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• Clickstream Data : Definition, Use Cases, and More

  15 avril 2024, par Erin

  Gaining a deeper understanding of user behaviour — customers’ different paths, digital footprints, and engagement patterns — is crucial for providing a personalised experience and making informed marketing decisions. 

  In that sense, clickstream data, or a comprehensive record of a user’s online activities, is one of the most valuable sources of actionable insights into users’ behavioural patterns. 

  This article will cover everything marketing teams need to know about clickstream data, from the basic definition and examples to benefits, use cases, and best practices. 

  What is clickstream data ? 

  As a form of web analytics, clickstream data focuses on tracking and analysing a user’s online activity. These digital breadcrumbs offer insights into the websites the user has visited, the pages they viewed, how much time they spent on a page, and where they went next.

  

  Your clickstream pipeline can be viewed as a “roadmap” that can help you recognise consistent patterns in how users navigate your website. 

  With that said, you won’t be able to learn much by analysing clickstream data collected from one user’s session. However, a proper analysis of large clickstream datasets can provide a wealth of information about consumers’ online behaviours and trends — which marketing teams can use to make informed decisions and optimise their digital marketing strategy. 

  Clickstream data collection can serve numerous purposes, but the main goal remains the same — gaining valuable insights into visitors’ behaviours and online activities to deliver a better user experience and improve conversion likelihood. 

  Depending on the specific events you’re tracking, clickstream data can reveal the following : 

  • How visitors reach your website 
  • The terms they type into the search engine
  • The first page they land on
  • The most popular pages and sections of your website
  • The amount of time they spend on a page 
  • Which elements of the page they interact with, and in what sequence
  • The click path they take 
  • When they convert, cancel, or abandon their cart
  • Where the user goes once they leave your website

  As you can tell, once you start collecting this type of data, you’ll learn quite a bit about the user’s online journey and the different ways they engage with your website — all without including any personal details about your visitors.

  Types of clickstream data 

  While all clickstream data keeps a record of the interactions that occur while the user is navigating a website or a mobile application — or any other digital platform — it can be divided into two types : 

  • Aggregated (web traffic) data provides comprehensive insights into the total number of visits and user interactions on a digital platform — such as your website — within a given timeframe 
  • Unaggregated data is broken up into smaller segments, focusing on an individual user’s online behaviour and website interactions 

  One thing to remember is that to gain valuable insights into user behaviour and uncover sequential patterns, you need a powerful tool and access to full clickstream datasets. Matomo’s Event Tracking can provide a comprehensive view of user interactions on your website or mobile app — everything from clicking a button and completing a form to adding (or removing) products from their cart. 

  On that note, based on the specific events you’re tracking when a user visits your website, clickstream data can include : 

  • Web navigation data : referring URL, visited pages, click path, and exit page
  • User interaction data : mouse movements, click rate, scroll depth, and button clicks
  • Conversion data : form submissions, sign-ups, and transactions 
  • Temporal data : page load time, timestamps, and the date and time of day of the user’s last login 
  • Session data : duration, start, and end times and number of pages viewed per session
  • Error data : 404 errors and network or server response issues 

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  Clickstream data benefits and use cases 

  Given the actionable insights that clickstream data collection provides, it can serve a wide range of use cases — from identifying behavioural patterns and trends and examining competitors’ performance to helping marketing teams map out customer journeys and improve ROI.

  

  According to the global Clickstream Analytics Market Report 2024, some key applications of clickstream analytics include click-path optimisation, website and app optimisation, customer analysis, basket analysis, personalisation, and traffic analysis. 

  

  The behavioural patterns and user preferences revealed by clickstream analytics data can have many applications — we’ve outlined the prominent use cases below. 

  Customer journey mapping 

  Clickstream data allows you to analyse the e-commerce customer’s online journey and provides insights into how they navigate your website. With such a comprehensive view of their click path, it becomes easier to understand user behaviour at each stage — from initial awareness to conversion — identify the most effective touchpoints and fine-tune that journey to improve their conversion likelihood. 

  Identifying customer trends 

  Clickstream data analytics can also help you identify trends and behavioural patterns — the most common sequences and similarities in how users reached your website and interacted with it — especially when you can access data from many website visitors. 

  Think about it — there are many ways in which you can use these insights into the sequence of clicks and interactions and recurring patterns to your team’s advantage. 

  Here’s an example : 

  It can reveal that some pieces of content and CTAs are performing well in encouraging visitors to take action — which shows how you should optimise other pages and what you should strive to create in the future, too. 

  Preventing site abandonment 

  Cart abandonment remains a serious issue for online retailers : 

  According to a recent report, the global cart abandonment rate in the fourth quarter of 2023 was at 83%. 

  That means that roughly eight out of ten e-commerce customers will abandon their shopping carts — most commonly due to additional costs, slow website loading times and the requirement to create an account before purchasing. 

  In addition to cart abandonment predictions, clickstream data analytics can reveal the pages where most visitors tend to leave your website. These drop-off points are clear indicators that something’s not working as it should — and once you can pinpoint them, you’ll be able to address the issue and increase conversion likelihood.

  Improving marketing campaign ROI 

  As previously mentioned, clickstream data analysis provides insights into the customer journey. Still, you may not realise that you can also use this data to keep track of your marketing effectiveness. 

  Global digital ad spending continues to grow — and is expected to reach $836 billion by 2026. It’s easy to see why relying on accurate data is crucial when deciding which marketing channels to invest in. 

  You want to ensure you’re allocating your digital marketing and advertising budget to the channels — be it SEO, pay-per-click (PPC) ads, or social media campaigns — that impact driving conversions. 

  When you combine clickstream e-commerce data with conversion rates, you’ll find the latter in Matomo’s goal reports and have a solid, data-driven foundation for making better marketing decisions.

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  Delivering a better user experience (UX) 

  Clickstream data analysis allows you to identify specific “pain points” — areas of the website that are difficult to use and may cause customer frustration. 

  It’s clear how this would be beneficial to your business : 

  Once you’ve identified these pain points, you can make the necessary changes to your website’s layout and address any technical issues that users might face, improving usability and delivering a smoother experience to potential customers. 

  Collecting clickstream data : Tools and legal implications 

  Your team will need a powerful tool capable of handling clickstream analytics to reap the benefits we’ve discussed previously. But at the same time, you need to respect users’ online privacy throughout clickstream data collection.

  

  Generally speaking, there are two ways to collect data about users’ online activity — web analytics tools and server log files.

  Web analytics tools are the more commonly used solution. Specifically designed to collect and analyse website data, these tools rely on JavaScript tags that run in the browser, providing actionable insights about user behaviour. Server log files can be a gold mine of data, too — but that data is raw and unfiltered, making it much more challenging to interpret and analyse. 

  That brings us to one of the major clickstream challenges to keep in mind as you move forward — compliance.

  While Google remains a dominant player in the web analytics market, there’s one area where Matomo has a significant advantage — user privacy. 

  Matomo operates according to privacy laws — including the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), making it an ethical alternative to Google Analytics. 

  It should go without saying, but compliance with data privacy laws — the most talked-about one being the GDPR framework introduced by the EU — isn’t something you can afford to overlook. 

  The GDPR was first implemented in the EU in 2018. Since then, several fines have been issued for non-compliance — including the record fine of €1.2 billion that Meta Platforms, Inc. received in 2023 for transferring personal data of EU-based users to the US.

  Clickstream analytics data best practices 

  

  As valuable as it might be, processing large amounts of clickstream analytics data can be a complex — and, at times, overwhelming — process. 

  Here are some best practices to keep in mind when it comes to clickstream analysis : 

  Define your goals 

  It’s essential to take the time to define your goals and objectives. 

  Once you have a clear idea of what you want to learn from a given clickstream dataset and the outcomes you hope to see, it’ll be easier to narrow down your scope — rather than trying to tackle everything at once — before moving further down the clickstream pipeline. 

  Here are a few examples of goals and objectives you can set for clickstream analysis : 

  • Understanding and predicting users’ behavioural patterns 
  • Optimising marketing campaigns and ROI 
  • Attributing conversions to specific marketing touchpoints and channels

  Analyse your data 

  Collecting clickstream analytics data is only part of the equation ; what you do with raw data and how you analyse it matters. You can have the most comprehensive dataset at your disposal — but it’ll be practically worthless if you don’t have the skill set to analyse and interpret it. 

  In short, this is the stage of your clickstream pipeline where you uncover common sequences and consistent patterns in user behaviour. 

  Clickstream data analytics can extract actionable insights from large datasets using various approaches, models, and techniques. 

  Here are a few examples : 

  • If you’re working with clickstream e-commerce data, you should perform funnel or conversion analyses to track conversion rates as users move through your sales funnel. 
  • If you want to group and analyse users based on shared characteristics, you can use Matomo for cohort analysis. 
  • If your goal is to predict future trends and outcomes — conversion and cart abandonment prediction, for example — based on available data, prioritise predictive analytics.

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  Organise and visualise your data

  As you reach the end of your clickstream pipeline, you need to start thinking about how you will present and communicate your data. And what better way to do that than to transform that data into easy-to-understand visualisations ? 

  Here are a few examples of easily digestible formats that facilitate quick decision-making : 

  • User journey maps, which illustrate the exact sequence of interactions and user flow through your website 
  • Heatmaps, which serve as graphical — and typically colour-coded — representations of a website visitor’s activity 
  • Funnel analysis, which are broader at the top but get increasingly narrower towards the bottom as users flow through and drop off at different stages of the pipeline 

  Collect clickstream data with Matomo 

  Clickstream data is hard to beat when tracking the website visitor’s journey — from first to last interaction — and understanding user behaviour. By providing real-time insights, your clickstream pipeline can help you see the big picture, stay ahead of the curve and make informed decisions about your marketing efforts. 

  Matomo accurate data and compliance with GDPR and other data privacy regulations — it’s an all-in-one, ethical platform that can meet all your web analytics needs. That’s why over 1 million websites use Matomo for their web analytics.

  Try Matomo 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.

  


• ffmpeg mp4 to mp3 conversion generates 1 second audio files only [closed]

  24 janvier 2024, par doesnotcompile

  I am having the following problem : I am having trouble converting an mp4 to an mp3.

  


  I have mp4 audio recordings that I need to convert in to mp3 audio files. The mp4 files have been recorded on iOS.

  


  I am currently doing this by using ffmpeg in python like so :

  


  subprocess.run(
            [
                "ffmpeg",
                "-loglevel", "error",
                "-i", ,
                "-q:a", "0",
                "-map", "a",
                
            ]
        )


  


  This runs with no error outputs.

  


  However, the resulting mp3 is only 1 second long, even though the input mp4 is around 5 seconds long.

  


  The mp4 is playable in Quicktime and Apple Music.

  


  ffprobe output :

  


  > ffprobe -v error -show_format input.mp4
[FORMAT]
filename=input.mp4
nb_streams=1
nb_programs=0
format_name=mov,mp4,m4a,3gp,3g2,mj2
format_long_name=QuickTime / MOV
start_time=0.000000
duration=5.120000
size=119636
bit_rate=186931
probe_score=100
TAG:major_brand=iso5
TAG:minor_version=1
TAG:compatible_brands=isomiso5hlsf
TAG:creation_time=2024-01-24T12:47:24.000000Z
[/FORMAT]


  


  No errors shown, and clearly 5 seconds long audio.

  


  Has anyone else experienced similar issues before ?

  


  Happy about any pointers !