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• FFMPEG update from 5.0 to 6.0 out_0_0 buffer queued [closed]

  16 mai 2023, par Kevitto

  I've been using ffmpeg 5.0 for some time, encoding an audio stream to an rtp server, but since I updated to ffmpeg 6.0 I get this :

  


  [out_0_0 @ 0x55ac187b60] 100 buffers queued in out_0_0, something may be wrong.


  


  Below is the ffmpeg call :

  


  ffmpeg -re -f alsa -i default:CARD:card1 -ac 2 -af aresample=async=1 -acodec libopus -b:a 48000 -f rtp "rtp://127.0.0.1:5002"


  


  And here is the full startup log :

  


  ffmpeg version 549430e Copyright (c) 2000-2023 the FFmpeg developers
  built with gcc 10 (Debian 10.2.1-6)
  configuration: --extra-cflags=-I/usr/local/include --extra-ldflags=-L/usr/local/lib --extra-libs='-lpthread -lm -latomic' --arch=arm64 --enable-gmp --enable-gpl --enable-libopus --enable-nonfree --enable-version3 --target-os=linux --enable-pthreads --enable-openssl --enable-hardcoded-tables
  libavutil      58.  2.100 / 58.  2.100
  libavcodec     60.  3.100 / 60.  3.100
  libavformat    60.  3.100 / 60.  3.100
  libavdevice    60.  1.100 / 60.  1.100
  libavfilter     9.  3.100 /  9.  3.100
  libswscale      7.  1.100 /  7.  1.100
  libswresample   4. 10.100 /  4. 10.100
  libpostproc    57.  1.100 / 57.  1.100
Guessed Channel Layout for Input Stream #0.0 : stereo
Input #0, alsa, from 'default:CARD=pisound':
  Duration: N/A, start: 1684250059.973334, bitrate: 1536 kb/s
  Stream #0:0: Audio: pcm_s16le, 48000 Hz, 2 channels, s16, 1536 kb/s
Stream mapping:
  Stream #0:0 -> #0:0 (pcm_s16le (native) -> opus (libopus))
Press [q] to stop, [?] for help
Output #0, rtp, to 'rtp://127.0.0.1:5002':
  Metadata:
    encoder         : Lavf60.3.100
  Stream #0:0: Audio: opus, 48000 Hz, stereo, s16, 48 kb/s
    Metadata:
      encoder         : Lavc60.3.100 libopus
SDP:
v=0
o=- 0 0 IN IP4 127.0.0.1
s=No Name
c=IN IP4 127.0.0.1
t=0 0
a=tool:libavformat 60.3.100
m=audio 5002 RTP/AVP 97
b=AS:48
a=rtpmap:97 opus/48000/2
a=fmtp:97 sprop-stereo=1

size=       0kB time=-577014:32:22.77 bitrate=  -0.0kbits/s speed=N/A    
size=       0kB time=-577014:32:22.77 bitrate=  -0.0kbits/s speed=N/A    
[out_0_0 @ 0x559f530c60] 100 buffers queued in out_0_0, something may be wrong.
size=       0kB time=-577014:32:22.77 bitrate=  -0.0kbits/s speed=N/A    
size=       0kB time=-577014:32:22.77 bitrate=  -0.0kbits/s speed=N/A    
size=       0kB time=-00:00:00.00 bitrate=  -0.0kbits/s speed=N/A    
size=       0kB time=-00:00:00.00 bitrate=  -0.0kbits/s speed=N/A    
[alsa @ 0x559f4db460] ALSA buffer xrun.
size=       6kB time=00:00:02.81 bitrate=  16.7kbits/s speed=0.93x    
size=       6kB time=00:00:02.81 bitrate=  16.7kbits/s speed=0.797x    
size=       6kB time=00:00:02.83 bitrate=  16.6kbits/s speed=0.703x    
size=       6kB time=00:00:02.83 bitrate=  16.6kbits/s speed=0.624x    
size=       6kB time=00:00:02.83 bitrate=  16.6kbits/s speed=0.562x    
size=       6kB time=00:00:02.83 bitrate=  16.6kbits/s speed=0.511x    
[alsa @ 0x559f4db460] ALSA buffer xrun.
size=      10kB time=00:00:05.67 bitrate=  14.8kbits/s speed=0.939x    
size=      10kB time=00:00:05.67 bitrate=  14.8kbits/s speed=0.866x    
size=      10kB time=00:00:05.67 bitrate=  14.8kbits/s speed=0.805x    
size=      10kB time=00:00:05.67 bitrate=  14.8kbits/s speed=0.751x    
size=      10kB time=00:00:05.69 bitrate=  14.8kbits/s speed=0.707x    
[alsa @ 0x559f4db460] ALSA buffer xrun.
size=      13kB time=00:00:05.95 bitrate=  17.8kbits/s speed=0.696x    
size=      16kB time=00:00:08.51 bitrate=  15.2kbits/s speed=0.939x    
size=      16kB time=00:00:08.51 bitrate=  15.2kbits/s speed=0.89x    
size=      16kB time=00:00:08.53 bitrate=  15.2kbits/s speed=0.847x    
size=      16kB time=00:00:08.53 bitrate=  15.2kbits/s speed=0.806x    
size=      16kB time=00:00:08.53 bitrate=  15.2kbits/s speed=0.77x    
[alsa @ 0x559f4db460] ALSA buffer xrun.
size=      21kB time=00:00:11.37 bitrate=  14.8kbits/s speed=0.981x  


  


  I tried changing the output to -f null /dev/null to see if the rtp was the issue, but I get the same thing. I made sure the user running it was a member to the "audio" group and arecord -l and aplay -l both show the card with the right name and information. I even tried to use its hw code instead of the default name, and same issue.

  


• 'unsupported input sample rate set' error while converting mkv to mp3 with ffmpeg on python

  15 décembre 2020, par Agent Merlot

  I'm getting this error on trying to convert some mkv files to mp3 via python. Nearly all files got converted, but some are facing this issue.
  
https://cdn.discordapp.com/attachments/663255565451001866/788424224661569596/Error.txt

  


  Please help me fix this issue.

  


  ffmpeg output extracted from the discord link above :

  


  ffmpeg version git-2020-06-04-7f81785 Copyright (c) 2000-2020 the FFmpeg developers
  built with gcc 9.3.1 (GCC) 20200523
  configuration: --enable-gpl --enable-version3 --enable-sdl2 --enable-fontconfig --enable-gnutls --enable-iconv --enable-libass --enable-libdav1d --enable-libbluray --enable-libfreetype --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libopus --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libsrt --enable-libtheora --enable-libtwolame --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libzimg --enable-lzma --enable-zlib --enable-gmp --enable-libvidstab --enable-libvmaf --enable-libvorbis --enable-libvo-amrwbenc --enable-libmysofa --enable-libspeex --enable-libxvid --enable-libaom --disable-w32threads --enable-libmfx --enable-ffnvcodec --enable-cuda-llvm --enable-cuvid --enable-d3d11va --enable-nvenc --enable-nvdec --enable-dxva2 --enable-avisynth --enable-libopenmpt --enable-amf
  libavutil      56. 49.100 / 56. 49.100
  libavcodec     58. 90.100 / 58. 90.100
  libavformat    58. 44.100 / 58. 44.100
  libavdevice    58.  9.103 / 58.  9.103
  libavfilter     7. 84.100 /  7. 84.100
  libswscale      5.  6.101 /  5.  6.101
  libswresample   3.  6.100 /  3.  6.100
  libpostproc    55.  6.100 / 55.  6.100
Input #0, matroska,webm, from 'J:\DC ED\Original\045 'Kimi no Egao ga Nani Yori mo Suki Datta' by 'Chicago Poodle'.mkv':
  Metadata:
    title           : 045 'Kimi no Egao ga Nani Yori mo Suki Datta' by 'Chicago Poodle'.mkv
    COPYRIGHT       : © 2013 APTX4869 Fansub
    creation_time   : 2020-11-18T05:03:06.000000Z
    COMPOSER        : Chicago Poodle
    ENCODER         : Lavf58.44.100
  Duration: 00:01:20.04, start: 0.000000, bitrate: 2023 kb/s
    Stream #0:0(jpn): Video: hevc (Main), yuv420p(tv), 1440x1080 [SAR 4:3 DAR 16:9], 23.98 fps, 23.98 tbr, 1k tbn, 23.98 tbc (default)
    Metadata:
      title           : VIDEO[AVC]
      ENCODER         : Lavc58.90.100 libx265
      BPS-eng         : 1831510
      DURATION-eng    : 00:01:20.039000000
      NUMBER_OF_FRAMES-eng: 1919
      NUMBER_OF_BYTES-eng: 18324032
      _STATISTICS_WRITING_APP-eng: mkvmerge v49.0.0 ('Sick Of Losing Soulmates') 64-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-11-18 05:03:06
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
    Stream #0:1(jpn): Audio: aac (LC), 96000 Hz, stereo, fltp (default)
    Metadata:
      title           : AUDIO[AAC]
      BPS-eng         : 188626
      DURATION-eng    : 00:01:19.999000000
      NUMBER_OF_FRAMES-eng: 3750
      NUMBER_OF_BYTES-eng: 1886246
      _STATISTICS_WRITING_APP-eng: mkvmerge v49.0.0 ('Sick Of Losing Soulmates') 64-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-11-18 05:03:06
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
Stream mapping:
  Stream #0:1 -> #0:0 (aac (native) -> mp3 (mp3_mf))
Press [q] to stop, [?] for help
[mp3_mf @ 000002142f4a5fc0] MFT name: 'MP3 Encoder ACM Wrapper MFT'
[mp3_mf @ 000002142f4a5fc0] unsupported input sample rate set
Error initializing output stream 0:0 -- Error while opening encoder for output stream #0:0 - maybe incorrect parameters such as bit_rate, rate, width or height
Conversion failed!


  


• A Comprehensive Guide to Robust Digital Marketing Analytics

  30 octobre 2023, par Erin

  First impressions are everything. This is not only true for dating and job interviews but also for your digital marketing strategy. Like a poorly planned resume getting tossed in the “no thank you” pile, 38% of visitors to your website will stop engaging with your content if they find the layout unpleasant. Thankfully, digital marketers can access data that can be harnessed to optimise websites and turn those “no thank you’s” into “absolutely’s.”

  So, how can we transform raw data into valuable insights that pay off ? The key is web analytics tools that can help you make sense of it all while collecting data ethically. In this article, we’ll equip you with ways to take your digital marketing strategy to the next level with the power of web analytics.

  What are the different types of digital marketing analytics ?

  Digital marketing analytics are like a cipher into the complex behaviour of your buyers. Digital marketing analytics help collect, analyse and interpret data from any touchpoint you interact with your buyers online. Whether you’re trying to gauge the effectiveness of a new email marketing campaign or improve your mobile app layout, there’s a way for you to make use of the insights you gain. 

  As we go through the eight commonly known types of digital marketing analytics, please note we’ll primarily focus on what falls under the umbrella of web analytics. 

  1. Web analytics help you better understand how users interact with your website. Good web analytics tools will help you understand user behaviour while securely handling user data. 
  2. Learn more about the effectiveness of your organisation’s social media platforms with social media analytics. Social media analytics include user engagement, post reach and audience demographics. 
  3. Email marketing analytics help you see how email campaigns are being engaged with.
  4. Search engine optimisation (SEO) analytics help you understand your website’s visibility in search engine results pages (SERPs). 
  5. Pay-per-click (PPC) analytics measure the performance of paid advertising campaigns.
  6. Content marketing analytics focus on how your content is performing with your audience. 
  7. Customer analytics helps organisations identify and examine buyer behaviour to retain the biggest spenders. 
  8. Mobile app analytics track user interactions within mobile applications. 

  Choosing which digital marketing analytics tools are the best fit for your organisation is not an easy task. When making these decisions, it’s critical to remember the ethical implications of data collection. Although data insights can be invaluable to your organisation, they won’t be of much use if you lose the trust of your users. 

  Tips and best practices for developing robust digital marketing analytics 

  So, what separates top-notch, robust digital marketing analytics from the rest ? We’ve already touched on it, but a big part involves respecting user privacy and ethically handling data. Data security should be on your list of priorities, alongside conversion rate optimisation when developing a digital marketing strategy. In this section, we will examine best practices for using digital marketing analytics while retaining user trust.

  

  Clear objectives

  Before comparing digital marketing analytics tools, you should define clear and measurable goals. Try asking yourself what you need your digital marketing analytics strategy to accomplish. Do you want to improve conversion rates while remaining data compliant ? Maybe you’ve noticed users are not engaging with your platform and want to fix that. Save yourself time and energy by focusing on the most relevant pain points and areas of improvement.

  Choose the right tools for the job

  Don’t just base your decision on what other people tell you. Take the tool for a test drive — free trials allow you to test features and user interfaces and learn more about the platform before committing. When choosing digital marketing analytics tools, look for ones that ensure compliance with privacy laws like GDPR.

  Don’t overlook data compliance

  GDPR ensures organisations prioritise data protection and privacy. You could be fined up to €20 million, or 4% of the previous year’s revenue for violations. Without data compliance practices, you can say goodbye to the time and money spent on digital marketing strategies. 

  Don’t sacrifice data quality and accuracy

  Inaccurate and low-quality data can taint your analysis, making it hard to glean valuable insights from your digital marketing analytics efforts. Regularly audit and clean your data to remove inaccuracies and inconsistencies. Address data discrepancies promptly to maintain the integrity of your analytics. Data validation measures also help to filter out inaccurate data.

  Communicate your findings

  Having insights is one thing ; effectively communicating complex data findings is just as important. Customise dashboards to display key metrics aligned with your objectives. Make sure to automate reports, allowing stakeholders to stay updated without manual intervention. 

  Understand the user journey

  To optimise your conversion rates, you need to understand the user journey. Start by analysing visitors interactions with your website — this will help you identify conversion bottlenecks in your sales or lead generation processes. Implement A/B testing for landing page optimisation, refining elements like call-to-action buttons or copy, and leverage Form Analytics to make informed, data-driven improvements to your forms.

  Continuous improvement

  Learn from the data insights you gain, and iterate your marketing strategies based on the findings. Stay updated with evolving web analytics trends and technologies to leverage new growth opportunities.

  Why you need web analytics to support your digital marketing analytics toolbox

  You wouldn’t set out on a roadtrip without a map, right ? Digital marketing analytics without insights into how users interact with your website are just as useless. Used ethically, web analytics tools can be an invaluable addition to your digital marketing analytics toolbox. 

  The data collected via web analytics reveals user interactions with your website. These could include anything from how long visitors stay on your page to their actions while browsing your website. Web analytics tools help you gather and understand this data so you can better understand buyer preferences. It’s like a domino effect : the more you understand your buyers and user behaviour, the better you can assess the effectiveness of your digital content and campaigns. 

  Web analytics reveal user behaviour, highlighting navigation patterns and drop-off points. Understanding these patterns helps you refine website layout and content, improving engagement and conversions for a seamless user experience.

  

  Concrete CMS harnessed the power of web analytics, specifically Form Analytics, to uncover a crucial insight within their user onboarding process. Their data revealed a significant issue : the “address” input field was causing visitors to drop off and not complete the form, severely impacting the overall onboarding experience and conversion rate.

  Armed with these insights, Concrete CMS made targeted optimisations to the form, resulting in a substantial transformation. By addressing the specific issue identified through Form Analytics, they achieved an impressive outcome – a threefold increase in lead generation.

  This case is a great example of how web analytics can uncover customer needs and preferences and positively impact conversion rates. 

  Ethical implications of digital marketing analytics

  As we’ve touched on, digital marketing analytics are a powerful tool to help better understand online user behaviour. With great power comes great responsibility, however, and it’s a legal and ethical obligation for organisations to protect individual privacy rights. Let’s get into the benefits of practising ethical digital marketing analytics and the potential risks of not respecting user privacy : 

  • If someone uses your digital platform and then opens their email one day to find it filled with random targeted ad campaigns, they won’t be happy. Avoid losing user trust — and facing a potential lawsuit — by informing users what their data will be used for. Give them the option to consent to opt-in or opt-out of letting you use their personal information. If users are also assured you’ll safeguard personal information against unauthorised access, they’ll be more likely to trust you to handle their data securely.
  • Protecting data against breaches means investing in technology that will let you end-to-end encrypt and securely store data. Other important data-security best practices include access control, backing up data regularly and network and physical security of assets.
  • A fine line separates digital marketing analytics and misusing user data — many companies have gotten into big trouble for crossing it. (By big trouble, we mean millions of dollars in fines.) When it comes to digital marketing analytics, you should never cut corners when it comes to user privacy and data security. This balance involves understanding what data can be collected and what should be collected and respecting user boundaries and preferences.

  

  Learn more 

  We discussed a lot of facets of digital marketing analytics, namely how to develop a robust digital marketing strategy while prioritising data compliance. With Matomo, you can protect user data and respect user privacy while gaining invaluable insights into user behaviour. Save your organisation time and money by investing in a web analytics solution that gives you the best of both worlds. 

  If you’re ready to begin using ethical and robust digital marketing analytics on your website, try Matomo. Start your 21-day free trial now — no credit card required.

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