# -*- coding: utf-8 -*-
"""Warum 'manchmal zu laut' + 'Stimme verändert sich am Anfang'? Stream-Kopf-Logik N× laufen lassen
und Gain, Crop-Punkt, Kopf-Segmente, Whisper-Start, rms/peak protokollieren."""
import os, numpy as np, librosa, soundfile as sf
from pocket_tts import TTSModel
from faster_whisper import WhisperModel

BASE = r"F:\Coding Stuff\mission-control-2\client\lucy-tts"
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)
TARGET_RMS=0.09; LEAD="Tja. "
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")
TEXTS = ["Hallo Commander, ich höre dich.",
         "Natürlich, Commander. Das Backup ist sauber durchgelaufen."]

def run_once(text):
    chunks=[]
    for c in m.generate_audio_stream(vs, LEAD+text, frames_after_eos=4):
        c=c.numpy() if hasattr(c,"numpy") else np.asarray(c)
        chunks.append(np.asarray(c,dtype=np.float32).reshape(-1))
    full=np.concatenate(chunks)
    # Kopf adaptiv (wie Server): bis >=2 Segmente oder 2.5s
    head=[]; hl=0; head_arr=None
    for c in chunks:
        head.append(c); hl+=c.size
        if hl < int(0.5*sr): continue
        a=np.concatenate(head)
        if len(librosa.effects.split(a, top_db=35))>=2 or hl>=int(2.5*sr):
            head_arr=a; break
    if head_arr is None: head_arr=np.concatenate(head)
    iv=librosa.effects.split(head_arr, top_db=35)
    segs=[(round(s/sr,2),round(e/sr,2)) for s,e in iv]
    # Gain aus voiced (aktuelle Server-Logik)
    voiced=np.concatenate([head_arr[s:e] for s,e in iv]) if len(iv) else head_arr
    rms=float(np.sqrt(np.mean(voiced**2))) or 1e-9
    gain=TARGET_RMS/rms
    # Crop (aktuell): kurz vor echtem Wort
    if len(iv)>=2:
        cut=max(iv[0][1], iv[1][0]-int(0.06*sr)); cropped=full[cut:]
    else:
        cropped=full
    out=np.clip(cropped*gain,-0.95,0.95)
    sf.write(os.path.join(BASE,"out_diag_s.wav"), out, sr)
    seg,_=w.transcribe(os.path.join(BASE,"out_diag_s.wav"), language="de", beam_size=5)
    start=(" ".join(x.text for x in seg)).strip()[:30]
    return dict(segs=segs, voiced_rms=round(rms,3), gain=round(gain,2),
                out_rms=round(float(np.sqrt(np.mean(out**2))),3), out_peak=round(float(np.abs(out).max()),3),
                start=start)

for text in TEXTS:
    print(f"=== {text[:30]!r} ===")
    for i in range(4):
        print(f"  run{i}:", run_once(text))
print("DIAG2_DONE")