"""Can whispering be instructed instead of cloned?

Six low-voicing clips all failed as prompts: either the clone lost the breath
(buy1take2, voicing 0.34 -> 0.71) or it kept the breath and invented words
(Rainnight, CER 0.53). Whisper has no F0, and the model's voice conditioning
leans on periodicity, so a 6 s breathy prompt is not enough to carry it.

The repo exposes two text-side controls that were never tried here:
  FREEFORM  a free-text instruction, e.g. "用气声耳语，很轻"
  BASIC     speed/pitch/volume from fixed vocabularies

Both keep a *working* prompt (a08) and move the style into the text, which is
the opposite of what we were doing. Success means low CER *and* low voicing of
the output - a soft normal voice is not a whisper.

Writes style_probe/*.wav and metrics.json.
"""
import json
import os
import sys
import time
import traceback
from pathlib import Path

import numpy as np

ROOT = Path(os.environ.get("WORK_ROOT", "/kaggle/working"))
REPO = ROOT / "Ming-3.0-tts"
MODEL = ROOT / "Ming-3.0-tts-0.5b"
BUNDLE = ROOT / "webfm-eval"
OUT = ROOT / "style_probe"
SEED = 20261031
VOICE = "a08"

LINES = ["后来店关了，歌还在。", "晚安，我们明天见。", "别急，先呼吸。"]

TRIALS = [
    ("plain", {}),
    ("freeform_whisper", {"task": "FREEFORM",
                          "instruction": "用气声耳语，很轻，像在深夜贴着耳朵说话。"}),
    ("freeform_soft", {"task": "FREEFORM",
                       "instruction": "轻声说，音量很低，气息多，接近耳语。"}),
    ("basic_low", {"task": "BASIC", "speed": "慢速", "pitch": "低", "volume": "低"}),
    ("freeform_basic", {"task": "FREEFORM",
                        "instruction": "用耳语说，很慢，很低，几乎只剩气息。"}),
]


def log(msg: str) -> None:
    print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)


def patch_fp32() -> None:
    for name in ("ming_tts.py", "modeling_bailingmm.py"):
        p = REPO / name
        s = p.read_text()
        s = s.replace("torch_dtype=torch.bfloat16", "torch_dtype=torch.float32")
        s = s.replace(".eval().to(torch.bfloat16).to(device)", ".eval().to(torch.float32).to(device)")
        s = s.replace("dtype=torch.bfloat16", "dtype=torch.float32")
        p.write_text(s)


def voicing(y, sr):
    win, hop = int(0.04 * sr), int(0.02 * sr)
    if len(y) < win * 2:
        return 0.0
    frames = np.lib.stride_tricks.sliding_window_view(y, win)[::hop]
    energy = (frames ** 2).mean(1)
    idx = np.argsort(energy)[-max(1, int(len(energy) * 0.3)):]
    lo, hi = int(sr / 400), int(sr / 70)
    peaks = []
    for f in frames[idx]:
        f = f - f.mean()
        ac = np.correlate(f, f, "full")[win - 1:]
        if ac[0] > 0:
            peaks.append(float(ac[lo:hi].max() / ac[0]))
    return float(np.median(peaks)) if peaks else 0.0


def main() -> None:
    import torch
    import soundfile as sf
    from transformers import pipeline

    OUT.mkdir(parents=True, exist_ok=True)
    sys.path.insert(0, str(REPO))
    patch_fp32()
    from ming_tts import MingTTS
    import tts_tasks
    sys.path.insert(0, str(BUNDLE / "eval"))
    from score import load_t2s, score_row

    table = load_t2s()
    voices = {v["slot"]: v for v in
              json.loads((BUNDLE / "mined/manifest.json").read_text())["voices"]}
    v = voices[VOICE]

    tts = MingTTS(str(MODEL), device="cuda", text_normalization=False)
    rows = []
    for name, kw in TRIALS:
        item = {"trial": name, "kwargs": {k: str(x) for k, x in kw.items()}}
        ws, vs = [], []
        for i, text in enumerate(LINES, 1):
            try:
                torch.manual_seed(SEED + i)
                w = tts.synthesize(text=text, language_id="Chinese",
                                   prompt_wav_path=str(BUNDLE / v["wav"]),
                                   prompt_text=v["prompt_text"], use_spk_emb=True,
                                   raw_text=True,
                                   max_decode_steps=max(32, min(400, int(len(text) * 3.5))),
                                   **kw)
                y = w.cpu().numpy().reshape(-1)
                sf.write(str(OUT / f"{name}_{i}.wav"), y, 44100)
                ws.append(round(y.size / 44100, 2))
                vs.append(voicing(y, 44100))
            except Exception:
                log(f"{name} line {i} failed\n{traceback.format_exc()}")
        item["seconds"] = ws
        item["voicing"] = float(np.mean(vs)) if vs else 0.0
        rows.append(item)
        log(f"{name}: v_out={item['voicing']:.2f} sec={ws}")

    asr = pipeline("automatic-speech-recognition", model="openai/whisper-small",
                   device=0, torch_dtype=torch.float16)
    for r in rows:
        ces = []
        for i, text in enumerate(LINES, 1):
            p = OUT / f"{r['trial']}_{i}.wav"
            if not p.exists():
                continue
            y, _ = sf.read(p, dtype="float32")
            out = asr({"array": y.astype(np.float32), "sampling_rate": 44100},
                      generate_kwargs={"language": "zh", "task": "transcribe"})
            hyp = (out.get("text", "") if isinstance(out, dict) else str(out)).strip()
            ces.append(score_row({"text": text, "hyp_text": hyp}, table)["cer_norm"])
            r[f"hyp_{i}"] = hyp
        r["cer"] = float(np.mean(ces)) if ces else 1.0
        log(f"{r['trial']} cer={r['cer']:.3f} v={r['voicing']:.2f}  {r.get('hyp_1','')[:22]}")

    rows.sort(key=lambda r: r["cer"])
    (OUT / "metrics.json").write_text(json.dumps(rows, ensure_ascii=False, indent=1))
    log("\n-- summary (CER low is good; voicing low means breathy) --")
    for r in rows:
        log(f"  cer={r['cer']:.2f} v={r['voicing']:.2f}  {r['trial']}")


if __name__ == "__main__":
    main()