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DJ Dolores - Oslodum 2004 (includes (cc) sample of “Oslodum” by Gilberto Gil)
15 septembre 2011, par
Mis à jour : Septembre 2011
Langue : English
Type : Audio
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Sur d’autres sites (10347)
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How to Choose the Optimal Multi-Touch Attribution Model for Your Organisation
13 mars 2023, par Erin — Analytics TipsIf you struggle to connect the dots on your customer journeys, you are researching the correct solution.
Multi-channel attribution models allow you to better understand the users’ paths to conversion and identify key channels and marketing assets that assist them.
That said, each attribution model has inherent limitations, which make the selection process even harder.
This guide explains how to choose the optimal multi-touch attribution model. We cover the pros and cons of popular attribution models, main evaluation criteria and how-to instructions for model implementation.
Pros and Cons of Different Attribution Models
First Interaction
First Interaction attribution model (also known as first touch) assigns full credit to the conversion to the first channel, which brought in a lead. However, it doesn’t report other interactions the visitor had before converting.
Marketers, who are primarily focused on demand generation and user acquisition, find the first touch attribution model useful to evaluate and optimise top-of-the-funnel (ToFU).
Pros
- Reflects the start of the customer journey
- Shows channels that bring in the best-qualified leads
- Helps track brand awareness campaigns
Cons
- Ignores the impact of later interactions at the middle and bottom of the funnel
- Doesn’t provide a full picture of users’ decision-making process
Last Interaction
Last Interaction attribution model (also known as last touch) shifts the entire credit allocation to the last channel before conversion. But it doesn’t account for the contribution of all other channels.
If your focus is conversion optimization, the last-touch model helps you determine which channels, assets or campaigns seal the deal for the prospect.
Pros
- Reports bottom-of-the-funnel events
- Requires minimal data and configurations
- Helps estimate cost-per-lead or cost-per-acquisition
Cons
- No visibility into assisted conversions and prior visitor interactions
- Overemphasise the importance of the last channel (which can often be direct traffic)
Last Non-Direct Interaction
Last Non-Direct attribution excludes direct traffic from the calculation and assigns the full conversion credit to the preceding channel. For example, a paid ad will receive 100% of credit for conversion if a visitor goes directly to your website to buy a product.
Last Non-Direct attribution provides greater clarity into the bottom-of-the-funnel (BoFU). events. Yet, it still under-reports the role other channels played in conversion.
Pros
- Improved channel visibility, compared to Last-Touch
- Avoids over-valuing direct visits
- Reports on lead-generation efforts
Cons
- Doesn’t work for account-based marketing (ABM)
- Devalues the quality over quantity of leads
Linear Model
Linear attribution model assigns equal credit for a conversion to all tracked touchpoints, regardless of their impact on the visitor’s decision to convert.
It helps you understand the full conversion path. But this model doesn’t distinguish between the importance of lead generation activities versus nurturing touches.
Pros
- Focuses on all touch points associated with a conversion
- Reflects more steps in the customer journey
- Helps analyse longer sales cycles
Cons
- Doesn’t accurately reflect the varying roles of each touchpoint
- Can dilute the credit if too many touchpoints are involved
Time Decay Model
Time decay models assumes that the closer a touchpoint is to the conversion, the greater its influence. Pre-conversion touchpoints get the highest credit, while the first ones are ranked lower (5%-5%-10%-15%-25%-30%).
This model better reflects real-life customer journeys. However, it devalues the impact of brand awareness and demand-generation campaigns.
Pros
- Helps track longer sales cycles and reports on each touchpoint involved
- Allows customising the half-life of decay to improve reporting
- Promotes conversion optimization at BoFu stages
Cons
- Can prompt marketers to curtail ToFU spending, which would translate to fewer qualified leads at lower stages
- Doesn’t reflect highly-influential events at earlier stages (e.g., a product demo request or free account registration, which didn’t immediately lead to conversion)
Position-Based Model
Position-Based attribution model (also known as the U-shaped model) allocates the biggest credit to the first and the last interaction (40% each). Then distributes the remaining 20% across other touches.
For many marketers, that’s the preferred multi-touch attribution model as it allows optimising both ToFU and BoFU channels.
Pros
- Helps establish the main channels for lead generation and conversion
- Adds extra layers of visibility, compared to first- and last-touch attribution models
- Promotes budget allocation toward the most strategic touchpoints
Cons
- Diminishes the importance of lead nurturing activities as more credit gets assigned to demand-gen and conversion-generation channels
- Limited flexibility since it always assigns a fixed amount of credit to the first and last touchpoints, and the remaining credit is divided evenly among the other touchpoints
How to Choose the Right Multi-Touch Attribution Model For Your Business
If you’re deciding which attribution model is best for your business, prepare for a heated discussion. Each one has its trade-offs as it emphasises or devalues the role of different channels and marketing activities.
To reach a consensus, the best strategy is to evaluate each model against three criteria : Your marketing objectives, sales cycle length and data availability.
Marketing Objectives
Businesses generate revenue in many ways : Through direct sales, subscriptions, referral fees, licensing agreements, one-off or retainer services. Or any combination of these activities.
In each case, your marketing strategy will look different. For example, SaaS and direct-to-consumer (DTC) eCommerce brands have to maximise both demand generation and conversion rates. In contrast, a B2B cybersecurity consulting firm is more interested in attracting qualified leads (as opposed to any type of traffic) and progressively nurturing them towards a big-ticket purchase.
When selecting a multi-touch attribution model, prioritise your objectives first. Create a simple scoreboard, where your team ranks various channels and campaign types you rely on to close sales.
Alternatively, you can survey your customers to learn how they first heard about your company and what eventually triggered their conversion. Having data from both sides can help you cross-validate your assumptions and eliminate some biases.
Then consider which model would best reflect the role and importance of different channels in your sales cycle. Speaking of which….
Sales Cycle Length
As shoppers, we spend less time deciding on a new toothpaste brand versus contemplating a new IT system purchase. Factors like industry, business model (B2C, DTC, B2B, B2BC), and deal size determine the average cycle length in your industry.
Statistically, low-ticket B2C sales can happen within just several interactions. The average B2B decision-making process can have over 15 steps, spread over several months.
That’s why not all multi-touch attribution models work equally well for each business. Time-decay suits better B2B companies, while B2C usually go for position-based or linear attribution.
Data Availability
Businesses struggle with multi-touch attribution model implementation due to incomplete analytics data.
Our web analytics tool captures more data than Google Analytics. That’s because we rely on a privacy-focused tracking mechanism, which allows you to collect analytics without showing a cookie consent banner in markets outside of Germany and the UK.
Cookie consent banners are mandatory with Google Analytics. Yet, almost 40% of global consumers reject it. This results in gaps in your analytics and subsequent inconsistencies in multi-touch attribution reports. With Matomo, you can compliantly collect more data for accurate reporting.
Some companies also struggle to connect collected insights to individual shoppers. With Matomo, you can cross-attribute users across browning sessions, using our visitors’ tracking feature.
When you already know a user’s identifier (e.g., full name or email address), you can track their on-site behaviours over time to better understand how they interact with your content and complete their purchases. Quick disclaimer, though, visitors’ tracking may not be considered compliant with certain data privacy laws. Please consult with a local authority if you have doubts.
How to Implement Multi-Touch Attribution
Multi-touch attribution modelling implementation is like a “seek and find” game. You have to identify all significant touchpoints in your customers’ journeys. And sometimes also brainstorm new ways to uncover the missing parts. Then figure out the best way to track users’ actions at those stages (aka do conversion and events tracking).
Here’s a step-by-step walkthrough to help you get started.
Select a Multi-Touch Attribution Tool
The global marketing attribution software is worth $3.1 billion. Meaning there are plenty of tools, differing in terms of accuracy, sophistication and price.
To make the right call prioritise five factors :
- Available models : Look for a solution that offers multiple options and allows you to experiment with different modelling techniques or develop custom models.
- Implementation complexity : Some providers offer advanced data modelling tools for creating custom multi-touch attribution models, but offer few out-of-the-box modelling options.
- Accuracy : Check if the shortlisted tool collects the type of data you need. Prioritise providers who are less dependent on third-party cookies and allow you to identify repeat users.
- Your marketing stack : Some marketing attribution tools come with useful add-ons such as tag manager, heatmaps, form analytics, user session recordings and A/B testing tools. This means you can collect more data for multi-channel modelling with them instead of investing in extra software.
- Compliance : Ensure that the selected multi-attribution analytics software wouldn’t put you at risk of GDPR non-compliance when it comes to user privacy and consent to tracking/analysis.
Finally, evaluate the adoption costs. Free multi-channel analytics tools come with data quality and consistency trade-offs. Premium attribution tools may have “hidden” licensing costs and bill you for extra data integrations.
Look for a tool that offers a good price-to-value ratio (i.e., one that offers extra perks for a transparent price).
Set Up Proper Data Collection
Multi-touch attribution requires ample user data. To collect the right type of insights you need to set up :
- Website analytics : Ensure that you have all tracking codes installed (and working correctly !) to capture pageviews, on-site actions, referral sources and other data points around what users do on page.
- Tags : Add tracking parameters to monitor different referral channels (e.g., “facebook”), campaign types (e.g., ”final-sale”), and creative assets (e.g., “banner-1”). Tags help you get a clearer picture of different touchpoints.
- Integrations : To better identify on-site users and track their actions, you can also populate your attribution tool with data from your other tools – CRM system, A/B testing app, etc.
Finally, think about the ideal lookback window — a bounded time frame you’ll use to calculate conversions. For example, Matomo has a default windows of 7, 30 or 90 days. But you can configure a custom period to better reflect your average sales cycle. For instance, if you’re selling makeup, a shorter window could yield better results. But if you’re selling CRM software for the manufacturing industry, consider extending it.
Configure Goals and Events
Goals indicate your main marketing objectives — more traffic, conversions and sales. In web analytics tools, you can measure these by tracking specific user behaviours.
For example : If your goal is lead generation, you can track :
- Newsletter sign ups
- Product demo requests
- Gated content downloads
- Free trial account registration
- Contact form submission
- On-site call bookings
In each case, you can set up a unique tag to monitor these types of requests. Then analyse conversion rates — the percentage of users who have successfully completed the action.
To collect sufficient data for multi-channel attribution modelling, set up Goal Tracking for different types of touchpoints (MoFU & BoFU) and asset types (contact forms, downloadable assets, etc).
Your next task is to figure out how users interact with different on-site assets. That’s when Event Tracking comes in handy.
Event Tracking reports notify you about specific actions users take on your website. With Matomo Event Tracking, you can monitor where people click on your website, on which pages they click newsletter subscription links, or when they try to interact with static content elements (e.g., a non-clickable banner).
Using in-depth user behavioural reports, you can better understand which assets play a key role in the average customer journey. Using this data, you can localise “leaks” in your sales funnel and fix them to increase conversion rates.
Test and Validated the Selected Model
A common challenge of multi-channel attribution modelling is determining the correct correlation and causality between exposure to touchpoints and purchases.
For example, a user who bought a discounted product from a Facebook ad would act differently than someone who purchased a full-priced product via a newsletter link. Their rate of pre- and post-sales exposure will also differ a lot — and your attribution model may not always accurately capture that.
That’s why you have to continuously test and tweak the selected model type. The best approach for that is lift analysis.
Lift analysis means comparing how your key metrics (e.g., revenue or conversion rates) change among users who were exposed to a certain campaign versus a control group.
In the case of multi-touch attribution modelling, you have to monitor how your metrics change after you’ve acted on the model recommendations (e.g., invested more in a well-performing referral channel or tried a new brand awareness Twitter ad). Compare the before and after ROI. If you see a positive dynamic, your model works great.
The downside of this approach is that you have to invest a lot upfront. But if your goal is to create a trustworthy attribution model, the best way to validate is to act on its suggestions and then test them against past results.
Conclusion
A multi-touch attribution model helps you measure the impact of different channels, campaign types, and marketing assets on metrics that matter — conversion rate, sales volumes and ROI.
Using this data, you can invest budgets into the best-performing channels and confidently experiment with new campaign types.
As a Matomo user, you also get to do so without breaching customers’ privacy or compromising on analytics accuracy.
Start using accurate multi-channel attribution in Matomo. Get your free 21-day trial now. No credit card required.
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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>

-
What permission ffmpeg-static need in AWS Lambda ?
17 février 2023, par JánosI have this code. It download a image, made a video from it and upload it to S3. It runs on Lambda. Added packages, intalled, zipped, uploaded.


npm install --production
zip -r my-lambda-function.zip ./


But get an
error code 126

2023-02-17T09:27:55.236Z 5c845bb6-02c1-41b0-8759-4459591b57b0 INFO Error: ffmpeg exited with code 126
 at ChildProcess.<anonymous> (/var/task/node_modules/fluent-ffmpeg/lib/processor.js:182:22)
 at ChildProcess.emit (node:events:513:28)
 at ChildProcess._handle.onexit (node:internal/child_process:291:12)
2023-02-17T09:27:55.236Z 5c845bb6-02c1-41b0-8759-4459591b57b0 INFO Error: ffmpeg exited with code 126 at ChildProcess.<anonymous> (/var/task/node_modules/fluent-ffmpeg/lib/processor.js:182:22) at ChildProcess.emit (node:events:513:28) at ChildProcess._handle.onexit (node:internal/child_process:291:12)
</anonymous></anonymous>

Do I need to set a specific premission for ffmpeg ?


import { PutObjectCommand, S3Client } from '@aws-sdk/client-s3'
import { fromNodeProviderChain } from '@aws-sdk/credential-providers'
import axios from 'axios'
import pathToFfmpeg from 'ffmpeg-static'
import ffmpeg from 'fluent-ffmpeg'
import fs from 'fs'
ffmpeg.setFfmpegPath(pathToFfmpeg)
const credentials = fromNodeProviderChain({
 clientConfig: {
 region: 'eu-central-1',
 },
})
const client = new S3Client({ credentials })

export const handler = async (event, context) => {
 try {
 let body
 let statusCode = 200
 const query = event?.queryStringParameters
 if (!query?.imgId && !query?.video1Id && !query?.video2Id) {
 return
 }

 const imgId = query?.imgId
 const video1Id = query?.video1Id
 const video2Id = query?.video2Id
 console.log(
 `Parameters received, imgId: ${imgId}, video1Id: ${video1Id}, video2Id: ${video2Id}`
 )
 const imgURL = getFileURL(imgId)
 const video1URL = getFileURL(`${video1Id}.mp4`)
 const video2URL = getFileURL(`${video2Id}.mp4`)
 const imagePath = `/tmp/${imgId}`
 const video1Path = `/tmp/${video1Id}.mp4`
 const video2Path = `/tmp/${video2Id}.mp4`
 const outputPath = `/tmp/${imgId}.mp4`
 await Promise.all([
 downloadFile(imgURL, imagePath),
 downloadFile(video1URL, video1Path),
 downloadFile(video2URL, video2Path),
 ])
 await new Promise((resolve, reject) => {
 console.log('Input files downloaded')
 ffmpeg()
 .input(imagePath)
 .inputFormat('image2')
 .inputFPS(30)
 .loop(1)
 .size('1080x1080')
 .videoCodec('libx264')
 .format('mp4')
 .outputOptions([
 '-tune animation',
 '-pix_fmt yuv420p',
 '-profile:v baseline',
 '-level 3.0',
 '-preset medium',
 '-crf 23',
 '-movflags +faststart',
 '-y',
 ])
 .output(outputPath)
 .on('end', () => {
 console.log('Output file generated')
 resolve()
 })
 .on('error', (e) => {
 console.log(e)
 reject()
 })
 .run()
 
 })
 await uploadFile(outputPath, imgId + '.mp4')
 .then((url) => {
 body = JSON.stringify({
 url,
 })
 })
 .catch((error) => {
 console.error(error)
 statusCode = 400
 body = error?.message ?? error
 })
 console.log(`File uploaded to S3`)
 const headers = {
 'Content-Type': 'application/json',
 'Access-Control-Allow-Headers': 'Content-Type',
 'Access-Control-Allow-Origin': 'https://tikex.com, https://borespiac.hu',
 'Access-Control-Allow-Methods': 'GET',
 }
 return {
 statusCode,
 body,
 headers,
 }
 } catch (error) {
 console.error(error)
 return {
 statusCode: 500,
 body: JSON.stringify('Error fetching data'),
 }
 }
}

const downloadFile = async (url, path) => {
 try {
 console.log(`Download will start: ${url}`)
 const response = await axios(url, {
 responseType: 'stream',
 })
 if (response.status !== 200) {
 throw new Error(
 `Failed to download file, status code: ${response.status}`
 )
 }
 response.data
 .pipe(fs.createWriteStream(path))
 .on('finish', () => console.log(`File downloaded to ${path}`))
 .on('error', (e) => {
 throw new Error(`Failed to save file: ${e}`)
 })
 } catch (e) {
 console.error(`Error downloading file: ${e}`)
 }
}
const uploadFile = async (path, id) => {
 const buffer = fs.readFileSync(path)
 const params = {
 Bucket: 't44-post-cover',
 ACL: 'public-read',
 Key: id,
 ContentType: 'video/mp4',
 Body: buffer,
 }
 await client.send(new PutObjectCommand(params))
 return getFileURL(id)
}
const getFileURL = (id) => {
 const bucket = 't44-post-cover'
 const url = `https://${bucket}.s3.eu-central-1.amazonaws.com/${id}`
 return url
}


Added
AWSLambdaBasicExecutionRole-16e770c8-05fa-4c42-9819-12c468cb5b49permission, with policy :

{
 "Version": "2012-10-17",
 "Statement": [
 {
 "Effect": "Allow",
 "Action": "logs:CreateLogGroup",
 "Resource": "arn:aws:logs:eu-central-1:634617701827:*"
 },
 {
 "Effect": "Allow",
 "Action": [
 "logs:CreateLogStream",
 "logs:PutLogEvents"
 ],
 "Resource": [
 "arn:aws:logs:eu-central-1:634617701827:log-group:/aws/lambda/promo-video-composer-2:*"
 ]
 },
 {
 "Effect": "Allow",
 "Action": [
 "s3:GetObject",
 "s3:PutObject",
 "s3:ListBucket"
 ],
 "Resource": [
 "arn:aws:s3:::example-bucket",
 "arn:aws:s3:::example-bucket/*"
 ]
 },
 {
 "Effect": "Allow",
 "Action": [
 "logs:CreateLogGroup",
 "logs:CreateLogStream",
 "logs:PutLogEvents"
 ],
 "Resource": [
 "arn:aws:logs:*:*:*"
 ]
 },
 {
 "Effect": "Allow",
 "Action": [
 "ec2:DescribeNetworkInterfaces"
 ],
 "Resource": [
 "*"
 ]
 },
 {
 "Effect": "Allow",
 "Action": [
 "sns:*"
 ],
 "Resource": [
 "*"
 ]
 },
 {
 "Effect": "Allow",
 "Action": [
 "cloudwatch:*"
 ],
 "Resource": [
 "*"
 ]
 },
 {
 "Effect": "Allow",
 "Action": [
 "kms:Decrypt"
 ],
 "Resource": [
 "*"
 ]
 }
 ]
}


What do I miss ?


janoskukoda@Janoss-MacBook-Pro promo-video-composer-2 % ls -l $(which ffmpeg)
lrwxr-xr-x 1 janoskukoda admin 35 Feb 10 12:50 /opt/homebrew/bin/ffmpeg -> ../Cellar/ffmpeg/5.1.2_4/bin/ffmpeg


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