locuta una carta/artículo entero de feadulta con la voz clonada (XTTS-v2 + GPU).
+
+Saca el texto del post ES, lo trocea por párrafos, lo locuta con la voz de
+referencia (calculando los latents del hablante UNA sola vez), concatena con
+pausas y añade comfort noise. Issue #76.
+
+Uso:
+  tts_carta.py <post_id> <muestra_voz.wav> [nombre_salida]
+"""
+import html
+import json
+import os
+import re
+import subprocess
+import sys
+from pathlib import Path
+
+os.environ.setdefault("COQUI_TOS_AGREED", "1")
+
+import numpy as np  # noqa: E402
+import soundfile as sf  # noqa: E402
+import torch  # noqa: E402
+from TTS.api import TTS  # noqa: E402
+
+DEVICE = "cuda" if torch.cuda.is_available() and not os.environ.get("FEA_CPU") else "cpu"
+OUT = Path(__file__).resolve().parent.parent / "wordpress/wp-content/uploads/tts-samples"
+SR = 24000
+CONTAINER = "wordpress-web"
+
+
+def get_post_text(pid):
+    subprocess.run(["docker", "exec", CONTAINER, "php", "/tmp/fea_post_io.php", "get", str(pid)],
+                   check=True, capture_output=True)
+    subprocess.run(["docker", "cp", f"{CONTAINER}:/tmp/fea_es.json", "/tmp/fea_es.json"], check=True)
+    d = json.load(open("/tmp/fea_es.json"))
+    raw = d["content"]
+    # Conserva límites de párrafo antes de quitar tags.
+    raw = re.sub(r"(?i)</p>|<br\s*/?>|</h[1-6]>", "\n", raw)
+    raw = re.sub(r"<[^>]+>", "", raw)          # quita tags
+    raw = re.sub(r"\[[^\]]+\]", "", raw)       # quita shortcodes
+    raw = html.unescape(raw)
+    paras = [re.sub(r"\s+", " ", p).strip() for p in raw.split("\n")]
+    paras = [p for p in paras if len(p) > 1]
+    return d["title"], paras
+
+
+def main():
+    if len(sys.argv) < 3:
+        sys.exit("uso: tts_carta.py <post_id> <muestra_voz.wav> [nombre_salida]")
+    pid = int(sys.argv[1])
+    spk = sys.argv[2]
+    name = sys.argv[3] if len(sys.argv) > 3 else f"carta-{pid}"
+
+    title, paras = get_post_text(pid)
+    print(f"Post #{pid}: «{title}»  ({len(paras)} párrafos, {sum(len(p) for p in paras)} car)")
+
+    print(f"Cargando XTTS-v2 en {DEVICE}…", flush=True)
+    tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(DEVICE)
+    model = tts.synthesizer.tts_model
+    print("Calculando timbre del hablante (1 vez)…", flush=True)
+    gpt_cond, spk_emb = model.get_conditioning_latents(audio_path=[spk])
+
+    pause = np.zeros(int(SR * 0.35), dtype=np.float32)
+    pieces = []
+    import time
+    t0 = time.time()
+    for i, para in enumerate(paras, 1):
+        out = model.inference(
+            para, "es", gpt_cond, spk_emb,
+            temperature=0.65, repetition_penalty=5.0, top_k=50, top_p=0.85,
+            enable_text_splitting=True,
+        )
+        pieces.append(np.asarray(out["wav"], dtype=np.float32))
+        pieces.append(pause)
+        print(f"  párrafo {i}/{len(paras)} ({len(para)} car) ok", flush=True)
+    audio = np.concatenate(pieces)
+    dt = time.time() - t0
+    dur = len(audio) / SR
+    print(f"Síntesis: {dt:.1f}s para {dur:.1f}s de audio (x{dur/dt:.1f} tiempo real) en {DEVICE}")
+
+    raw = OUT / f"{name}.raw.wav"
+    sf.write(raw, audio, SR)
+    wav = OUT / f"{name}.wav"
+    subprocess.run([
+        "ffmpeg", "-y", "-i", str(raw), "-filter_complex",
+        "anoisesrc=color=brown:amplitude=0.004:sample_rate=24000[n];"
+        "[n]highpass=f=120,lowpass=f=3800[nf];"
+        "[0:a][nf]amix=inputs=2:duration=first:dropout_transition=0:normalize=0[a]",
+        "-map", "[a]", "-ar", "24000", str(wav),
+    ], capture_output=True)
+    raw.unlink(missing_ok=True)
+    mp3 = OUT / f"{name}.mp3"
+    subprocess.run(["ffmpeg", "-y", "-i", str(wav), "-b:a", "96k", str(mp3)], capture_output=True)
+    print(f"OK -> {mp3}  ({dur:.0f}s de audio)")
+
+
+if __name__ == "__main__":
+    main()