# -*- coding: utf-8 -*-
"""Fix: (1) 'vorne abgeschnitten' = Modell startet mid-Phonem -> Lead-Wort voranstellen,
in flow generieren, dann in der STILLE-LÜCKE davor schneiden (voller Onset bleibt).
(2) 'zu laut' = RMS-Normalisierung auf Zielpegel statt Peak 0.95."""
import os, numpy as np, soundfile as sf, librosa
from pocket_tts import TTSModel
from faster_whisper import WhisperModel

BASE = r"F:\Coding Stuff\mission-control-2\client\lucy-tts"
OUT = r"C:\Users\TobisPC\Desktop\lucy_front_loud"; os.makedirs(OUT, exist_ok=True)
src, _ = librosa.load(os.path.join(BASE, "ref.mp3"), sr=24000, mono=True)
srt, _ = librosa.effects.trim(src, top_db=30); ref = os.path.join(BASE, "ref.wav")
sf.write(ref, srt[:int(18*24000)], 24000)

SENT = {
 "kurz": "Hallo Commander, ich höre dich.",
 "lang": "Natürlich kümmere ich mich darum, Commander. Ich starte den Dienst neu, prüfe die Protokolle und melde mich, sobald alles wieder läuft.",
}
LEAD = "Tja. "  # Wegwerf-Lead -> erzeugt natürlichen Onset fürs echte erste Wort

def drop_tail_blip(a, sr):
    for _ in range(3):
        iv = librosa.effects.split(a, top_db=35)
        if len(iv) < 2: break
        srms = float(np.median([np.sqrt(np.mean(a[s:e]**2)) for s,e in iv[:-1]]))
        s,e = iv[-1]; dur=(e-s)/sr; rms=float(np.sqrt(np.mean(a[s:e]**2))); gap=(s-iv[-2][1])/sr
        if gap>0.35 and rms<0.30*srms and dur<0.35: a = a[:iv[-2][1]]
        else: break
    return a

def crop_lead(a, sr):
    """Schneide in der Lücke NACH dem Lead-Wort -> echtes 1. Wort voll erhalten."""
    iv = librosa.effects.split(a, top_db=35)
    if len(iv) >= 2:
        # Ende des 1. (Lead-)Segments + kleiner Sicherheitsabstand in die Lücke
        cut = iv[0][1] + int(0.03*sr)
        nxt = iv[1][0]
        cut = min(cut, max(iv[0][1], nxt - int(0.04*sr)))  # mind. 40ms Stille vor echtem Wort lassen
        a = a[cut:]
    return a

def finalize(a, sr, target_rms, peak_cap=0.9, front_trim=False):
    a = np.asarray(a, dtype=np.float32).reshape(-1)
    if front_trim:
        yt,_ = librosa.effects.trim(a, top_db=45); a = yt if yt.size else a
    else:
        # nur HINTEN trimmen (vorne unangetastet lassen)
        rev,_ = librosa.effects.trim(a[::-1], top_db=45); a = rev[::-1] if rev.size else a
    # RMS-Normalisierung auf Zielpegel
    rms = float(np.sqrt(np.mean(a**2))) or 1e-9
    a = a * (target_rms / rms)
    peak = float(np.max(np.abs(a)))
    if peak > peak_cap: a = a * (peak_cap / peak)  # Sicherheits-Clamp
    fi = min(int(0.008*sr), a.size//2)
    if fi>0:
        a[:fi]*=np.linspace(0.,1.,fi,dtype=np.float32); a[-fi:]*=np.linspace(1.,0.,fi,dtype=np.float32)
    pad = np.zeros(int(0.08*sr), dtype=np.float32)
    return np.concatenate([pad, a, pad])

m = TTSModel.load_model(language="german_24l", lsd_decode_steps=6, temp=0.9)
vs = m.get_state_for_audio_prompt(ref); sr = m.sample_rate
w = WhisperModel("small", device="cpu", compute_type="int8")

for name, text in SENT.items():
    # Onset-Fix-Roh: mit Lead generieren, Tail-Blip weg, Lead wegschneiden
    raw = m.generate_audio(vs, LEAD + text, frames_after_eos=4)
    raw = raw.numpy() if hasattr(raw,"numpy") else np.asarray(raw)
    raw = np.asarray(raw, dtype=np.float32).reshape(-1)
    raw = drop_tail_blip(raw, sr)
    cropped = crop_lead(raw, sr)
    for tr in [0.12, 0.09, 0.06]:
        out = finalize(cropped, sr, tr, front_trim=False)
        sf.write(os.path.join(OUT, f"{name}_onset_rms{int(tr*100):02d}.wav"), out, sr)
    # Whisper-Check: Output darf NICHT mit Lead beginnen
    sf.write(os.path.join(OUT, f"_chk_{name}.wav"), finalize(cropped, sr, 0.09), sr)
    seg,_ = w.transcribe(os.path.join(OUT, f"_chk_{name}.wav"), language="de", beam_size=5)
    txt = " ".join(s.text for s in seg).strip()
    print(f"[{name}] Whisper-Start: {txt[:55]!r}", flush=True)
print("FRONT_LOUD_DONE", flush=True)