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- 拍照顺序 正脸→左脸→右脸:步骤文案/语音文件(step2-leftface/step3-rightface)/payload 归属对齐 - 蒙版轮廓由 Figma 拍照示意位图 alpha 镂空区逐像素描摹生成(0 正脸含耳/1 左脸鼻朝左/2 右脸鼻朝右),框外纯黑 - 侧脸步预览与抓帧不镜像(左右不再迷惑),HeadPose 侧脸自校准阈值 30 帧防误翻转 - 语音:「保持不动」播完再进倒计时(afterCurrent 接续,不再拦腰切断) - 正脸步角度不合格只提示「请正对屏幕」,阈值放宽 |yaw|≤15°/|pitch|≤18°;转头提示仅侧脸步 - 抓帧改完整帧(修 Face++ INVALID_IMAGE_FACE) - 报告页按 Figma 08:内联白底顶栏(返回+标题)、总分双独立白卡(30px 大数字+15px 单位后缀)、 维度短竖分隔线+最低分标红、Tab 黄条、免责左对齐、护理建议去 tag 加粗+收尾句、 产品大标题移出白卡、COREⅡ 换 Figma 导出 SVG 字标、产品图 151×174、卡圆角 10px - 精华卡判据措辞用户化(均分档→综合均分/最差项约束→最低分维度/年龄组档→年龄段,引擎值不动) - 修报告页滚动 P0(去 position:relative 覆写),文档同步(语音文档/README)
233 lines
7.9 KiB
Python
233 lines
7.9 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""按 docs/语音播报内容设计文档_V1.0.md 生成 h5/assets/voice/*.mp3。
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音色：Qwen3-TTS CustomVoice · Serena（温暖柔和年轻女声）+ 甜美亲和 instruct。
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后处理：裁首尾静音 ≤150ms → 响度 −16 LUFS → MP3 48kbps CBR 单声道 22050Hz。
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依赖 SoundGame 的 .venv-tts 与模型缓存（见 /home/xsl/SoundGame/env.d/tts.sh）。
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"""
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from __future__ import annotations
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import json
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import os
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import subprocess
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import sys
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import tempfile
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from pathlib import Path
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os.environ.setdefault("HF_HOME", "/home/xsl/SoundGame/models/hf")
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os.environ.setdefault("HF_ENDPOINT", "https://hf-mirror.com")
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import numpy as np
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import soundfile as sf
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import torch
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from qwen_tts import Qwen3TTSModel
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ROOT = Path(__file__).resolve().parents[1]
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OUT_DIR = ROOT / "h5" / "assets" / "voice"
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STAGING = ROOT / "tools" / "voice_wav_tmp"
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SPEAKER = "Serena"
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# 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
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INSTRUCT = (
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"甜美柔和的年轻女声，亲切温暖，像贴心的美肤顾问，"
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"语速适中偏缓，吐字清晰，带着微笑感"
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)
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# (文件名不含扩展名, 文案) — 与设计文档第 2 节逐字一致
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LINES: list[tuple[str, str]] = [
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("step1-front", "请单人正脸出镜,面部对准取景框"),
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("step2-leftface", "请慢慢向右转头,露出左脸"),
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("step3-rightface", "请保持单人出镜,慢慢向左转头,露出右脸"),
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("hint-noface", "未检测到人脸,请正对镜头"),
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("hint-near", "请靠近一点"),
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("hint-far", "请远一点"),
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("hint-front", "请正对屏幕"),
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("hint-turn-left", "请向左转头"),
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("hint-turn-right", "请向右转头"),
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("hint-hold", "保持不动"),
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("countdown-321", "三、二、一"),
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("shot-done", "拍摄完成"),
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("analyzing", "正在生成您的肌肤报告,请稍候"),
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("analyze-fail", "检测失败,请调整后重新拍摄"),
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]
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PEAK_NORM = 0.89
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MAX_EDGE_SILENCE_MS = 150
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SILENCE_THRESH = 0.01
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def trim_edges(wav: np.ndarray, sr: int) -> np.ndarray:
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"""裁掉过长首尾静音，保留 ≤150ms 边沿，避免提示显迟钝。"""
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abs_w = np.abs(wav)
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above = np.where(abs_w > SILENCE_THRESH)[0]
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if len(above) == 0:
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return wav
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keep = int(sr * MAX_EDGE_SILENCE_MS / 1000)
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start = max(0, int(above[0]) - keep)
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end = min(len(wav), int(above[-1]) + 1 + keep)
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return wav[start:end]
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def to_mp3(wav_path: Path, mp3_path: Path) -> None:
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"""响度归一 −16 LUFS + 转 MP3 48k CBR mono 22050Hz。"""
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# 两遍 loudnorm：先测再应用。探测不能用 -v error，否则 JSON 统计会被压掉。
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probe = subprocess.run(
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[
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"ffmpeg", "-hide_banner", "-nostats",
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"-i", str(wav_path),
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"-af", "loudnorm=I=-16:TP=-1.5:LRA=11:print_format=json",
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"-f", "null", "-",
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],
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capture_output=True,
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text=True,
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check=True,
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)
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err = probe.stderr
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start = err.rfind("{")
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end = err.rfind("}")
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if start < 0 or end < 0:
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raise RuntimeError(f"loudnorm probe failed for {wav_path}: {err[-500:]}")
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stats = json.loads(err[start : end + 1])
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af = (
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f"loudnorm=I=-16:TP=-1.5:LRA=11:"
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f"measured_I={stats['input_i']}:"
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f"measured_TP={stats['input_tp']}:"
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f"measured_LRA={stats['input_lra']}:"
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f"measured_thresh={stats['input_thresh']}:"
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f"offset={stats['target_offset']}:"
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f"linear=true"
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)
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subprocess.run(
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[
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"ffmpeg", "-y", "-v", "error",
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"-i", str(wav_path),
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"-af", af,
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"-ar", "22050",
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"-ac", "1",
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"-c:a", "libmp3lame",
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"-b:a", "48k",
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str(mp3_path),
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],
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check=True,
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)
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def normalize_clip(wav: np.ndarray, sr: int) -> np.ndarray:
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wav = trim_edges(np.asarray(wav, dtype=np.float32).reshape(-1), sr)
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peak = float(np.max(np.abs(wav)))
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if peak < 0.05:
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raise RuntimeError(f"peak too low ({peak:.4f})")
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wav = wav / peak * PEAK_NORM
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dur = len(wav) / sr
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if dur < 0.35:
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wav = np.concatenate([wav, np.zeros(int((0.35 - dur) * sr), dtype=np.float32)])
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return wav
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def write_mp3(name: str, wav: np.ndarray, sr: int) -> None:
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wav_path = STAGING / f"{name}.wav"
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mp3_path = OUT_DIR / f"{name}.mp3"
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sf.write(str(wav_path), wav, sr)
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to_mp3(wav_path, mp3_path)
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dur = len(wav) / sr
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print(f"[ok] {name}.mp3 {dur:.2f}s {mp3_path.stat().st_size} bytes", flush=True)
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def gen_countdown(model) -> tuple[np.ndarray, int]:
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"""倒计时单独按字生成再拼接，避免「三、二、一」整句被读成播报腔、与提示音气质漂移。
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与提示音共用同一 speaker + instruct；句间留约 0.35s，总长约 2.5s。
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"""
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digits = ["三", "二", "一"]
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# 同批带上一条短提示作音色锚点，丢弃锚点音频
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texts = ["保持不动", *digits]
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wavs, sr = model.generate_custom_voice(
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text=texts,
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language=["Chinese"] * len(texts),
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speaker=[SPEAKER] * len(texts),
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instruct=[INSTRUCT] * len(texts),
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max_new_tokens=2048,
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)
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gap = np.zeros(int(0.35 * sr), dtype=np.float32)
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parts: list[np.ndarray] = []
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for i, digit in enumerate(digits):
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clip = normalize_clip(wavs[i + 1], sr)
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parts.append(clip)
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if i < len(digits) - 1:
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parts.append(gap)
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print(f" countdown digit {digit}: {len(clip) / sr:.2f}s", flush=True)
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return np.concatenate(parts), sr
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def main() -> int:
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import argparse
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ap = argparse.ArgumentParser()
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ap.add_argument(
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"--only",
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default="",
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help="逗号分隔的 id，只生成这些；空则全量",
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)
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args = ap.parse_args()
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only = {x.strip() for x in args.only.split(",") if x.strip()}
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todo = [(n, t) for n, t in LINES if not only or n in only]
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if only and not todo:
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print(f"unknown --only ids: {sorted(only)}", flush=True)
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return 1
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OUT_DIR.mkdir(parents=True, exist_ok=True)
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STAGING.mkdir(parents=True, exist_ok=True)
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print("loading Qwen3-TTS CustomVoice ...", flush=True)
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model = Qwen3TTSModel.from_pretrained(
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"Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
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device_map="cuda:0",
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dtype=torch.bfloat16,
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)
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# 倒计时走专用拼接路径，其余整句生成
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normal = [(n, t) for n, t in todo if n != "countdown-321"]
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do_countdown = any(n == "countdown-321" for n, _ in todo)
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if normal:
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names = [n for n, _ in normal]
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texts = [t for _, t in normal]
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print(f"generating {len(names)} lines with speaker={SPEAKER} ...", flush=True)
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wavs, sr = model.generate_custom_voice(
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text=texts,
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language=["Chinese"] * len(names),
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speaker=[SPEAKER] * len(names),
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instruct=[INSTRUCT] * len(names),
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max_new_tokens=2048,
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)
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for name, wav in zip(names, wavs):
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try:
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write_mp3(name, normalize_clip(wav, sr), sr)
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except RuntimeError as e:
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print(f"[FAIL] {name}: {e}", flush=True)
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return 1
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if do_countdown:
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print(f"generating countdown-321 (digit stitch, speaker={SPEAKER}) ...", flush=True)
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wav, sr = gen_countdown(model)
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# 整段再峰值归一一次，保证与提示音响度一致
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peak = float(np.max(np.abs(wav)))
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wav = wav / peak * PEAK_NORM
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write_mp3("countdown-321", wav, sr)
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# 清单始终覆盖文档全量已落地文件（缺文件则跳过写入名）
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existing = [
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n for n, _ in LINES if (OUT_DIR / f"{n}.mp3").is_file()
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]
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manifest = {"files": existing}
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(OUT_DIR / "manifest.json").write_text(
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json.dumps(manifest, ensure_ascii=False, indent=2) + "\n",
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encoding="utf-8",
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)
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print(f"wrote {OUT_DIR / 'manifest.json'} ({len(existing)} files)", flush=True)
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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