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• PyDub can't locate FFmpeg, even after comfirming that FFmpeg is properly installed

  14 avril 2023, par TordG

  After trying many solutions, checking that FFmpeg is installed correctly through cmd and other modules, PyDub still cannot find it. I am running Windows 11, Python 3.10.6, PyDub 0.25.1 and FFmpeg version 2023-04-12-git-1179bb703e-full_build-www.gyan.dev

  


  I've tried to run the following code that should speed up an mp3 file within the directory by 100%

  


  import os
from pydub import AudioSegment

file = r'test.mp3'

# Load the MP3 file
audio = AudioSegment.from_file(file, format="mp3")

# Increase the speed
audio = audio.speedup(playback_speed=2, chunk_size=150)

# Export the modified file to the same file name
audio.export(file, format="mp3")

# Delete the original file
os.remove(file)


  


  From this I get the following error :

  


  C:\Users\xxx\AppData\Local\Programs\Python\Python310\lib\site-packages\pydub\utils.py:170: RuntimeWarning: Couldn&#x27;t find ffmpeg or avconv - defaulting to ffmpeg, but may not work&#xA;  warn("Couldn&#x27;t find ffmpeg or avconv - defaulting to ffmpeg, but may not work", RuntimeWarning)&#xA;C:\Users\xxx\AppData\Local\Programs\Python\Python310\lib\site-packages\pydub\utils.py:198: RuntimeWarning: Couldn&#x27;t find ffprobe or avprobe - defaulting to ffprobe, but may not work&#xA;  warn("Couldn&#x27;t find ffprobe or avprobe - defaulting to ffprobe, but may not work", RuntimeWarning)&#xA;Traceback (most recent call last):&#xA;  File "c:\Users\xxx\xxx\xxx\xxx\xxx\speed_up.py", line 7, in <module>&#xA;    audio = AudioSegment.from_file(file, format="mp3")&#xA;  File "C:\Users\xxx\AppData\Local\Programs\Python\Python310\lib\site-packages\pydub\audio_segment.py", line 728, in from_file&#xA;    info = mediainfo_json(orig_file, read_ahead_limit=read_ahead_limit)&#xA;  File "C:\Users\xxx\AppData\Local\Programs\Python\Python310\lib\site-packages\pydub\utils.py", line 274, in mediainfo_json&#xA;    res = Popen(command, stdin=stdin_parameter, stdout=PIPE, stderr=PIPE)&#xA;  File "C:\Users\xxx\AppData\Local\Programs\Python\Python310\lib\subprocess.py", line 969, in __init__&#xA;    self._execute_child(args, executable, preexec_fn, close_fds,&#xA;  File "C:\Users\xxx\AppData\Local\Programs\Python\Python310\lib\subprocess.py", line 1438, in _execute_child&#xA;    hp, ht, pid, tid = _winapi.CreateProcess(executable, args,&#xA;FileNotFoundError: [WinError 2] The system cannot find the file specified&#xA;</module>

  


  I've added FFmpeg to my path variable and checked that it worked by typing 'ffmpeg' in my command prompt. I also made sure that it was installed and pathed correctly by testing it with other modules that should use ffmpeg as well

  


  The following code worked flawlessly (where moviepy uses ffmpeg as far as I know) :

  


  from moviepy.editor import ColorClip

# Create a black clip with a duration of 5 seconds
empty_clip = ColorClip(size=(640, 480), color=(0, 0, 0), duration=5)

# Write the empty clip to a video file
empty_clip.write_videofile("empty.mp4", codec="libx264", fps=25)



  


  I've specified the abs path in my code as :

  


  AudioSegment.ffmpeg_path = r"C:\ffmpeg\bin\ffmpeg.exe"

  


  Again this didn't work and I cannot find any more solutions anywhere

  


• Generating DASH manifest with ffmpeg works in Docker exec shell but not in os.exec golang

  22 février 2023, par Inquisitor

  I am trying to generate a DASH manifest with ffmpeg using this command

  


  /usr/bin/ffmpeg -f webm_dash_manifest -i ./480.webm -f webm_dash_manifest -i ./audio.webm -c copy -map 0:0 -map 1:0 -y -f webm_dash_manifest -adaptation_sets "id=0,streams=0 id=1,streams=1" ./manifest.mpd -loglevel repeat+level+verbose


  


  I run this in go, in a docker container as shown :

  


  func runFFmpeg(arguments []string) {
    arguments = append(arguments, "-loglevel", "repeat+level+verbose")
    cmd := exec.Command("ffmpeg", arguments...)

    fmt.Printf("cmd: %v\n", cmd)

    var out bytes.Buffer
    var stderr bytes.Buffer
    cmd.Stdout = &out
    cmd.Stderr = &stderr
    err := cmd.Run()

    if err != nil {
        log.Println(err)
        log.Println(stderr.String())
    }
}
runFFmpeg([]string{"-f", "webm_dash_manifest", "-i", "./480.webm", "-f", "webm_dash_manifest", "-i", "./audio.webm", "-c", "copy", "-map", "0:0", "-map", "1:0", "-y", "-f", "webm_dash_manifest", "-adaptation_sets", "\"id=0,streams=0 id=1,streams=1\"", "./manifest.mpd"})


  


  But I get this output :

  


   cmd: /usr/bin/ffmpeg -f webm_dash_manifest -i ./480.webm -f webm_dash_manifest -i ./audio.webm -c copy -map 0:0 -map 1:0 -y -copy_unknown -f webm_dash_manifest -adaptation_sets "id=0,streams=0 id=1,streams=1" ./manifest.mpd -loglevel repeat+level+verbose
 2023/02/22 04:45:17 exit status 1
 2023/02/22 04:45:17 [info] ffmpeg version 5.1.2 Copyright (c) 2000-2022 the FFmpeg developers
 [info]   built with gcc 12.2.1 (Alpine 12.2.1_git20220924-r3) 20220924
 [info]   configuration: --prefix=/usr --enable-avfilter --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-gnutls --enable-gpl --enable-libass --enable-libmp3lame --enable-libpulse --enable-libvorbis --enable-libvpx --enable-libxvid --enable-libx264 --enable-libx265 --enable-libtheora --enable-libv4l2 --enable-libdav1d --enable-lto --enable-postproc --enable-pic --enable-pthreads --enable-shared --enable-libxcb --enable-librist --enable-libsrt --enable-libssh --enable-libvidstab --disable-stripping --disable-static --disable-librtmp --disable-lzma --enable-libaom --enable-libopus --enable-libsoxr --enable-libwebp --enable-vaapi --enable-vdpau --enable-vulkan --enable-libdrm --enable-libzmq --optflags=-O2 --disable-debug --enable-libsvtav1
 [info]   libavutil      57. 28.100 / 57. 28.100
 [info]   libavcodec     59. 37.100 / 59. 37.100
 [info]   libavformat    59. 27.100 / 59. 27.100
 [info]   libavdevice    59.  7.100 / 59.  7.100
 [info]   libavfilter     8. 44.100 /  8. 44.100
 [info]   libswscale      6.  7.100 /  6.  7.100
 [info]   libswresample   4.  7.100 /  4.  7.100
 [info]   libpostproc    56.  6.100 / 56.  6.100
 [webm_dash_manifest @ 0x7fcec2836100] [warning] Could not find codec parameters for stream 0 (Video: vp9, 1 reference frame, none(tv, progressive, left), 640x480): unspecified pixel format
 Consider increasing the value for the 'analyzeduration' (0) and 'probesize' (5000000) options
 [info] Input #0, webm_dash_manifest, from './480.webm':
 [info]   Metadata:
 [info]     title           : Big Buck Bunny, Sunflower version
 [info]     GENRE           : Animation
 [info]     MAJOR_BRAND     : isom
 [info]     MINOR_VERSION   : 1
 [info]     COMPATIBLE_BRANDS: isomavc1
 [info]     COMPOSER        : Sacha Goedegebure
 [info]     ARTIST          : Blender Foundation 2008, Janus Bager Kristensen 2013
 [info]     COMMENT         : Creative Commons Attribution 3.0 - http://bbb3d.renderfarming.net
 [info]     ENCODER         : Lavf59.27.100
 [info]   Duration: 00:10:34.60, bitrate: 352 kb/s
 [info]   Stream #0:0: Video: vp9, 1 reference frame, none(tv, progressive, left), 640x480, SAR 853:640 DAR 853:480, 30 fps, 30 tbr, 1k tbn (default)
 [info]     Metadata:
 [info]       HANDLER_NAME    : GPAC ISO Video Handler
 [info]       VENDOR_ID       : [0][0][0][0]
 [info]       ENCODER         : Lavc59.37.100 libvpx-vp9
 [info]       DURATION        : 00:10:34.600000000
 [info]       webm_dash_manifest_duration: 634600
 [info]       webm_dash_manifest_initialization_range: 889
 [info]       webm_dash_manifest_file_name: 480.webm
 [info]       webm_dash_manifest_track_number: 1
 [info]       webm_dash_manifest_cues_start: 27924928
 [info]       webm_dash_manifest_cues_end: 27927508
 [info]       webm_dash_manifest_bandwidth: 738287
 [info]       webm_dash_manifest_cluster_keyframe: 1
 [info]       webm_dash_manifest_cue_timestamps: 67,5067,10067,15067,20067,25067,30067,35067,40067,45067,50067,55067,60067,65067,70067,75067,80067,85067,90067,95067,100067,105067,110067,115067,120067,125067,130067,135067,140067,145067,150067,155067,160067,165067,170067,175067,180067,185067,190067,195067
 [info] Input #1, webm_dash_manifest, from './audio.webm':
 [info]   Metadata:
 [info]     title           : Big Buck Bunny, Sunflower version
 [info]     GENRE           : Animation
 [info]     MAJOR_BRAND     : isom
 [info]     MINOR_VERSION   : 1
 [info]     COMPATIBLE_BRANDS: isomavc1
 [info]     COMPOSER        : Sacha Goedegebure
 [info]     ARTIST          : Blender Foundation 2008, Janus Bager Kristensen 2013
 [info]     COMMENT         : Creative Commons Attribution 3.0 - http://bbb3d.renderfarming.net
 [info]     ENCODER         : Lavf59.27.100
 [info]   Duration: 00:05:35.91, bitrate: 120 kb/s
 [info]   Stream #1:0: Audio: vorbis, 48000 Hz, 5.1, fltp (default)
 [info]     Metadata:
 [info]       HANDLER_NAME    : GPAC ISO Audio Handler
 [info]       VENDOR_ID       : [0][0][0][0]
 [info]       ENCODER         : Lavc59.37.100 libvorbis
 [info]       DURATION        : 00:05:35.907000000
 [info]       webm_dash_manifest_duration: 335907
 [info]       webm_dash_manifest_initialization_range: 7411
 [info]       webm_dash_manifest_file_name: audio.webm
 [info]       webm_dash_manifest_track_number: 1
 [info]       webm_dash_manifest_cues_start: 5047497
 [info]       webm_dash_manifest_cues_end: 5048846
 [info]       webm_dash_manifest_bandwidth: 126994
 [info]       webm_dash_manifest_cluster_keyframe: 1
 [info]       webm_dash_manifest_cue_timestamps: 0,4986,9980,14962,19959,24956,29946,34930,39916,44911,49908,54892,59874,64868,69858,74858,79855,84847,89834,94834,99826,104826,109826,114818,119812,124799,129780,134770,139756,144743,149727,154719,159714,164706,169698,174687,179668,184667,189658,194650,19
 HERE'S THE ERROR --> [error] Could not write header for output file #0 (incorrect codec parameters ?): Operation not permitted
 [error] Error initializing output stream 0:1 -- 
 [info] Stream mapping:
 [info]   Stream #0:0 -> #0:0 (copy)
 [info]   Stream #1:0 -> #0:1 (copy)
 [error] 
 [AVIOContext @ 0x7fcebe23a840] [verbose] Statistics: 0 bytes written, 0 seeks, 0 writeouts
 [AVIOContext @ 0x7fcebe23a980] [verbose] Statistics: 5940786 bytes read, 133 seeks
 [AVIOContext @ 0x7fcebe23acc0] [verbose] Statistics: 2260628 bytes read, 72 seeks


  


  It works perfectly however if I run it within docker exec (exec into the container and run the command in bash). Also, note in the last output that the command is well formed when I print it out from go. File permissions are also not the problem since the command to create the video variants works with os.exec i.e

  


  runFFmpeg([]string{"-i", originalFilePath, "-c:v", "libvpx-vp9", "-keyint_min", "150", "-g", "150", "-tile-columns", "4", "-frame-parallel", "1", "-f", "webm", "-dash", "1", "-an", "-vf", fmt.Sprintf("scale=%s:%s", width, height), "-b:v", bitRate, "-dash", "1", "-y", destinationFilePath})


  


• What is Behavioural Segmentation and Why is it Important ?

  28 septembre 2023, par Erin — Analytics Tips

  Amidst the dynamic landscape of web analytics, understanding customers has grown increasingly vital for businesses to thrive. While traditional demographic-focused strategies possess merit, they need to uncover the nuanced intricacies of individual online behaviours and preferences. As customer expectations evolve in the digital realm, enterprises must recalibrate their approaches to remain relevant and cultivate enduring digital relationships.

  In this context, the surge of technology and advanced data analysis ushers in a marketing revolution : behavioural segmentation. Businesses can unearth invaluable insights by meticulously scrutinising user actions, preferences and online interactions. These insights lay the foundation for precisely honed, high-performing, personalised campaigns. The era dominated by blanket, catch-all marketing strategies is yielding to an era of surgical precision and tailored engagement. 

  While the insights from user behaviours empower businesses to optimise customer experiences, it’s essential to strike a delicate balance between personalisation and respecting user privacy. Ethical use of behavioural data ensures that the power of segmentation is wielded responsibly and in compliance, safeguarding user trust while enabling businesses to thrive in the digital age.

  What is behavioural segmentation ?

  Behavioural segmentation is a crucial concept in web analytics and marketing. It involves categorising individuals or groups of users based on their online behaviour, actions and interactions with a website. This segmentation method focuses on understanding how users engage with a website, their preferences and their responses to various stimuli. Behavioural segmentation classifies users into distinct segments based on their online activities, such as the pages they visit, the products they view, the actions they take and the time they spend on a site.

  Behavioural segmentation plays a pivotal role in web analytics for several reasons :

  1. Enhanced personalisation :

  Understanding user behaviour enables businesses to personalise online experiences. This aids with delivering tailored content and recommendations to boost conversion, customer loyalty and customer satisfaction.

  2. Improved user experience :

  Behavioural segmentation optimises user interfaces (UI) and navigation by identifying user paths and pain points, enhancing the level of engagement and retention.

  3. Targeted marketing :

  Behavioural segmentation enhances marketing efficiency by tailoring campaigns to user behaviour. This increases the likelihood of interest in specific products or services.

  4. Conversion rate optimisation :

  Analysing behavioural data reveals factors influencing user decisions, enabling website optimisation for a streamlined purchasing process and higher conversion rates.

  5. Data-driven decision-making :

  Behavioural segmentation empowers data-driven decisions. It identifies trends, behavioural patterns and emerging opportunities, facilitating adaptation to changing user preferences and market dynamics.

  6. Ethical considerations :

  Behavioural segmentation provides valuable insights but raises ethical concerns. User data collection and use must prioritise transparency, privacy and responsible handling to protect individuals’ rights.

  The significance of ethical behavioural segmentation will be explored more deeply in a later section, where we will delve into the ethical considerations and best practices for collecting, storing and utilising behavioural data in web analytics. It’s essential to strike a balance between harnessing the power of behavioural segmentation for business benefits and safeguarding user privacy and data rights in the digital age.

  

  Different types of behavioural segments with examples

  1. Visit-based segments : These segments hinge on users’ visit patterns. Analyse visit patterns, compare first-time visitors to returning ones, or compare users landing on specific pages to those landing on others.
     • Example : The real estate website Zillow can analyse how first-time visitors and returning users behave differently. By understanding these patterns, Zillow can customise its website for each group. For example, they can highlight featured listings and provide navigation tips for first-time visitors while offering personalised recommendations and saved search options for returning users. This could enhance user satisfaction and boost the chances of conversion.
  2. Interaction-based segments : Segments can be created based on user interactions like special events or goals completed on the site.
     • Example : Airbnb might use this to understand if users who successfully book accommodations exhibit different behaviours than those who don’t. This insight could guide refinements in the booking process for improved conversion rates.
  3. Campaign-based segments : Beyond tracking visit numbers, delve into usage differences of visitors from specific sources or ad campaigns for deeper insights.
     • Example : Nike might analyse user purchase behaviour from various traffic sources (referral websites, organic, direct, social media and ads). This informs marketing segmentation adjustments, focusing on high-performance channels. It also customises the website experience for different traffic sources, optimising content, promotions and navigation. This data-driven approach could boost user experiences and maximise marketing impact for improved brand engagement and sales conversions.
  4. Ecommerce segments : Separate users based on purchases, even examining the frequency of visits linked to specific products. Segment heavy users versus light users. This helps uncover diverse customer types and browsing behaviours.
     • Example : Amazon could create segments to differentiate between visitors who made purchases and those who didn’t. This segmentation could reveal distinct usage patterns and preferences, aiding Amazon in tailoring its recommendations and product offerings.
  5. Demographic segments : Build segments based on browser language or geographic location, for instance, to comprehend how user attributes influence site interactions.
     • Example : Netflix can create user segments based on demographic factors like geographic location to gain insight into how a visitor’s location can influence content preferences and viewing behaviour. This approach could allow for a more personalised experience.
  6. Technographic segments : Segment users by devices or browsers, revealing variations in site experience and potential platform-specific issues or user attitudes.
     • Example : Google could create segments based on users’ devices (e.g., mobile, desktop) to identify potential issues in rendering its search results. This information could be used to guide Google in providing consistent experiences regardless of device.

  

  The importance of ethical behavioural segmentation

  Respecting user privacy and data protection is crucial. Matomo offers features that align with ethical segmentation practices. These include :

  • Anonymization : Matomo allows for data anonymization, safeguarding individual identities while providing valuable insights.
  • GDPR compliance : Matomo is GDPR compliant, ensuring that user data is handled following European data protection regulations.
  • Data retention and deletion : Matomo enables businesses to set data retention policies and delete user data when it’s no longer needed, reducing the risk of data misuse.
  • Secured data handling : Matomo employs robust security measures to protect user data, reducing the risk of data breaches.

  Real-world examples of ethical behavioural segmentation :

  1. Content publishing : A leading news website could utilise data anonymization tools to ethically monitor user engagement. This approach allows them to optimise content delivery based on reader preferences while ensuring the anonymity and privacy of their target audience.
  2. Non-profit organisations : A charity organisation could embrace granular user control features. This could be used to empower its donors to manage their data preferences, building trust and loyalty among supporters by giving them control over their personal information.

  

  Examples of effective behavioural segmentation

  Companies are constantly using behavioural insights to engage their audiences effectively. In this section, we’ll delve into real-world examples showcasing how top companies use behavioural segmentation to enhance their marketing efforts.

  

  1. Coca-Cola’s behavioural insights for marketing strategy : Coca-Cola employs behavioural segmentation to evaluate its advertising campaigns. Through analysing user engagement across TV commercials, social media promotions and influencer partnerships, Coca-Cola’s marketing team can discover that video ads shared by influencers generate the highest ROI and web traffic.

     This insight guides the reallocation of resources, leading to increased sales and a more effective advertising strategy.

  2. eBay’s custom conversion approach : eBay excels in conversion optimisation through behavioural segmentation. When users abandon carts, eBay’s dynamic system sends personalised email reminders featuring abandoned items and related recommendations tailored to user interests and past purchase decisions.

     This strategy revives sales, elevates conversion rates and sparks engagement. eBay’s adeptness in leveraging behavioural insights transforms user experience, steering a customer journey toward conversion.

  3. Sephora’s data-driven conversion enhancement : Data analysts can use Sephora’s behavioural segmentation strategy to fuel revenue growth through meticulous data analysis. By identifying a dedicated subset of loyal customers who exhibit a consistent preference for premium skincare products, data analysts enable Sephora to customise loyalty programs.

     These personalised rewards programs provide exclusive discounts and early access to luxury skincare releases, resulting in heightened customer engagement and loyalty. The data-driven precision of this approach directly contributes to amplified revenue from this specific customer segment.

  Examples of the do’s and don’ts of behavioural segmentation 

  

  Behavioural segmentation is a powerful marketing and data analysis tool, but its success hinges on ethical and responsible practices. In this section, we will explore real-world examples of the do’s and don’ts of behavioural segmentation, highlighting companies that have excelled in their approach and those that have faced challenges due to lapses in ethical considerations.

  Do’s of behavioural segmentation :

  • Personalised messaging :
    • Example : Spotify
      • Spotify’s success lies in its ability to use behavioural data to curate personalised playlists and user recommendations, enhancing its music streaming experience.
  • Transparency :
    • Example : Basecamp
      • Basecamp’s transparency in sharing how user data is used fosters trust. They openly communicate data practices, ensuring users are informed and comfortable.
  • Anonymization
    • Example : Matomo’s anonymization features
      • Matomo employs anonymization features to protect user identities while providing valuable insights, setting a standard for responsible data handling.
  • Purpose limitation :
    • Example : Proton Mail
      • Proton Mail strictly limits the use of user data to email-related purposes, showcasing the importance of purpose-driven data practices.
  • Dynamic content delivery : 
    • Example : LinkedIn
      • LinkedIn uses behavioural segmentation to dynamically deliver job recommendations, showcasing the potential for relevant content delivery.
  • Data security :
    • Example : Apple
      • Apple’s stringent data security measures protect user information, setting a high bar for safeguarding sensitive data.
  • Adherence to regulatory compliance : 
    • Example : Matomo’s regulatory compliance features
      • Matomo’s regulatory compliance features ensure that businesses using the platform adhere to data protection regulations, further promoting responsible data usage.

  Don’ts of behavioural segmentation :

  • Ignoring changing regulations
    • Example : Equifax
      • Equifax faced major repercussions for neglecting evolving regulations, resulting in a data breach that exposed the sensitive information of millions.
  • Sensitive attributes
    • Example : Twitter
      • Twitter faced criticism for allowing advertisers to target users based on sensitive attributes, sparking concerns about user privacy and data ethics.
  • Data sharing without consent
    • Example : Meta & Cambridge Analytica
      • The Cambridge Analytica scandal involving Meta (formerly Facebook) revealed the consequences of sharing user data without clear consent, leading to a breach of trust.
  • Lack of control
    • Example : Uber
      • Uber faced backlash for its poor data security practices and a lack of control over user data, resulting in a data breach and compromised user information.
  • Don’t be creepy with invasive personalisation
    • Example : Offer Moment
      • Offer Moment’s overly invasive personalisation tactics crossed ethical boundaries, unsettling users and eroding trust.

  These examples are valuable lessons, emphasising the importance of ethical and responsible behavioural segmentation practices to maintain user trust and regulatory compliance in an increasingly data-driven world.

  Continue the conversation

  Diving into customer behaviours, preferences and interactions empowers businesses to forge meaningful connections with their target audience through targeted marketing segmentation strategies. This approach drives growth and fosters exceptional customer experiences, as evident from the various common examples spanning diverse industries.

  In the realm of ethical behavioural segmentation and regulatory compliance, Matomo is a trusted partner. Committed to safeguarding user privacy and data integrity, our advanced web analytics solution empowers your business to harness the power of behavioral segmentation, all while upholding the highest standards of compliance with stringent privacy regulations.

  To gain deeper insight into your visitors and execute impactful marketing campaigns, explore how Matomo can elevate your efforts. Try Matomo free for 21-days, no credit card required.