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• Everytime I run my code ffmpeg responds with this instead of doing its function. How do I fix ?

  25 juillet 2023, par Oreo F

  Output from ffmpeg/avlib:

ffmpeg version 2023-07-19-git-efa6cec759-full_build-www.gyan.dev Copyright (c) 2000-2023 the FFmpeg developers
  built with gcc 12.2.0 (Rev10, Built by MSYS2 project)
  configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-libsnappy --enable-zlib --enable-librist --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libaribb24 --enable-libaribcaption --enable-libdav1d --enable-libdavs2 --enable-libuavs3d --enable-libzvbi --enable-librav1e --enable-libsvtav1 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libaom --enable-libjxl --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-libharfbuzz --enable-liblensfun --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-ffnvcodec --enable-nvdec --enable-nvenc --enable-d3d11va --enable-dxva2 --enable-libvpl --enable-libshaderc --enable-vulkan --enable-libplacebo --enable-opencl --enable-libcdio --enable-libgme --enable-libmodplug --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libshine --enable-libtheora --enable-libtwolame --enable-libvo-amrwbenc --enable-libcodec2 --enable-libilbc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-ladspa --enable-libbs2b --enable-libflite --enable-libmysofa --enable-librubberband --enable-libsoxr --enable-chromaprint
  libavutil      58. 14.100 / 58. 14.100
  libavcodec     60. 22.100 / 60. 22.100
  libavformat    60. 10.100 / 60. 10.100
  libavdevice    60.  2.101 / 60.  2.101
  libavfilter     9.  8.102 /  9.  8.102
  libswscale      7.  3.100 /  7.  3.100
  libswresample   4. 11.100 /  4. 11.100
  libpostproc    57.  2.100 / 57.  2.100


  


  Everytime I run the code it does this.

  


  code :

  


  import praw
import requests
import cv2
import os
from pydub import AudioSegment

def download_video(url, filename):
    response = requests.get(url)
    with open(filename, 'wb') as f:
        f.write(response.content)

def combine_videos(video_urls, output_filename):
    video_clips = []
    audio_clips = []
    for i, url in enumerate(video_urls):
        temp_filename = f'temp_video_{i}.mp4'
        download_video(url, temp_filename)
        video_clip = cv2.VideoCapture(temp_filename)
        audio_clip = AudioSegment.from_file(temp_filename, format="mp4")
        video_clips.append(video_clip)
        audio_clips.append(audio_clip)
    
    if not video_clips:
        print("No video clips to combine.")
        return

    frame_width = int(video_clips[0].get(cv2.CAP_PROP_FRAME_WIDTH))
    frame_height = int(video_clips[0].get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = int(video_clips[0].get(cv2.CAP_PROP_FPS))

    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    output_clip = cv2.VideoWriter(output_filename, fourcc, fps, (frame_width, frame_height))

    for i, video_clip in enumerate(video_clips):
        while True:
            ret, frame = video_clip.read()
            if not ret:
                break
            output_clip.write(frame)
    
    for video_clip in video_clips:
        video_clip.release()
    
    output_clip.release()

    # Combining audio using pydub
    combined_audio = sum(audio_clips)
    combined_audio.export("combined_audio.mp3", format="mp3")

    # Merging audio with video using ffmpeg
    os.system(f'ffmpeg -i {output_filename} -i combined_audio.mp3 -c:v copy -c:a aac -strict experimental -map 0:v:0 -map 1:a:0 final_output.mp4')

    # Cleaning up temporary files
    os.remove("combined_audio.mp3")

def main():
    reddit = praw.Reddit(
       client_id='XXX',
        client_secret='XXX',
        user_agent='Reddit Video Downloader'
    )
    
    subreddit_name = input("Enter the name of the subreddit: ")
    limit = int(input("Enter the number of videos to download: "))
    
    subreddit = reddit.subreddit(subreddit_name)
    submissions = subreddit.hot(limit=limit)
    
    video_urls = [submission.url for submission in submissions if submission.media and 'reddit_video' in submission.media]
    
    if video_urls:
        output_filename = input("Enter the output filename (e.g., output.mp4): ")
        combine_videos(video_urls, output_filename)
        print("Videos combined successfully!")
    else:
        print("No Reddit videos found in the subreddit.")

if __name__ == "__main__":
    main()


  


  Anyone got any idea why this happens ?

  


  I'm making a script that scrapes videos from a specific subreddit.

  


  Also if it helps the temp video file is corrupted when it gets made.

  


  I've put this into chatGPT as well and brought an expert friend and he hasn't been able to help me.

  


• What is Audience Segmentation ? The 5 Main Types & Examples

  16 novembre 2023, par Erin — Analytics Tips

  The days of mass marketing with the same message for millions are long gone. Today, savvy marketers instead focus on delivering the most relevant message to the right person at the right time.

  They do this at scale by segmenting their audiences based on various data points. This isn’t an easy process because there are many types of audience segmentation. If you take the wrong approach, you risk delivering irrelevant messages to your audience — or breaking their trust with poor data management.

  In this article, we’ll break down the most common types of audience segmentation, share examples highlighting their usefulness and cover how you can segment campaigns without breaking data regulations.

  What is audience segmentation ?

  Audience segmentation is when you divide your audience into multiple smaller specific audiences based on various factors. The goal is to deliver a more targeted marketing message or to glean unique insights from analytics.

  It can be as broad as dividing a marketing campaign by location or as specific as separating audiences by their interests, hobbies and behaviour.

  

  Audience segmentation inherently makes a lot of sense. Consider this : an urban office worker and a rural farmer have vastly different needs. By targeting your marketing efforts towards agriculture workers in rural areas, you’re honing in on a group more likely to be interested in farm equipment. 

  Audience segmentation has existed since the beginning of marketing. Advertisers used to select magazines and placements based on who typically read them. They would run a golf club ad in a golf magazine, not in the national newspaper.

  How narrow you can make your audience segments by leveraging multiple data points has changed.

  Why audience segmentation matters

  In a survey by McKinsey, 71% of consumers said they expected personalisation, and 76% get frustrated when a vendor doesn’t deliver.

  

  These numbers reflect expectations from consumers who have actively engaged with a brand — created an account, signed up for an email list or purchased a product.

  They expect you to take that data and give them relevant product recommendations — like a shoe polishing kit if you bought nice leather loafers.

  If you don’t do any sort of audience segmentation, you’re likely to frustrate your customers with post-sale campaigns. If, for example, you just send the same follow-up email to all customers, you’d damage many relationships. Some might ask : “What ? Why would you think I need that ?” Then they’d promptly opt out of your email marketing campaigns.

  To avoid that, you need to segment your audience so you can deliver relevant content at all stages of the customer journey.

  5 key types of audience segmentation

  To help you deliver the right content to the right person or identify crucial insights in analytics, you can use five types of audience segmentation : demographic, behavioural, psychographic, technographic and transactional.

  

  Demographic segmentation 

  Demographic segmentation is when you segment a larger audience based on demographic data points like location, age or other factors.

  The most basic demographic segmentation factor is location, which is easy to leverage in marketing efforts. For example, geographic segmentation can use IP addresses and separate marketing efforts by country. 

  But more advanced demographic data points are becoming increasingly sensitive to handle. Especially in Europe, GDPR makes advanced demographics a more tentative subject. Using age, education level and employment to target marketing campaigns is possible. But you need to navigate this terrain thoughtfully and responsibly, ensuring meticulous adherence to privacy regulations.

  Potential data points :

  • Location
  • Age
  • Marital status
  • Income
  • Employment 
  • Education

  Example of effective demographic segmentation :

  A clothing brand targeting diverse locations needs to account for the varying weather conditions. In colder regions, showcasing winter collections or insulated clothing might resonate more with the audience. Conversely, in warmer climates, promoting lightweight or summer attire could be more effective. 

  Here are two ads run by North Face on Facebook and Instagram to different audiences to highlight different collections :

  

  Each collection is featured differently and uses a different approach with its copy and even the media. With social media ads, targeting people based on advanced demographics is simple enough — you can just single out the factors when making your campaign. But if you don’t want to rely on these data-mining companies, that doesn’t mean you have no options for segmentation.

  Consider allowing people to self-select their interests or preferences by incorporating a short survey within your email sign-up form. This simple addition can enhance engagement, decrease bounce rates, and ultimately improve conversion rates, offering valuable insights into audience preferences.

  This is a great way to segment ethically and without the need of data-mining companies.

  Behavioural segmentation

  Behavioural segmentation segments audiences based on their interaction with your website or app.

  You use various data points to segment your target audience based on their actions.

  Potential data points :

  • Page visits
  • Referral source
  • Clicks
  • Downloads
  • Video plays
  • Goal completion (e.g., signing up for a newsletter or purchasing a product)

  Example of using behavioural segmentation to improve campaign efficiency :

  One effective method involves using a web analytics tool such as Matomo to uncover patterns. By segmenting actions like specific clicks and downloads, pinpoint valuable trends—identifying actions that significantly enhance visitor conversions. 

  

  For instance, if a case study video substantially boosts conversion rates, elevate its prominence to capitalise on this success.

  Then, you can set up a conditional CTA within the video player. Make it pop up after the user has watched the entire video. Use a specific form and sign them up to a specific segment for each case study. This way, you know the prospect’s ideal use case without surveying them.

  This is an example of behavioural segmentation that doesn’t rely on third-party cookies.

  Psychographic segmentation

  Psychographic segmentation is when you segment audiences based on your interpretation of their personality or preferences.

  Potential data points :

  • Social media patterns
  • Follows
  • Hobbies
  • Interests

  Example of effective psychographic segmentation :

  Here, Adidas segments its audience based on whether they like cycling or rugby. It makes no sense to show a rugby ad to someone who’s into cycling and vice versa. But to rugby athletes, the ad is very relevant.

  

  If you want to avoid social platforms, you can use surveys about hobbies and interests to segment your target audience in an ethical way.

  Technographic segmentation

  Technographic segmentation is when you single out specific parts of your audience based on which hardware or software they use.

  Potential data points :

  • Type of device used
  • Device model or brand
  • Browser used

  Example of segmenting by device type to improve user experience :

  Upon noticing a considerable influx of tablet users accessing their platform, a leading news outlet decided to optimise their tablet browsing experience. They overhauled the website interface, focusing on smoother navigation and better readability for tablet users. These changes offered tablet users a seamless and enjoyable reading experience tailored precisely to their device.

  Transactional segmentation

  Transactional segmentation is when you use your customers’ purchase history to better target your marketing message to their needs.

  When consumers prefer personalisation, they typically mean based on their actual transactions, not their social media profiles.

  Potential data points :

  • Average order value
  • Product categories purchased within X months
  • X days since the last purchase of a consumable product

  Example of effective transactional segmentation :

  A pet supply store identifies a segment of customers consistently purchasing cat food but not other pet products. They create targeted email campaigns offering discounts or loyalty rewards specifically for cat-related items to encourage repeat purchases within this segment.

  If you want to improve customer loyalty and increase revenue, the last thing you should do is send generic marketing emails. Relevant product recommendations or coupons are the best way to use transactional segmentation.

  B2B-specific : Firmographic segmentation

  Beyond the five main segmentation types, B2B marketers often use “firmographic” factors when segmenting their campaigns. It’s a way to segment campaigns that go beyond the considerations of the individual.

  Potential data points :

  • Company size
  • Number of employees
  • Company industry
  • Geographic location (office)

  Example of effective firmographic segmentation :

  Companies of different sizes won’t need the same solution — so segmenting leads by company size is one of the most common and effective examples of B2B audience segmentation.

  The difference here is that B2B campaigns are often segmented through manual research. With an account-based marketing approach, you start by researching your potential customers. You then separate the target audience into smaller segments (or even a one-to-one campaign).

  Start segmenting and analysing your audience more deeply with Matomo

  Segmentation is a great place to start if you want to level up your marketing efforts. Modern consumers expect to get relevant content, and you must give it to them.

  But doing so in a privacy-sensitive way is not always easy. You need the right approach to segment your customer base without alienating them or breaking regulations.

  That’s where Matomo comes in. Matomo champions privacy compliance while offering comprehensive insights and segmentation capabilities. With robust privacy controls and cookieless configuration, it ensures GDPR and other regulations are met, empowering data-driven decisions without compromising user privacy.

  Take advantage of our 21-day free trial to get insights that can help you improve your marketing strategy and better reach your target audience. No credit card required.

• High pitched audio or no audio at all when converting AVI video to MP4 with FFmpeg [closed]

  15 août 2023, par Karen S

  I'm pretty new to FFmpeg and I'm having trouble converting a video from AVI format to MP4. I'm trying to convert pcm_s16le to aac. No matter what audio stream I use, the resulting audio in the MP4 file is either very high pitched and choppy (stream 1) or the audio stream is silent (stream 2).

  


  The AVI video has two audio streams :

  


  Stream #0:1: Audio: pcm_s16le, 48000 Hz, stereo, s16, 1536 kb/s
Stream #0:2: Audio: pcm_s16le, 32000 Hz, stereo, s16, 1024 kb/s


  


  In VLC Media Player the video plays fine and it uses the second audio stream.

  


  When I run ffmpeg -i input.avi -vcodec libx264 -crf 27 output.mp4 in my terminal, the output audio in the MP4 file is very high pitched and choppy in VLC.
This is the codec information for the audio stream in VLC after the conversion :

  


  Codec: MPEG AAC Audio (mp4a)
Type: Audio
Channels: Stereo
Sample rate: 48000 Hz
Bits per sample: 32


  


  When I specify the audio stream and codec the resulting video is silent.

  


  ffmpeg -i input.avi -map 0:v:0 -vcodec libx264 -crf 29 -map 0:a:1 -acodec aac output.mp4


  


  This is the terminal output from FFmpeg :

  


  FILEPATH -i "input.avi" -map 0:v:0 -vcodec libx264 -crf 29 -map 0:a:1 -acodec aac "output.mp4"
ffmpeg version 2023-06-11-git-09621fd7d9-full_build-www.gyan.dev Copyright (c) 2000-2023 the FFmpeg developers
  built with gcc 12.2.0 (Rev10, Built by MSYS2 project)
  configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-libsnappy --enable-zlib --enable-librist --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libaribb24 --enable-libaribcaption --enable-libdav1d --enable-libdavs2 --enable-libuavs3d --enable-libzvbi --enable-librav1e --enable-libsvtav1 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libaom --enable-libjxl --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-liblensfun --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-ffnvcodec --enable-nvdec --enable-nvenc --enable-d3d11va --enable-dxva2 --enable-libvpl --enable-libshaderc --enable-vulkan --enable-libplacebo --enable-opencl --enable-libcdio --enable-libgme --enable-libmodplug --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libshine --enable-libtheora --enable-libtwolame --enable-libvo-amrwbenc --enable-libcodec2 --enable-libilbc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-ladspa --enable-libbs2b --enable-libflite --enable-libmysofa --enable-librubberband --enable-libsoxr --enable-chromaprint
  libavutil      58. 13.100 / 58. 13.100
  libavcodec     60. 17.100 / 60. 17.100
  libavformat    60.  6.100 / 60.  6.100
  libavdevice    60.  2.100 / 60.  2.100
  libavfilter     9.  8.101 /  9.  8.101
  libswscale      7.  3.100 /  7.  3.100
  libswresample   4. 11.100 /  4. 11.100
  libpostproc    57.  2.100 / 57.  2.100
[avi @ 000002533665a400] Switching to NI mode, due to poor interleaving
Input #0, avi, from 'input.avi':
  Duration: 00:10:40.64, start: 0.000000, bitrate: 28852 kb/s
  Stream #0:0: Video: dvvideo, yuv411p, 720x480 [SAR 32:27 DAR 16:9], 25000 kb/s, SAR 8:9 DAR 4:3, 60k fps, 29.97 tbr, 60k tbn
  Stream #0:1: Audio: pcm_s16le, 48000 Hz, stereo, s16, 1536 kb/s
  Stream #0:2: Audio: pcm_s16le, 32000 Hz, stereo, s16, 1024 kb/s
File 'output.mp4' already exists. Overwrite? [y/N] y
Stream mapping:
  Stream #0:0 -> #0:0 (dvvideo (native) -> h264 (libx264))
  Stream #0:2 -> #0:1 (pcm_s16le (native) -> aac (native))
Press [q] to stop, [?] for help
[libx264 @ 00000253366702c0] using SAR=8/9
[libx264 @ 00000253366702c0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 00000253366702c0] profile High 4:2:2, level 3.0, 4:2:2, 8-bit
[libx264 @ 00000253366702c0] 264 - core 164 r3107 a8b68eb - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=29.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, mp4, to 'output.mp4':
  Metadata:
    encoder         : Lavf60.6.100
  Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv422p(tv, bottom coded first (swapped)), 720x480 [SAR 8:9 DAR 4:3], q=2-31, 29.97 fps, 30k tbn
    Metadata:
      encoder         : Lavc60.17.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
  Stream #0:1: Audio: aac (LC) (mp4a / 0x6134706D), 32000 Hz, stereo, fltp, 128 kb/s
    Metadata:
      encoder         : Lavc60.17.100 aac
[dvvideo @ 000002533879d780] Concealing bitstream errors.41 bitrate= 901.6kbits/s speed=2.01x
    Last message repeated 76 times
[dvvideo @ 000002533878f640] Concealing bitstream errors.43 bitrate= 925.1kbits/s speed=1.97x
    Last message repeated 19 times
[out#0/mp4 @ 00000253366770c0] video:72743kB audio:119kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.649282%
frame=19200 fps= 58 q=-1.0 Lsize=   73335kB time=00:10:40.61 bitrate= 937.8kbits/s speed=1.92x
[libx264 @ 00000253366702c0] frame I:99    Avg QP:28.84  size: 35252
[libx264 @ 00000253366702c0] frame P:4922  Avg QP:31.82  size:  9208
[libx264 @ 00000253366702c0] frame B:14179 Avg QP:36.03  size:  1811
[libx264 @ 00000253366702c0] consecutive B-frames:  1.2%  0.8%  1.1% 97.0%
[libx264 @ 00000253366702c0] mb I  I16..4: 13.7% 74.6% 11.7%
[libx264 @ 00000253366702c0] mb P  I16..4:  1.7%  6.7%  1.1%  P16..4: 38.6% 13.2%  9.3%  0.0%  0.0%    skip:29.3%
[libx264 @ 00000253366702c0] mb B  I16..4:  0.2%  0.5%  0.0%  B16..8: 37.3%  2.8%  0.7%  direct: 2.1%  skip:56.3%  L0:42.5% L1:52.2% BI: 5.3%
[libx264 @ 00000253366702c0] 8x8 transform intra:69.6% inter:71.7%
[libx264 @ 00000253366702c0] coded y,uvDC,uvAC intra: 65.4% 59.0% 8.7% inter: 12.9% 7.0% 0.1%
[libx264 @ 00000253366702c0] i16 v,h,dc,p: 12% 64%  7% 16%
[libx264 @ 00000253366702c0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 11% 26% 26%  5%  6%  5%  8%  5%  8%
[libx264 @ 00000253366702c0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu:  9% 48% 15%  4%  5%  4%  7%  3%  5%
[libx264 @ 00000253366702c0] i8c dc,h,v,p: 61% 19% 16%  4%
[libx264 @ 00000253366702c0] Weighted P-Frames: Y:3.5% UV:1.3%
[libx264 @ 00000253366702c0] ref P L0: 55.4% 15.5% 20.6%  8.2%  0.3%
[libx264 @ 00000253366702c0] ref B L0: 89.3%  8.2%  2.5%
[libx264 @ 00000253366702c0] ref B L1: 95.5%  4.5%
[libx264 @ 00000253366702c0] kb/s:930.17
[aac @ 00000253382c31c0] Qavg: 65536.000


  


  Could someone point me in the right direction please ? I've tried doing the conversion with Handbrake as well and the audio is still choppy.