#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""按 docs/语音播报内容设计文档_V1.0.md 生成 h5/assets/voice/*.mp3。

音色：Qwen3-TTS CustomVoice · Serena（温暖柔和年轻女声）+ 甜美亲和 instruct。
后处理：裁首尾静音 ≤150ms → 响度 −16 LUFS → MP3 48kbps CBR 单声道 22050Hz。
依赖 SoundGame 的 .venv-tts 与模型缓存（见 /home/xsl/SoundGame/env.d/tts.sh）。
"""
from __future__ import annotations

import json
import os
import subprocess
import sys
import tempfile
from pathlib import Path

os.environ.setdefault("HF_HOME", "/home/xsl/SoundGame/models/hf")
os.environ.setdefault("HF_ENDPOINT", "https://hf-mirror.com")

import numpy as np
import soundfile as sf
import torch
from qwen_tts import Qwen3TTSModel

ROOT = Path(__file__).resolve().parents[1]
OUT_DIR = ROOT / "h5" / "assets" / "voice"
STAGING = ROOT / "tools" / "voice_wav_tmp"
SPEAKER = "Serena"
# 甜美、亲和、语速适中偏缓；全表同一 instruct，保证步骤/姿态/倒计时同音色同气质
INSTRUCT = (
    "甜美柔和的年轻女声，亲切温暖，像贴心的美肤顾问，"
    "语速适中偏缓，吐字清晰，带着微笑感"
)

# (文件名不含扩展名, 文案) — 与设计文档第 2 节逐字一致
LINES: list[tuple[str, str]] = [
    ("step1-front", "请单人正脸出镜,面部对准取景框"),
    ("step2-rightface", "请保持单人出镜,慢慢向左转头,露出右脸"),
    ("step3-leftface", "请慢慢向右转头,露出左脸"),
    ("hint-noface", "未检测到人脸,请正对镜头"),
    ("hint-near", "请靠近一点"),
    ("hint-far", "请远一点"),
    ("hint-front", "请正对屏幕"),
    ("hint-turn-left", "请向左转头"),
    ("hint-turn-right", "请向右转头"),
    ("hint-hold", "保持不动"),
    ("countdown-321", "三、二、一"),
    ("shot-done", "拍摄完成"),
    ("analyzing", "正在生成您的肌肤报告,请稍候"),
    ("analyze-fail", "检测失败,请调整后重新拍摄"),
]

PEAK_NORM = 0.89
MAX_EDGE_SILENCE_MS = 150
SILENCE_THRESH = 0.01


def trim_edges(wav: np.ndarray, sr: int) -> np.ndarray:
    """裁掉过长首尾静音，保留 ≤150ms 边沿，避免提示显迟钝。"""
    abs_w = np.abs(wav)
    above = np.where(abs_w > SILENCE_THRESH)[0]
    if len(above) == 0:
        return wav
    keep = int(sr * MAX_EDGE_SILENCE_MS / 1000)
    start = max(0, int(above[0]) - keep)
    end = min(len(wav), int(above[-1]) + 1 + keep)
    return wav[start:end]


def to_mp3(wav_path: Path, mp3_path: Path) -> None:
    """响度归一 −16 LUFS + 转 MP3 48k CBR mono 22050Hz。"""
    # 两遍 loudnorm：先测再应用。探测不能用 -v error，否则 JSON 统计会被压掉。
    probe = subprocess.run(
        [
            "ffmpeg", "-hide_banner", "-nostats",
            "-i", str(wav_path),
            "-af", "loudnorm=I=-16:TP=-1.5:LRA=11:print_format=json",
            "-f", "null", "-",
        ],
        capture_output=True,
        text=True,
        check=True,
    )
    err = probe.stderr
    start = err.rfind("{")
    end = err.rfind("}")
    if start < 0 or end < 0:
        raise RuntimeError(f"loudnorm probe failed for {wav_path}: {err[-500:]}")
    stats = json.loads(err[start : end + 1])
    af = (
        f"loudnorm=I=-16:TP=-1.5:LRA=11:"
        f"measured_I={stats['input_i']}:"
        f"measured_TP={stats['input_tp']}:"
        f"measured_LRA={stats['input_lra']}:"
        f"measured_thresh={stats['input_thresh']}:"
        f"offset={stats['target_offset']}:"
        f"linear=true"
    )
    subprocess.run(
        [
            "ffmpeg", "-y", "-v", "error",
            "-i", str(wav_path),
            "-af", af,
            "-ar", "22050",
            "-ac", "1",
            "-c:a", "libmp3lame",
            "-b:a", "48k",
            str(mp3_path),
        ],
        check=True,
    )


def main() -> int:
    OUT_DIR.mkdir(parents=True, exist_ok=True)
    STAGING.mkdir(parents=True, exist_ok=True)

    print("loading Qwen3-TTS CustomVoice ...", flush=True)
    model = Qwen3TTSModel.from_pretrained(
        "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
        device_map="cuda:0",
        dtype=torch.bfloat16,
    )

    names = [n for n, _ in LINES]
    texts = [t for _, t in LINES]
    print(f"generating {len(names)} lines with speaker={SPEAKER} ...", flush=True)
    wavs, sr = model.generate_custom_voice(
        text=texts,
        language=["Chinese"] * len(names),
        speaker=[SPEAKER] * len(names),
        instruct=[INSTRUCT] * len(names),
        max_new_tokens=2048,
    )

    manifest_ids: list[str] = []
    for name, wav in zip(names, wavs):
        wav = np.asarray(wav, dtype=np.float32).reshape(-1)
        wav = trim_edges(wav, sr)
        peak = float(np.max(np.abs(wav)))
        if peak < 0.05:
            print(f"[FAIL] {name}: peak too low ({peak:.4f})", flush=True)
            return 1
        wav = wav / peak * PEAK_NORM
        dur = len(wav) / sr
        if dur < 0.35:
            pad = np.zeros(int((0.35 - dur) * sr), dtype=np.float32)
            wav = np.concatenate([wav, pad])
            dur = len(wav) / sr

        wav_path = STAGING / f"{name}.wav"
        mp3_path = OUT_DIR / f"{name}.mp3"
        sf.write(str(wav_path), wav, sr)
        to_mp3(wav_path, mp3_path)
        size = mp3_path.stat().st_size
        print(f"[ok] {name}.mp3  {dur:.2f}s  {size} bytes", flush=True)
        manifest_ids.append(name)

    manifest = {"files": manifest_ids}
    (OUT_DIR / "manifest.json").write_text(
        json.dumps(manifest, ensure_ascii=False, indent=2) + "\n",
        encoding="utf-8",
    )
    print(f"wrote {OUT_DIR / 'manifest.json'} ({len(manifest_ids)} files)", flush=True)
    return 0


if __name__ == "__main__":
    sys.exit(main())