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Médias (2)
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GetID3 - Bloc informations de fichiers
9 avril 2013, par
Mis à jour : Mai 2013
Langue : français
Type : Image
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GetID3 - Boutons supplémentaires
9 avril 2013, par
Mis à jour : Avril 2013
Langue : français
Type : Image
Autres articles (13)
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Changer son thème graphique
22 février 2011, parLe thème graphique ne touche pas à la disposition à proprement dite des éléments dans la page. Il ne fait que modifier l’apparence des éléments.
Le placement peut être modifié effectivement, mais cette modification n’est que visuelle et non pas au niveau de la représentation sémantique de la page.
Modifier le thème graphique utilisé
Pour modifier le thème graphique utilisé, il est nécessaire que le plugin zen-garden soit activé sur le site.
Il suffit ensuite de se rendre dans l’espace de configuration du (...) -
Encoding and processing into web-friendly formats
13 avril 2011, parMediaSPIP automatically converts uploaded files to internet-compatible formats.
Video files are encoded in MP4, Ogv and WebM (supported by HTML5) and MP4 (supported by Flash).
Audio files are encoded in MP3 and Ogg (supported by HTML5) and MP3 (supported by Flash).
Where possible, text is analyzed in order to retrieve the data needed for search engine detection, and then exported as a series of image files.
All uploaded files are stored online in their original format, so you can (...) -
Other interesting software
13 avril 2011, parWe don’t claim to be the only ones doing what we do ... and especially not to assert claims to be the best either ... What we do, we just try to do it well and getting better ...
The following list represents softwares that tend to be more or less as MediaSPIP or that MediaSPIP tries more or less to do the same, whatever ...
We don’t know them, we didn’t try them, but you can take a peek.
Videopress
Website : http://videopress.com/
License : GNU/GPL v2
Source code : (...)
Sur d’autres sites (4351)
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How to fix error FFmpeg "Error during encoding : failed to allocate memory (-4)"
22 avril 2022, par SCAR101I have a problem with FFmpeg encoding. It can write for an hour or two hours or 5 minutes, and then he gives out error :
[h264_qsv @ 000001a47094f340] Error during encoding: failed to allocate memory (-4). What am I doing wrong ? Here is the FFmpeg string :

-y -f dshow -video_size 1920x1080 -rtbufsize 702000k -framerate 25 
-i video="Decklink Video Capture":audio="Decklink Audio Capture
-vf "drawtext=fontfile=C\\:\\\\Windows\\\\Fonts\\\\arial.ttf:fontsize=36:fontcolor=red:text='%{localtime}'" 
-s 720x540 -pix_fmt yuv420p -c:v h264_qsv 
-preset veryfast -b:v 512k -acodec libmp3lame 
-b:a 96k -ac 2 -ar 44100 D:\Input_1\2022\4\20\Input_1_20.04.2022_08.16.31.mkv


Computer Specifications : Core I5 11600, 16Gb.


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Benefits and Shortcomings of Multi-Touch Attribution
13 mars 2023, par Erin — Analytics TipsFew sales happen instantly. Consumers take their time to discover, evaluate and become convinced to go with your offer.
Multi-channel attribution (also known as multi-touch attribution or MTA) helps businesses better understand which marketing tactics impact consumers’ decisions at different stages of their buying journey. Then double down on what’s working to secure more sales.
Unlike standard analytics, multi-channel modelling combines data from various channels to determine their cumulative and independent impact on your conversion rates.
The main benefit of multi-touch attribution is obvious : See top-performing channels, as well as those involved in assisted conversions. The drawback of multi-touch attribution : It comes with a more complex setup process.
If you’re on the fence about getting started with multi-touch attribution, here’s a summary of the main arguments for and against it.
What Are the Benefits of Multi-Touch Attribution ?
Remember an old parable of blind men and an elephant ?
Each one touched the elephant and drew conclusions about how it might look. The group ended up with different perceptions of the animal and thought the others were lying…until they decided to work together on establishing the truth.
Multi-channel analytics works in a similar way : It reconciles data from various channels and campaign types into one complete picture. So that you can get aligned on the efficacy of different campaign types and gain some other benefits too.
Better Understanding of Customer Journeys
On average, it takes 8 interactions with a prospect to generate a conversion. These interactions happen in three stages :
- Awareness : You need to introduce your company to the target buyers and pique their interest in your solution (top-of-the-funnel).
- Consideration : The next step is to channel this casual interest into deliberate research and evaluation of your offer (middle-of-the-funnel).
- Decision : Finally, you need to get the buyer to commit to your offer and close the deal (bottom-of-the-funnel).
You can analyse funnels using various attribution models — last-click, fist-click, position-based attribution, etc. Each model, however, will spotlight the different element(s) of your sales funnel.
For example, a single-touch attribution model like last-click zooms in on the bottom-of-the-funnel stage. You can evaluate which channels (or on-site elements) sealed the deal for the prospect. For example, a site visitor arrived from an affiliate link and started a free trial. In this case, the affiliate (referral traffic) gets 100% credit for the conversion.
This measurement tactic, however, doesn’t show which channels brought the customer to the very bottom of your funnel. For instance, they may have interacted with a social media post, your landing pages or a banner ad before that.
Multi-touch attribution modelling takes funnel analysis a notch further. In this case, you map more steps in the customer journey — actions, events, and pages that triggered a visitor’s decision to convert — in your website analytics tool.
Then, select a multi-touch attribution model, which provides more backward visibility aka allows you to track more than one channel, preceding the conversion.
For example, a Position Based attribution model reports back on all interactions a site visitor had between their first visit and conversion.
A prospect first lands at your website via search results (Search traffic), which gets a 40% credit in this model. Two days later, the same person discovers a mention of your website on another blog and visits again (Referral traffic). This time, they save the page as a bookmark and revisit it again in two more days (Direct traffic). Each of these channels will get a 10% credit. A week later, the prospect lands again on your site via Twitter (Social) and makes a request for a demo. Social would then receive a 40% credit for this conversion. Last-click would have only credited social media and first-click — search engines.
The bottom line : Multi-channel attribution models show how different channels (and marketing tactics) contribute to conversions at different stages of the customer journey. Without it, you get an incomplete picture.
Improved Budget Allocation
Understanding causal relationships between marketing activities and conversion rates can help you optimise your budgets.
First-click/last-click attribution models emphasise the role of one channel. This can prompt you toward the wrong conclusions.
For instance, your Facebook ads campaigns do great according to a first-touch model. So you decide to increase the budget. What you might be missing though is that you could have an even higher conversion rate and revenue if you fix “funnel leaks” — address high drop-off rates during checkout, improve page layout and address other possible reasons for exiting the page.
Funnel reports at Matomo allow you to see how many people proceed to the next conversion stage and investigate why they drop off. By knowing when and why people abandon their purchase journey, you can improve your marketing velocity (aka the speed of seeing the campaign results) and your marketing costs (aka the budgets you allocate toward different assets, touchpoints and campaign types).
Or as one of the godfathers of marketing technology, Dan McGaw, explained in a webinar :
“Once you have a multi-touch attribution model, you [can] actually know the return on ad spend on a per-campaign basis. Sometimes, you can get it down to keywords. Sometimes, you can get down to all kinds of other information, but you start to realise, “Oh, this campaign sucks. I should shut this off.” And then really, that’s what it’s about. It’s seeing those campaigns that suck and turning them off and then taking that budget and putting it into the campaigns that are working”.
More Accurate Measurements
The big boon of multi-channel marketing attribution is that you can zoom in on various elements of your funnel and gain granular data on the asset’s performance.
In other words : You get more accurate insights into the different elements involved in customer journeys. But for accurate analytics measurements, you must configure accurate tracking.
Define your objectives first : How do you want a multi-touch attribution tool to help you ? Multi-channel attribution analysis helps you answer important questions such as :
- How many touchpoints are involved in the conversions ?
- How long does it take for a lead to convert on average ?
- When and where do different audience groups convert ?
- What is your average win rate for different types of campaigns ?
Your objectives will dictate which multi-channel modelling approach will work best for your business — as well as the data you’ll need to collect.
At the highest level, you need to collect two data points :
- Conversions : Desired actions from your prospects — a sale, a newsletter subscription, a form submission, etc. Record them as tracked Goals.
- Touchpoints : Specific interactions between your brand and targets — specific page visits, referral traffic from a particular marketing channel, etc. Record them as tracked Events.
Your attribution modelling software will then establish correlation patterns between actions (conversions) and assets (touchpoints), which triggered them.
The accuracy of these measurements, however, will depend on the quality of data and the type of attribution modelling used.
Data quality stands for your ability to procure accurate, complete and comprehensive information from various touchpoints. For instance, some data won’t be available if the user rejected a cookie consent banner (unless you’re using a privacy-focused web analytics tool like Matomo).
Different attribution modelling techniques come with inherent shortcomings too as they don’t accurately represent the average sales cycle length or track visitor-level data, which allows you to understand which customer segments convert best.
Learn more about selecting the optimal multi-channel attribution model for your business.
What Are the Limitations of Multi-Touch Attribution ?
Overall, multi-touch attribution offers a more comprehensive view of the conversion paths. However, each attribution model (except for custom ones) comes with inherent assumptions about the contribution of different channels (e.g,. 25%-25%-25%-25% in linear attribution or 40%-10%-10%-40% in position-based attribution). These conversion credit allocations may not accurately represent the realities of your industry.
Also, most attribution models don’t reflect incremental revenue you gain from existing customers, which aren’t converting through analysed channels. For example, account upgrades to a higher tier, triggered via an in-app offer. Or warranty upsell, made via a marketing email.
In addition, you should keep in mind several other limitations of multi-touch attribution software.
Limited Marketing Mix Analysis
Multi-touch attribution tools work in conjunction with your website analytics app (as they draw most data from it). Because of that, such models inherit the same visibility into your marketing mix — a combo of tactics you use to influence consumer decisions.
Multi-touch attribution tools cannot evaluate the impact of :
- Dark social channels
- Word-of-mouth
- Offline promotional events
- TV or out-of-home ad campaigns
If you want to incorporate this data into your multi-attribution reporting, you’ll have to procure extra data from other systems — CRM, ad measurement partners, etc, — and create complex custom analytics models for its evaluation.
Time-Based Constraints
Most analytics apps provide a maximum 90-day lookback window for attribution. This can be short for companies with longer sales cycles.
Source : Marketing Charts Marketing channels can be overlooked or underappreciated when your attribution window is too short. Because of that, you may curtail spending on brand awareness campaigns, which, in turn, will reduce the number of people entering the later stages of your funnel.
At the same time, many businesses would also want to track a look-forward window — the revenue you’ll get from one customer over their lifetime. In this case, not all tools may allow you to capture accurate information on repeat conversions — through re-purchases, account tier updates, add-ons, upsells, etc.
Again, to get an accurate picture you’ll need to understand how far into the future you should track conversions. Will you only record your first sales as a revenue number or monitor customer lifetime value (CLV) over 3, 6 or 12 months ?
The latter is more challenging to do. But CLV data can add another depth of dimension to your modelling accuracy. With Matomo, you set up this type of tracking by using our visitors’ tracking feature. We can help you track select visitors with known identifiers (e.g. name or email address) to discover their visiting patterns over time.
Limited Access to Raw Data
In web analytics, raw data stands for unprocessed website visitor information, stripped from any filters, segmentation or sampling applied.
Data sampling is a practice of analysing data subsets (instead of complete records) to extrapolate findings towards the entire data set. Google Analytics 4 applies data sampling once you hit over 500k sessions at the property level. So instead of accurate, real-life reporting, you receive approximations, generated by machine learning models. Data sampling is one of the main reasons behind Google Analytics’ accuracy issues.
In multi-channel attribution modelling, usage of sampled data creates further inconsistencies between the reports and the actual state of affairs. For instance, if your website generates 5 million page views, GA multi-touch analytical reports are based on the 500K sample size aka only 90% of the collected information. This hardly represents the real effect of all marketing channels and can lead to subpar decision-making.
With Matomo, the above is never an issue. We don’t apply data sampling to any websites (no matter the volume of traffic) and generate all the reports, including multi-channel attribution ones, based on 100% real user data.
AI Application
On the other hand, websites with smaller traffic volumes often have limited sampling datasets for building attribution models. Some tracking data may also be not available because the visitor rejected a cookie banner, for instance. On average, less than 50% of users in Australia, France, Germany, Denmark and the US among other countries always consent to all cookies.
To compensate for such scenarios, some multi-touch attribution solutions apply AI algorithms to “fill in the blanks”, which impacts the reporting accuracy. Once again, you get approximate data of what probably happened. However, Matomo is legally exempt from showing a cookie consent banner in most EU markets. Meaning you can collect 100% accurate data to make data-driven decisions.
Difficult Technical Implementation
Ever since attribution modelling got traction in digital marketing, more and more tools started to emerge.
Source : Markets and Markets Most web analytics apps include multi-touch attribution reports. Then there are standalone multi-channel attribution platforms, offering extra features for conversion rate optimization, offline channel tracking, data-driven custom modelling, etc.
Most advanced solutions aren’t available out of the box. Instead, you have to install several applications, configure integrations with requested data sources, and then use the provided interfaces to code together custom data models. Such solutions are great if you have a technical marketer or a data science team. But a steep learning curve and high setup costs make them less attractive for smaller teams.
Conclusion
Multi-touch attribution modelling lifts the curtain in more steps, involved in various customer journeys. By understanding which touchpoints contribute to conversions, you can better plan your campaign types and budget allocations.
That said, to benefit from multi-touch attribution modelling, marketers also need to do the preliminary work : Determine the key goals, set up event and conversion tracking, and then — select the optimal attribution model type and tool.
Matomo combines simplicity with sophistication. We provide marketers with familiar, intuitive interfaces for setting up conversion tracking across the funnel. Then generate attribution reports, based on 100% accurate data (without any sampling or “guesstimation” applied). You can also get access to raw analytics data to create custom attribution models or plug it into another tool !
Start using accurate, easy-to-use multi-channel attribution with Matomo. Start your free 21-day trial now. No credit card requried.
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FFMPEG Output File is Empty Nothing was Encoded (for a Picture) ?
4 mars 2023, par Sarah SzaboI have a strange issue effecting one of my programs that does bulk media conversions using ffmpeg from the command line, however this effects me using it directly from the shell as well :


ffmpeg -i INPUT.mkv -ss 0:30 -y -qscale:v 2 -frames:v 1 -f image2 -huffman optimal "OUTPUT.png"
fails every run with the error message :
Output file is empty, nothing was encoded (check -ss / -t / -frames parameters if used)

This only happens with very specific videos, and seemingly no other videos. File type is usually .webm. These files have been downloaded properly (usually from yt-dlp), and I have tried re-downloading them just to verify their integrity.


One such file from a colleague was : https://www.dropbox.com/s/xkucr2z5ra1p2oh/Triggerheart%20Execlica%20OST%20%28Arrange%29%20-%20Crueltear%20Ending.mkv?dl=0


Is there a subtle issue with the command string ?


Notes :


removing
-huffman optimalhad no effect

moving
-ssto before-ihad no effect

removing
-f image2 had no effect

Full Log :


sarah@MidnightStarSign:~/Music/Playlists/Indexing/Indexing Temp$ ffmpeg -i Triggerheart\ Execlica\ OST\ \(Arrange\)\ -\ Crueltear\ Ending.mkv -ss 0:30 -y -qscale:v 2 -frames:v 1 -f image2 -huffman optimal "TEST.png"
ffmpeg version n5.1.2 Copyright (c) 2000-2022 the FFmpeg developers
 built with gcc 12.2.0 (GCC)
 configuration: --prefix=/usr --disable-debug --disable-static --disable-stripping --enable-amf --enable-avisynth --enable-cuda-llvm --enable-lto --enable-fontconfig --enable-gmp --enable-gnutls --enable-gpl --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libdav1d --enable-libdrm --enable-libfreetype --enable-libfribidi --enable-libgsm --enable-libiec61883 --enable-libjack --enable-libmfx --enable-libmodplug --enable-libmp3lame --enable-libopencore_amrnb --enable-libopencore_amrwb --enable-libopenjpeg --enable-libopus --enable-libpulse --enable-librav1e --enable-librsvg --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libtheora --enable-libv4l2 --enable-libvidstab --enable-libvmaf --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxcb --enable-libxml2 --enable-libxvid --enable-libzimg --enable-nvdec --enable-nvenc --enable-opencl --enable-opengl --enable-shared --enable-version3 --enable-vulkan
 libavutil 57. 28.100 / 57. 28.100
 libavcodec 59. 37.100 / 59. 37.100
 libavformat 59. 27.100 / 59. 27.100
 libavdevice 59. 7.100 / 59. 7.100
 libavfilter 8. 44.100 / 8. 44.100
 libswscale 6. 7.100 / 6. 7.100
 libswresample 4. 7.100 / 4. 7.100
 libpostproc 56. 6.100 / 56. 6.100
[matroska,webm @ 0x55927f484740] Could not find codec parameters for stream 2 (Attachment: none): unknown codec
Consider increasing the value for the 'analyzeduration' (0) and 'probesize' (5000000) options
Input #0, matroska,webm, from 'Triggerheart Execlica OST (Arrange) - Crueltear Ending.mkv':
 Metadata:
 title : TriggerHeart Exelica PS2 & 360 Arrange ー 16 - Crueltear Ending
 PURL : https://www.youtube.com/watch?v=zJ0bEa_8xEg
 COMMENT : https://www.youtube.com/watch?v=zJ0bEa_8xEg
 ARTIST : VinnyVynce
 DATE : 20170905
 ENCODER : Lavf59.27.100
 Duration: 00:00:30.00, start: -0.007000, bitrate: 430 kb/s
 Stream #0:0(eng): Video: vp9 (Profile 0), yuv420p(tv, bt709), 720x720, SAR 1:1 DAR 1:1, 25 fps, 25 tbr, 1k tbn (default)
 Metadata:
 DURATION : 00:00:29.934000000
 Stream #0:1(eng): Audio: opus, 48000 Hz, stereo, fltp (default)
 Metadata:
 DURATION : 00:00:30.001000000
 Stream #0:2: Attachment: none
 Metadata:
 filename : cover.webp
 mimetype : image/webp
Codec AVOption huffman (Huffman table strategy) specified for output file #0 (TEST.png) has not been used for any stream. The most likely reason is either wrong type (e.g. a video option with no video streams) or that it is a private option of some encoder which was not actually used for any stream.
Stream mapping:
 Stream #0:0 -> #0:0 (vp9 (native) -> png (native))
Press [q] to stop, [?] for help
Output #0, image2, to 'TEST.png':
 Metadata:
 title : TriggerHeart Exelica PS2 & 360 Arrange ー 16 - Crueltear Ending
 PURL : https://www.youtube.com/watch?v=zJ0bEa_8xEg
 COMMENT : https://www.youtube.com/watch?v=zJ0bEa_8xEg
 ARTIST : VinnyVynce
 DATE : 20170905
 encoder : Lavf59.27.100
 Stream #0:0(eng): Video: png, rgb24, 720x720 [SAR 1:1 DAR 1:1], q=2-31, 200 kb/s, 25 fps, 25 tbn (default)
 Metadata:
 DURATION : 00:00:29.934000000
 encoder : Lavc59.37.100 png
frame= 0 fps=0.0 q=0.0 Lsize=N/A time=00:00:00.00 bitrate=N/A speed= 0x 
video:0kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
Output file is empty, nothing was encoded (check -ss / -t / -frames parameters if used)


Manjaro OS System Specs :


System:
 Kernel: 6.1.12-1-MANJARO arch: x86_64 bits: 64 compiler: gcc v: 12.2.1
 parameters: BOOT_IMAGE=/@/boot/vmlinuz-6.1-x86_64
 root=UUID=f11386cf-342d-47ac-84e6-484b7b2f377d rw rootflags=subvol=@
 radeon.modeset=1 nvdia-drm.modeset=1 quiet
 cryptdevice=UUID=059df4b4-5be4-44d6-a23a-de81135eb5b4:luks-disk
 root=/dev/mapper/luks-disk apparmor=1 security=apparmor
 resume=/dev/mapper/luks-swap udev.log_priority=3
 Desktop: KDE Plasma v: 5.26.5 tk: Qt v: 5.15.8 wm: kwin_x11 vt: 1 dm: SDDM
 Distro: Manjaro Linux base: Arch Linux
Machine:
 Type: Desktop Mobo: ASUSTeK model: PRIME X570-PRO v: Rev X.0x
 serial: <superuser required="required"> UEFI: American Megatrends v: 4408
 date: 10/27/2022
Battery:
 Message: No system battery data found. Is one present?
Memory:
 RAM: total: 62.71 GiB used: 27.76 GiB (44.3%)
 RAM Report: permissions: Unable to run dmidecode. Root privileges required.
CPU:
 Info: model: AMD Ryzen 9 5950X bits: 64 type: MT MCP arch: Zen 3+ gen: 4
 level: v3 note: check built: 2022 process: TSMC n6 (7nm) family: 0x19 (25)
 model-id: 0x21 (33) stepping: 0 microcode: 0xA201016
 Topology: cpus: 1x cores: 16 tpc: 2 threads: 32 smt: enabled cache:
 L1: 1024 KiB desc: d-16x32 KiB; i-16x32 KiB L2: 8 MiB desc: 16x512 KiB
 L3: 64 MiB desc: 2x32 MiB
 Speed (MHz): avg: 4099 high: 4111 min/max: 2200/6358 boost: disabled
 scaling: driver: acpi-cpufreq governor: schedutil cores: 1: 4099 2: 4095
 3: 4102 4: 4100 5: 4097 6: 4100 7: 4110 8: 4111 9: 4083 10: 4099 11: 4100
 12: 4094 13: 4097 14: 4101 15: 4100 16: 4099 17: 4100 18: 4097 19: 4098
 20: 4095 21: 4100 22: 4099 23: 4099 24: 4105 25: 4098 26: 4100 27: 4100
 28: 4092 29: 4103 30: 4101 31: 4100 32: 4099 bogomips: 262520
 Flags: 3dnowprefetch abm adx aes aperfmperf apic arat avic avx avx2 bmi1
 bmi2 bpext cat_l3 cdp_l3 clflush clflushopt clwb clzero cmov cmp_legacy
 constant_tsc cpb cpuid cqm cqm_llc cqm_mbm_local cqm_mbm_total
 cqm_occup_llc cr8_legacy cx16 cx8 de decodeassists erms extapic
 extd_apicid f16c flushbyasid fma fpu fsgsbase fsrm fxsr fxsr_opt ht
 hw_pstate ibpb ibrs ibs invpcid irperf lahf_lm lbrv lm mba mca mce
 misalignsse mmx mmxext monitor movbe msr mtrr mwaitx nonstop_tsc nopl npt
 nrip_save nx ospke osvw overflow_recov pae pat pausefilter pclmulqdq
 pdpe1gb perfctr_core perfctr_llc perfctr_nb pfthreshold pge pku pni popcnt
 pse pse36 rapl rdpid rdpru rdrand rdseed rdt_a rdtscp rep_good sep sha_ni
 skinit smap smca smep ssbd sse sse2 sse4_1 sse4_2 sse4a ssse3 stibp succor
 svm svm_lock syscall tce topoext tsc tsc_scale umip v_spec_ctrl
 v_vmsave_vmload vaes vgif vmcb_clean vme vmmcall vpclmulqdq wbnoinvd wdt
 x2apic xgetbv1 xsave xsavec xsaveerptr xsaveopt xsaves
 Vulnerabilities:
 Type: itlb_multihit status: Not affected
 Type: l1tf status: Not affected
 Type: mds status: Not affected
 Type: meltdown status: Not affected
 Type: mmio_stale_data status: Not affected
 Type: retbleed status: Not affected
 Type: spec_store_bypass mitigation: Speculative Store Bypass disabled via
 prctl
 Type: spectre_v1 mitigation: usercopy/swapgs barriers and __user pointer
 sanitization
 Type: spectre_v2 mitigation: Retpolines, IBPB: conditional, IBRS_FW,
 STIBP: always-on, RSB filling, PBRSB-eIBRS: Not affected
 Type: srbds status: Not affected
 Type: tsx_async_abort status: Not affected
Graphics:
 Device-1: NVIDIA GA104 [GeForce RTX 3070] vendor: ASUSTeK driver: nvidia
 v: 525.89.02 alternate: nouveau,nvidia_drm non-free: 525.xx+
 status: current (as of 2023-02) arch: Ampere code: GAxxx
 process: TSMC n7 (7nm) built: 2020-22 pcie: gen: 4 speed: 16 GT/s lanes: 8
 link-max: lanes: 16 bus-ID: 0b:00.0 chip-ID: 10de:2484 class-ID: 0300
 Device-2: AMD Cape Verde PRO [Radeon HD 7750/8740 / R7 250E]
 vendor: VISIONTEK driver: radeon v: kernel alternate: amdgpu arch: GCN-1
 code: Southern Islands process: TSMC 28nm built: 2011-20 pcie: gen: 3
 speed: 8 GT/s lanes: 8 link-max: lanes: 16 ports: active: DP-3,DP-4
 empty: DP-1, DP-2, DP-5, DP-6 bus-ID: 0c:00.0 chip-ID: 1002:683f
 class-ID: 0300 temp: 54.0 C
 Device-3: Microdia USB 2.0 Camera type: USB driver: snd-usb-audio,uvcvideo
 bus-ID: 9-2:3 chip-ID: 0c45:6367 class-ID: 0102 serial: <filter>
 Display: x11 server: X.Org v: 21.1.7 with: Xwayland v: 22.1.8
 compositor: kwin_x11 driver: X: loaded: modesetting,nvidia dri: radeonsi
 gpu: radeon display-ID: :0 screens: 1
 Screen-1: 0 s-res: 5760x2160 s-dpi: 80 s-size: 1829x686mm (72.01x27.01")
 s-diag: 1953mm (76.91")
 Monitor-1: DP-1 pos: 1-2 res: 1920x1080 dpi: 93
 size: 527x296mm (20.75x11.65") diag: 604mm (23.8") modes: N/A
 Monitor-2: DP-1-3 pos: 2-1 res: 1920x1080 dpi: 82
 size: 598x336mm (23.54x13.23") diag: 686mm (27.01") modes: N/A
 Monitor-3: DP-1-4 pos: 1-1 res: 1920x1080 dpi: 93
 size: 527x296mm (20.75x11.65") diag: 604mm (23.8") modes: N/A
 Monitor-4: DP-3 pos: primary,2-2 res: 1920x1080 dpi: 82
 size: 598x336mm (23.54x13.23") diag: 686mm (27.01") modes: N/A
 Monitor-5: DP-4 pos: 2-4 res: 1920x1080 dpi: 82
 size: 598x336mm (23.54x13.23") diag: 686mm (27.01") modes: N/A
 Monitor-6: HDMI-0 pos: 1-3 res: 1920x1080 dpi: 93
 size: 527x296mm (20.75x11.65") diag: 604mm (23.8") modes: N/A
 API: OpenGL v: 4.6.0 NVIDIA 525.89.02 renderer: NVIDIA GeForce RTX
 3070/PCIe/SSE2 direct-render: Yes
Audio:
 Device-1: NVIDIA GA104 High Definition Audio vendor: ASUSTeK
 driver: snd_hda_intel bus-ID: 5-1:2 v: kernel chip-ID: 30be:1019 pcie:
 class-ID: 0102 gen: 4 speed: 16 GT/s lanes: 8 link-max: lanes: 16
 bus-ID: 0b:00.1 chip-ID: 10de:228b class-ID: 0403
 Device-2: AMD Oland/Hainan/Cape Verde/Pitcairn HDMI Audio [Radeon HD 7000
 Series] vendor: VISIONTEK driver: snd_hda_intel v: kernel pcie: gen: 3
 speed: 8 GT/s lanes: 8 link-max: lanes: 16 bus-ID: 0c:00.1
 chip-ID: 1002:aab0 class-ID: 0403
 Device-3: AMD Starship/Matisse HD Audio vendor: ASUSTeK
 driver: snd_hda_intel v: kernel pcie: gen: 4 speed: 16 GT/s lanes: 16
 bus-ID: 0e:00.4 chip-ID: 1022:1487 class-ID: 0403
 Device-4: Schiit Audio Unison Universal Dac type: USB driver: snd-usb-audio
 Device-5: JMTek LLC. Plugable USB Audio Device type: USB
 driver: hid-generic,snd-usb-audio,usbhid bus-ID: 5-2:3 chip-ID: 0c76:120b
 class-ID: 0300 serial: <filter>
 Device-6: ASUSTek ASUS AI Noise-Cancelling Mic Adapter type: USB
 driver: hid-generic,snd-usb-audio,usbhid bus-ID: 5-4:4 chip-ID: 0b05:194e
 class-ID: 0300 serial: <filter>
 Device-7: Microdia USB 2.0 Camera type: USB driver: snd-usb-audio,uvcvideo
 bus-ID: 9-2:3 chip-ID: 0c45:6367 class-ID: 0102 serial: <filter>
 Sound API: ALSA v: k6.1.12-1-MANJARO running: yes
 Sound Interface: sndio v: N/A running: no
 Sound Server-1: PulseAudio v: 16.1 running: no
 Sound Server-2: PipeWire v: 0.3.65 running: yes
Network:
 Device-1: Intel I211 Gigabit Network vendor: ASUSTeK driver: igb v: kernel
 pcie: gen: 1 speed: 2.5 GT/s lanes: 1 port: f000 bus-ID: 07:00.0
 chip-ID: 8086:1539 class-ID: 0200
 IF: enp7s0 state: up speed: 1000 Mbps duplex: full mac: <filter>
 IP v4: <filter> type: dynamic noprefixroute scope: global
 broadcast: <filter>
 IP v6: <filter> type: noprefixroute scope: link
 IF-ID-1: docker0 state: down mac: <filter>
 IP v4: <filter> scope: global broadcast: <filter>
 WAN IP: <filter>
Bluetooth:
 Device-1: Cambridge Silicon Radio Bluetooth Dongle (HCI mode) type: USB
 driver: btusb v: 0.8 bus-ID: 5-5.3:7 chip-ID: 0a12:0001 class-ID: e001
 Report: rfkill ID: hci0 rfk-id: 0 state: up address: see --recommends
Logical:
 Message: No logical block device data found.
 Device-1: luks-c847cf9f-c6b5-4624-a25e-4531e318851a maj-min: 254:2
 type: LUKS dm: dm-2 size: 3.64 TiB
 Components:
 p-1: sda1 maj-min: 8:1 size: 3.64 TiB
 Device-2: luks-swap maj-min: 254:1 type: LUKS dm: dm-1 size: 12 GiB
 Components:
 p-1: nvme0n1p2 maj-min: 259:2 size: 12 GiB
 Device-3: luks-disk maj-min: 254:0 type: LUKS dm: dm-0 size: 919.01 GiB
 Components:
 p-1: nvme0n1p3 maj-min: 259:3 size: 919.01 GiB
RAID:
 Message: No RAID data found.
Drives:
 Local Storage: total: 9.1 TiB used: 2.79 TiB (30.6%)
 SMART Message: Unable to run smartctl. Root privileges required.
 ID-1: /dev/nvme0n1 maj-min: 259:0 vendor: Western Digital
 model: WDS100T3X0C-00SJG0 size: 931.51 GiB block-size: physical: 512 B
 logical: 512 B speed: 31.6 Gb/s lanes: 4 type: SSD serial: <filter>
 rev: 111110WD temp: 53.9 C scheme: GPT
 ID-2: /dev/nvme1n1 maj-min: 259:4 vendor: Western Digital
 model: WDS100T2B0C-00PXH0 size: 931.51 GiB block-size: physical: 512 B
 logical: 512 B speed: 31.6 Gb/s lanes: 4 type: SSD serial: <filter>
 rev: 211070WD temp: 46.9 C scheme: GPT
 ID-3: /dev/sda maj-min: 8:0 vendor: Western Digital
 model: WD4005FZBX-00K5WB0 size: 3.64 TiB block-size: physical: 4096 B
 logical: 512 B speed: 6.0 Gb/s type: HDD rpm: 7200 serial: <filter>
 rev: 1A01 scheme: GPT
 ID-4: /dev/sdb maj-min: 8:16 vendor: Western Digital
 model: WD4005FZBX-00K5WB0 size: 3.64 TiB block-size: physical: 4096 B
 logical: 512 B speed: 6.0 Gb/s type: HDD rpm: 7200 serial: <filter>
 rev: 1A01 scheme: GPT
 ID-5: /dev/sdc maj-min: 8:32 type: USB vendor: SanDisk
 model: Gaming Xbox 360 size: 7.48 GiB block-size: physical: 512 B
 logical: 512 B type: N/A serial: <filter> rev: 8.02 scheme: MBR
 SMART Message: Unknown USB bridge. Flash drive/Unsupported enclosure?
 Message: No optical or floppy data found.
Partition:
 ID-1: / raw-size: 919.01 GiB size: 919.01 GiB (100.00%)
 used: 611.14 GiB (66.5%) fs: btrfs dev: /dev/dm-0 maj-min: 254:0
 mapped: luks-disk label: N/A uuid: N/A
 ID-2: /boot/efi raw-size: 512 MiB size: 511 MiB (99.80%)
 used: 40.2 MiB (7.9%) fs: vfat dev: /dev/nvme0n1p1 maj-min: 259:1 label: EFI
 uuid: 8922-E04D
 ID-3: /home raw-size: 919.01 GiB size: 919.01 GiB (100.00%)
 used: 611.14 GiB (66.5%) fs: btrfs dev: /dev/dm-0 maj-min: 254:0
 mapped: luks-disk label: N/A uuid: N/A
 ID-4: /run/media/sarah/ConvergentRefuge raw-size: 3.64 TiB
 size: 3.64 TiB (100.00%) used: 2.19 TiB (60.1%) fs: btrfs dev: /dev/dm-2
 maj-min: 254:2 mapped: luks-c847cf9f-c6b5-4624-a25e-4531e318851a
 label: ConvergentRefuge uuid: 7d295e73-4143-4eb1-9d22-75a06b1d2984
 ID-5: /run/media/sarah/MSS_EXtended raw-size: 475.51 GiB
 size: 475.51 GiB (100.00%) used: 1.48 GiB (0.3%) fs: btrfs
 dev: /dev/nvme1n1p1 maj-min: 259:5 label: MSS EXtended
 uuid: f98b3a12-e0e4-48c7-91c2-6e3aa6dcd32c
Swap:
 Kernel: swappiness: 60 (default) cache-pressure: 100 (default)
 ID-1: swap-1 type: partition size: 12 GiB used: 6.86 GiB (57.2%)
 priority: -2 dev: /dev/dm-1 maj-min: 254:1 mapped: luks-swap label: SWAP
 uuid: c8991364-85a7-4e6c-8380-49cd5bd7a873
Unmounted:
 ID-1: /dev/nvme1n1p2 maj-min: 259:6 size: 456 GiB fs: ntfs label: N/A
 uuid: 5ECA358FCA356485
 ID-2: /dev/sdb1 maj-min: 8:17 size: 3.64 TiB fs: ntfs
 label: JerichoVariance uuid: 1AB22D5664889CBD
 ID-3: /dev/sdc1 maj-min: 8:33 size: 3.57 GiB fs: iso9660
 ID-4: /dev/sdc2 maj-min: 8:34 size: 4 MiB fs: vfat label: MISO_EFI
 uuid: 5C67-4BF8
USB:
 Hub-1: 1-0:1 info: Hi-speed hub with single TT ports: 4 rev: 2.0
 speed: 480 Mb/s chip-ID: 1d6b:0002 class-ID: 0900
 Hub-2: 1-2:2 info: Hitachi ports: 4 rev: 2.1 speed: 480 Mb/s
 chip-ID: 045b:0209 class-ID: 0900
 Device-1: 1-2.4:3 info: Microsoft Xbox One Controller (Firmware 2015)
 type: <vendor specific="specific"> driver: xpad interfaces: 3 rev: 2.0 speed: 12 Mb/s
 power: 500mA chip-ID: 045e:02dd class-ID: ff00 serial: <filter>
 Hub-3: 2-0:1 info: Super-speed hub ports: 4 rev: 3.0 speed: 5 Gb/s
 chip-ID: 1d6b:0003 class-ID: 0900
 Hub-4: 2-2:2 info: Hitachi ports: 4 rev: 3.0 speed: 5 Gb/s
 chip-ID: 045b:0210 class-ID: 0900
 Hub-5: 3-0:1 info: Hi-speed hub with single TT ports: 1 rev: 2.0
 speed: 480 Mb/s chip-ID: 1d6b:0002 class-ID: 0900
 Hub-6: 3-1:2 info: VIA Labs Hub ports: 4 rev: 2.1 speed: 480 Mb/s
 power: 100mA chip-ID: 2109:3431 class-ID: 0900
 Hub-7: 3-1.2:3 info: VIA Labs VL813 Hub ports: 4 rev: 2.1 speed: 480 Mb/s
 chip-ID: 2109:2813 class-ID: 0900
 Hub-8: 4-0:1 info: Super-speed hub ports: 4 rev: 3.0 speed: 5 Gb/s
 chip-ID: 1d6b:0003 class-ID: 0900
 Hub-9: 4-2:2 info: VIA Labs VL813 Hub ports: 4 rev: 3.0 speed: 5 Gb/s
 chip-ID: 2109:0813 class-ID: 0900
 Hub-10: 5-0:1 info: Hi-speed hub with single TT ports: 6 rev: 2.0
 speed: 480 Mb/s chip-ID: 1d6b:0002 class-ID: 0900
 Device-1: 5-1:2 info: Schiit Audio Unison Universal Dac type: Audio
 driver: snd-usb-audio interfaces: 2 rev: 2.0 speed: 480 Mb/s power: 500mA
 chip-ID: 30be:1019 class-ID: 0102
 Device-2: 5-2:3 info: JMTek LLC. Plugable USB Audio Device type: Audio,HID
 driver: hid-generic,snd-usb-audio,usbhid interfaces: 4 rev: 1.1
 speed: 12 Mb/s power: 100mA chip-ID: 0c76:120b class-ID: 0300
 serial: <filter>
 Device-3: 5-4:4 info: ASUSTek ASUS AI Noise-Cancelling Mic Adapter
 type: Audio,HID driver: hid-generic,snd-usb-audio,usbhid interfaces: 4
 rev: 1.1 speed: 12 Mb/s power: 100mA chip-ID: 0b05:194e class-ID: 0300
 serial: <filter>
 Hub-11: 5-5:5 info: Genesys Logic Hub ports: 4 rev: 2.0 speed: 480 Mb/s
 power: 100mA chip-ID: 05e3:0608 class-ID: 0900
 Device-1: 5-5.3:7 info: Cambridge Silicon Radio Bluetooth Dongle (HCI mode)
 type: Bluetooth driver: btusb interfaces: 2 rev: 2.0 speed: 12 Mb/s
 power: 100mA chip-ID: 0a12:0001 class-ID: e001
 Hub-12: 5-6:6 info: Genesys Logic Hub ports: 4 rev: 2.0 speed: 480 Mb/s
 power: 100mA chip-ID: 05e3:0608 class-ID: 0900
 Hub-13: 6-0:1 info: Super-speed hub ports: 4 rev: 3.1 speed: 10 Gb/s
 chip-ID: 1d6b:0003 class-ID: 0900
 Hub-14: 7-0:1 info: Hi-speed hub with single TT ports: 6 rev: 2.0
 speed: 480 Mb/s chip-ID: 1d6b:0002 class-ID: 0900
 Device-1: 7-2:2 info: SanDisk Cruzer Micro Flash Drive type: Mass Storage
 driver: usb-storage interfaces: 1 rev: 2.0 speed: 480 Mb/s power: 200mA
 chip-ID: 0781:5151 class-ID: 0806 serial: <filter>
 Device-2: 7-4:3 info: ASUSTek AURA LED Controller type: HID
 driver: hid-generic,usbhid interfaces: 2 rev: 2.0 speed: 12 Mb/s power: 16mA
 chip-ID: 0b05:18f3 class-ID: 0300 serial: <filter>
 Hub-15: 8-0:1 info: Super-speed hub ports: 4 rev: 3.1 speed: 10 Gb/s
 chip-ID: 1d6b:0003 class-ID: 0900
 Hub-16: 9-0:1 info: Hi-speed hub with single TT ports: 4 rev: 2.0
 speed: 480 Mb/s chip-ID: 1d6b:0002 class-ID: 0900
 Hub-17: 9-1:2 info: Terminus FE 2.1 7-port Hub ports: 7 rev: 2.0
 speed: 480 Mb/s power: 100mA chip-ID: 1a40:0201 class-ID: 0900
 Device-1: 9-1.1:4 info: Sunplus Innovation Gaming mouse [Philips SPK9304]
 type: Mouse driver: hid-generic,usbhid interfaces: 1 rev: 2.0 speed: 1.5 Mb/s
 power: 98mA chip-ID: 1bcf:08a0 class-ID: 0301
 Device-2: 9-1.5:6 info: Microdia Backlit Gaming Keyboard
 type: Keyboard,Mouse driver: hid-generic,usbhid interfaces: 2 rev: 2.0
 speed: 12 Mb/s power: 400mA chip-ID: 0c45:652f class-ID: 0301
 Device-3: 9-1.6:7 info: HUION H420 type: Mouse,HID driver: uclogic,usbhid
 interfaces: 3 rev: 1.1 speed: 12 Mb/s power: 100mA chip-ID: 256c:006e
 class-ID: 0300
 Hub-18: 9-1.7:8 info: Terminus Hub ports: 4 rev: 2.0 speed: 480 Mb/s
 power: 100mA chip-ID: 1a40:0101 class-ID: 0900
 Device-1: 9-2:3 info: Microdia USB 2.0 Camera type: Video,Audio
 driver: snd-usb-audio,uvcvideo interfaces: 4 rev: 2.0 speed: 480 Mb/s
 power: 500mA chip-ID: 0c45:6367 class-ID: 0102 serial: <filter>
 Device-2: 9-4:11 info: VKB-Sim © Alex Oz 2021 VKBsim Gladiator EVO L
 type: HID driver: hid-generic,usbhid interfaces: 1 rev: 2.0 speed: 12 Mb/s
 power: 500mA chip-ID: 231d:0201 class-ID: 0300
 Hub-19: 10-0:1 info: Super-speed hub ports: 4 rev: 3.1 speed: 10 Gb/s
 chip-ID: 1d6b:0003 class-ID: 0900
Sensors:
 System Temperatures: cpu: 38.0 C mobo: 41.0 C
 Fan Speeds (RPM): fan-1: 702 fan-2: 747 fan-3: 938 fan-4: 889 fan-5: 3132
 fan-6: 0 fan-7: 0
 GPU: device: nvidia screen: :0.0 temp: 49 C fan: 0% device: radeon
 temp: 53.0 C
Info:
 Processes: 842 Uptime: 3h 11m wakeups: 0 Init: systemd v: 252
 default: graphical tool: systemctl Compilers: gcc: 12.2.1 alt: 10/11
 clang: 15.0.7 Packages: 2158 pm: pacman pkgs: 2110 libs: 495 tools: pamac,yay
 pm: flatpak pkgs: 31 pm: snap pkgs: 17 Shell: Bash v: 5.1.16
 running-in: yakuake inxi: 3.3.25
</filter></filter></filter></filter></filter></filter></vendor></filter></filter></filter></filter></filter></filter></filter></filter></filter></filter></filter></filter></filter></filter></filter></filter></filter></superuser>

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