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There are two types of people in this world: those who watch TV shows with captions on, and those who are weird. All joking aside, the importance of closed captions for video cannot be understated. In addition to being crucial for the deaf and hard-of-hearing, captions also are important when audio is unavailable or not clearly audible. Maybe you're watching a video in a public place and the audio is drowned out by ambient noise. Or maybe the person speaking in the video is using a microphone that isn't the best quality, or speaks with an accent or dialect that is unfamiliar to the viewer. Captions are a good thing. Unfortunately, captioning audio in a live stream is tricky. always Before we dig into the problem of captioning live streams, let's talk about semantics a bit. Did you know that there is a difference between the terms and ? describes as: closed caption subtitle HTML spec subtitles transcription or translation of the dialogue, suitable for when the sound is available but not understood (e.g. because the user does not understand the language of the media resource's audio track). Overlaid on the video. The spec describes as: captions Transcription or translation of the dialogue, sound effects, relevant musical cues, and other relevant audio information, suitable for when sound is unavailable or not clearly audible (e.g. because it is muted, drowned-out by ambient noise, or because the user is deaf). Overlaid on the video; labeled as appropriate for the hard-of-hearing. This means that when we talk about "closed captions" for live videos, we're usually referring to since usually include descriptive information. Think about a scene in a TV show where an actor gets in the car to leave home and says goodbye to their spouse. The caption for this scene might read "Goodbye, dear. [car engine starts]." subtitles captions We're not close to having AI systems describe contextual information like this for us, so we're limited to adding pure "speech-to-text" subtitles captions to our live stream; we can do that using the method below. You’ll notice that the title and body of this blog post uses the terms ‘captions’ or ‘closed captions’ even though what we’re really talking about here are subtitles based on the definitions above. Unfortunately, since the term ‘closed captions’ is so commonly misused, it makes the most sense to use this term improperly to help developers find this blog post and learn how to add this feature to their live streams. Just know that what we’re really talking about here are subtitles! Note: Adding Captions to Amazon IVS Live Streams The solution that we look at in this post focuses on broadcasting to an Amazon Interactive Video Service (Amazon IVS) live stream from . OBS doesn't offer native support for captioning, but there are several plugins that can perform the necessary speech-to-text conversion and publish the captions to an RTMP stream in the . OBS Studio CEA-708/EIA-608 format supported by Amazon IVS For this demo, I've chosen to use the by ratwithacompiler ( and ). To get started with this plugin, and . Once you've got it installed in OBS, select and make sure the dock is enabled. OBS-captions-plugin GitHub plugin page download it install it Docks Captions Next, select the 'gear' icon in the dock to modify the settings. Captions Make sure that a is selected, and modify the plugin configuration to suit your needs. For example, the default for me was set to seconds, but I found seconds to be a better value. Caption Source Caption Timeout 15.0 5.0 Once you've saved your configuration and started a new live stream, the plugin handles converting your speech to text and produce the required caption information to the live stream. To play back the caption data with the Amazon IVS player, we can add an event listener to listen for the event ( ). TextCue docs ivsPlayer.addEventListener(IVSPlayer.PlayerEventType.TEXT_CUE, (evt) => {
  console.log(evt);
} The handler as configured above logs all incoming events to the console. TextCue The property of the event contains the caption data. text TextCue With some HTML and CSS, we can render the caption data as an overlay on the element. This implementation is highly dependent on your needs, but you should take into account auto-hiding the overlay after a specified period of no caption data. <video> https://youtu.be/spFpCIqGSm8?embedable=true Summary In this post, we looked at how to use an OBS plugin to convert speech to text and publish that text as caption data on an Amazon IVS live stream.

How to Add Closed Captions (CC) to Amazon IVS Live Streams

অ্যামাজন আইভিএস লাইভ স্ট্রিমগুলিতে কীভাবে ক্লোজড ক্যাপশন (সিসি) যুক্ত করবেন

So fügen Sie Untertitel (CC) zu Amazon IVS Live Streams hinzu

Cómo agregar subtítulos (CC) a transmisiones en vivo de Amazon IVS

Comment ajouter des sous-titres codés (CC) à un flux en direct Amazon IVS

Amazon IVS लाइव स्ट्रीम में क्लोज्ड कैप्शन (CC) कैसे जोड़ें

Amazon IVS ライブ ストリームにクローズド キャプション (CC) を追加する方法

Amazon IVS 라이브 스트림에 자막(CC)을 추가하는 방법

Como adicionar legendas ocultas (CC) a uma transmissão ao vivo do Amazon IVS

Amazon IVS Canlı Yayınlarına Altyazı (CC) Nasıl Eklenir?

Cách thêm chú thích chi tiết (CC) vào luồng trực tiếp Amazon IVS

如何将隐藏式字幕 (CC) 添加到 Amazon IVS 实时流

In approaching the specifically sexual aspect of odor in the human species

In this article, we will compare the new product from Nvidia, the GeForce RTX 4090, with various professional cards from this manufacturer and try to answer the question, "Is it more beneficial to use a new graphics card in workflows, or is it still better to use ?" server graphics cards have a number of significant differences, determined by the purpose of use: Professional and gaming GPU cards Server graphics cards are used in ML development, rendering and modeling of complex objects, scientific research, cinematography, and more. Gaming graphics cards are designed for individual use. Scope of application. The professional card cooling system blows hot air out of the server or workstation. Their cooling turbine is designed for continuous operation. Gaming cards blow air over the cards. They must be installed in special cases with a good ventilation system. Gaming card fans are not designed for long-term operation and fail during long-term, continuous operation. Cooling. Professional GPUs allow you to do more computing with less power. This feature largely accounts for the high cost of server video cards. Performance and energy efficiency. Quality control in the manufacture of professional cards is stricter than in the creation of gaming cards. Production features. Professional cards are not equipped with connectors (HDMI, DVI) for video output - there is only DisplayPort. Connectors. Not all server GPUs can be used for gaming. Additional functionality. GeForce RTX 4090 Technology Overview The GeForce RTX 4090 GPU was released at the end of 2022 and represents a continuation of the line of desktop accelerators from , which aroused great interest among players around the world. NVIDIA The key features of the card are: As with the entire GeForce RTX 40 line, the new AD10x GPUs (AD102 in the 4090) are based on the Ada Lovelace architecture and use the 4N process (TSMC). Improved performance of ray tracing and machine compute operations on tensor cores. The 4N technological process allows them to increase energy efficiency by several percentage points. The size of the card (304 by 137 mm, 3 slots) makes it difficult to mount it in both desktop PCs and servers. The gaming cooling system often makes it impossible to mount the 4090 in GPU servers. Compared to the 3090, the AD102 has 70% more CUDA cores. NVIDIA DLSS 3 technology uses motion vector analysis and OFA algorithms. The low-latency NVIDIA Reflex platform enhances the gaming experience for professional gamers. 8th generation NVEnc encoder with AV1 encoding support. NVIDIA Broadcast app. NVIDIA Studio. Specifications for NVIDIA RTX A4000, NVIDIA RTX A5000, NVIDIA RTX 3090 and NVIDIA RTX 4090 Graphics Cards RTX A4000 RTX A5000 RTX 3090 RTX 4090 Archi­tec­ture Ampere Ampere Ampere Ada Lovelace Techno­logy pro­cess 8 nm 8 nm 8 nm 4N GPU GA104 GA102 GA102 AD102 Number of transis­tors (billion) 17,4 28,3 28,3 76,3 Clock frequency (GHz) 0,74 1.17 1,39 2,23 Boost Clock (GHz) 1.56 1.70 1.70 2,52 Memory frequency (MHz) 1,750 2,000 1,219 1,325 Memory Band­width (Gb/s) 448 768 936.2 1008 GPU memory(Gb) 16 Gb 24 24 24 Memory type GDDR6 GDDR6 GDDR6X GDDR6X Cache memory (Mb) 4 6 6 72 ECC memory yes yes no no CUDA cores 6 144 8192 10496 16384 Tensor cores 192 256 328 512 RT cores 48 64 82 128 Number of texture modules 192 256 328 512 Maximum power (W) 140 230 350 450 Compu­ting perfor­mance FP16 (half) (tera­flops) 19.2 27.8 35.6 82.6 Compu­ting perfor­mance FP32 (float) (tera­flops) 19.2 27.8 35.6 Up to 82,6 Compu­ting perfor­mance FP64 (double) 599 giga­flops 867.8 giga­flops 556 giga­flops 1.3 tera­flops Theore­tical maximum fill rate (giga­pixels/s) 149.8 162.7 189.8 444 Theore­tical texture fetch rate (giga­texels/s) 149.8 433.9 566 1290 Inter­face PCI-E 4.0 x16 PCI-E 4.0 x16 PCI-E 4.0 x16 PCI-E 4.0 x16 NVIDIA DLSS no no yes 3 Nvlink no 2-board low-profile confi­gura­tion (2-slot and 3-slot bridges) no no CUDA Support 8.6 8.6 8.6 8.9 VULKAN Support 1.3 1.3 1.2 1.3 DirectX 12 Ulti­mate 12 Ulti­mate 12 Ulti­mate 12 Ulti­mate Shader Model 6.6 6.6 6.7 6.7 OpenGL 4.6 4.6 4.6 4.6 OpenCL 3.0 3.0 3.0 3.0 Virtual GPU (vGPU) software support — NVIDIA Virtual PC (vPC) and Vir­tual Appli­cations (vApps), NVIDIA RTX vWS, NVIDIA Vir­tual Com­pute Ser­ver — — Cost (€) 1 500 3 000 1 600 from 1 900 The new architecture, memory bandwidth, number of tensor cores, DLSS 3 technology, and other characteristics of the GeForce RTX 4090 define a wide range of applications for the GPU. It’s not only for gaming but also for working with artificial intelligence and complex calculations. Testing the RTX 4090 and A5000 Description of the test environment AMD Ryzen 9 5900 X 12-Core Processor (3.80 GHz) 32 GB DDR4-3200 ECC DDR4 SDRAM 1600 MHz Microsoft Windows 10 Professional 64-bit V-Ray GPU CUDA test V-Ray GPU RTX Test Blender Benchmark In this test and in LuxMark, we will only compare the RTX A5000 and RTX 4090 cards, as they are the most relevant in the context of this article. LuxMark We measured relative GPU performance in rendering. The performance of the GeForce RTX 4090 in the tests looks impressive and almost doubles not only the results of the but also the professional GPUs. The V-Ray GPU RTX benchmark demonstrates ray-traced GPU performance. The RTX 4090 also outperforms the RTX 3090 by two times. RTX 3090 "Dogs vs. Cats" To compare GPU performance for neural networks, we used the Dogs vs. Cats dataset. The test analyzes the contents of a photo and distinguishes between a cat or a dog in the photo. All necessary initial data is . We ran this test on different GPUs and in different cloud services, and we got the following results: here Full cycle of training (min.) The full training cycle of the test neural network took from 31 to 60 minutes. The result for the GeForce RTX 4090 was 31 minutes and surpassed the performance of all the other GPUs. The most noticeable difference in the results was between the RTX 3090 and RTX 4090 cards: the new generation of GPUs from NVIDIA coped with the calculations almost twice as fast as the previous Nvidia generation. Tests have shown that the closest competitor to the 4090 card is the A5000. It remains to compare these cards in terms of their price-quality ratio. In all the tests, the new card from Nvidia showed a result that exceeded the RTX A5000 by about two times. At the same time, the cost of the RTX 4090 is much lower: $1,599 USD (minimum price) compared to $2,599 USD. It would seem that the choice is obvious, but there are nuances. The A5000 GPU consumes significantly less power and can be a cost-effective solution for tasks with a constant high load on the GPU over the long term. The RTX A5000 also supports NVLink technology, which is useful when training neural networks. A5000 GPUs have no restrictions on using NVENC/NVDEC for parallel video transcoding tasks. With the purchase of a specialized license, the A5000-class professional GPUs can be virtualized and available in the server as several smaller virtual GPUs. Another problem is on using drivers for its gaming cards in data centers and remotely outside the office. Nvidia's ban Although NVidia's promotional photo shows a lot of 4090s with large fans and a 3U format, in reality, this configuration is almost impossible to buy. In stock, there are only large-size gaming cards for 4 units with an increased height and fans blowing out up and down. Such cards cannot be used in servers and in most workstations. Conclusion The transition to the new Ada Lovelace architecture has significantly increased the performance of the GeForce RTX 4090. Improved tensor cores and RT cores significantly improve the quality and expand the capabilities of real-time ray tracing. The 24 GB memory capacity allows you to process large amounts of data. The GeForce RTX 4090 is primarily designed for gaming and is great for various types of computing solutions: AI, data analysis, and machine learning. The new architecture greatly outperforms the previous generation of NVIDIA GPUs. An important limitation in the professional use of this video card is its high power consumption and the inability to combine several cards using Nvlink. An alternative to purchasing a video card is to rent a GPU server. Our calculations show that monthly rentals for the GeForce RTX 4090 and RTX A5000 cards are comparable in price. Accordingly, if you need to perform professional tasks, renting a GeForce RTX 4090 card can be beneficial due to its high performance. or a server with a with professional-grade NVIDIA RTX 5500 / 5000 / A4000 cards. are also available . The GPU card is dedicated to the VM and cannot be used by other clients. GPU performance in virtual machines matches GPU performance in dedicated servers. Rent GPU servers with instant deployment custom configuration VPS with dedicated GPU cards

Which GPU is Better for Business, the RTX 4090 or a Server RTX A5000?

IDEALS AND A REALITY

We’re in the era of widespread video communications in our daily personal and professional lives. Now that people are more accustomed to using video technology, AR stands to dial up both the functionality and fun factor of the medium. From facilitating realistic makeup and beauty effects to virtual accessory try-ons and real-time background replacements, the powerful multiplatform SDK from DeepAR is being used by almost 8,000 developers worldwide for web-based and mobile applications. The startup’s proprietary AR technology allows companies of all sizes to add AR effects to any iOS, Android, macOS or HTML5 application with just a few lines of code, an integration process that takes hours, not days. Once created, AR assets can be reused automatically for any platform. According to DeepAR’s research, AR users in ecommerce typically receive a 15-25% clickthrough rate increase on product pages; up to 120% increased customer dwell time; more engaged customers that are twice as likely to make purchases; and double the conversion rate. The following details how the DeepAR Android SDK can be integrated with Amazon IVS for AR-enhanced live streams. Requirements First, we are going to need to set up a few things. 1. Create a . DeepAR developer account 2. Setup . AWS Account 3. Setup . IAM Permissions 4. . Create a channel Once you have a channel created, you are going to need to use these: a.Ingest server b.Stream key Integration Steps We will showcase the Amazon IVS <> DeepAR integration with a simple Android app that shows a camera preview and lets you add fun AR masks and filters. To follow along with this tutorial clone this . GitHub repo Custom image-input sources allow an application to provide its own image input to the Amazon IVS broadcast SDK, instead of being limited to the preset cameras or screen share. A custom image source can be as simple as a semi-transparent watermark or static "be right back" scene, or it can allow the app to do additional custom processing like adding beauty filters to the camera. You can have multiple custom image sources, like a watermark plus a camera with beauty filters. When you use a custom image-input source for custom control of the camera (such as using beauty-filter libraries that require camera access), the Amazon IVS broadcast SDK is no longer responsible for managing the camera. Instead, the application is responsible for handling the camera’s lifecycle correctly. You will find more details of Amazon IVS broadcast SDK custom image sources . here First, download the . DeepAR Android SDK Copy the deepar.aar located in the lib folder from the downloaded zip into the <repo>/deepar directory. Now open the repo as Android Studio project. Once the project loads, there might be some red errors. If this happens, run the Gradle Sync to fix the errors. Now it’s time to pair your app with the DeepAR account via license key. To generate the license key, you need to on the DeepAR developer site. For testing, create a project with a free plan. Give your project a name and then click add Android app. Copy the App ID from the build.grade of the app. create a project Now copy the generated key and paste it in the MainActivity.java Copy the ingest server and stream key from the Amazon IVS console, and paste them in the MainActivity.java. In this example app we are using Android’s CameraX API to get the frames from the camera. We have set it up with a 1280x720 image resolution. You can look up this part in the bindImageAnalisys() method. The pipeline with DeepAR and Amazon IVS broadcast SDK includes: 1. CameraX sends camera frames in the ARSurfaceProvider class. 2. Feed those frames into DeepAR. 3. DeepAR renders a preview onto the surface provided by Amazon IVS broadcast SDK. Here is where the DeepAR SDK and Amazon IVS broadcast SDK come together. Breaking it down: ●      Create a BroadcastSession with the default device MICROPHONE so that the Amazon IVS broadcast SDK handles the sound part. ●      Create a custom input image source from the broadcast session. Set its size the same as camera (in this case 720p), and set the rotation to zero. ●      Bind broadcast session to the slot. ●      Set DeepAR to render to the custom image source surface. ●      Get the preview view from the broadcast session and add it to the view hierarchy to show the preview on the screen. ●      Start the broadcast session with ingest server and stream key. This will start streaming frames that DeepAR renders to the surface provided by the broadcast session. Testing Time Now run the app on the device. You should see a camera preview. With left and right buttons, you can change the AR masks and filters. Open the channel you created in the Amazon IVS console and open the live stream tab, then you should be streaming the video augmented with AR effects.

How to Use DeepAR For AR Effects on Amazon IVS Live Streams 

Cómo usar DeepAR para efectos AR en transmisiones en vivo de Amazon IVS

Comment utiliser DeepAR pour les effets AR sur les flux en direct Amazon IVS

Amazon IVS लाइव स्ट्रीम पर AR प्रभावों के लिए DeepAR का उपयोग कैसे करें

Amazon IVS ライブ ストリームでの AR 効果に DeepAR を使用する方法

Como usar o DeepAR para efeitos AR em transmissões ao vivo do Amazon IVS

Cách sử dụng DeepAR cho Hiệu ứng AR trên Luồng trực tiếp Amazon IVS

如何在 Amazon IVS 直播流上使用 DeepAR 实现 AR 效果

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Since n.d.

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