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• Compiled FFmpeg not accepting -c:v and -c:a

  13 mars 2020, par King Horse

  I compiled FFmpeg with libsrt, with the online compile guide. https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu & how to compile ffmpeg with enabling libsrt

  It seems to compile correctly.

  ffmpeg version N-96575-g843c24a Copyright (c) 2000-2020 the FFmpeg developers
  
  built with gcc 7 (Ubuntu 7.4.0-1ubuntu1~18.04.1)
  
  configuration: --prefix=/home/ubuntu/ffmpeg_build --pkg-config-flags=--static --extra-cflags=-I/home/ubuntu/ffmpeg_build/include --extra-ldflags=-L/home/ubuntu/ffmpeg_build/lib --extra-libs='-lpthread -lm' --bindir=/home/ubuntu/bin --enable-gpl --enable-libaom --enable-libass --enable-libfdk-aac --enable-libfreetype --enable-libmp3lame --enable-libopus --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 --enable-libsrt --enable-nonfree
  
  libavutil      56. 38.100 / 56. 38.100
  
  libavcodec     58. 67.100 / 58. 67.100
  
  libavformat    58. 37.100 / 58. 37.100
  
  libavdevice    58.  9.103 / 58.  9.103
  
  libavfilter     7. 72.100 /  7. 72.100
  
  libswscale      5.  6.100 /  5.  6.100
  
  libswresample   3.  6.100 /  3.  6.100
  
  libpostproc    55.  6.100 / 55.  6.100

  But when running this command to convert a incoming SRT stream to HLS, it doesn’t know the -c:a command. When switching the order, it runs that it doesn’t know about the -c:v command.

  ffmpeg -re -i srt://0.0.0.0:25000?pkt_size=1316&mode=listener -c:a copy -c:v copy -strict -f hls -hls_time 4 -hls_playlist_type event stream.m3u8

  ~$ ffmpeg -re -i srt://0.0.0.0:25000?pkt_size=1316&mode=listener -c:a copy -c:v copy -strict -f hls -hls_time 4 -hls_playlist_type event stream.m3u8
  
  [2] 9930
  
  ffmpeg version N-96575-g843c24a Copyright (c) 2000-2020 the FFmpeg developers
  
    built with gcc 7 (Ubuntu 7.4.0-1ubuntu1~18.04.1)
  
    configuration: --prefix=/home/ubuntu/ffmpeg_build --pkg-config-flags=--static --extra-cflags=-I/home/ubuntu/ffmpeg_build/include --extra-ldflags=-L/home/ubuntu/ffmpeg_build/lib --extra-libs='-lpthread -lm' --bindir=/home/ubuntu/bin --enable-gpl --enable-libaom --enable-libass --enable-libfdk-aac --enable-libfreetype --enable-libmp3lame --enable-libopus --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 --enable-libsrt --enable-nonfree
  
    libavutil      56. 38.100 / 56. 38.100
  
    libavcodec     58. 67.100 / 58. 67.100
  
    libavformat    58. 37.100 / 58. 37.100
  
    libavdevice    58.  9.103 / 58.  9.103
  
    libavfilter     7. 72.100 /  7. 72.100
  
    libswscale      5.  6.100 /  5.  6.100
  
    libswresample   3.  6.100 /  3.  6.100
  
    libpostproc    55.  6.100 / 55.  6.100
  
  -c:a: command not found
  
  
  
  [2]+  Stopped                 ffmpeg -re -i srt://0.0.0.0:25000?pkt_size=1316

  I have searched the issue, but I could not find anything similar.
  Does someone know what I have missed in the setup ?

  Everything is manually compiled through the guide, this was the final command I run to compile FFmpeg :

  cd ~/ffmpeg_sources && \
  
  wget -O ffmpeg-snapshot.tar.bz2 https://ffmpeg.org/releases/ffmpeg-snapshot.tar.bz2 && \
  
  tar xjvf ffmpeg-snapshot.tar.bz2 && \
  
  cd ffmpeg && \
  
  PATH="$HOME/bin:$PATH" PKG_CONFIG_PATH="$HOME/ffmpeg_build/lib/pkgconfig" ./configure \
  
    --prefix="$HOME/ffmpeg_build" \
  
    --pkg-config-flags="--static" \
  
    --extra-cflags="-I$HOME/ffmpeg_build/include" \
  
    --extra-ldflags="-L$HOME/ffmpeg_build/lib" \
  
    --extra-libs="-lpthread -lm" \
  
    --bindir="$HOME/bin" \
  
    --enable-gpl \
  
    --enable-libaom \
  
    --enable-libass \
  
    --enable-libfdk-aac \
  
    --enable-libfreetype \
  
    --enable-libmp3lame \
  
    --enable-libopus \
  
    --enable-libvorbis \
  
    --enable-libvpx \
  
    --enable-libx264 \
  
    --enable-libx265 \
  
    --enable-libsrt \
  
    --enable-nonfree && \
  
  PATH="$HOME/bin:$PATH" make && \
  
  make install && \
  
  hash -r

• ffmpeg : video to aligned audio (audio too short)

  16 octobre 2020, par mcb

  I want to extract an aligned audio stream from a video. The goal is to obtain an audio sequence that is precisely aligned with the video.

  


  Issue : The video and audio sequences are not aligned. The output audio duration is shorter than the video input.

  


  Script to reproduce :

  


  fn=TV-20200617-2242-4900.websm.h264
url=https://download.media.tagesschau.de/video/2020/0617/$fn.mp4
wget -nc $url

ffmpeg -y -i "$fn.mp4" -vsync 1 -async 1 -map 0:a "$fn.wav" -map 0:v "$fn.flv"

ffprobe -i $fn.mp4  # Duration: 00:01:51.68
ffprobe -i $fn.flv  # Duration: 00:01:51.68
ffprobe -i $fn.wav  # Duration: 00:01:49.61


  


  What I have tried (without success) :

  


  

  • Adding -async 1 as suggested in this answer.

  


  • Adding -acodec copy and exporting the video at the same time (link).

  


  • Opening the mp4 in Audacity. The duration there is 00:01:49.61.

  


  • Opening the mp4 in VLC. Duration : 00:01:51.68.

  


  • Explicitly setting the framerate.

  


  • Other video files.

  


  


  ffmpeg version 4.2.4-1ubuntu0.1

  


  I would appreciate any hint on how to make this work. Thank you.

  


• What is data anonymization in web analytics ?

  11 février 2020, par Joselyn Khor — Analytics Tips, Privacy
  Collecting information via web analytics platforms is needed to help a website grow and improve. When doing so, it’s best to strike a balance between getting valuable insights, and keeping the trust of your users by protecting their privacy.

  This means not collecting or processing any personally identifiable information (PII). But what if your organisation requires you to collect PII ?

  That’s where data anonymization comes in.

  

  What is data anonymization ?

  Data anonymization makes identifiable information unidentifiable. This is done through data processing techniques which remove or modify PII data. So data becomes anonymous and can’t be linked to any individual.

  In the context of web analytics, data anonymization is handy because you can collect useful data while protecting the privacy of website visitors.

  Why is data anonymization important ?

  Through modern threats of identity theft, credit card fraud and the like, data anonymization is a way to protect the identity and privacy of individuals. As well as protect private and sensitive information of organisations. 

  Data anonymization lets you follow the many laws around the world which protect user privacy. These laws provide safeguards around collecting personal data or personally identifiable information (PII), so data anonymization is a good solution to ensure you’re not processing such sensitive information.

  In some cases, implementing data anonymization techniques means you can avoid having to show your users a consent screen. Which means you may not need to ask for consent in order to track data. This is a bonus as consent screens can annoy and stop people from engaging with your site.

  GDPR and data anonymization

  

  The GDPR is a law in the EU that limits the collection and processing of personal data. The aim is to give people more control over their online personal information. Which is why website owners need to follow certain rules to become GDPR compliant and protect user privacy. According to the GDPR, you can be fined up to 4% of your yearly revenue for data breaches or non-compliance. 

  In the case of web analytics, tools can be easily made compliant by following a number of steps. 

  This is why anonymizing data is a big deal.

  Anonymized data isn’t personal data according to the GDPR : 

  “The principles of data protection should therefore not apply to anonymous information, namely information which does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable.”

  This means, you still get the best of both worlds. By anonymizing data, you’re still able to collect useful information like visitor behavioural data.

  US privacy laws and data anonymization

  In the US, there isn’t one single law that governs the protection of personal data, called personally identifiable information (PII). There are hundreds of federal and state laws that protect the personal data of US residents. As well as, industry-specific statutes related to data privacy, like the California Consumer Privacy Act (CCPA) and the Health Insurance Portability and Accountability Act (HIPAA).

  Website owners in the US need to know exactly what laws govern their area of business in order to follow them.

  A general guideline is to protect user privacy regardless of whether you are or aren’t allowed to collect PII. This means anonymizing identifiable information so your website users aren’t put at risk.

  Data anonymization techniques in Matomo Analytics

  If you carry these out, you won’t need to ask your website visitors for tracking consent since anonymized data is no longer considered personal data under the GDPR.

  • Anonymize previously tracked raw data – like visitors/visits
  • Anonymize Visitor IP addresses
  • Anonymize geo-location information (country, region, city)
  • Pseudonymize User ID ( if you don’t want this data to land on your Matomo server, the solution is simply to not set the User ID. Then Matomo will use the normal anonymized IDs)
  • Data can also be anonymized before it’s even sent to Matomo
  • Learn how to further configure privacy settings in a web analytics tool like Matomo
  • 11 ways Matomo Analytics helps you to protect your visitors privacy

  The techniques listed above make it easy for you when using a tool like Matomo, as they are automatically anonymized.

  Tools like Google Analytics on the other hand don’t provide some of the privacy options and leave it up to you to take on the burden of implementation without providing steps.

  Data anonymization tools

  If you’re a website owner who wants to grow your business or learn more about your website visitors, privacy-friendly tools like Matomo Analytics are a great option. By following the easy steps to be GDPR compliant, you can anonymize all data that could put your visitors at risk.