响度归一化

复制import subprocess
import json

# 使用 ffmpeg 将音频归一化到 -14 LUFS (ITU-R BS.1770-4)
input_file = "input.mp3"
output_file = "output.mp3"

# 第一步：分析当前响度
analysis = subprocess.run(
    ["ffmpeg", "-i", input_file, "-af", "loudnorm=I=-14:print_format=json",
     "-f", "null", "-"],
    capture_output=True, text=True
)

# 从 stderr 提取 JSON 分析结果（ffmpeg 将分析数据输出到 stderr）
stderr_lines = analysis.stderr.split('\n')
json_start = next(i for i, l in enumerate(stderr_lines) if l.strip().startswith('{'))
params = json.loads('\n'.join(stderr_lines[json_start:]))

# 第二步：应用测量参数进行精确归一化
subprocess.run([
    "ffmpeg", "-i", input_file,
    "-af", f"loudnorm=I=-14:LRA=7:TP=-1.5:"
            f"measured_I={params['input_i']}:"
            f"measured_LRA={params['input_lra']}:"
            f"measured_TP={params['input_tp']}:"
            f"measured_thresh={params['input_thresh']}:"
            f"offset={params['target_offset']}",
    output_file
], check=True)

print(f"归一化完成: {output_file}")

复制package main

import (
	"encoding/json"
	"fmt"
	"os/exec"
	"strings"
)

func main() {
	input := "input.wav"
	output := "output.wav"

	// 第一遍：分析原始响度
	cmd := exec.Command("ffmpeg", "-i", input,
		"-af", "loudnorm=I=-14:print_format=json",
		"-f", "null", "-")
	out, _ := cmd.CombinedOutput()

	// 从输出中提取 JSON 块
	outStr := string(out)
	start := strings.Index(outStr, "{")
	end := strings.LastIndex(outStr, "}") + 1
	if start == -1 || end == 0 {
		panic("无法解析响度分析结果")
	}

	var params struct {
		InputI     float64 `json:"input_i"`
		InputLRA   float64 `json:"input_lra"`
		InputTP    float64 `json:"input_tp"`
		InputThresh float64 `json:"input_thresh"`
		TargetOffset float64 `json:"target_offset"`
	}
	json.Unmarshal([]byte(outStr[start:end]), &params)

	// 第二遍：应用精确归一化
	filter := fmt.Sprintf(
		"loudnorm=I=-14:LRA=7:TP=-1.5:"+
			"measured_I=%.2f:measured_LRA=%.2f:"+
			"measured_TP=%.2f:measured_thresh=%.2f:"+
			"offset=%.2f",
		params.InputI, params.InputLRA,
		params.InputTP, params.InputThresh,
		params.TargetOffset)

	cmd = exec.Command("ffmpeg", "-i", input, "-af", filter, output)
	if err := cmd.Run(); err != nil {
		panic(err)
	}
	fmt.Println("归一化完成:", output)
}

复制// 浏览器端：使用 Web Audio API 分析并调整响度（简化版 LUFS 近似）
// 注意：完整 LUFS 需要多通道分析，此处展示单通道 RMS 能量归一化
async function normalizeLoudness(audioBuffer) {
  const ctx = new OfflineAudioContext(
    audioBuffer.numberOfChannels,
    audioBuffer.length,
    audioBuffer.sampleRate
  );

  // 复制音频数据到离线上下文
  const source = ctx.createBufferSource();
  source.buffer = audioBuffer;
  source.connect(ctx.destination);
  source.start();

  // 渲染并获取原始数据
  const rendered = await ctx.startRendering();
  const channelData = rendered.getChannelData(0);

  // 计算 RMS（均方根）能量
  let sumSquares = 0;
  for (let i = 0; i < channelData.length; i++) {
    sumSquares += channelData[i] ** 2;
  }
  const rms = Math.sqrt(sumSquares / channelData.length);

  // 目标 RMS 对应 -14 LUFS 的近似值（0 dBFS = 0 LUFS 时）
  const targetRMS = 0.2; // 约 -14 dBFS
  const gain = targetRMS / (rms || 1);

  // 应用增益
  const normalized = new Float32Array(channelData.length);
  for (let i = 0; i < channelData.length; i++) {
    normalized[i] = Math.max(-1, Math.min(1, channelData[i] * gain));
  }

  return normalized;
}

// 使用示例（假设已从 <audio> 或 fetch 获得 AudioBuffer）
// const audioCtx = new AudioContext();
// const response = await fetch('audio.mp3');
// const arrayBuffer = await response.arrayBuffer();
// const audioBuffer = await audioCtx.decodeAudioData(arrayBuffer);
// const normalized = await normalizeLoudness(audioBuffer);
// console.log('归一化完成，样本数:', normalized.length);