完成语音生成

2026-09-27 00:03:34 +08:00
parent 7d72d7dd5a
commit f8de5f881a
30 changed files with 188 additions and 0 deletions
@@ -0,0 +1,170 @@
+#!/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())