video api

video industry

Normal

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Emilien Gobillot

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Video trends

**The comeback of specialized hardware and its impact on the video industry**

Over the past few decades, the trend in computing has been toward general-purpose computing systems that can run a wide variety of applications. This has led to the widespread use of [CPUs and GPUs](https://www.intel.com/content/www/us/en/products/docs/processors/cpu-vs-gpu.html). However, there has been a recent resurgence of interest in specialized hardware, which is designed specifically to perform one or a few tasks extremely efficiently. In this blog post, we will explore the reasons behind this trend and some of the critical examples of specialized hardware being used in the video industry today.

**Why is specialized hardware making a comeback in computing?**

Specialized hardware in the computing industry refers to computer components or devices designed for a specific purpose, such as application-specific integrated circuits (ASICs)  or systems-on-a-chip (SoCs) . These hardware components are optimized for their specific task, resulting in faster and more efficient performance than general-purpose hardware.

Several factors have contributed to the recent resurgence of specialized hardware in computer science:

* **High efficiency : Why we want it quick and ressource-efficient**

One key driver is the increasing demand for high-performance solutions. Artificial intelligence, machine learning and video processing are domains where CPUs are far from being the right solution. Regarding AI workloads, training, and running models are more efficient on GPU and even better on specialized hardware like Tensor processing units (TPUs). Google has been the first to design TPUs. These ASICs (Application Specific Integrated Circuits) outperform the CPUs.

Why are CPUs outperformed ? In early 2023 commodity servers come with 1 or 2 sockets and maximum 96 cores per socket for the x86 microarchitecture (128 for ARM). On the other hand, a server with a mid range CPUs can receive several ASICs/TPUs on the PCIe ports. Those boosted systems outperforms by a lot the CPUs based system. The TPUv4, in terms of Tflops is 2000x faster than a laptop according to Google.

The benefits of a specialized hardware is that it provides better performance, higher density and a lower energy consumption than the classic CPUs.

In most cases, TPU/ASIC > GPU > CPU on the 3 criteria : performance, density, and energy savings.

* **Total Cost of Ownership (TCO): Why It's Worth the cost**

Specialized hardware exhibits smaller size, higer speed, and greater electrical efficiency. As a result the density increases and the number of computing units decreases. At the end, the TCO is improved. After the switch to ASICs based VCUs (Video coding unit), Youtube is [reported](https://www.semianalysis.com/p/google-new-custom-silicon-replaces) to have saved millions of CPUs ! At this scale you also save data centers 💰

**How specialized hardware is changing the game in the video industry**

In the video industry, one of the most computationally intensive processes is video transcoding, especially for high-quality videos. Transcoding decodes a video from one codec and encodes it to another, such as from H.264 to H.265. This process can also involve changing other video parameters like resolution, bit rate, frame rate, etc. This has led to a need for fast and efficient video transcoding solutions without sacrificing the video quality output.

Transcoding is a service api.video provides in addition to hosting and delivery. Via an API call, a video is uploaded, and then the URL of the video is returned immediately. Note that high resolution and new codecs increase the computational cost.

Similarly to Artificial Intelligence (AI), several types of hardware are commonly used in video transcoding applications:

1. ASICs (Application Specific Integrated Circuits): Video ASICs are specialized hardware accelerators designed specifically for video processing tasks, such as video encoding/ decoding / transcoding. They are sometimes called VPU (Video Processing Unit) or VCU (Video coding Unit). They significantly improve performance over traditional CPUs, particularly for high-resolution or high-frame-rate video. Alphabet, the mother company of Google and YouTube has designed its own ASICs : codename Argos. For a system with 20 VCUs, the TCO has decreased by a 33x factor for VP9 encoding, and throughput increased by a 100x factor!

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Facebook/ Meta made the same move to specific hardware. They partnered with silicon vendors to [design custom ASICs](https://engineering.fb.com/2019/03/14/data-center-engineering/accelerating-infrastructure/).

The electric consumption of an ASIC is around a few watts.

2. FPGAs (Field-programmable gate arrays): FPGAs are programmable hardware devices that can be customized for specific applications, including video transcoding. They can provide significant performance improvements over traditional CPUs for certain types of video transcoding tasks, such as simultaneously encoding or decoding multiple video streams. Twitch is using an [FPGA from Xilinx](https://blog.twitch.tv/en/2018/12/19/how-v-p9-delivers-value-for-twitch-s-esports-live-streaming-35db26f6322f/).

The electric consumption is around 25 W for a PCIe card

3. Graphics processing units (GPUs): GPUs are another type of hardware. This hardware is more generic than FPGA and ASIC, but specialized video encoders/decoders are added to these cards. Nvidia was the first company to build a datacenter GPU for transcoding. Intel enters the market in late 2022.

The electric consumption is generally around 70W.

Regarding energy consumption ASICs > (are better than) FPGAs > GPU > CPU. This order is the same for the transcoding speed.

**Reducing the carbon footprint of video transcoding : api.video’s approach**

At api.video we are really focused on the energy consumption of our transcoding pipeline. We want to minimize the ecological impact of video transcoding while keeping the transcoding speed high. The infrastructure team has performed multiple benchmarks, and here are the results.

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Those numbers are based on public information (marketing sheet) for the GPUs and FPGAs and real benchmarks for the CPU and ASICs. The quality of the video's output could change the results slightly, but we clearly see a winner regarding power efficiency. In a future blog post, we will share precise benchmarks about the ASICs vs. CPU battle with you. Stay tuned.

Along this article, we have seen the resurgence of dedicated hardware in computer science is driven by a growing need for high-performance computing, particularly in fields like artificial intelligence, machine learning, and video transcoding. Dedicated hardware can provide better performance and energy efficiency than traditional general-purpose computing units, making it an attractive option for many applications. As computing continues to evolve, we will likely see more specialized hardware being developed for specific tasks, driving innovation and progress in the field. 

Our video API allows you to add videos to your products while prioritizing the reduction of your carbon footprint. [Join us](https://api.video/) and take part of the transition to an environmentally responsible video infrastructure.

The comeback of specialized hardware

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Announcements

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Cédric Montet

Company News

AI, models, assistants, agents and prompts sound and look magical on social networks. But from the countless conversations we have had with startups and Enterprise customers over the last months, we have realized that the gap between existing video workflow and concrete use cases is real.

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Whether it’s getting transcripts in seconds, changing the language of your video or being able to search for items in your content – the benefits, value or potential savings you see and can get with AI are obvious.

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This is why we are thrilled to unveil how we are bringing AI into video, where we are heading and how you can get more value from it!

## What does the future of video streaming look like?

AI will completely upend video consumption as we know it today. We envision a world where video is not a content piece anymore which you can only start and stop – because AI will make videos discoverable and universally understandable. More than ever it resonates fully with our core mission – connecting people through their cameras.

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Since Day 1, we are building api.video to make your use cases, first, a reality and, then, a success.

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Back in the days, the fragmented video streaming market, the number of services you had to aggregate, the lack of innovation by the incumbents was preventing you from joining our video-first world, while your efforts and budgets were consumed by maintaining mashups instead of focusing on what matters. 

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This is why we have built the video backbone and seeing that, now, you host 2.5 millions videos on api.video and deliver dozens of millions videos every month demonstrate why simplicity, exceptional performances and an E2E approach were key.


&nbsp;

What’s happening today with AI, the efforts it requires to benchmark, setup and customize LLMs, to run and maintain them, the Infrastructure you have to build to execute them before getting any value, remind us of the state of the streaming market when we had launched api.video.

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Helping you to benefit from the AI revolution by abstracting all the underlying complexity became obvious and a natural next step in our evolution.

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To get there and to bring you there, here is what we are building, api.video’s very first – **Video Model Hub**.

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Let’s start with the root principles encompassing our work on this Hub:

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- **Open-source models over private ones.** We are long time believers and contributors of the open source community and we have chosen to run only models that are available publicly.

- **Your data is… your data.** You already trust us to host and deliver sensitive content,and this is exactly why we won’t train our own models or sell the data generated from your videos to anyone.

- **Privacy at the core.** We host and execute the models inside our own infrastructure. Your data remains inside the api.video infrastructure. Your videos or data is not sent to external partner, SaaS providers like OpenAI, or others.

- **We believe that every organization will eventually build their own models to leverage their unique data sets**. Next year, alongside the ability to run public and open source models on api.video, we will offer you the ability to host your own.

- **One API to rule them all.** As we have already replaced a dozen of services and vendors under GET/videos to make your videos available worldwide in seconds, we are abstracting all the complex work of executing LLMs behind clear and straightforward endpoints.

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At a glance, api.video brings to you today the power of AI by:

- Leveraging its years of experience in video management and building infrastructure, with GPUs.
- Combining with the most advanced LLMs available on the market.

so that you get more value from your videos in a few lines of code!

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## Here are the first three new features from the Video Model Hub

### 1. Summarization of long-form videos

Upload a video in English, Spanish, French, Portuguese or Chinese and get a text summary of 120 characters with the 3 key takeaways discussed in the video (within seconds!). Ideal to index or preview long form recorded meetings or courses!

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_The video below gives you a glimpse into AI summarization._


### 2. Transcription of your video in seconds

Get a transcript (in .VTTformat) from your uploaded video with the timecodes and offer the captions to your audiences. The best part? The api.video player supports VTT files so there’s nothing new that you need to build on your side!
This also makes your content more inclusive by making it accessible to people with hearing impairments. 


### 3. Translation of subtitles in 5 different languages (and more to come!)

Is your video in English? You can get Spanish and French subtitles. In Chinese? You will get English subtitles and three more languages. In short, this is the perfect solution to spread your message to your international audience!

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_Check out the video below for a glimpse into AI translation._ 


_Try it for yourself now by uploading a video in your [sandbox account](https://dashboard.api.video/overview)._ 

## What’s next

From video classification, alteration, generation, audio alteration, metadata generation, we have identified and plan to implement more than 200 additional use cases and features within the upcoming months!

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From next year, we will let you bring your own models on the infrastructure to get even better and more personalized outcomes.

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This is a long-journey and we’re only getting started. The whole team is thrilled to be at the forefront of this next chapter!


AI  >< Video: The Video Model Hub unveiled

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Arushi Gupta

Product updates

Our mission at api.video has always been to turn your use cases into successful realities. We built the video backbone with this goal in mind, and today, you host 2.5 million videos and deliver tens of millions more!

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Helping you harness the power of AI, while simplifying the problems that come along with, was the next direction to take. 

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To achieve this and to support your journey, we bring to you –  **AI features for your videos.**



## What are the AI video features?

### 1. AI video transcription & captions

With AI transcription, you can convert spoken words in your videos into written text, providing an accurate, searchable record of your content. This means you can easily create subtitles, enhancing accessibility for the hearing impaired or getting a written record of your meetings or events. 






### 2. AI video translation

With AI translation, you can translate your videos into multiple languages, ensuring that viewers from different linguistic backgrounds can understand and engage with your content. This can be useful if you’re hosting an international virtual event, creating multilingual educational materials, or expanding your marketing reach.

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Translation will be available for 5 languages – French, English, Spanish, Portuguese and Chinese.


### 3. AI video summarization

Using AI summarization, you can distill lengthy content into concise, digestible summaries, allowing viewers to quickly grasp the key points without watching the entire video with AI video summarization. This can prove invaluable for educational lectures, business presentations, and lengthy seminars where time is of the essence.

## Do you really need this?

Well, the potential [uses of our AI features](https://api.video/use-cases/generative-ai/) are limitless! From e-learning and e-commerce to online streaming, these [AI features](https://api.video/product/ai-features/) are going to be the difference between ordinary and excellent. What can you do really?

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- Summarize educational lectures for a quick review
- Translate virtual events into multiple languages for global reach
- Transcribe video interviews for easy reference
- And so much more!

## More than just encoding, hosting, and delivery

We’ve gone beyond the basics of video hosting and delivery to give you features that are a step up from the ordinary. Now, not only can you ensure that your customers get video delivery at minimal cost, but also that they get the flexibility to offer content  in their preferred language and in a crisp manner. 

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Additionally, when the features are in production, you may also get the option of adding them via your API. This means that you get complete power to add the features you like without having to go through the dashboard.

## Your videos stay private to you

Your data is yours, and we respect that. You already trust us to host and deliver your most sensitive content, which is why we will never train our own models or sell data generated from your videos. Privacy is at the core of everything we do, and that’s why we host and execute the models within our own infrastructure. Your data stays entirely within the api.video ecosystem and is never sent to external partners, SaaS providers like OpenAI or others.

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_Our CEO and founder, Cedric Montet, speaks more about privacy being our topmost concern in his latest [blog](https://api.video/blog/company/video-model-hub-ai/)._

## Give it a try!

See a glimpse how these AI features look like with a free sandbox account! [Upload a video now in your dashboard](https://dashboard.api.video/overview) and witness AI make a difference to your videos. As this is a Beta release, any feedback, questions, doubts on the features are welcome and much appreciated.

Create a sandbox account now 

Lights, Camera, AI Action! Discover our AI features

Introducing AI features to compliment your videos

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AI >< Video: The Video Model Hub unveiled

How to integrate AI facial expression detection to control your video – Javascript 

5 AI tools that will save you 10+ hours on video production

Lights, Camera, AI Action! 

In a world where time is money and creativity reigns supreme, AI tools that convert text to video are game-changers! These AI-powered wonders are designed to save your day—and countless hours. Join us as we unveil the top 5 AI video tools that can dramatically cut down your video creation and editing time. From transforming text into captivating visuals to adding that perfect touch of magic to your edits, these tools are set to become your new best friends in the digital playground!

## **1. CapCut**

At number 1, we have Capcut. 

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[CapCut](https://www.capcut.com/) surged in popularity due to its user-friendly interface and powerful editing features, making it a favorite among video creators. Its integration with TikTok and social media platforms is also reason for its widespread adoption.

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The best part about using CapCut is that most of these important features are available for free, which kicks out budget-constraints out of the window. CapCut uses AI to empower users to create stunning videos without needing any fancy editing experience. With mobile and desktop apps, as well as a web version, CapCut can easily be used on the go. 

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CapCut offers a range of AI-driven features that are very, very impressive. With 'Smart Cuts & Trims', CapCut's AI automatically detects shaky parts, long pauses, or unnecessary bits, suggesting cuts to tighten up your video and eliminate boring footage. 'Effortless Audio Magic' helps isolate voices and enhance audio quality, making your message crystal clear even if there’s noisy background interference. 'Instant Captions' auto-generate captions, making your videos more accessible and saving you time and effort. Moreover, if your filming didn't capture the perfect lighting, 'Light Like a Pro' allows CapCut’s AI to adjust lighting in your videos post-production, creating the ideal mood.

What I most love about CapCut AI video generator is its AI-driven background removal feature which eliminates the need for a green screen, allowing me to replace any background with an image or color of my choice.

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Something to note about Capcut is that the free version includes watermarks on exported videos. which can be removed only with a paid subscription. This may potentially pose a barrier for you if you’re a budget-conscious user.

## **2. Runway**

[Runway](https://runwayml.com/) can be called a virtual sorcerer's wand for video editing! It has the potential to be an entire AI video production studio, so it can basically create an entire movie without filming a single frame — if you have the time, that is. Runway can generate videos and animations from simple text prompts, create animations from still images, generate lip-syncing avatars based on a recording of your voice, and even allow you to train your own AI models. 

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Runway might be a little intimidating in the beginning. That’s because it has an array of features that take time to understand, execute and implement, and that could be slightly time-taking and if I can say, complex.

Another aspect to note is that while Runway offers a range of pricing plans, the advanced features that truly set it apart are locked behind higher-tier subscriptions, making it less accessible for small businesses or individual creators on a tight budget.

## **3. Invideo AI**

[Invideo AI](https://invideo.io/ai/) is an excellent tool for generating a starting point for your videos. It helps you to quickly create a basic structure for your videos, which can then be further customized and polished. The  features allow you to input simple prompts, from which the tool generates an initial draft that includes relevant footage, transitions, and effects. 

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A few standout features of Invideo AI are an extensive stock media library that offers a range of options to choose from, AI-powered script generation which creates a complete script for your videos and, voiceover addition. Their AI voiceover generator is particularly good because it has an option to add your own voice which it then uses to narrate scripts in. The narration is also decent, with multiple accents and focussing well on the feeling, intent, and emotion behind each word.

Overall, the tool is great for YouTubers and people who want to start off their video making journey with minimal effort. 

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One of the shortcomings of Invideo AI is its dependency on a stable internet connection, as the tool is cloud-based. This can be a significant limitation for users in areas with unreliable internet access. I also noticed that rendering videos on Invideo AI can sometimes be slow, especially for longer or high-resolution projects, so keep enough time in hand before you expect a final video. 

## **4. Wondershare Filmora**

[Wondershare Filmora](https://filmora.wondershare.net/de/ad/videobearbeitungsprogramm-brand.html) has been a staple in the video editing world for over 15 years. Despite its long history, it's continuously evolving, with a suite of AI features that enhance video quality without requiring professional training or extensive time investments. 

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Filmora is a popular choice among full-time influencers and video editors for advanced-level video editing. It is also a preference because the tool keeps evolving based on new technological developments, and therefore is easily able to meet the demanding standards of modern digital media.

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Some of Filmora’s features include 'AI smart cutout', which allows you to cut objects out of scenes simply by painting their area on the screen, and 'AI audio denoise', which helps control background noise. 'AI audio stretch' lets you extend music to match scene transitions, while AI motion tracking enables face blurring for privacy or fun effects. They also have AI remove background which allows you to create imaginative scenes without hefty budgets. 

Wondershare also provides detailed explanations of these features on their website, so if you ever face a problem using any of them, be sure to check their dedicated pages. 


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However, do note that Filmora's performance can be sluggish when handling large files or high-resolution videos, leading to slower rendering times and potential crashes at times.

## **5. Canva Magic Studio**

Easy to use. Neat interface. Quick results. 

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Those were the three things I liked best about [Canva Magic Studio](https://www.canva.com/magic/). 

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Canva Magic Studio, though currently in a very basic state, looks very promising as an AI video tool. Having been a fan of Canva for infographics, presentations and social posts since the past few years, I have full confidence that their AI video tool will evolve too. 

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As of now, Canva Magic Studio gives you amazing results when it comes to video quality and is also very quick to generate the final video. The good video quality comes from its integration with [Runway](https://zapier.com/blog/best-ai-video-generator/#runway) (our #2 in the list).

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To show you an example, I used the prompt: "A cute rabbit watching a video on a laptop”. And the [result](https://www.canva.com/design/DAGGCqwwf4A/GS7q42J5C9nx26gxJGR9lA/watch) was surprisingly very apt!

Canva Magic Studio is a great option if you want to bulk-create your social media posts – create a chunk of Instagram reels or Facebook ads in a go or create similar kinds of videos using a single template. The template options Canva offers have a wide range and are decent for beginners to start with.

## Sora – Open AI’s video generator

Open AI sparked a wave of excitement around AI with ChatGPT, demonstrating its incredible potential in natural language processing. Now, it's taking things a step further with [Sora, an innovative AI](https://openai.com/index/sora/) video creator.

Open AI recently shared a series of tweets showing the potential of Sora and I must say, I am amazed. Be it [several giant mammoths](https://x.com/OpenAI/status/1758192960116785459) rushing through a snowy meadow or a [fluffy monster](https://x.com/OpenAI/status/1758192964222988506) behind a melted candle, the kind of videos Sora will be able to create is astounding! It looks like it will be able to take detailed prompts into consideration, just like it did with ChatGPT, to create exactly what the user needs.

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As Sora continues to evolve and improve, it will undoubtedly shape the future of video production, showing way to new forms of storytelling and engagement in the years to come.

## AI is great – but it cannot replace you

While these AI video tools can ease the load for you and save some time, they cannot really take the place of your creativity, intuition, and emotional intelligence. In the end, it is your perspective, personal touch, and experiences that shape the videos you create. 

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_If you’re finding a place to store, host and deliver your [AI generated videos](https://api.video/use-cases/generative-ai/), api.video could be just what you’re looking for. With quick video upload options, flexible hosting capabilities, ability to add watermarks and keep videos private, you can choose api.video for all your video needs. If you would like to test out our features, [sign-up](https://dashboard.api.video/register) for a sandbox account now and you wound’t be disappointed._

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Yohann Martzolff

Tutorials

React

In today's tech-driven world, enhancing user interaction and creating immersive experiences is crucial. One exciting way to achieve this is by integrating AI facial expression detection into your video content.  In this blog, we'll guide you through the steps to <u>integrate AI facial expression detection to control your videos using React</u>. 

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Let's start.

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_*To learn how to do the integration with Javascript, head to this [blog](https://api.video/blog/tutorials/AI-facial-expression-detection-javascript/).*_

## 1. Create a minimal React app

As the first step, we need to create a new React app. Run the following command for the same. 

Now add the MediaPipe and the api.video React player packages

## 2. Add a video player

You’re going to use the official [api.video’s React player](https://github.com/apivideo/api.video-react-player) in this application, but feel free to use your favorite one and adapt the callbacks to it.

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Replace the code inside `App.tsx` with the following.
The `playerRef` allows us to control the [api.video player](https://api.video/product/player/).

Run your application with the npm run start command and go to `http://localhost:3000/`, you should see a video that you can play, pause or mute. 

## 3. Record your facial expressions

To control the player with your facial expressions, you need to stream your face through your webcam and pass this streamed data to the trained machine-learning model.

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Add a `<video />` HTML tag to `App.tsx`. 
Its `webcamRef` allows the video element to be targeted.
Apply a `display: "none"` inline style to hide the element, and an `autoPlay` attribute so the webcam stream time is updated. 

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**Note:** You can remove the `display: "none"` style and customize your CSS, it doesn’t matter.

## 4. Load the machine-learning model

Before we can use the machine-learning model, we must wait for it to finish loading. These models can be large and take a moment to get everything needed to run.

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Put this logic inside a `useEffect` and store the `faceLandmarker` class as a React stateful value.

## 5. Start detecting the webcam stream

Once the machine-learning model has been loaded and the `faceLandmarker` class has been instantiated, you can start detecting the stream.

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Create a new `useEffect`

Run your application with the `npm run start` command, go to `http://localhost:3000/`, and open your console. You should see a lot of console logs appearing each millisecond.

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Each one of them logs an array of objects of type:

## 6. Use the facial expression detection results

You now have access to a list of facial expression detection results. Use them to do whatever you want and in this case, to control your video player.

Let’s assume that you want to play the video when you smile, and pause it when you do a kiss.

Check the facial expression detection results on each frame and play or pause your video depending on the score of the wanted result by replacing your console.log with the following.

Once again, run your application with the `npm run start` command, go to `http://localhost:3000/`, and try to smile at your webcam. It should trigger the player’s `play` callback and play the video. Then, try to do a kiss ( 😗 ) in front of your webcam, and the video should pause.

## 7. Display the results

It can be helpful to have a visual representation of the detection results. Display these results as a list and highlight the detected facial expressions by adding a `<ul />` HTML tag to your JSX and creating a new ref to target it.

Add the code to update the HTML according to the results.

Now, you should see a list of results displayed in your application, with a green highlight on the ones with more than 60% confidence.

## 8. Code samples

Check the full `App.tsx` code below.

We hope this blog helped you understand the coding behind integrating AI facial expression detection to your videos.

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_For any questions or doubts you may have, chat with us on our [website](https://api.video/contact/). If you would like to try out api.video for your videos, [sign-up](https://dashboard.api.video/register) for a free sandbox account here._

How to build AI facial expression detection into your videos with React

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