How to connect Google Cloud Speech-To-Text and PostgreSQL
Create a New Scenario to Connect Google Cloud Speech-To-Text and PostgreSQL
In the workspace, click the “Create New Scenario” button.

Add the First Step
Add the first node – a trigger that will initiate the scenario when it receives the required event. Triggers can be scheduled, called by a Google Cloud Speech-To-Text, triggered by another scenario, or executed manually (for testing purposes). In most cases, Google Cloud Speech-To-Text or PostgreSQL will be your first step. To do this, click "Choose an app," find Google Cloud Speech-To-Text or PostgreSQL, and select the appropriate trigger to start the scenario.

Add the Google Cloud Speech-To-Text Node
Select the Google Cloud Speech-To-Text node from the app selection panel on the right.

Google Cloud Speech-To-Text
Configure the Google Cloud Speech-To-Text
Click on the Google Cloud Speech-To-Text node to configure it. You can modify the Google Cloud Speech-To-Text URL and choose between DEV and PROD versions. You can also copy it for use in further automations.
Add the PostgreSQL Node
Next, click the plus (+) icon on the Google Cloud Speech-To-Text node, select PostgreSQL from the list of available apps, and choose the action you need from the list of nodes within PostgreSQL.

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PostgreSQL

Authenticate PostgreSQL
Now, click the PostgreSQL node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your PostgreSQL settings. Authentication allows you to use PostgreSQL through Latenode.
Configure the Google Cloud Speech-To-Text and PostgreSQL Nodes
Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.
Set Up the Google Cloud Speech-To-Text and PostgreSQL Integration
Use various Latenode nodes to transform data and enhance your integration:
- Branching: Create multiple branches within the scenario to handle complex logic.
- Merging: Combine different node branches into one, passing data through it.
- Plug n Play Nodes: Use nodes that don’t require account credentials.
- Ask AI: Use the GPT-powered option to add AI capabilities to any node.
- Wait: Set waiting times, either for intervals or until specific dates.
- Sub-scenarios (Nodules): Create sub-scenarios that are encapsulated in a single node.
- Iteration: Process arrays of data when needed.
- Code: Write custom code or ask our AI assistant to do it for you.

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AI Anthropic Claude 3
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PostgreSQL
Trigger on Webhook
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Webhook response

Save and Activate the Scenario
After configuring Google Cloud Speech-To-Text, PostgreSQL, and any additional nodes, don’t forget to save the scenario and click "Deploy." Activating the scenario ensures it will run automatically whenever the trigger node receives input or a condition is met. By default, all newly created scenarios are deactivated.
Test the Scenario
Run the scenario by clicking “Run once” and triggering an event to check if the Google Cloud Speech-To-Text and PostgreSQL integration works as expected. Depending on your setup, data should flow between Google Cloud Speech-To-Text and PostgreSQL (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Google Cloud Speech-To-Text and PostgreSQL
Google Cloud Speech-To-Text + PostgreSQL + Slack: When a new audio file of a customer support call is stored, Google Cloud Speech-To-Text transcribes the audio using asynchronous processing. The resulting transcript is then stored in a PostgreSQL database, and a summary of the call is sent to a designated Slack channel for review.
Google Cloud Speech-To-Text + Google Docs + PostgreSQL: Automatically transcribe archived meeting audio using Google Cloud Speech-To-Text. Then, use the transcribed text to create meeting minutes in Google Docs, and save key information in a PostgreSQL database.
Google Cloud Speech-To-Text and PostgreSQL integration alternatives
About Google Cloud Speech-To-Text
Automate audio transcription using Google Cloud Speech-To-Text within Latenode. Convert audio files to text and use the results to populate databases, trigger alerts, or analyze customer feedback. Latenode provides visual tools to manage the flow, plus code options for custom parsing or filtering. Scale voice workflows without complex coding.
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About PostgreSQL
Use PostgreSQL in Latenode to automate database tasks. Build flows that react to database changes or use stored data to trigger actions in other apps. Automate reporting, data backups, or sync data across systems without code. Scale complex data workflows easily within Latenode's visual editor.
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FAQ Google Cloud Speech-To-Text and PostgreSQL
How can I connect my Google Cloud Speech-To-Text account to PostgreSQL using Latenode?
To connect your Google Cloud Speech-To-Text account to PostgreSQL on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Google Cloud Speech-To-Text and click on "Connect".
- Authenticate your Google Cloud Speech-To-Text and PostgreSQL accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I archive transcribed call center audio in PostgreSQL?
Yes, you can! Latenode simplifies this. Automatically transcribe audio with Google Cloud Speech-To-Text, then store the text and metadata securely in PostgreSQL for analysis and compliance.
What types of tasks can I perform by integrating Google Cloud Speech-To-Text with PostgreSQL?
Integrating Google Cloud Speech-To-Text with PostgreSQL allows you to perform various tasks, including:
- Storing transcribed meeting notes directly into a database.
- Analyzing customer support call transcripts for sentiment.
- Creating searchable archives of dictated voice memos.
- Building a voice-enabled data entry system.
- Automatically updating database records based on voice commands.
How do I handle long audio files with Google Cloud Speech-To-Text in Latenode?
Latenode lets you split large files, process in parallel, then aggregate the output. This speeds up transcription and database entry.
Are there any limitations to the Google Cloud Speech-To-Text and PostgreSQL integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Transcription accuracy depends on the audio quality and language clarity.
- Large data volumes may impact workflow execution speed without optimization.
- PostgreSQL database size limits can affect long-term storage capacity.