Quick Start Guide for Data Center Platforms

NVIDIA Riva supports two architectures, Linux x86_64 and Linux ARM64. These are referred to as data center (x86_64) and embedded (ARM64). These instructions are applicable to data center users.

For more information and questions, visit the NVIDIA Riva Developer Forum.

Prerequisites

Before using Riva Speech AI, ensure you meet the following prerequisites:

\n
You have access and are logged into NVIDIA NGC. For step-by-step instructions, refer to the NGC Getting Started Guide.
\n
\n
You have access to an NVIDIA Volta, NVIDIA Turing, or an NVIDIA Ampere architecture-based A100 GPU. For more information, refer to the Support Matrix.
\n
\n
You have Docker installed with support for NVIDIA GPUs. For more information, refer to the Support Matrix.
\n

Models Available for Deployment

There are two push-button deployment options to deploy Riva Speech AI, which use pretrained models available from the NGC catalog:

Local Docker: You can use the Quick Start scripts to set up a local workstation and deploy the Riva services using Docker. Continue with this guide to use the Quick Start scripts.

Kubernetes: The Riva Helm Chart is designed to automate the steps for push-button deployment to a Kubernetes cluster. For more information, refer to Kubernetes deployment.

In addition to using pretrained models, Riva Speech AI can run with fine-tune custom models using NVIDIA NeMo. Refer to the {ref}nemo-development section for details regarding the advanced option to create a model repository with NVIDIA NeMo.

Getting Started with Riva for Data Center Platforms

Riva includes Quick Start scripts to help you get started with Riva Speech AI Skills. These scripts are meant for deploying the services locally, for testing, and running the example applications.

\n
Download the Riva Quick Start scripts. You can either use the command-line interface or you can download the scripts directly from your browser. Click the Download drop-down button in the upper right corner and select:
\n
- \n
  CLI - the download command is copied. Ensure you have the NGC CLI tool installed. Once installed, open the command prompt and paste the copied command to start your download.
  \n
- \n
  Browser (Direct Download) - the download begins in a location of your choosing.
  \n
\n
\n
Initialize and start Riva. The initialization step downloads and prepares Docker images and models. The start script launches the server.
\n
:::{note}\nThis process can take up to an hour on an average internet connection. On the data center, each model is individually optimized for the target GPU after download.\n:::
\n
Optional: Modify the config.sh file within the quickstart directory with your preferred configuration. Options include:
\n
- which services to enable
- which models to retrieve from NGC
- where to store them
- which GPU to use if more than one is installed on your system (refer to Local (Docker) for more details)
- locations of the SSL/TLS certificate
- key files if using a secure connection
\n
:::{note}\nFor using the Riva translation services, refer to the Configure translation services instructions in the config.sh file within the quickstart directory.\n:::
\n
Data center
\n
```
cd riva_quickstart_v2.19.0\n
```
\n
:::{note}\nIf you are using a vGPU environment, set the parameter for enabling unified memory pciPassthru<vgpu-id>.cfg.enable_uvm to 1, where <vgpu-id> should be replaced by the vGPU-id assigned to a VM. For example, to enable unified memory for two vGPUs that are assigned to a VM, set pciPassthru0.cfg.enable_uvm and pciPassthru1.cfg.enable_uvm to 1. For more information, refer to the NVIDIA Virtual GPU Software User Guide.\n:::
\n
Initialize and start Riva
\n
```
bash riva_init.sh\nbash riva_start.sh\n
```
\n
\n
Try walking through the different tutorials on GitHub. If running the Riva Quick Start scripts on a cloud service provider (such as AWS or GCP), ensure that your compute instance has an externally visible IP address. To run the tutorials, connect a browser window to the correct port (8888 by default) of that external IP address.
\n
\n
Shut down the server when finished. After you've completed these steps and experimented with inferencing, run the riva_stop.sh script to stop the server.
\n

For more information on how to customize a local deployment, refer to the Local (Docker) section.

Transcribe Audio Files with Riva

For Automatic Speech Recognition (ASR), run the following commands from inside the Riva client container to perform streaming and offline transcription of audio files. If using SSL/TLS, ensure to include the --ssl_server_cert /ssl/server.crt option.

\n
Issue the riva_start_client.sh script to start the client container with sample clients for each service. The script is located in the Quick Start folder.
\n
```
bash riva_start_client.sh\n
```
\n

For offline recognition, run:

riva_asr_client --audio_file=/opt/riva/wav/en-US_sample.wav\n

For streaming recognition, run:

riva_streaming_asr_client --audio_file=/opt/riva/wav/en-US_sample.wav\n

Synthesize Speech with Riva

From within the Riva client container, run the following command to synthesize the audio files.

riva_tts_client --voice_name=English-US.Female-1 \\\n                --text=\"Hello, this is a speech synthesizer.\" \\\n                --audio_file=/opt/riva/wav/output.wav\n

The audio files are stored in the /opt/riva/wav directory.

The streaming API can be tested by using the command-line option --online=true. However, there is no difference between both\noptions with the command-line client since it saves the entire audio to a .wav file.

Translate Text or Speech with Riva

Translate Text-to-Text (T2T)

From within the Riva client container, run the following command to perform a text-to-text translation from English to German.

riva_nmt_t2t_client --source_language_code=\"en-US\" --target_language_code=\"de-DE\" --text=\"This will become German words.\"\n

Translate Speech-to-Text (S2T)

From within the Riva client container, run the following commands to perform a speech-to-text translation from English audio to German text.

riva_nmt_streaming_s2t_client --audio_file=/opt/riva/wav/en-US_sample.wav --source_language_code=\"en-US\" --target_language_code=\"de-DE\"\n

Translate Speech-to-Speech (S2S)

From within the Riva client container, run the following commands to perform an speech-to-speech translation from Spanish audio to English audio.

riva_nmt_streaming_s2s_client --audio_file=/opt/riva/wav/es-US_sample.wav --source_language_code=\"es-US\" --target_language_code=\"en-US\"\n

Riva Collections

The Riva Collection contains the Riva Speech AI server, Riva Speech AI client containers, Riva Quick Start scripts resources, and Riva Speech AI Skills Helm chart.

Additional Resources

For organizations looking to deploy Riva-based applications in production to unlimited workloads and get full NVIDIA support with direct access to NVIDIA AI experts globally, explore NVIDIA Riva or try NVIDIA Riva for free on NVIDIA LaunchPad.

License

By downloading and using Riva software, you accept the terms and conditions of this license.

","resourceVersions","resourceVersionsTotal","hasMoreVersions","versionMetadata","versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/riva/resources/riva_quickstart/-","version","-","imageReference","cliDownloadCommand","ngc registry resource download-version nvidia/riva/riva_quickstart:2.19.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/riva/resources/riva_quickstart/-/version-history","accessCheckResult",{"_189":190,"_191":26},"hasRelatedCollections",true,"context",{"_86":87,"_88":89},1786360464531,[],[],[222,223,224,225,226,227,228],{"_40":55,"_42":67,"_44":57,"_46":58,"_48":68,"_50":51},{"_40":55,"_42":64,"_44":45,"_46":65,"_48":66,"_50":51},{"_40":55,"_42":62,"_44":45,"_46":65,"_48":229,"_50":51},{"_40":55,"_42":60,"_44":57,"_46":58,"_48":61,"_50":51},{"_40":55,"_42":56,"_44":57,"_46":58,"_48":59,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":54,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51},45,"nvidia/riva/riva_quickstart","nvidia/riva/resources/riva_quickstart","riva_quickstart","Scripts and utilities for getting started with Riva Speech Skills","# Quick Start Guide for Data Center Platforms\n\nNVIDIA Riva supports two architectures, Linux x86_64 and Linux ARM64. These are referred to as **data center** (x86_64) and **embedded** (ARM64). These instructions are applicable to data center users.\n\nFor more information and questions, visit the [NVIDIA Riva Developer Forum](https://forums.developer.nvidia.com/c/ai-data-science/deep-learning/riva/475).\n\n\n## Prerequisites\n\nBefore using Riva Speech AI, ensure you meet the following prerequisites:\n\n1. You have access and are [logged into](https://ngc.nvidia.com/signin) NVIDIA NGC. For step-by-step instructions, refer to the [NGC Getting Started Guide](https://docs.nvidia.com/ngc/ngc-overview/index.html#registering-activating-ngc-account).\n\n2. You have access to an NVIDIA Volta, NVIDIA Turing, or an NVIDIA Ampere architecture-based A100 GPU. For more information, refer to the [Support Matrix](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/support-matrix/support-matrix.html).\n\n3. You have Docker installed with support for NVIDIA GPUs. For more information, refer to the [Support Matrix](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/support-matrix/support-matrix.html).\n\n## Models Available for Deployment\n\nThere are two push-button deployment options to deploy Riva Speech AI, which use [pretrained models](https://catalog.ngc.nvidia.com/models?query=label:%22Riva%22) available from the NGC catalog:\n\n**Local Docker:** You can use the Quick Start scripts to set up a local workstation and deploy the Riva services using Docker. Continue with this guide to use the Quick Start scripts.\n\n**Kubernetes:** The [Riva Helm Chart](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/riva/helm-charts/riva-api) is designed to automate the steps for push-button deployment to a Kubernetes cluster. For more information, refer to [Kubernetes deployment](installation/deploy-kubernetes).\n\nIn addition to using pretrained models, Riva Speech AI can run with fine-tune custom models using [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). Refer to the {ref}`nemo-development` section for details regarding the advanced option to create a model repository with NVIDIA NeMo.\n\n\n## Getting Started with Riva for Data Center Platforms\n\nRiva includes Quick Start scripts to help you get started with Riva Speech AI Skills. These scripts are meant for deploying the services locally, for testing, and running the example applications.\n\n1. Download the Riva Quick Start scripts. You can either use the command-line interface or you can download the scripts directly from your browser. Click the **Download** drop-down button in the upper right corner and select:\n\n - **CLI** - the download command is copied. Ensure you have the [NGC CLI tool](https://ngc.nvidia.com/setup/installers/cli) installed. Once installed, open the command prompt and paste the copied command to start your download.\n\n - **Browser (Direct Download)** - the download begins in a location of your choosing.\n\n2. Initialize and start Riva. The initialization step downloads and prepares Docker images and models. The start script launches the server.\n\n :::{note}\n This process can take up to an hour on an average internet connection. On the data center, each model is individually optimized for the target GPU after download.\n :::\n\n *Optional:* Modify the `config.sh` file within the `quickstart` directory with your preferred configuration. Options include:\n\n - which services to enable\n - which models to retrieve from NGC\n - where to store them\n - which GPU to use if more than one is installed on your system (refer to [Local (Docker)](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/installation/deploy-local.html#local-docker) for more details)\n - locations of the SSL/TLS certificate\n - key files if using a secure connection\n\n :::{note}\n For using the Riva translation services, refer to the `Configure translation services` instructions in the `config.sh` file within the `quickstart` directory.\n :::\n\n **Data center**\n ```bash\n cd riva_quickstart_v2.19.0\n ```\n\n :::{note}\n If you are using a vGPU environment, set the parameter for enabling unified memory `pciPassthru.cfg.enable_uvm` to 1, where `` should be replaced by the vGPU-id assigned to a VM. For example, to enable unified memory for two vGPUs that are assigned to a VM, set `pciPassthru0.cfg.enable_uvm` and `pciPassthru1.cfg.enable_uvm` to 1. For more information, refer to the [NVIDIA Virtual GPU Software User Guide](https://docs.nvidia.com/grid/latest/pdf/grid-vgpu-user-guide.pdf).\n :::\n\n **Initialize and start Riva**\n ```bash\n bash riva_init.sh\n bash riva_start.sh\n ```\n\n3. Try walking through the different [tutorials on GitHub](https://github.com/nvidia-riva/tutorials). If running the Riva Quick Start scripts on a cloud service provider (such as AWS or GCP), ensure that your compute instance has an externally visible IP address. To run the tutorials, connect a browser window to the correct port (`8888` by default) of that external IP address.\n\n4. Shut down the server when finished. After you've completed these steps and experimented with inferencing, run the ``riva_stop.sh`` script to stop the server.\n\nFor more information on how to customize a local deployment, refer to the [Local (Docker)](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/installation/deploy-local.html#local-docker) section.\n\n\n## Transcribe Audio Files with Riva\n\nFor Automatic Speech Recognition (ASR), run the following commands from inside the Riva client container to perform streaming and offline transcription of audio files. If using SSL/TLS, ensure to include the `--ssl_server_cert /ssl/server.crt` option.\n\n1. Issue the `riva_start_client.sh` script to start the client container with sample clients for each service. The script is located in the Quick Start folder.\n\n ```bash\n bash riva_start_client.sh\n ```\n\n2. For offline recognition, run:\n\n ```bash\n riva_asr_client --audio_file=/opt/riva/wav/en-US_sample.wav\n ```\n\n3. For streaming recognition, run:\n\n ```bash\n riva_streaming_asr_client --audio_file=/opt/riva/wav/en-US_sample.wav\n ```\n\n\n## Synthesize Speech with Riva\n\nFrom within the Riva client container, run the following command to synthesize the audio files.\n\n```bash\nriva_tts_client --voice_name=English-US.Female-1 \\\n --text=\"Hello, this is a speech synthesizer.\" \\\n --audio_file=/opt/riva/wav/output.wav\n```\n\nThe audio files are stored in the `/opt/riva/wav` directory.\n\nThe streaming API can be tested by using the command-line option `--online=true`. However, there is no difference between both\noptions with the command-line client since it saves the entire audio to a `.wav` file.\n\n\n## Translate Text or Speech with Riva\n\n### Translate Text-to-Text (T2T)\n\nFrom within the Riva client container, run the following command to perform a text-to-text translation from English to German.\n\n```bash\nriva_nmt_t2t_client --source_language_code=\"en-US\" --target_language_code=\"de-DE\" --text=\"This will become German words.\"\n```\n\n### Translate Speech-to-Text (S2T)\n\nFrom within the Riva client container, run the following commands to perform a speech-to-text translation from English audio to German text.\n\n```bash\nriva_nmt_streaming_s2t_client --audio_file=/opt/riva/wav/en-US_sample.wav --source_language_code=\"en-US\" --target_language_code=\"de-DE\"\n```\n\n### Translate Speech-to-Speech (S2S)\n\nFrom within the Riva client container, run the following commands to perform an speech-to-speech translation from Spanish audio to English audio.\n\n```bash\nriva_nmt_streaming_s2s_client --audio_file=/opt/riva/wav/es-US_sample.wav --source_language_code=\"es-US\" --target_language_code=\"en-US\"\n```\n\n\n## Riva Collections\n\nThe [Riva Collection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/riva/collections/riva-speech) contains the Riva Speech AI server, Riva Speech AI client containers, Riva Quick Start scripts resources, and Riva Speech AI Skills Helm chart.\n\n## Suggested Reading\n\nFor the latest product documentation, supported hardware and software, and release notes, refer to the [Riva User's Guide](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/index.html).\n\n## Additional Resources\n\nFor organizations looking to deploy Riva-based applications in production to unlimited workloads and get full NVIDIA support with direct access to NVIDIA AI experts globally, explore [NVIDIA Riva](https://www.nvidia.com/en-us/ai-data-science/products/riva/) or [try NVIDIA Riva for free](https://www.nvidia.com/en-us/launchpad/ai/riva/) on NVIDIA LaunchPad.\n\n## License\n\nBy downloading and using Riva software, you accept the terms and conditions of this [license](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).","Riva Skills Quick Start",["D",1743757484104],"2.19.0","/api/image/nvidia/riva/resources/riva_quickstart","https://raw.githubusercontent.com/kbojo/images/master/Nvidia-Centric-Riva.png",163196,"bias","explainability","privacy","safetyAndSecurity",[],[250,251,252,253,254,255,256],[],[],[],{"_93":272,"_129":273,"_130":274,"_132":133},{"_93":269,"_129":270,"_130":271,"_132":133},{"_93":266,"_129":267,"_130":268,"_132":133},{"_93":140,"_129":141,"_130":265,"_132":133},{"_93":262,"_129":263,"_130":264,"_132":133},{"_93":135,"_129":135,"_130":261,"_132":133},{"_93":257,"_129":258,"_130":259,"_132":133},"Text to Speech","uscs_text_to_speech",{"_134":260,"_129":258},"application",{"_134":137,"_129":135},"Natural Language 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609],[556,557,558,559,560],{"_394":470,"_93":470,"_92":396,"_367":580},{"_394":444,"_93":444,"_92":396,"_367":575},{"_394":570,"_93":570,"_92":396,"_367":571},{"_394":565,"_93":565,"_92":396,"_367":566},{"_394":561,"_93":561,"_92":396,"_367":562},"nvidia.partners",[563],{"_361":564,"_401":561,"_402":26,"_92":438},"edge","app.hushly.com",[567],{"_361":568,"_401":565,"_402":26,"_92":569},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[572],{"_361":573,"_401":570,"_402":26,"_92":574},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696],[648,649,650,651],{"_394":663,"_93":663,"_92":396,"_367":664},{"_394":658,"_93":658,"_92":396,"_367":659},{"_394":457,"_93":457,"_92":396,"_367":655},{"_394":570,"_93":570,"_92":396,"_367":652},[653],{"_361":654,"_401":570,"_402":215,"_92":574},"_gh_sess",[656],{"_361":657,"_401":457,"_402":215,"_92":464},"YSC","bizible.com",[660],{"_361":661,"_401":658,"_402":26,"_92":662},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[665],{"_361":666,"_401":663,"_402":26,"_92":667},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_361":738,"_401":516,"_402":26,"_92":739},{"_361":723,"_401":737,"_402":215,"_92":724},{"_361":706,"_401":519,"_402":26,"_92":631},{"_361":706,"_401":619,"_402":26,"_92":631},{"_361":735,"_401":516,"_402":26,"_92":736},{"_361":723,"_401":522,"_402":215,"_92":724},{"_361":733,"_401":516,"_402":26,"_92":734},{"_361":723,"_401":519,"_402":215,"_92":724},{"_361":731,"_401":618,"_402":26,"_92":732},{"_361":723,"_401":640,"_402":215,"_92":724},{"_361":703,"_401":519,"_402":26,"_92":705},{"_361":729,"_401":516,"_402":215,"_92":730},{"_361":727,"_401":516,"_402":26,"_92":728},{"_361":725,"_401":516,"_402":215,"_92":726},{"_361":723,"_401":614,"_402":215,"_92":724},{"_361":721,"_401":516,"_402":26,"_92":722},{"_361":719,"_401":516,"_402":26,"_92":720},{"_361":717,"_401":516,"_402":215,"_92":718},{"_361":715,"_401":623,"_402":26,"_92":716},{"_361":703,"_401":614,"_402":26,"_92":705},{"_361":713,"_401":516,"_402":26,"_92":714},{"_361":711,"_401":516,"_402":215,"_92":712},{"_361":709,"_401":516,"_402":26,"_92":710},{"_361":707,"_401":516,"_402":26,"_92":708},{"_361":706,"_401":516,"_402":26,"_92":631},{"_361":703,"_401":704,"_402":26,"_92":705},{"_361":701,"_401":516,"_402":26,"_92":702},{"_361":699,"_401":516,"_402":215,"_92":700},{"_361":697,"_401":516,"_402":26,"_92":698},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. 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