Speech Synthesis#

Local Deployment using Quick Start Scripts#

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.

Download the scripts. Go to the Riva Quick Start for Data center or Embedded depending on the platform that you are using. Select the File Browser tab to download the scripts or use the NGC CLI tool to download from the command line.

Data center
```
ngc registry resource download-version nvidia/riva/riva_quickstart:2.19.0
```
Embedded
```
ngc registry resource download-version nvidia/riva/riva_quickstart_arm64:2.19.0
```

Configure RIVA deployment: Modify the config.sh file within the quickstart directory with your preferred configuration.

Description	Parameter in config.sh
Services to enable	`service_enabled_asr`, `service_enabled_tts`, `service_enabled_nmt`
Models to retrieve from NGC	`asr_acoustic_model`, `asr_language_code`, `tts_language_code`
Model storage location	`riva_model_loc` (default value: `riva-model-repo` docker volume. Can be set to a local directory in host machine if needed)
GPU selection for multi-GPU systems	`gpus_to_use` (refer to Local (Docker) for more details)
SSL/TLS certificate, key file location	`ssl_server_cert`, `ssl_server_key`, `ssl_root_cert`, `ssl_use_mutual_auth`

Initialize and start Riva. The initialization step downloads and prepares Docker images and models. The start script launches the server.

Note

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 they have been downloaded. On embedded platforms, preoptimized models for the GPU on the NVIDIA Jetson are downloaded.

Data center
```
cd riva_quickstart_v2.19.0
```
Note

If 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.

Embedded
```
cd riva_quickstart_arm64_v2.19.0
```
Note

If you are using the Jetson AGX Xavier or the Jetson NX Xavier platform, set the $riva_tegra_platform variable to xavier in the config.sh file within the quickstart directory.

To use a USB device for audio input/output, connect it to the Jetson platform so it gets automatically mounted into the container.

Initialize and start Riva
```
bash riva_init.sh
bash riva_start.sh
```
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.
Shut down the server when finished. After you have completed these steps and experimented with inferencing, run the riva_stop.sh script to stop the server.

For further details on how to customize a local deployment, refer to Local (Docker).

If using SSL/TLS, ensure to include the options described in this section to enable the secure deployment of the Riva server.

Synthesize Speech with Riva#

For Data center, 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 (downloaded earlier in the Local Deployment using Quick Start Scripts section, step 1 above).
```
bash riva_start_client.sh
```
For Embedded, this step is not needed because the sample clients are already present in the Riva server container launched in the previous step.
Run the following command to list the available models and choose the model to use.
```
python3 /opt/riva/examples/talk.py --list-voices
```
Note

By default, the RIVA server deploys English (US) Fastpitch-HifiGAN model for Speech Synthesis. You can customize this configuration by adjusting the following parameters in config.sh:
- tts_model: Select the model to use for synthesis (e.g. “magpie”, “fastpitch_hifigan”)
- tts_language_code: Select the language for synthesis (e.g. “en-US”, “es-US”)
If you have set the following parameters in config.sh,
```
service_enabled_tts=true
tts_model="fastpitch_hifigan"
tts_language_code="en-US"
```
the above command should display output as following:
```
$ python3 /opt/riva/examples/talk.py --list-voices
{
   "en-US": {
      "voices": [
            "English-US.Female-1"
      ]
   }
```

Run the following command to synthesize the audio files.

python3 /opt/riva/examples/talk.py \
         --text " This is text" \
         --voice English-US.Female-1 \
         --language-code en-US \
         --output /opt/riva/wav/output.wav

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

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

Next Steps#

In this Quick Start Guide, you learned the basics of deploying the Riva server with pretrained models and using the API. Specifically, you:

Installed the Riva server and pretrained models
Walked through some tutorials to use the Riva API
Executed Riva command-line clients to synthesize text-to-speech (TTS).

For more examples of how you can use Riva Speech AI Skills in real applications, follow the tutorials in GitHub. Additionally, you can build your own speech AI applications with Riva using available APIs like gRPC, Python libraries, and command-line clients.

To learn more about Riva Speech AI Skills, visit the NVIDIA Riva Developer page.

NVIDIA Riva

Speech Synthesis

Contents

Speech Synthesis#

Local Deployment using Quick Start Scripts#

Synthesize Speech with Riva#

Next Steps#