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• Error transcoding with FFmpeg : Error : Output format hls is not available

  6 mai 2024, par asif mohmd

  I am using FFmpeg library to transcode a video file into multiple resolutions and create an HLS (HTTP Live Streaming) master playlist.

  


  It takes a video file as input but its does give me the output with HLS playlist.I got a error called "Output format hls is not available". Only the Output directory is creating

  


  I am using FFMpeg 7.0 full build version and also tried older versions and ffmpeg essentials and also tried chocolatey.

  


  if i remove the implementation of HLS from this code.it will create 4 different resolution videos in my output.

  


  Note:I just tried this same code on my friend MAC Book by only changing the setffmpegPath : "ffmpeg.setFfmpegPath("C :\ffmpeg\bin\ffmpeg.exe") ;" to his ffmpeg directory.
Its working perfectly in his mac book

  


  import "dotenv/config";&#xA;import * as fs from "fs";&#xA;import * as path from "path";&#xA;import ffmpeg from "fluent-ffmpeg";&#xA;import crypto from "crypto";&#xA;&#xA;ffmpeg.setFfmpegPath("C:\\ffmpeg\\bin\\ffmpeg.exe");&#xA;&#xA;export const FFmpegTranscoder = async (file: any): Promise<any> => {&#xA;  try {&#xA;    console.log("Starting script");&#xA;    console.time("req_time");&#xA;&#xA;    const randomName = (bytes = 32) =>&#xA;      crypto.randomBytes(bytes).toString("hex");&#xA;    const fileName = randomName();&#xA;    const directoryPath = path.join(__dirname, "..", "..", "input");&#xA;    const filePath = path.join(directoryPath, `${fileName}.mp4`);&#xA;&#xA;    if (!fs.existsSync(directoryPath)) {&#xA;      fs.mkdirSync(directoryPath, { recursive: true });&#xA;    }&#xA;&#xA;    const paths = await new Promise<any>((resolve, reject) => {&#xA;      fs.writeFile(filePath, file, async (err) => {&#xA;        if (err) {&#xA;          console.error("Error saving file:", err);&#xA;          throw err;&#xA;        }&#xA;        console.log("File saved successfully:", filePath);&#xA;&#xA;        try {&#xA;          const outputDirectoryPath = await transcodeWithFFmpeg(&#xA;            fileName,&#xA;            filePath&#xA;          );&#xA;          resolve({ directoryPath, filePath, fileName, outputDirectoryPath });&#xA;        } catch (error) {&#xA;          console.error("Error transcoding with FFmpeg:", error);&#xA;        }&#xA;      });&#xA;    });&#xA;    return paths;&#xA;  } catch (e: any) {&#xA;    console.log(e);&#xA;  }&#xA;};&#xA;&#xA;const transcodeWithFFmpeg = async (fileName: string, filePath: string) => {&#xA;  const directoryPath = path.join(&#xA;    __dirname,&#xA;    "..",&#xA;    "..",&#xA;    `output/hls/${fileName}`&#xA;  );&#xA;&#xA;  if (!fs.existsSync(directoryPath)) {&#xA;    fs.mkdirSync(directoryPath, { recursive: true });&#xA;  }&#xA;&#xA;  const resolutions = [&#xA;    {&#xA;      resolution: "256x144",&#xA;      videoBitrate: "200k",&#xA;      audioBitrate: "64k",&#xA;    },&#xA;    {&#xA;      resolution: "640x360",&#xA;      videoBitrate: "800k",&#xA;      audioBitrate: "128k",&#xA;    },&#xA;    {&#xA;      resolution: "1280x720",&#xA;      videoBitrate: "2500k",&#xA;      audioBitrate: "192k",&#xA;    },&#xA;    {&#xA;      resolution: "1920x1080",&#xA;      videoBitrate: "5000k",&#xA;      audioBitrate: "256k",&#xA;    },&#xA;  ];&#xA;&#xA;  const variantPlaylists: { resolution: string; outputFileName: string }[] = [];&#xA;&#xA;  for (const { resolution, videoBitrate, audioBitrate } of resolutions) {&#xA;    console.log(`HLS conversion starting for ${resolution}`);&#xA;    const outputFileName = `${fileName}_${resolution}.m3u8`;&#xA;    const segmentFileName = `${fileName}_${resolution}_%03d.ts`;&#xA;&#xA;    await new Promise<void>((resolve, reject) => {&#xA;      ffmpeg(filePath)&#xA;        .outputOptions([&#xA;          `-c:v h264`,&#xA;          `-b:v ${videoBitrate}`,&#xA;          `-c:a aac`,&#xA;          `-b:a ${audioBitrate}`,&#xA;          `-vf scale=${resolution}`,&#xA;          `-f hls`,&#xA;          `-hls_time 10`,&#xA;          `-hls_list_size 0`,&#xA;          `-hls_segment_filename ${directoryPath}/${segmentFileName}`,&#xA;        ])&#xA;        .output(`${directoryPath}/${outputFileName}`)&#xA;        .on("end", () => resolve())&#xA;        .on("error", (err) => reject(err))&#xA;        .run();&#xA;    });&#xA;    const variantPlaylist = {&#xA;      resolution,&#xA;      outputFileName,&#xA;    };&#xA;    variantPlaylists.push(variantPlaylist);&#xA;    console.log(`HLS conversion done for ${resolution}`);&#xA;  }&#xA;  console.log(`HLS master m3u8 playlist generating`);&#xA;&#xA;  let masterPlaylist = variantPlaylists&#xA;    .map((variantPlaylist) => {&#xA;      const { resolution, outputFileName } = variantPlaylist;&#xA;      const bandwidth =&#xA;        resolution === "256x144"&#xA;          ? 264000&#xA;          : resolution === "640x360"&#xA;          ? 1024000&#xA;          : resolution === "1280x720"&#xA;          ? 3072000&#xA;          : 5500000;&#xA;      ``;&#xA;      return `#EXT-X-STREAM-INF:BANDWIDTH=${bandwidth},RESOLUTION=${resolution}\n${outputFileName}`;&#xA;    })&#xA;    .join("\n");&#xA;  masterPlaylist = `#EXTM3U\n` &#x2B; masterPlaylist;&#xA;&#xA;  const masterPlaylistFileName = `${fileName}_master.m3u8`;&#xA;&#xA;  const masterPlaylistPath = `${directoryPath}/${masterPlaylistFileName}`;&#xA;  fs.writeFileSync(masterPlaylistPath, masterPlaylist);&#xA;  console.log(`HLS master m3u8 playlist generated`);&#xA;  return directoryPath;&#xA;};&#xA;</void></any></any>

  


  My console.log is :

  


      Starting script&#xA;    HLS conversion starting for 256x144&#xA;    Error transcoding with FFmpeg: Error: Output format hls is not available&#xA;        at C:\Users\asifa\Desktop\Genius Grid\Transcode-service\node_modules\fluent-ffmpeg\lib\capabilities.js:589:21&#xA;        at nextTask (C:\Users\asifa\Desktop\Genius Grid\Transcode-service\node_modules\async\dist\async.js:5791:13)&#xA;        at next (C:\Users\asifa\Desktop\Genius Grid\Transcode-service\node_modules\async\dist\async.js:5799:13)&#xA;        at C:\Users\asifa\Desktop\Genius Grid\Transcode-service\node_modules\async\dist\async.js:329:20&#xA;        at C:\Users\asifa\Desktop\Genius Grid\Transcode-service\node_modules\fluent-ffmpeg\lib\capabilities.js:549:7&#xA;        at handleExit (C:\Users\asifa\Desktop\Genius Grid\Transcode-service\node_modules\fluent-ffmpeg\lib\processor.js:170:11)&#xA;        at ChildProcess.<anonymous> (C:\Users\asifa\Desktop\Genius Grid\Transcode-service\node_modules\fluent-ffmpeg\lib\processor.js:184:11)&#xA;        at ChildProcess.emit (node:events:518:28)&#xA;        at ChildProcess.emit (node:domain:488:12)&#xA;        at Process.ChildProcess._handle.onexit (node:internal/child_process:294:12) &#xA;</anonymous>

  


  I am using Windows 11 and FFMpeg version 7.0. I repeatedly checked, using CMD commands, that my FFMpeg was installed correctly and confirmed the environment variables path, experimented with various FFMpeg versions, and tried with FFMpeg full build Chocolatey package.

  


  In Command Line its working perfectly :

  


  PS C:\Users\asifa\Desktop\test fmmpeg> ffmpeg -hide_banner -y -i .\SampleVideo_1280x720_30mb.mp4 -vf scale=w=640:h=360:force_original_aspect_ratio=decrease -c:a aac -b:v 800k -c:v h264 -b:a 128k -f hls -hls_time 14 -hls_list_size 0 -hls_segment_filename beach/480p_%03d.ts beach/480p.m3u8
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '.\SampleVideo_1280x720_30mb.mp4':
  Metadata:
    major_brand     : isom
    minor_version   : 512
    compatible_brands: isomiso2avc1mp41
    creation_time   : 1970-01-01T00:00:00.000000Z
    encoder         : Lavf53.24.2
  Duration: 00:02:50.86, start: 0.000000, bitrate: 1474 kb/s
  Stream #0:0[0x1](und): Video: h264 (Main) (avc1 / 0x31637661), yuv420p(progressive), 1280x720 [SAR 1:1 DAR 16:9], 1086 kb/s, 25 fps, 25 tbr, 12800 tbn (default)
      Metadata:
        creation_time   : 1970-01-01T00:00:00.000000Z
        handler_name    : VideoHandler
        vendor_id       : [0][0][0][0]
  Stream #0:1[0x2](und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, 5.1, fltp, 383 kb/s (default)
      Metadata:
        creation_time   : 1970-01-01T00:00:00.000000Z
        handler_name    : SoundHandler
        vendor_id       : [0][0][0][0]
Stream mapping:
  Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))
  Stream #0:1 -> #0:1 (aac (native) -> aac (native))
Press [q] to stop, [?] for help
[libx264 @ 000001ef1288ec00] using SAR=1/1
[libx264 @ 000001ef1288ec00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 000001ef1288ec00] profile High, level 3.0, 4:2:0, 8-bit
[libx264 @ 000001ef1288ec00] 264 - core 164 r3190 7ed753b - H.264/MPEG-4 AVC codec - Copyleft 2003-2024 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=11 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=abr mbtree=1 bitrate=800 ratetol=1.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, hls, to 'beach/480p.m3u8':
  Metadata:
    major_brand     : isom
    minor_version   : 512
    compatible_brands: isomiso2avc1mp41
    encoder         : Lavf61.1.100
  Stream #0:0(und): Video: h264, yuv420p(progressive), 640x360 [SAR 1:1 DAR 16:9], q=2-31, 800 kb/s, 25 fps, 90k tbn (default)
      Metadata:
        creation_time   : 1970-01-01T00:00:00.000000Z
        handler_name    : VideoHandler
        vendor_id       : [0][0][0][0]
        encoder         : Lavc61.3.100 libx264
      Side data:
        cpb: bitrate max/min/avg: 0/0/800000 buffer size: 0 vbv_delay: N/A
  Stream #0:1(und): Audio: aac (LC), 48000 Hz, 5.1, fltp, 128 kb/s (default)
      Metadata:
        creation_time   : 1970-01-01T00:00:00.000000Z
        handler_name    : SoundHandler
        vendor_id       : [0][0][0][0]
        encoder         : Lavc61.3.100 aac
[hls @ 000001ef12482040] Opening 'beach/480p_000.ts' for writing speed=15.5x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_001.ts' for writing speed=17.9x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_002.ts' for writing speed=17.3x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_003.ts' for writing speed=19.4x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_004.ts' for writing speed=19.3x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_005.ts' for writing speed=19.2x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_006.ts' for writing
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_007.ts' for writing speed=19.4x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_008.ts' for writing speed=19.5x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_009.ts' for writing speed=19.5x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_010.ts' for writing speed=19.4x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_011.ts' for writing/A    =19.4x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[out#0/hls @ 000001ef11d4e880] video:17094KiB audio:2680KiB subtitle:0KiB other streams:0KiB global headers:0KiB muxing overhead: unknown
frame= 4271 fps=485 q=-1.0 Lsize=N/A time=00:02:50.76 bitrate=N/A speed=19.4x
[libx264 @ 000001ef1288ec00] frame I:45    Avg QP:10.29  size: 60418
[libx264 @ 000001ef1288ec00] frame P:1914  Avg QP:14.53  size:  5582
[libx264 @ 000001ef1288ec00] frame B:2312  Avg QP:20.63  size:  1774
[libx264 @ 000001ef1288ec00] consecutive B-frames: 22.9% 11.9%  8.6% 56.6%
[libx264 @ 000001ef1288ec00] mb I  I16..4: 15.6% 32.1% 52.2%
[libx264 @ 000001ef1288ec00] mb P  I16..4:  0.3%  3.4%  1.2%  P16..4: 20.3% 10.0% 13.1%  0.0%  0.0%    skip:51.8%
[libx264 @ 000001ef1288ec00] mb B  I16..4:  0.1%  0.9%  0.4%  B16..8: 17.2%  5.6%  2.8%  direct: 2.0%  skip:71.0%  L0:41.5% L1:44.1% BI:14.4%
[libx264 @ 000001ef1288ec00] final ratefactor: 16.13
[libx264 @ 000001ef1288ec00] 8x8 transform intra:58.4% inter:51.7%
[libx264 @ 000001ef1288ec00] coded y,uvDC,uvAC intra: 86.7% 94.3% 78.8% inter: 12.6% 15.0% 4.5%
[libx264 @ 000001ef1288ec00] i16 v,h,dc,p: 17% 42% 14% 28%
[libx264 @ 000001ef1288ec00] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 23% 19% 11%  6%  7%  8%  8%  9%  9%
[libx264 @ 000001ef1288ec00] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 23% 18% 12%  6%  9%  9%  8%  8%  7%
[libx264 @ 000001ef1288ec00] i8c dc,h,v,p: 44% 24% 20% 12%
[libx264 @ 000001ef1288ec00] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 000001ef1288ec00] ref P L0: 78.3%  9.7%  8.8%  3.2%
[libx264 @ 000001ef1288ec00] ref B L0: 92.5%  6.0%  1.5%
[libx264 @ 000001ef1288ec00] ref B L1: 97.1%  2.9%
[libx264 @ 000001ef1288ec00] kb/s:819.63
[aac @ 000001ef128f7c80] Qavg: 452.137


  


  When I use the .on('start', (cmdline) => console.log(cmdline))} code with the -f hls command, the error "Output format hls is not available" appears, as previously mentioned. But my Console.log looks like this if I run my code without using -f hls command :

  


  Without -f hls command

  


  await new Promise<void>((resolve, reject) => {&#xA;  ffmpeg(filePath)&#xA;    .outputOptions([&#xA;      `-c:v h264`,&#xA;      `-b:v ${videoBitrate}`,&#xA;      `-c:a aac`,&#xA;      `-b:a ${audioBitrate}`,&#xA;      `-vf scale=${resolution}`,&#xA; &#xA;      `-hls_time 10`,&#xA;      `-hls_list_size 0`,&#xA;      `-hls_segment_filename ${directoryPath}/${segmentFileName}`,&#xA;    ])&#xA;    .output(`${directoryPath}/${outputFileName}`)&#xA;    .on(&#x27;start&#x27;, (cmdline) => console.log(cmdline)) &#xA;    .on("end", () => resolve())&#xA;    .on("error", (err) => reject(err))&#xA;    .run();&#xA;});&#xA;</void>

  


  Console.log is :

  


  `Starting script
File saved successfully: C:\Users\asifa\Desktop\Genius Grid\Transcode-service\input\c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1.mp4
HLS conversion starting for 256x144
ffmpeg -i C:\Users\asifa\Desktop\Genius Grid\Transcode-service\input\c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1.mp4 -y -c:v h264 -b:v 200k -c:a aac -b:a 64k -vf scale=256x144 -hls_time 10 -hls_list_size 0 -hls_segment_filename C:\Users\asifa\Desktop\Genius Grid\Transcode-service\output\hls\c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1/c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1_256x144_%03d.ts C:\Users\asifa\Desktop\Genius Grid\Transcode-service\output\hls\c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1/c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1_256x144.m3u8
Error transcoding with FFmpeg: Error: ffmpeg exited with code 2880417800: Unrecognized option 'hls_segment_filename C:\Users\asifa\Desktop\Genius Grid\Transcode-service\output\hls\c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1/c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1_256x144_%03d.ts'.
Error splitting the argument list: Option not found`


  


• How to Implement Cross-Channel Analytics : A Guide for Marketers

  17 avril 2024, par Erin

  Every modern marketer knows they have to connect with consumers across several channels. But do you know how well Instagram works alongside organic traffic or your email list ? Are you even tracking the impacts of these channels in one place ?

  You need a cross-channel analytics solution if you answered no to either of these questions. 

  In this article, we’ll explain cross-channel analytics, why your company probably needs it and how to set up a cross-channel analytics solution as quickly and easily as possible.

  What is cross-channel analytics ? 

  Cross-channel analytics is a form of marketing analytics that collects and analyses data from every channel and campaign you use.

  The result is a comprehensive view of your customer’s journey and each channel’s role in converting customers. 

  Cross-channel analytics lets you track every channel you use to convert customers, including :

  • Your website
  • Social media profiles
  • Email
  • Paid search
  • E-commerce
  • Retargeting campaigns

  Cross-channel analytics solves one of the most significant issues of cross-channel or multi-channel marketing efforts : measurement. 

  Research shows that only 16% of marketing tech stacks allow for accurate measurement of multi-channel initiatives across channels. 

  That’s a problem, given the staggering number of touchpoints in a typical buyer’s conversion path. However, it can be fixed using a cross-channel analytics approach that lets you measure the performance of every channel and assign a dollar value to its role in every conversion. 

  The difference between cross-channel analytics and multi-channel analytics

  Cross-channel analytics and multi-channel analytics sound very similar, but there’s one key difference you need to know. Multi-channel analytics measures the performance of several channels, but not necessarily all of them, nor the extent to which they work together to drive conversions. Conversely, cross-channel analytics measures the performance of all your marketing channels and how they work together. 

  What are the benefits of cross-channel analytics 

  Cross-channel analytics offers a lot of marketing and business benefits. Here are the ones marketing managers love most.

  Get a complete view of the customer journey

  Implementing a cross-channel analytics solution is the only way to get a complete view of your customer journey. 

  Cross-channel marketing analytics lets you see your customer journey in high definition, allowing you to build comprehensive customer profiles using data from multiple sources across every touchpoint. 

  

  The result ? You get to understand how every customer behaves at every point of the customer journey, why they convert or leave your funnel, and which channels play the biggest role. 

  In short, you get to see why customers convert so you can learn how to convert more of them.

  Personalise the customer experience

  According to a McKinsey study, customers demand personalisation, and brands that excel at it generate 40% more revenue. Deliver the personalisation they desire and reap the benefits with cross-channel analytics. 

  When you understand the customer journey in detail, it becomes much easier to personalise your website and marketing efforts to their preferences and behaviours.

  Identify your most effective marketing channels

  Cross-channel marketing helps you understand your marketing efforts to see how every channel impacts conversions. 

  Take a look at the screenshot from Matomo below. Cross-channel analytics lets you get incredibly granular — we can see the number of conversions of organic search drives and the performance of individual search engines. 

  

  This makes it easy to identify your most effective marketing channels and allocate your resources appropriately. It also allows you to ask (and answer) which channels are the most effective.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Attribute conversions accurately 

  An attribution model decides how you assign credit for each customer conversion to different touchpoints on the customer journey. Without a cross-channel analytics solution, you’re stuck using a standard attribution model like first or last click. 

  These models will show you how customers first found your brand or which channel finally convinced them to convert, but it doesn’t help you understand the role all your channels played in the conversion. 

  Cross-channel analytics solves this attribution problem. Rather than attributing a conversion to the touchpoint that directly led to the sale, cross-channel data gives you the real picture and allows you to use multi-touch attribution to understand which touchpoints generate the most revenue.

  How to set up cross-channel analytics

  Now that you know what cross-channel analytics is and why you should use it, here’s how to set up your solution. 

  1. Determine your objectives

  Defining your marketing goals will help you build a more relevant and actionable cross-channel analytics solution. 

  If you want to improve marketing attribution, for example, you can choose a platform with that feature built-in. If you care about personalisation, you could choose a platform with A/B testing capabilities to measure the impact of your personalisation efforts. 

  1. Set relevant KPIs

  You’ll want to track relevant KPIs to measure the marketing effectiveness of each channel. Put top-of-the-funnel metrics aside and focus on conversion metrics. 

  These include :

  • Conversion rate
  • Average visit duration
  • Bounce rate

  1. Implement tracking and analytics tools

  Gathering customer data from every channel and centralising it in a single location is one of the biggest challenges of cross-channel analytics. Still, it’s made easier with the right tracking tool or analytics platform. 

  The trick is to choose a platform that lets you measure as many of your channels as possible in a single platform. With Matomo, for example, you can track search, paid search, social and email campaigns and your website analytics.

  1. Set up a multi-touch attribution model

  Now that you have all of your data in one place, you can set up a multi-touch attribution model that lets you understand the extent to which each marketing channel contributes to your overall success. 

  There are several attribution models to choose from, including :

  

  Each model has benefits and drawbacks, so choosing the right model for your organisation can be tricky. Rather than take a wild guess, evaluate each model against your marketing objectives, sales length cycle and data availability.

  For example, if you want to focus on optimising customer acquisition costs, a model that prioritises earlier touchpoints will be better. If you care about conversions, you might try a time decay model. 

  1. Turn data into insights with reports

  One of the big benefits of choosing a tool like Matomo, which consolidates data in one place, is that it significantly speeds up and simplifies reporting.

  When all the data is stored in one platform, you don’t need to spend hours combing through your social media platforms and copying and pasting analytics data into a spreadsheet. It’s all there and ready for you to run reports.

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  1. Take action

  There’s no point implementing a cross-channel analytics system if you aren’t going to take action. 

  But where should you start ?

  Optimising your budgets and prioritising marketing spend is a great starting point. Use your cross-channel insights to find your most effective marketing channels (they’re the ones that convert the most customers or have the highest ROI) and allocate more of your budget to them. 

  You can also optimise the channels that aren’t pulling their weight if social media is letting you down ; for example, experiment with tactics like social commerce that could drive more conversions. Alternatively, you could choose to stop investing entirely in these channels.

  Cross-channel analytics best practices

  If you already have a cross-channel analytics solution, take things to the next level with the following best practices. 

  Use a centralised solution to track everything

  Centralising your data in one analytics tool can streamline your marketing efforts and help you stay on top of your data. It won’t just save you from tabbing between different browsers or copying and pasting everything into a spreadsheet, but it can also make it easier to create reports. 

  Think about consumer privacy 

  If you are looking at a new cross-channel analytics tool, consider how it accounts for data privacy regulations in your area. 

  You’re going to be collecting a lot of data, so it’s important to respect their privacy wishes. 

  It’s best to choose a platform like Matomo that complies with the strictest privacy laws (CCPA, GDPR, etc.).

  Monitor data in real time

  So, you’ve got a holistic view of your marketing efforts by integrating all your channels into a single tool ?

  Great, now go further by monitoring the impact of your marketing efforts in real time.

  

  With a web analytics platform like Matomo, you can see who visits your site, what they do, and where they come from through features like the visits log report, which even lets you view individual user sessions. This lets you measure the impact of posting on a particular social channel or launching a new offer. 

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

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  Reallocate marketing budgets based on performance

  When you track every channel, you can use a multi-touch attribution model like position-based or time-decay to give every channel the credit it deserves. But don’t just credit each channel ; turn your valuable insights into action. 

  Use cross-channel attribution analytics data to reallocate your marketing budget to the most profitable channels or spend time optimising the channels that aren’t pulling their weight. 

  Cross-channel analytics platforms to get started with 

  The marketing analytics market is huge. Mordor Intelligence valued it at $6.31 billion in 2024 and expects it to reach $11.54 billion by 2029. Many of these platforms offer cross-channel analytics, but few can track the impact of multiple marketing channels in one place. 

  So, rather than force you to trawl through confusing product pages, we’ve shortlisted three of the best cross-channel analytics solutions. 

  Matomo

  

  Matomo is a web analytics platform that lets you collect and centralise your marketing data while giving you 100% accurate data. That includes search, social, e-commerce, campaign tracking data and comprehensive website analytics.

  Better still, you get the necessary tools to turn those insights into action. Custom reporting lets you track and visualise the metrics that matter, while conversion optimisation tools like built-in A/B testing, heatmaps, session recordings and more let you test your theories. 

  Google Analytics

  

  Google Analytics is the most popular and widely used tool on the market. The level of analysis and customisation you can do with it is impressive for a free tool. That includes tracking just about any event and creating reports from scratch. 

  Google Analytics provides some cross-channel marketing features and lets you track the impact of various channels, such as social and search, but there are a couple of drawbacks. 

  Privacy can be a concern because Google Analytics collects data from your customers for its own remarketing purposes. 

  It also uses data sampling to generate wider insights from a small subset of your data. This lack of accurate data reporting can cause you to generate false insights.

  With Google Analytics, you’ll also need to subscribe to additional tools to gain advanced insights into the user experience. So, consider that while this tool is free, you’ll need to pay for heatmaps, session recording and A/B testing tools to optimise effectively.

  Improvado

  

  Improvado is an analytics tool for sales and marketing teams that extracts thousands of metrics from hundreds of sources. It centralises data in data warehouses, from which you can create a range of marketing dashboards.

  While Improvado does have analytics capabilities, it is primarily an ETL (extraction, transform, load) tool for organisations that want to centralise all their data. That means marketers who aren’t familiar with data transformations may struggle to get their heads around the complexity of the platform.

  Make the most of cross-channel analytics with Matomo

  Cross-channel analytics is the only way to get a comprehensive view of your customer journey and understand how your channels work together to drive conversions.

  Then you’re dealing with so many channels and data ; keeping things as simple as possible is the key to success. That’s why over 1 million websites choose Matomo. 

  Our all-in-one analytics solution measures traditional web analytics, behavioural analytics, attribution and SEO, so you have 100% accurate data in one place. 

  Try it free for 21 days. No credit card required.

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• ffmpeg produces duplicate pts with "wallclock_as_timestamps 1" option on MKV

  15 avril 2024, par Jax2171

  I need to get real time reference of every keyframe captured by an IP camera. The -wallclock_as_timestamps 1 option seems to do the trick for us, however we are forced to replace the TS output container with MKV to get a correct PTS epoch value 1712996356.833000.

  


  Here is the ffmpeg command used :

  


  ffmpeg -report -use_wallclock_as_timestamps 1 -rtsp_transport tcp -i rtsp://user:password1@192.168.5.21/cam/realmonitor?channel=1channel1[1]=1subtype=0 -c:v copy -c:a aac -copyts -f matroska -y rec.mkv


  


  The capture process runs without any relevant worning or error messages.

  


  However, playing the captured video with any player shows very short and evident but very annoying lags. Upon investigation I discovered that many frame PTSs have the same value. The command I used to show duplicate PTSs is as follows :

  


  ffprobe -v error -show_entries frame=pkt_pts_time -select_streams v -of csv=p=0 rec.mkv | sort | uniq -d


  


  On a recording of about 10 minutes the result of the duplicate PTS is the following :

  


  1713086493.367000
1713086493.368000
1713086493.370000
1713086493.372000
1713086543.714000
1713086558.793000
1713086558.817000
1713086558.872000
1713086561.780000
1713086564.642000
1713086564.657000
1713086564.778000
1713086565.794000
...


  


  I'm not sure if the lag problem is caused by this, however the problem does not occur with the TS container, which however I cannot use due to the PTS values being roundly 33 bit.

  


  The -vsync 0 or -vsync 2 options on input or output didn't help.

  


  This is the log using the -report option :

  


      ffmpeg started on 2024-04-15 at 09:04:38
Report written to "ffmpeg-20240415-090438.log"
Log level: 48
Command line:
ffmpeg -report -stats -hide_banner -use_wallclock_as_timestamps 1 -rtsp_transport tcp -i "rtsp://user:password1@192.168.5.21/cam/realmonitor?channel=1channel1[1]=1subtype=0" -c:v copy -c:a aac -copyts -f matroska -y rec.mkv
Splitting the commandline.
Reading option '-report' ... matched as option 'report' (generate a report) with argument '1'.
Reading option '-stats' ... matched as option 'stats' (print progress report during encoding) with argument '1'.
Reading option '-hide_banner' ... matched as option 'hide_banner' (do not show program banner) with argument '1'.
Reading option '-use_wallclock_as_timestamps' ... matched as AVOption 'use_wallclock_as_timestamps' with argument '1'.
Reading option '-rtsp_transport' ... matched as AVOption 'rtsp_transport' with argument 'tcp'.
Reading option '-i' ... matched as input url with argument 'rtsp://user:password1@192.168.5.21/cam/realmonitor?channel=1channel1[1]=1subtype=0'.
Reading option '-c:v' ... matched as option 'c' (codec name) with argument 'copy'.
Reading option '-c:a' ... matched as option 'c' (codec name) with argument 'aac'.
Reading option '-copyts' ... matched as option 'copyts' (copy timestamps) with argument '1'.
Reading option '-f' ... matched as option 'f' (force format) with argument 'matroska'.
Reading option '-y' ... matched as option 'y' (overwrite output files) with argument '1'.
Reading option 'rec.mkv' ... matched as output url.
Finished splitting the commandline.
Parsing a group of options: global .
Applying option report (generate a report) with argument 1.
Applying option stats (print progress report during encoding) with argument 1.
Applying option hide_banner (do not show program banner) with argument 1.
Applying option copyts (copy timestamps) with argument 1.
Applying option y (overwrite output files) with argument 1.
Successfully parsed a group of options.
Parsing a group of options: input url rtsp://user:password1@192.168.5.21/cam/realmonitor?channel=1channel1[1]=1subtype=0.
Successfully parsed a group of options.
Opening an input file: rtsp://user:password1@192.168.5.21/cam/realmonitor?channel=1channel1[1]=1subtype=0.
[tcp @ 0x1646660] No default whitelist set
[tcp @ 0x1646660] Original list of addresses:
[tcp @ 0x1646660] Address 192.168.5.21 port 554
[tcp @ 0x1646660] Interleaved list of addresses:
[tcp @ 0x1646660] Address 192.168.5.21 port 554
[tcp @ 0x1646660] Starting connection attempt to 192.168.5.21 port 554
[tcp @ 0x1646660] Successfully connected to 192.168.5.21 port 554
[rtsp @ 0x1645e70] SDP:
v=0
o=- 2251950012 2251950012 IN IP4 0.0.0.0
s=Media Server
c=IN IP4 0.0.0.0
t=0 0
a=control:*
a=packetization-supported:DH
a=rtppayload-supported:DH
a=range:npt=now-
a=x-packetization-supported:IV
a=x-rtppayload-supported:IV
m=video 0 RTP/AVP 96
a=control:trackID=0
a=framerate:25.000000
a=rtpmap:96 H264/90000
a=fmtp:96 packetization-mode=1;profile-level-id=4D4028;sprop-parameter-sets=Z01AKKaAeAIn5ZuAgICgAAADACAAAAZQgAA=,aO48gAA=
a=recvonly
m=audio 0 RTP/AVP 97
a=control:trackID=1
a=rtpmap:97 MPEG4-GENERIC/16000
a=fmtp:97 streamtype=5;profile-level-id=1;mode=AAC-hbr;sizelength=13;indexlength=3;indexdeltalength=3;config=1408
a=recvonly

[rtsp @ 0x1645e70] video codec set to: h264
[rtsp @ 0x1645e70] RTP Packetization Mode: 1
[rtsp @ 0x1645e70] RTP Profile IDC: 4d Profile IOP: 40 Level: 28
[rtsp @ 0x1645e70] Extradata set to 0x164af98 (size: 39)
[rtsp @ 0x1645e70] audio codec set to: aac
[rtsp @ 0x1645e70] audio samplerate set to: 16000
[rtsp @ 0x1645e70] audio channels set to: 1
[rtsp @ 0x1645e70] setting jitter buffer size to 0
[rtsp @ 0x1645e70] setting jitter buffer size to 0
[rtsp @ 0x1645e70] hello state=0
Failed to parse interval end specification ''
[h264 @ 0x164ab30] nal_unit_type: 7(SPS), nal_ref_idc: 3
[h264 @ 0x164ab30] nal_unit_type: 8(PPS), nal_ref_idc: 3
[h264 @ 0x164ab30] nal_unit_type: 7(SPS), nal_ref_idc: 3
[h264 @ 0x164ab30] nal_unit_type: 8(PPS), nal_ref_idc: 3
[h264 @ 0x164ab30] nal_unit_type: 7(SPS), nal_ref_idc: 3
[h264 @ 0x164ab30] nal_unit_type: 8(PPS), nal_ref_idc: 3
[h264 @ 0x164ab30] nal_unit_type: 5(IDR), nal_ref_idc: 3
[h264 @ 0x164ab30] Format yuvj420p chosen by get_format().
[h264 @ 0x164ab30] Reinit context to 1920x1088, pix_fmt: yuvj420p
[h264 @ 0x164ab30] nal_unit_type: 1(Coded slice of a non-IDR picture), nal_ref_idc: 3
[h264 @ 0x164ab30] nal_unit_type: 1(Coded slice of a non-IDR picture), nal_ref_idc: 3
[h264 @ 0x164ab30] nal_unit_type: 1(Coded slice of a non-IDR picture), nal_ref_idc: 3
[h264 @ 0x164ab30] nal_unit_type: 1(Coded slice of a non-IDR picture), nal_ref_idc: 3
[h264 @ 0x164ab30] nal_unit_type: 1(Coded slice of a non-IDR picture), nal_ref_idc: 3
[h264 @ 0x164ab30] nal_unit_type: 1(Coded slice of a non-IDR picture), nal_ref_idc: 3
[rtsp @ 0x1645e70] All info found
Input #0, rtsp, from 'rtsp://user:password1@192.168.5.21/cam/realmonitor?channel=1channel1[1]=1subtype=0':
  Metadata:
    title           : Media Server
  Duration: N/A, start: 1713164678.794625, bitrate: N/A
    Stream #0:0, 22, 1/90000: Video: h264 (Main), yuvj420p(pc, bt709, progressive), 1920x1080, 25 fps, 25 tbr, 90k tbn, 50 tbc
    Stream #0:1, 15, 1/16000: Audio: aac (LC), 16000 Hz, mono, fltp
Successfully opened the file.
Parsing a group of options: output url rec.mkv.
Applying option c:v (codec name) with argument copy.
Applying option c:a (codec name) with argument aac.
Applying option f (force format) with argument matroska.
Successfully parsed a group of options.
Opening an output file: rec.mkv.
[file @ 0x1699f30] Setting default whitelist 'file,crypto,data'
Successfully opened the file.
Stream mapping:
  Stream #0:0 -> #0:0 (copy)
  Stream #0:1 -> #0:1 (aac (native) -> aac (native))
Press [q] to stop, [?] for help
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:1 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
detected 4 logical cores
[graph_0_in_0_1 @ 0x1682bb0] Setting 'time_base' to value '1/16000'
[graph_0_in_0_1 @ 0x1682bb0] Setting 'sample_rate' to value '16000'
[graph_0_in_0_1 @ 0x1682bb0] Setting 'sample_fmt' to value 'fltp'
[graph_0_in_0_1 @ 0x1682bb0] Setting 'channel_layout' to value '0x4'
[graph_0_in_0_1 @ 0x1682bb0] tb:1/16000 samplefmt:fltp samplerate:16000 chlayout:0x4
[format_out_0_1 @ 0x187f2e0] Setting 'sample_fmts' to value 'fltp'
[format_out_0_1 @ 0x187f2e0] Setting 'sample_rates' to value '96000|88200|64000|48000|44100|32000|24000|22050|16000|12000|11025|8000|7350'
[AVFilterGraph @ 0x164fd70] query_formats: 4 queried, 9 merged, 0 already done, 0 delayed
[matroska @ 0x169c330] get_metadata_duration returned: 0
Output #0, matroska, to 'rec.mkv':
  Metadata:
    title           : Media Server
    encoder         : Lavf58.45.100
    Stream #0:0, 0, 1/1000: Video: h264 (Main) (H264 / 0x34363248), yuvj420p(pc, bt709, progressive), 1920x1080, q=2-31, 25 fps, 25 tbr, 1k tbn, 90k tbc
    Stream #0:1, 0, 1/1000: Audio: aac (LC) ([255][0][0][0] / 0x00FF), 16000 Hz, mono, fltp, 69 kb/s
    Metadata:
      encoder         : Lavc58.91.100 aac
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:1 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[matroska @ 0x169c330] Starting new cluster with timestamp 1713164678731 at offset 770 bytes
[matroska @ 0x169c330] Writing block of size 581 with pts 1713164678731, dts 1713164678731, duration 64 at relative offset 14 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 517 with pts 1713164678795, dts 1713164678795, duration 64 at relative offset 602 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 376900 with pts 1713164678872, dts 1713164678872, duration 40 at relative offset 1126 in cluster at offset 770. TrackNumber 1, keyframe 1
[matroska @ 0x169c330] Writing block of size 8172 with pts 1713164678912, dts 1713164678912, duration 40 at relative offset 378034 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 672 with pts 1713164678912, dts 1713164678912, duration 64 at relative offset 386213 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 550 with pts 1713164679177, dts 1713164679177, duration 64 at relative offset 386892 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 7654 with pts 1713164679178, dts 1713164679178, duration 40 at relative offset 387449 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 7483 with pts 1713164679213, dts 1713164679213, duration 40 at relative offset 395110 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 7703 with pts 1713164679242, dts 1713164679242, duration 40 at relative offset 402600 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 565 with pts 1713164679242, dts 1713164679242, duration 64 at relative offset 410310 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 7650 with pts 1713164679271, dts 1713164679271, duration 40 at relative offset 410882 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 585 with pts 1713164679271, dts 1713164679271, duration 64 at relative offset 418539 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 8682 with pts 1713164679301, dts 1713164679301, duration 40 at relative offset 419131 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 8888 with pts 1713164679330, dts 1713164679330, duration 40 at relative offset 427820 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 506 with pts 1713164679330, dts 1713164679330, duration 64 at relative offset 436715 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 8019 with pts 1713164679360, dts 1713164679360, duration 40 at relative offset 437228 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 7919 with pts 1713164679361, dts 1713164679361, duration 40 at relative offset 445254 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 7822 with pts 1713164679361, dts 1713164679361, duration 40 at relative offset 453180 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 699 with pts 1713164679361, dts 1713164679361, duration 64 at relative offset 461009 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 619 with pts 1713164679361, dts 1713164679361, duration 64 at relative offset 461715 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 7768 with pts 1713164679362, dts 1713164679362, duration 40 at relative offset 462341 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 8469 with pts 1713164679362, dts 1713164679362, duration 40 at relative offset 470116 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 601 with pts 1713164679362, dts 1713164679362, duration 64 at relative offset 478592 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 559 with pts 1713164679363, dts 1713164679363, duration 64 at relative offset 479200 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 8265 with pts 1713164679366, dts 1713164679366, duration 40 at relative offset 479766 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 7766 with pts 1713164679406, dts 1713164679406, duration 40 at relative offset 488038 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 531 with pts 1713164679415, dts 1713164679415, duration 64 at relative offset 495811 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 7753 with pts 1713164679446, dts 1713164679446, duration 40 at relative offset 496349 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 8274 with pts 1713164679486, dts 1713164679486, duration 40 at relative offset 504109 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 569 with pts 1713164679496, dts 1713164679496, duration 64 at relative offset 512390 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 8445 with pts 1713164679526, dts 1713164679526, duration 40 at relative offset 512966 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 522 with pts 1713164679535, dts 1713164679535, duration 64 at relative offset 521418 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 7922 with pts 1713164679566, dts 1713164679566, duration 40 at relative offset 521947 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 7954 with pts 1713164679606, dts 1713164679606, duration 40 at relative offset 529876 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 503 with pts 1713164679615, dts 1713164679615, duration 64 at relative offset 537837 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 11167 with pts 1713164679646, dts 1713164679646, duration 40 at relative offset 538347 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 503 with pts 1713164679655, dts 1713164679655, duration 64 at relative offset 549521 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 10534 with pts 1713164679686, dts 1713164679686, duration 40 at relative offset 550031 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 7607 with pts 1713164679726, dts 1713164679726, duration 40 at relative offset 560572 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 478 with pts 1713164679772, dts 1713164679772, duration 64 at relative offset 568186 in cluster at offset 770. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 7842 with pts 1713164679774, dts 1713164679774, duration 40 at relative offset 568671 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 9862 with pts 1713164679806, dts 1713164679806, duration 40 at relative offset 576520 in cluster at offset 770. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Starting new cluster with timestamp 1713164679815 at offset 587166 bytes
[matroska @ 0x169c330] Writing block of size 449 with pts 1713164679815, dts 1713164679815, duration 64 at relative offset 14 in cluster at offset 587166. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 379456 with pts 1713164679870, dts 1713164679870, duration 40 at relative offset 470 in cluster at offset 587166. TrackNumber 1, keyframe 1
[matroska @ 0x169c330] Writing block of size 415 with pts 1713164679903, dts 1713164679903, duration 64 at relative offset 379934 in cluster at offset 587166. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 7008 with pts 1713164679905, dts 1713164679905, duration 40 at relative offset 380356 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 6917 with pts 1713164679925, dts 1713164679925, duration 40 at relative offset 387371 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 513 with pts 1713164679935, dts 1713164679935, duration 64 at relative offset 394295 in cluster at offset 587166. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 7111 with pts 1713164679966, dts 1713164679966, duration 40 at relative offset 394815 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 753 with pts 1713164679975, dts 1713164679975, duration 64 at relative offset 401933 in cluster at offset 587166. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 7091 with pts 1713164680006, dts 1713164680006, duration 40 at relative offset 402693 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 7045 with pts 1713164680045, dts 1713164680045, duration 40 at relative offset 409791 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 659 with pts 1713164680055, dts 1713164680055, duration 64 at relative offset 416843 in cluster at offset 587166. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 6983 with pts 1713164680086, dts 1713164680086, duration 40 at relative offset 417509 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 6932 with pts 1713164680127, dts 1713164680127, duration 40 at relative offset 424499 in cluster at offset 587166. TrackNumber 1, keyframe 0
frame=   35 fps=0.0 q=-1.0 size=     512kB time=475879:04:40.20 bitrate=   0.0kbits/s speed=3.35e+09x    
[matroska @ 0x169c330] Writing block of size 691 with pts 1713164680135, dts 1713164680135, duration 64 at relative offset 431438 in cluster at offset 587166. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 6990 with pts 1713164680166, dts 1713164680166, duration 40 at relative offset 432136 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 651 with pts 1713164680176, dts 1713164680176, duration 64 at relative offset 439133 in cluster at offset 587166. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 7046 with pts 1713164680206, dts 1713164680206, duration 40 at relative offset 439791 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 7130 with pts 1713164680246, dts 1713164680246, duration 40 at relative offset 446844 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 601 with pts 1713164680255, dts 1713164680255, duration 64 at relative offset 453981 in cluster at offset 587166. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 7205 with pts 1713164680286, dts 1713164680286, duration 40 at relative offset 454589 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 561 with pts 1713164680295, dts 1713164680295, duration 64 at relative offset 461801 in cluster at offset 587166. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 6936 with pts 1713164680326, dts 1713164680326, duration 40 at relative offset 462369 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 6822 with pts 1713164680366, dts 1713164680366, duration 40 at relative offset 469312 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 621 with pts 1713164680375, dts 1713164680375, duration 64 at relative offset 476141 in cluster at offset 587166. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 6845 with pts 1713164680405, dts 1713164680405, duration 40 at relative offset 476769 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 6848 with pts 1713164680445, dts 1713164680445, duration 40 at relative offset 483621 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 588 with pts 1713164680455, dts 1713164680455, duration 64 at relative offset 490476 in cluster at offset 587166. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 6828 with pts 1713164680486, dts 1713164680486, duration 40 at relative offset 491071 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 546 with pts 1713164680495, dts 1713164680495, duration 64 at relative offset 497906 in cluster at offset 587166. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 6845 with pts 1713164680526, dts 1713164680526, duration 40 at relative offset 498459 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 6924 with pts 1713164680566, dts 1713164680566, duration 40 at relative offset 505311 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 508 with pts 1713164680576, dts 1713164680576, duration 64 at relative offset 512242 in cluster at offset 587166. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 6844 with pts 1713164680606, dts 1713164680606, duration 40 at relative offset 512757 in cluster at offset 587166. TrackNumber 1, keyframe 0
frame=   48 fps= 47 q=-1.0 size=     512kB time=475879:04:40.72 bitrate=   0.0kbits/s speed=1.66e+09x    
[matroska @ 0x169c330] Writing block of size 587 with pts 1713164680615, dts 1713164680615, duration 64 at relative offset 519608 in cluster at offset 587166. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 6859 with pts 1713164680645, dts 1713164680645, duration 40 at relative offset 520202 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 6855 with pts 1713164680686, dts 1713164680686, duration 40 at relative offset 527068 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 573 with pts 1713164680695, dts 1713164680695, duration 64 at relative offset 533930 in cluster at offset 587166. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 6881 with pts 1713164680726, dts 1713164680726, duration 40 at relative offset 534510 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 10773 with pts 1713164680766, dts 1713164680766, duration 40 at relative offset 541398 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 520 with pts 1713164680775, dts 1713164680775, duration 64 at relative offset 552178 in cluster at offset 587166. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 6923 with pts 1713164680805, dts 1713164680805, duration 40 at relative offset 552705 in cluster at offset 587166. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Starting new cluster with timestamp 1713164680815 at offset 1146808 bytes
[matroska @ 0x169c330] Writing block of size 580 with pts 1713164680815, dts 1713164680815, duration 64 at relative offset 14 in cluster at offset 1146808. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 380085 with pts 1713164680864, dts 1713164680864, duration 40 at relative offset 601 in cluster at offset 1146808. TrackNumber 1, keyframe 1
[matroska @ 0x169c330] Writing block of size 9916 with pts 1713164680896, dts 1713164680896, duration 40 at relative offset 380694 in cluster at offset 1146808. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 541 with pts 1713164680901, dts 1713164680901, duration 64 at relative offset 390617 in cluster at offset 1146808. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 5877 with pts 1713164680925, dts 1713164680925, duration 40 at relative offset 391165 in cluster at offset 1146808. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] Writing block of size 529 with pts 1713164680935, dts 1713164680935, duration 64 at relative offset 397049 in cluster at offset 1146808. TrackNumber 2, keyframe 1
[matroska @ 0x169c330] Writing block of size 6661 with pts 1713164680966, dts 1713164680966, duration 40 at relative offset 397585 in cluster at offset 1146808. TrackNumber 1, keyframe 0
[matroska @ 0x169c330] end duration = 1713164681006
[matroska @ 0x169c330] stream 0 end duration = 1713164681006
[matroska @ 0x169c330] stream 1 end duration = 1713164680999
frame=   54 fps= 42 q=-1.0 Lsize=    1515kB time=475879:04:40.99 bitrate=   0.0kbits/s speed=1.33e+09x    
video:1493kB audio:20kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.099897%
Input file #0 (rtsp://user:password1@192.168.5.21/cam/realmonitor?channel=1channel1[1]=1subtype=0):
  Input stream #0:0 (video): 54 packets read (1529156 bytes); 
  Input stream #0:1 (audio): 35 packets read (9268 bytes); 35 frames decoded (35840 samples); 
  Total: 89 packets (1538424 bytes) demuxed
Output file #0 (rec.mkv):
  Output stream #0:0 (video): 54 packets muxed (1529156 bytes); 
  Output stream #0:1 (audio): 35 frames encoded (35840 samples); 36 packets muxed (20446 bytes); 
  Total: 90 packets (1549602 bytes) muxed
35 frames successfully decoded, 0 decoding errors
[AVIOContext @ 0x1667620] Statistics: 2 seeks, 7 writeouts
[aac @ 0x1673880] Qavg: 142.738
Exiting normally, received signal 15.


  


  In this short 3 second capture the duplicate timestamps are 1713164679.361000 and 1713164679.362000.

  


  How can I solve this problem ? What different approach could I use to achieve this goal ?

  


  Thanks in advance.