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  • Keeping control of your media in your hands

    13 avril 2011, par

    The vocabulary used on this site and around MediaSPIP in general, aims to avoid reference to Web 2.0 and the companies that profit from media-sharing.
    While using MediaSPIP, you are invited to avoid using words like "Brand", "Cloud" and "Market".
    MediaSPIP is designed to facilitate the sharing of creative media online, while allowing authors to retain complete control of their work.
    MediaSPIP aims to be accessible to as many people as possible and development is based on expanding the (...)

  • Submit bugs and patches

    13 avril 2011

    Unfortunately a software is never perfect.
    If you think you have found a bug, report it using our ticket system. Please to help us to fix it by providing the following information : the browser you are using, including the exact version as precise an explanation as possible of the problem if possible, the steps taken resulting in the problem a link to the site / page in question
    If you think you have solved the bug, fill in a ticket and attach to it a corrective patch.
    You may also (...)

  • Organiser par catégorie

    17 mai 2013, par

    Dans MédiaSPIP, une rubrique a 2 noms : catégorie et rubrique.
    Les différents documents stockés dans MédiaSPIP peuvent être rangés dans différentes catégories. On peut créer une catégorie en cliquant sur "publier une catégorie" dans le menu publier en haut à droite ( après authentification ). Une catégorie peut être rangée dans une autre catégorie aussi ce qui fait qu’on peut construire une arborescence de catégories.
    Lors de la publication prochaine d’un document, la nouvelle catégorie créée sera proposée (...)

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  • Multiple nVidia GPU transcoding (NOT computing) bottleneck

    11 décembre 2018, par Daniel Cantarin

    I’m doing some nVidia multi-GPU testing. However, this tests are not on the computing field but transcoding, using nvenc/nvdec.

    I have a setup with 3 Quadro GPUs (max 4 on this motherboard), and running some transcoding jobs using ffmpeg.

    Thing is, up to 8 jobs everything is fine, with tolerable GPU and CPU stats.
    But when reaching the 9th job, the nVidia cards metrics start to fall down. Whats troublesome is this : it doesn’t matter the job distribution. That is, if I send 8 jobs to GPU0, and 1 job to GPU1, is the same as 4-5, or 4-4-1, or 0-4-5, etc.

    What I see beggining on the 9th job is :

    1. CPU gets to about 60% usage (30% up to 8th jobs) and doesn’t go up much after adding more jobs.
    2. DECODING metrics falls from about 75% (when single card has 8 jobs) to about 20%.
    3. Every card behaves the same when this problem starts, no matter how many jobs they have.

    And the last strange thing I see : when that problem happens, and I kill all the jobs, the cards keep working for a while (sometimes even minutes).

    All this points to some bottleneck somewhere on the motherboard. Maybe the PCIe bus, maybe some CPU subsystem, I’m not sure. It also points to some buffering happening somewhere. I’m using the usual popular tools to see high-level metrics and curves (top/htop, nvidia-smi, nvtop, etc).

    My question : does anybody knows some common bottlenecks regarding multi-GPU setups that could lead to a problem like this ?

    Any tip would be nice.

    Thanks in advance.

  • ffmpeg v4l2 taking 100% cpu usage

    16 novembre 2022, par didentifier

    I am on Fedora 37, version is irrelevant as I had same issue on Fedora 36.

    


    I am using the following command to send video from my usb video card to v4l2loopback and the CPU usage goes to 100% and I can't do much more on my computer (i5-8600k, nvidia 2060 rtx, 16GB ram).

    


    ffmpeg -nostdin -threads 1 -f video4linux2 -video_size 1920x1080 -input_format mjpeg -i /dev/video1 -f v4l2 -preset fast -c:v copy /dev/video0

    


    previously the command was like that and the rest of the arguments was added to see if it makes any difference, it seems it doesn't change much

    


    ffmpeg -f video4linux2 -video_size 1920x1080 -input_format mjpeg -i /dev/video1 -f v4l2 -c copy /dev/video0

    


    To be honest I don't know much about ffmpeg or video in general, I was using the same command a few months ago on the same hardware with almost 0 impact to my performance, I could play games and watch videos at the same time while now I get 15 FPS in my games while I am using ffmpeg. Did anything change in ffmpeg ? Any improvements to my ffmpeg command ? Shouldn't ffmpeg not encode anything if I am using pixelformat and frame rate supported by my usb video card and if that is true shouldn't be super light ?

    


  • How to record audio with ffmpeg on ubuntu 22.04 with python ?

    22 juillet 2024, par Dinh Chu

    I encountered the problem of recording output audio on Ubuntu with Python.
I want to record the output sound from the browser.
I used USB Headphone as the output device, then record sounded with FFMPEG.

    


    pactl list short sources

    


    0. alsa_output.usb-0c76_USB_PnP_Audio_Device-00.analog-stereo.monitor  module-alsa-card.c      s16le 2ch 48000Hz       SUSPENDED
1. alsa_input.usb-0c76_USB_PnP_Audio_Device-00.mono-fallback  module-alsa-card.c      s16le 1ch 48000Hz       SUSPENDED 
2. alsa_output.pci-0000_00_1f.3.iec958-stereo.monitor     module-alsa-card.c      s16le 2ch 44100Hz       SUSPENDED


    


    When I run command on Ubuntu, It recorded audio successfully.

    


    ffmpeg -f pulse -i alsa_output.usb-0c76_USB_PnP_Audio_Device-00.analog-stereo.monitor -t 10 output.wav


    


    However, when I executed the command in the project Python, the error reported :

    


    alsa_output.usb-0c76_USB_PnP_Audio_Device-00.analog-stereo.monitor: Operation not permitted


    


    or if using orther ouput :

    


    alsa_output.pci-0000_00_1f.3.iec958-stereo.monitor: Operation not permitted


    


    Details can see more attached photos :
enter image description here
Record error :
enter image description here

    


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