speech to text online ibm

Watson speech to text

This service listens for the word Watson. When it is detected, the service captures an audio clip and sends it to an instance of Speech to Text. Stop words are removed (optionally), and the transcribed text is sent to IBM Event Streams Service.

Before you begin

Ensure that your system meets these requirements:

• You must register and unregister by performing the steps in Preparing an edge device .
• A USB sound card and microphone is installed on your Raspberry Pi.

This service requires both an instance of IBM Event Streams Service and IBM Speech to Text to run correctly. For instructions about how to deploy an instance of event streams, see Host CPU load percentage example (cpu2evtstreams) .

Ensure the necessary IBM Event Streams Service environment variables are set:

The event streams topic this sample uses is myeventstreams by default, but you can use any topic by setting the following environment variable:

Deploying an instance of IBM Speech to Text

If an instance is deployed currently, obtain the access information and set the environment variables, or follow these steps:

• Navigate to the IBM Cloud.
• Click Create resource .
• Enter Speech to Text in the search box.
• Select the Speech to Text tile.
• Select a region, select a pricing plan, enter a service name, and click Create to provision the instance.

After provisioning is complete, click the instance and note the credentials API Key and URL and export them as the following environment variable:

Go to the Getting Started section for instructions of how to test the Speech to Text service.

Registering your edge device

To run the watsons2text service example on your edge node, you must register your edge node with the IBM/pattern-ibm.watsons2text-arm deployment pattern. Perform the steps in the Using Watson Speech to Text to IBM Event Streams Service with Deployment Pattern section of the readme file.

Additional information

The processtect example source code is also available in the Horizon GitHub repository as an example for IBM Edge Application Managerdevelopment. This source includes the code for all of the four services that run on the edge nodes for this example.

These services include:

• The hotworddetect service listens and detects the hot word Watson, and then records an audio clip and published it to the mqtt broker.
• The watsons2text service receives an audio clip and sends it to the IBM Speech to Text service and publishes the deciphered text to the mqtt broker.
• The stopwordremoval {:target="_blank"}{: .externalLink}service runs as a WSGI server takes a JSON object, such as {"text": "how are you today"} and removes common stop words and returns {"result": "how you today"}.
• The mqtt2kafka service publishes data to IBM Event Streams Service when it receives something on the mqtt topic where it is subscribed.
• The mqtt_broker is responsible for all inter-container communication.

What to do next

For instructions about building and publishing your own version of the Offline Voice Assistant Edge Service, see Offline Voice Assistant Edge Service . Follow the steps in the watson_speech2text directory of the Open Horizon examples repository.

See the Open Horizon examples repository .

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Convert Video Speech to Text with Watson

Closed captions have grown to be an important part of the video experience. While they assist deaf and hard of hearing people in enjoying video content, a  study in the UK  discovered that 80% of closed caption use was from those with no hearing issues. Not only that, but Facebook found out that adding captions to a video increased view times on their network by 12% . These reasons, along with regulations such as the  Americans with Disabilities Act and rules from the FCC , have realized the need to caption video assets. However, caption generation can be time consuming, taking 5-10 times the length of the video asset, or costly if you are paying someone else to create them.

A solution is automatic speech recognition from machine learning. This the ability to identify words and phrases in spoken language and convert them to text. The process offers content owners a way to quickly and cost effectively provide captions for their videos. To address this, IBM introduced the ability to convert video speech to text through IBM Watson. This was added to IBM’s video streaming solutions in late 2017 for VODs (video on-demand). It has recently been expanded to recognize additional languages.

Integrated live captioning for enterprises is also available, although differs in several ways from the VOD feature talked about here.

How it works for VODs

Supported languages, caption accuracy, steps to convert video speech to text, exporting and editing captions, additional professional services available for captioning.

IBM Watson uses machine intelligence to transcribe speech accurately through combining information about grammar and language structure with knowledge about the composition of the audio signal. As the transcription process is underway, Watson will continue to learn as more of the speech is heard, providing additional context. Through this process, it will apply this added knowledge retroactively, so if clarity to an earlier statement is introduced toward the end of the speech Watson will go back and update the earlier part to maintain accuracy.

To convert video speech to text, content owners simply need to upload their video content to IBM’s video streaming or enterprise video streaming offerings. If the video is selected as being in a supported language, Watson will automatically start to caption the content through using speech to text. This process takes roughly the length of the video to transcribe, producing quick, useable captions.

At launch, this feature supported 7 different languages, with English variants for either the United Kingdom or the United States. This was expanded to support 11 different languages in 2020. Right now the system can recognize the following languages:

• English (UK) or English (US)
• Portuguese (Brazil)

Being a supported language means that the technology can be set to recognize audio in that language and transcribe it. So English audio would be transcribed to English text or captions, while Italian audio would be transcribed into Italian text.

More languages will be supported as time goes on as we are constantly working on expanding the list of supported languages.

Two elements go into the accuracy when converting speech to text.

The first is that any automated speech to text service can only transcribe words that it knows. Watson will determine the most likely results for spoken words or phrases, but might misinterpret names, brands and technical terms. The service continues to advance and learn, though, and as mentioned is setup to review the full speech and make corrections based on context. For example, Watson might transcribe someone as saying “they have defective jeans”, but later context is added that they are talking about genetics and the statement could be amended as “they have defective genes”.  In addition, training can be performed on the specific content you plan to feed the speech to text engine, which can dramatically improve accuracy on these specific words. Contact IBM sales to learn more about this optional service.

Second is the quality of the video’s audio, which has a big impact on accuracy. The best results are observed when there is one speaker in your video talking at a normal pace with good audio quality present. Factors that can work against speech to text accuracy include a lot of background noise, including loud soundtracks or soundtracks with vocals, or instances where multiple people are talking simultaneously. Speakers with accents that cause words to be slurred or pronounced differently might also be misinterpreted unless the whole speech provides proper context.

To start automatically generating captions on your videos, you will first need to designate a language for IBM Watson to use. This can be done in two ways:

• On a per video basis, by editing the video and going to Overview and setting the language

Once a language is selected… that’s it. Watson will automatically start to review the available video content and create caption files through speech to text. This process takes roughly the length of the video to create the captions. So a ten minute video would take roughly ten minutes to caption. Once a video has finished the caption process an email will be sent to confirm it’s been captioned.

By default, captions will publish with the content. In other words, when the caption process is done they will be immediately accessible to end viewers. If you prefer to review the captions first, it’s recommended to leave the video unpublished until Watson is finished. After this, you can preview the video without publishing it to check for accuracy. Once satisfied, the video and captions can be published together for viewers to watch.

IBM Watson Media provides a closed caption editor inside the web based platform. It is recommended to use this to edit captions for a variety of reasons, including ease of use in checking scene to captions for accuracy easily. It also makes it easier to have multiple people editing captions as well.

That said, captions can be edited offline if desired. This is made possible because the captions are generated in a WebVTT file format . This means each caption is associated with a timestamp that designates both when the caption should appear and also when it should disappear. An example caption entry in a WebVTT file would look like this:

0000:03:54.000 --> 0000:04:08.000 The fires were contained through the extraordinary efforts of the Derry firefighters.

In this case, the WebVTT is indicating that a caption will appear at 3:54 minutes into the video, and disappear at the 4:08 minute mark. However, let’s say that we wanted to edit these captions. Maybe, for example, we want to note that Derry is located in Maine by changing it to “Derry (Maine) firefighters”. To do this we would need to export the captions and then edit them.

To export captions, a content owner can select a video and navigate to the Closed Captions tab. This will show a list of available captions, which can be a combination of those generated through Watson and also WebVTT files that were uploaded as well.

Mousing over a caption entry will provide three options: Download, Settings and Unpublish.

When clicking download, a prompt to save a local copy will appear. This copy can be used for a variety of purposes, from creating a DVD version to just saving a local transcript for records. If you intend to edit it, though, there are a variety of options at your disposal.

First off, WebVTT files can be edited in a simple text editor. So Notepad for Windows or TextEdit for Macs would be able to edit the raw file. In the example above, you could search (Ctrl + F on Windows, Command + F on Macs) to find the passage about the firefighters and quickly edit it. If, however, you prefer something that is a little more graphic oriented to edit the caption file, there are programs specifically designed for this. While many are Windows based, like  Subtitle Edit  or  Subtitle Workshop , these programs will allow you to watch the video file while editing. This might aid people in finding the specific moments they want to edit more easily.

After the captions have been sufficiently edited, you can go back to the Closed Captions tab for that video to add them. This is done through clicking the Add Captions button. This requires that a language for the caption be selected.

IBM offers a comprehensive suite of professional services for importing, maintaining and performing quality control on video libraries and live events. For video captioning, IBM can provide services ranging from making available professional transcription services and translators, to file format conversions for libraries with non-HTML5 compatible captions. In addition, IBM can provide training services for Watson to significantly improve the accuracy of the automatic captions on proper nouns, technical terms, names and industry or company-specific jargon.  Contact IBM sales to learn more.

Converting video speech to text with IBM Watson provides content owners a fast way to make their libraries of video content more accessible. It also ties into general shifting viewer preferences that is seeing more people opt to have captions on videos they watch. As the technology behind the service continues to adapt and grow, expect this feature to improve with time as well, being able to interpret more words, more accents and more languages.

Watson Speech to Text review

Find out how ibm does speech recognition in our watson speech to text review.

TechRadar Verdict

There’s plenty to be said in favor of IBM’s Watson Speech to Text service, such as its ability to convert hours of audio into text quickly and accurately. But price, integration complexity, and somewhat patchy BETA features may put some businesses off.

Fast and accurate speech recognition

Grammar, language, and acoustic model training

More expensive than AWS or Google

Multi-speaker recognition is hit-and-miss

Why you can trust TechRadar We spend hours testing every product or service we review, so you can be sure you’re buying the best. Find out more about how we test.

Watson is IBM’s natural-language-processing computer system. It powers the famous question-answering supercomputer as well as a series of AI-based enterprise products, including Watson Speech to Text . In our Watson Speech to Text review, we’ll take a look at one of the best speech-to-text apps around, ideal for anyone who wants to convert audio to text at scale.

The Watson speech processing platform is available on IBM Cloud. It’s a versatile tool and can be used in many contexts including dictation and conference call transcription. What’s more, unlike most other speech-to-text apps, it’s available as an API, allowing developers to embed it into voice control systems, among other things. 

Watson Speech to Text: Plans and pricing

You can use Watson Speech to Text to process up to 500 minutes of audio for free per month. If you want to convert more than that, you’ll need to pay for each audio minute, and the rate changes based on the duration of audio processed. Costs range from $0.01 to $0.02 per minute, and there’s an add-on charge of $0.03 per minute if you require IBM’s Custom Language Model. Premium quote-only Watson plans are available too, and these grant access to enhanced data privacy features and uptime guarantees.

You can also access the Watson Speech to Text system through a general-purpose IBM Cloud subscription. Natural language processing is just one app in a wide range of AI services you can get through IBM Cloud, so this is a good option for any organization that needs access to high-speed data transfers, chatbots, or text-to-speech tools.  

Watson Speech to Text: Features

Thanks to flexible API integration and other pre-build IBM tools, the Watson speech recognition service goes well beyond basic transcription. If you want to use it in a customer service context, for example, the Watson Assistant can be set up to process natural language questions directly or answer queries over the phone.

Watson works with live audio in 11 languages and can import sounds in a variety of pre-recorded formats. When streaming, real-time diagnostic support means Watson can prompt users to move closer to their microphone or change their environment. Also impressive is the fact that Watson can distinguish between different speakers in a shared conversation thanks to Speaker Diarization, a feature still undergoing beta testing.

Watson Speech to Text: Setup

To use Watson, the first thing you need to do is create an IBM Bluemix account. Registration is free and painless, requiring just an email address and password. Once logged in you need to add a provision on your account for the Speech to Text service. You’ll be given a couple of credentials at this stage that you should save in your own records. 

After you’ve done that, things get significantly more complex. To access Watson, you’ll need to add those credentials to a batch of client uniform resource locator (cURL) code and then run it on your machine. To find out exactly what command to call, check out this handy guide. Alternatively, if you just want to see how well the Watson system works without having to jump through all those hoops you can try it out on IBM’s demo site instead.

Watson Speech to Text: Interface

Unlike consumer-facing voice-to-text apps, Watson’s services are designed to be accessed through APIs and code embedded in other systems. For this reason, there’s no real Watson “interface”. Instead, Watson can be accessed through three different internet protocols. These are WebSockets, REST API, and Watson Developer Cloud.

To control Watson, you will need to use a command-line tool that connects to IBM’s cloud via one of those three routes. The interface that the end-user interacting with Watson sees will need to be built by someone on your development team separately. 

Watson Speech to Text: Performance

Overall, we were impressed by the way that this natural-language-processing platform handled real speech. We used Watson to transcribe clips we recorded in a range of challenging environments as well as soundbites of famous speeches given in several of Watson’s 11 supported languages.

Although errors grew more frequent for clips with lots of background noise, in general, Watson produced incredibly accurate results. We’d estimate from our tests that unprompted mistakes occurred only once every 150 words on average. However, it did become clear why Watson’s Speaker Diarization feature remains in BETA testing as, several times during our evaluation, one voice was mislabelled as separate speakers.

 Watson Speech to Text: Support 

The IBM resource center offers plenty of documentation to better understand how to apply Watson to your particular use case. It’s also worth making use of the API-integrations and SDKs created by the Watson developer community and posted to GitHub .

If you don’t find the solution to your problem there, you can reach out to IBM directly by opening a support ticket or contacting them over the phone. As long as you opted for one of the premium Watson packages, your Watson use will be protected by a Service Level Uptime agreement.

Watson Speech to Text: Final verdict

If your organization has the know-how and resources to properly integrate the IBM Watson Speech to Text platform into your system, you’ll benefit from advanced functions like real-time sound environment diagnostics and interim transcription results. However, small businesses and organizations will struggle with the technical challenge of setting Watson up properly.

The competition

The IBM Watson Speech to Text service is a direct competitor to bulk transcription services Google Cloud Speech-to-Text and Amazon Transcribe. Both of these are significantly cheaper than Watson, with Google Cloud transcription, for example, starting at $0.006 per minute. All three services share similar functions, such as customized vocabulary, but one feature sorely missing from IBM Watson but available with both competitors is automatic punctuation recognition.

Looking for another spoeech-to-text solution? Check out our Best speech-to-text software guide.

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A Fundamental End-to-End Speech Recognition Toolkit and Open Source SOTA Pretrained Models, Supporting Speech Recognition, Voice Activity Detection, Text Post-processing etc.

modelscope/FunASR

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( 简体中文 |English)

FunASR hopes to build a bridge between academic research and industrial applications on speech recognition. By supporting the training & finetuning of the industrial-grade speech recognition model, researchers and developers can conduct research and production of speech recognition models more conveniently, and promote the development of speech recognition ecology. ASR for Fun！

Highlights | News | Installation | Quick Start | Tutorial | Runtime | Model Zoo | Contact

• FunASR is a fundamental speech recognition toolkit that offers a variety of features, including speech recognition (ASR), Voice Activity Detection (VAD), Punctuation Restoration, Language Models, Speaker Verification, Speaker Diarization and multi-talker ASR. FunASR provides convenient scripts and tutorials, supporting inference and fine-tuning of pre-trained models.
• We have released a vast collection of academic and industrial pretrained models on the ModelScope and huggingface , which can be accessed through our Model Zoo . The representative Paraformer-large , a non-autoregressive end-to-end speech recognition model, has the advantages of high accuracy, high efficiency, and convenient deployment, supporting the rapid construction of speech recognition services. For more details on service deployment, please refer to the service deployment document .

What's new:

• 2024/05/15：emotion recognition models are new supported. emotion2vec+large ， emotion2vec+base ， emotion2vec+seed . currently supports the following categories: 0: angry 1: happy 2: neutral 3: sad 4: unknown.
• 2024/05/15: Offline File Transcription Service 4.5, Offline File Transcription Service of English 1.6，Real-time Transcription Service 1.10 released，adapting to FunASR 1.0 model structure；( docs )
• 2024/03/05：Added the Qwen-Audio and Qwen-Audio-Chat large-scale audio-text multimodal models, which have topped multiple audio domain leaderboards. These models support speech dialogue, usage .
• 2024/03/05：Added support for the Whisper-large-v3 model, a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. It can be downloaded from the modelscope , and openai .
• 2024/03/05: Offline File Transcription Service 4.4, Offline File Transcription Service of English 1.5，Real-time Transcription Service 1.9 released，docker image supports ARM64 platform, update modelscope；( docs )
• 2024/01/30：funasr-1.0 has been released ( docs )
• 2024/01/30：emotion recognition models are new supported. model link , modified from repo .
• 2024/01/25: Offline File Transcription Service 4.2, Offline File Transcription Service of English 1.3 released，optimized the VAD (Voice Activity Detection) data processing method, significantly reducing peak memory usage, memory leak optimization; Real-time Transcription Service 1.7 released，optimizatized the client-side；( docs )
• 2024/01/09: The Funasr SDK for Windows version 2.0 has been released, featuring support for The offline file transcription service (CPU) of Mandarin 4.1, The offline file transcription service (CPU) of English 1.2, The real-time transcription service (CPU) of Mandarin 1.6. For more details, please refer to the official documentation or release notes( FunASR-Runtime-Windows )
• 2024/01/03: File Transcription Service 4.0 released, Added support for 8k models, optimized timestamp mismatch issues and added sentence-level timestamps, improved the effectiveness of English word FST hotwords, supported automated configuration of thread parameters, and fixed known crash issues as well as memory leak problems, refer to ( docs ).
• 2024/01/03: Real-time Transcription Service 1.6 released，The 2pass-offline mode supports Ngram language model decoding and WFST hotwords, while also addressing known crash issues and memory leak problems, ( docs )
• 2024/01/03: Fixed known crash issues as well as memory leak problems, ( docs ).
• 2023/12/04: The Funasr SDK for Windows version 1.0 has been released, featuring support for The offline file transcription service (CPU) of Mandarin, The offline file transcription service (CPU) of English, The real-time transcription service (CPU) of Mandarin. For more details, please refer to the official documentation or release notes( FunASR-Runtime-Windows )
• 2023/11/08: The offline file transcription service 3.0 (CPU) of Mandarin has been released, adding punctuation large model, Ngram language model, and wfst hot words. For detailed information, please refer to docs .
• 2023/10/17: The offline file transcription service (CPU) of English has been released. For more details, please refer to ( docs ).
• 2023/10/13: SlideSpeech : A large scale multi-modal audio-visual corpus with a significant amount of real-time synchronized slides.
• 2023/10/10: The ASR-SpeakersDiarization combined pipeline Paraformer-VAD-SPK is now released. Experience the model to get recognition results with speaker information.
• 2023/10/07: FunCodec : A Fundamental, Reproducible and Integrable Open-source Toolkit for Neural Speech Codec.
• 2023/09/01: The offline file transcription service 2.0 (CPU) of Mandarin has been released, with added support for ffmpeg, timestamp, and hotword models. For more details, please refer to ( docs ).
• 2023/08/07: The real-time transcription service (CPU) of Mandarin has been released. For more details, please refer to ( docs ).
• 2023/07/17: BAT is released, which is a low-latency and low-memory-consumption RNN-T model. For more details, please refer to ( BAT ).
• 2023/06/26: ASRU2023 Multi-Channel Multi-Party Meeting Transcription Challenge 2.0 completed the competition and announced the results. For more details, please refer to ( M2MeT2.0 ).

Installation

• Requirements
• Install for pypi
• Or install from source code
• Install modelscope or huggingface_hub for the pretrained models (Optional)

FunASR has open-sourced a large number of pre-trained models on industrial data. You are free to use, copy, modify, and share FunASR models under the Model License Agreement . Below are some representative models, for more models please refer to the Model Zoo .

(Note: ⭐ represents the ModelScope model zoo, 🤗 represents the Huggingface model zoo, 🍀 represents the OpenAI model zoo)

Quick Start

Below is a quick start tutorial. Test audio files ( Mandarin , English ).

Command-line usage

Notes: Support recognition of single audio file, as well as file list in Kaldi-style wav.scp format: wav_id wav_pat

Speech Recognition (Non-streaming)

Note: hub : represents the model repository, ms stands for selecting ModelScope download, hf stands for selecting Huggingface download.

Speech Recognition (Streaming)

Note: chunk_size is the configuration for streaming latency. [0,10,5] indicates that the real-time display granularity is 10*60=600ms , and the lookahead information is 5*60=300ms . Each inference input is 600ms (sample points are 16000*0.6=960 ), and the output is the corresponding text. For the last speech segment input, is_final=True needs to be set to force the output of the last word.

Voice Activity Detection (Non-Streaming)

Note: The output format of the VAD model is: [[beg1, end1], [beg2, end2], ..., [begN, endN]] , where begN/endN indicates the starting/ending point of the N-th valid audio segment, measured in milliseconds.

Voice Activity Detection (Streaming)

Note: The output format for the streaming VAD model can be one of four scenarios:

• [[beg1, end1], [beg2, end2], .., [begN, endN]] ：The same as the offline VAD output result mentioned above.
• [[beg, -1]] ：Indicates that only a starting point has been detected.
• [[-1, end]] ：Indicates that only an ending point has been detected.
• [] ：Indicates that neither a starting point nor an ending point has been detected.

The output is measured in milliseconds and represents the absolute time from the starting point.

Punctuation Restoration

Timestamp prediction, speech emotion recognition.

More usages ref to docs , more examples ref to demo

Export ONNX

More examples ref to demo

Deployment Service

FunASR supports deploying pre-trained or further fine-tuned models for service. Currently, it supports the following types of service deployment:

• File transcription service, Mandarin, CPU version, done
• The real-time transcription service, Mandarin (CPU), done
• File transcription service, English, CPU version, done
• File transcription service, Mandarin, GPU version, in progress

For more detailed information, please refer to the service deployment documentation .

Community Communication

If you encounter problems in use, you can directly raise Issues on the github page.

You can also scan the following DingTalk group or WeChat group QR code to join the community group for communication and discussion.

Contributors

The contributors can be found in contributors list

This project is licensed under The MIT License . FunASR also contains various third-party components and some code modified from other repos under other open source licenses. The use of pretraining model is subject to model license

Used by 156

Contributors 74

• Python 72.2%
• JavaScript 3.8%

NVIDIA Collaborates with Hugging Face to Simplify Generative AI Model Deployments

As generative AI experiences rapid growth, the community has stepped up to foster this expansion in two significant ways: swiftly publishing state-of-the-art foundational models, and streamlining their integration into application development and production.

NVIDIA is aiding this effort by optimizing foundation models to enhance performance, allowing enterprises to generate tokens faster, reduce the costs of running the models, and improve end user experience with NVIDIA NIM.

NVIDIA NIM inference microservices are designed to streamline and accelerate the deployment of generative AI models across NVIDIA accelerated infrastructure anywhere, including cloud, data center, and workstations.

NIM leverages TensorRT-LLM inference optimization engine, industry-standard APIs, and prebuilt containers to provide low-latency, high-throughput AI inference that scales with demand. It supports a wide range of LLMs including Llama 3 , Mixtral 8x22B , Phi-3 , and Gemma , as well as optimizations for domain-specific applications in speech , image , video , healthcare , and more.

NIM delivers superior throughput, enabling enterprises to generate tokens up to 5x faster. For generative AI applications, token processing is the key performance metric, and increased token throughput directly translates to higher revenue for enterprises.

By simplifying the integration and deployment process, NIM enables enterprises to rapidly move from AI model development to production, enhancing efficiency, reducing operational costs, and allowing businesses to focus on innovation and growth.

And now, we’re going a step further with Hugging Face to help developers run models in a matter of minutes.

Deploy NIM on Hugging Face with a few clicks

Hugging Face is a leading platform for AI models and has become the go-to destination for AI developers as it enhances the accessibility of AI models.

Leverage the power of seamless deployment with NVIDIA NIM, starting with Llama 3 8B and Llama 3 70B , on your preferred cloud service provider, all directly accessible from Hugging Face.

NIM delivers superior throughput and achieves near-100% utilization with multiple concurrent requests, enabling enterprises to generate text 3x faster. For generative AI applications, token processing is the key performance metric, and increased token throughput directly translates to higher revenue for enterprises.

The dedicated NIM endpoint on Hugging Face spins up instances on your preferred cloud, automatically fetches and deploys the NVIDIA optimized model, and enables you to start inference with just a few clicks, all in a matter of minutes.

Let’s take a closer look.

Step 1: Navigate to the Llama 3 8B or 70B instruct model page on Hugging Face, and click on those ‘Deploy’ drop-down, and then select ‘NVIDIA NIM Endpoints’ from the menu.

Step 2: A new page with ‘Create a new Dedicated Endpoint’ with NVIDIA NIM is presented. Select your preferred CSP instance type to run the model on. The A10G/A100 on AWS, and A100/H100 on GCP instances leverage NVIDIA optimized model engines for best performance.

Step 3: In the ‘Advanced configuration’ section, choose ‘NVIDIA NIM’ from the Container Type drop-down, and then click on ‘Create Endpoint’.

Step 4: Within a matter of minutes, an inference endpoint is up and running.

Get started

Deploy Llama 3 8B and 70B NIMs from Hugging Face to speed time to market for generative AI solutions, boost revenue with high token throughput, and reduce inference costs.

To experience and prototype applications with over 40 multimodal NIMs available today, visit ai.nvidia.com .

With free NVIDIA cloud credits, you can build and test prototype applications by integrating NVIDIA-hosted API endpoints with just a few lines of code.

Related resources

• GTC session: Building Accelerated AI With Hugging Face and NVIDIA
• GTC session: Building an End-to-End Solution for Enterprise-Ready Generative AI
• GTC session: Navigating Generative AI Challenges and Harnessing Potential: Insights from NVIDIA's Enterprise Deployments
• NGC Containers: GenAI SD NIM
• Webinar: Fast-Track to Generative AI With NVIDIA

About the Authors

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NVIDIA GTC Training Labs On Demand Available Now

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FACT SHEET: President   Biden Announces New Actions to Secure the   Border

New actions will bar migrants who cross our Southern border unlawfully from receiving asylum Biden taking action as Congressional Republicans put partisan politics ahead of national security, twice voting against toughest reforms in decades

Since his first day in office, President Biden has called on Congress to secure our border and address our broken immigration system. Over the past three years, while Congress has failed to act, the President has acted to secure our border. His Administration has deployed the most agents and officers ever to address the situation at the Southern border, seized record levels of illicit fentanyl at our ports of entry, and brought together world leaders on a framework to deal with changing migration patterns that are impacting the entire Western Hemisphere.  Earlier this year, the President and his team reached a historic bipartisan agreement with Senate Democrats and Republicans to deliver the most consequential reforms of America’s immigration laws in decades. This agreement would have added critical border and immigration personnel, invested in technology to catch illegal fentanyl, delivered sweeping reforms to the asylum system, and provided emergency authority for the President to shut down the border when the system is overwhelmed. But Republicans in Congress chose to put partisan politics ahead of our national security, twice voting against the toughest and fairest set of reforms in decades. President Biden believes we must secure our border. That is why today, he announced executive actions to bar migrants who cross our Southern border unlawfully from receiving asylum. These actions will be in effect when high levels of encounters at the Southern Border exceed our ability to deliver timely consequences, as is the case today. They will make it easier for immigration officers to remove those without a lawful basis to remain and reduce the burden on our Border Patrol agents. But we must be clear: this cannot achieve the same results as Congressional action, and it does not provide the critical personnel and funding needed to further secure our Southern border. Congress still must act. The Biden-Harris Administration’s executive actions will:   Bar Migrants Who Cross the Southern Border Unlawfully From Receiving Asylum

• President Biden issued a proclamation under Immigration and Nationality Act sections 212(f) and 215(a) suspending entry of noncitizens who cross the Southern border into the United States unlawfully. This proclamation is accompanied by an interim final rule from the Departments of Justice and Homeland Security that restricts asylum for those noncitizens.
• These actions will be in effect when the Southern border is overwhelmed, and they will make it easier for immigration officers to quickly remove individuals who do not have a legal basis to remain in the United States.
• These actions are not permanent. They will be discontinued when the number of migrants who cross the border between ports of entry is low enough for America’s system to safely and effectively manage border operations. These actions also include similar humanitarian exceptions to those included in the bipartisan border agreement announced in the Senate, including those for unaccompanied children and victims of trafficking.

Recent Actions to secure our border and address our broken immigration system: Strengthening the Asylum Screening Process

• The Department of Homeland Security published a proposed rule to ensure that migrants who pose a public safety or national security risk are removed as quickly in the process as possible rather than remaining in prolonged, costly detention prior to removal. This proposed rule will enhance security and deliver more timely consequences for those who do not have a legal basis to remain in the United States.

Announced new actions to more quickly resolve immigration cases

• The Department of Justice and Department of Homeland Security launched a Recent Arrivals docket to more quickly resolve a portion of immigration cases for migrants who attempt to cross between ports of entry at the Southern border in violation of our immigration laws.
• Through this process, the Department of Justice will be able to hear these cases more quickly and the Department of Homeland Security will be able to more quickly remove individuals who do not have a legal basis to remain in the United States and grant protection to those with valid claims.
• The bipartisan border agreement would have created and supported an even more efficient framework for issuing final decisions to all asylum seekers. This new process to reform our overwhelmed immigration system can only be created and funded by Congress.

Revoked visas of CEOs and government officials who profit from migrants coming to the U.S. unlawfully

• The Department of State imposed visa restrictions on executives of several Colombian transportation companies who profit from smuggling migrants by sea. This action cracks down on companies that help facilitate unlawful entry into the United States, and sends a clear message that no one should profit from the exploitation of vulnerable migrants.
• The State Department also imposed visa restrictions on over 250 members of the Nicaraguan government, non-governmental actors, and their immediate family members for their roles in supporting the Ortega-Murillo regime, which is selling transit visas to migrants from within and beyond the Western Hemisphere who ultimately make their way to the Southern border.
• Previously, the State Department revoked visas of executives of charter airlines for similar actions.

Expanded Efforts to Dismantle Human Smuggling and Support Immigration Prosecutions

• The Departments of State and Justice launched an “Anti-Smuggling Rewards” initiative designed to dismantle the leadership of human smuggling organizations that bring migrants through Central America and across the Southern U.S. border. The initiative will offer financial rewards for information leading to the identification, location, arrest, or conviction of those most responsible for significant human smuggling activities in the region.
• The Department of Justice will seek new and increased penalties against human smugglers to properly account for the severity of their criminal conduct and the human misery that it causes.
• The Department of Justice is also partnering with the Department of Homeland Security to direct additional prosecutors and support staff to increase immigration-related prosecutions in crucial border U.S. Attorney’s Offices. Efforts include deploying additional DHS Special Assistant United States Attorneys to different U.S. Attorneys’ offices, assigning support staff to critical U.S. Attorneys’ offices, including DOJ Attorneys to serve details in U.S. Attorneys’ Offices in several border districts, and partnering with federal agencies to identify additional resources to target these crimes.

Enhancing Immigration Enforcement

• The Department of Homeland Security has surged agents to the Southern border and is referring a record number of people into expedited removal.
• The Department of Homeland Security is operating more repatriation flights per week than ever before. Over the past year, DHS has removed or returned more than 750,000 people, more than in every fiscal year since 2010.
• Working closely with partners throughout the region, the Biden-Harris Administration is identifying and collaborating on enforcement efforts designed to stop irregular migration before migrants reach our Southern border, expand investment and integration opportunities in the region to support those who may otherwise seek to migrate, and increase lawful pathways for migrants as an alternative to irregular migration.

Seizing Fentanyl at our Border

• Border officials have seized more fentanyl at ports of entry in the last two years than the past five years combined, and the President has added 40 drug detection machines across points of entry to disrupt the fentanyl smuggling into the Homeland. The bipartisan border agreement would fund the installation of 100 additional cutting-edge inspection machines to help detect fentanyl at our Southern border ports of entry.
• In close partnership with the Government of Mexico, the Department of Justice has extradited Nestor Isidro Perez Salaz, known as “El Nini,” from Mexico to the United States to face prosecution for his role in illicit fentanyl trafficking and human rights abuses. This is one of many examples of joint efforts with Mexico to tackle the fentanyl and synthetic drug epidemic that is killing so many people in our countries and globally, and to hold the drug trafficking organizations to account.

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