System Maintenance

Periodically, you will update configuration files and models, install product updates for your self-hosted deployment, and rotate licensing credentials. A regularly updated system will be both more secure and more performant than a lagging one.

Updating Models

Deepgram model updates include releases of new architectures, features, and language support, as well an incremental improvements for existing models. Please contact your Deepgram Account Representative to find out whether there are updates available for the models you use.

Model Selection

Your Deepgram Account Representative provides you with a list of several different models depending on your use cases. For selecting language models to download, consider:

• Language(s) you will transcribe. Each model file name contains a language code, such as en (English) or es (Spanish).
• Pre-recorded (batch) or streaming transcription. Look for batch or streaming in the model file name.
• Whether you prefer smart-formatted ("human-readable", with grammar and punctuation) or non-formatted transcription output. Look for formatted or non-formatted in the model file name.
• Model architecture(s) you prefer, such as nova-2-general or enhanced.
• Domain models, such as nova-2-phonecall.
• Additional features you plan to use, including diarization, keywords, redaction, audio intelligence, etc.
  • Keywords requires the phoneme and g2p models.
  • Sentiment analysis, intent recognition, and topic detection require the sit model.
  • Each of the language detection (language-detector), diarization (diarizer), profanity filtering, summarization, and search features require its own model.

Putting it all together, the model nova-2-general.en.formatted.streaming.a12b345.dg will transcribe English with streaming audio input, using the Nova-2 General model, with smart-formatted output.

📘

Legacy Model File Naming

Models delivered prior to May 2024 may have different naming conventions for the model architecture. For example, they may read as 2-general-nova instead of nova-2-general in the filename.

The underlying model is identical, and both can be called with model=nova-2-general in your API calls.

Model-Dependent Feature Support

An individual inference request to your self-hosted environment will require one or more deep learning models to be present. If your request attempts to use a feature that is powered by its own model, and the model is not present in your models directory in your self-hosted deployment, you will get a 400 Bad Request HTTP response.

For example, if you make a speech-to-text request that doesn't have the appropriate language or streaming/batch mode available, you may see this error:

{
  "err_code":"Bad Request",
  "err_msg":"Bad Request: No such model/language/tier combination found. You could try the \"general\" model (language: en-US, base tier).",
  "request_id":"154637c0-65b7-4ec4-a74c-467f8e51afd9"
}   

Additionally, if you request one of the following model-based features, and the model is not present, your request may be incomplete or receive an error response.

• Diarization
• Keywords
• Language Detection
• Entity Detection
• Profanity Filter (Base Tier only)
• Search
• Audio Intelligence Features (Summarization, Sentiment, Topics, Intents)

Contact your Deepgram Account Representative with the set of features you'd like to utilize on your requests, and you'll receive the necessary models to support that desired feature set.

Adding New Models to your Deployment

Docker/Podman

You can download new models into your models/ directory to expose them to your system. You can look for a log line like the following in your Engine logs to demonstrate that a new model has been loaded:

2024-02-26T15:41:40.770498319Z  INFO load_model{path=/models/general.tar}: impeller::model_suppliers::autoload: Inserting model key=AsrKey { name: "general", version: "2023-02-22.3", languages: List(["en", ...]), aliases: {}, tags: [], uuid: 96a295ec-6336-43d5-b1cb-1e48b5e6d9a4, formatted: false, mode: All, architecture: None }

You may also verify that a new model UUID is serving your requests by inspecting the models or model_info fields in the request response metadata.

Kubernetes

Model files are typically exposed to Engine Pods via PersistentVolumeClaim backed by a PersistentVolume. See the deepgram-self-hosted Helm chart documentation on Persistent Storage Options for details on updating models in your cluster.

Installing Product Updates

Deepgram regularly updates the container images for its self-hosted offering. Some updates are recommended, while others are mandatory and, if not installed, will result in Deepgram products ceasing to function.

You can identify the latest self-hosted release in the Deepgram Changelog. Filter by "Self-Hosted", and select the latest release. You can use either the Semver version tag or the release tag (release-XXXXXX) for each container image.

Docker/Podman

1. Update the image field for all services in your docker-compose.yml or podman-compose.yml file with the desired tag.

2. Refresh your container image repository credentials:

   Shell

   docker login quay.io
   

3. Restart existing containers. The new image tags in your Compose file will be automatically detected and the necessary container images will be downloaded and deployed.

   Shell

   # Docker
   docker compose -f /path/to/docker-compose.yml up -d
   # Podman
   podman-compose -f /path/to/docker-compose.yml up -d
   

Kubernetes

In the deepgram-self-hosted Helm chart, you can specify the container image tag to use with the {api,engine,license-proxy}.image.tag values. See the values documentation for more details.

You can then run helm upgrade to roll these changes out to your cluster.

Updating Configuration Files

Besides the container image tags, there may be other updates to configuration files that are available. Deepgram's self-hosted-resources repository contains up-to-date configuration files for Docker and Podman, as well as the deepgram-self-hosted Helm chart. Consult this repository regularly to see if updates are available.

Docker/Podman

Updated files may include the Compose file and the API/Engine/License Proxy toml configuration files. If you modify your configuration files, you will need to restart your existing containers for changes to take effect.

# Docker
docker compose -f /path/to/docker-compose.yml up -d --force-recreate
# Podman
podman-compose -f /path/to/podman-compose.yml up -d --force-recreate

Kubernetes

The deepgram-self-hosted Helm chart has regular releases, and you should regularly update your Helm installation to use the latest version. The chart Changelog describes changes between versions, and the chart README contains upgrade instructions, as well as migration notes if a specific version contains breaking changes.

Existing Pods will need to be restarted when underlying ConfigMaps are changed. This is handled automatically in version >=0.2.1 of the deepgram-self-hosted Helm chart.

Managing Deepgram Licenses

You may manage your own Deepgram licensing per the Self Service Licensing & Credentials guide, including rotation and expiration of credentials.

If you have ever been issued a license file for specialized, offline/air-gapped deployments, please contact your Deepgram Account Representative for additional documentation about maintaining your license.

Backing up Deepgram Products

When backing up a Deepgram installation, you should back up all Infrastructure-as-Code artifacts.

• Docker/Podman
  • Compose files (docker-compose.yml or podman-compose.yml)
  • api.toml
  • engine.toml
  • license-proxy.toml, if necessary
  • models directory
• Kubernetes
  • Version of deepgram-self-hosted Helm chart that is in use
  • values.yaml configuration file

We highly recommend backing up your entire environment state, if possible. If you are using Docker/Podman, this can be done with a VM snapshot. If you are using Kubernetes, you may consider a cluster backup tool, such as Velero.