.svg){kind=icon}
How to connect Google Cloud Speech-To-Text and Landbot.io
Create a New Scenario to Connect Google Cloud Speech-To-Text and Landbot.io
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 Landbot.io will be your first step. To do this, click "Choose an app," find Google Cloud Speech-To-Text or Landbot.io, 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.
+
1
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 Landbot.io Node
Next, click the plus (+) icon on the Google Cloud Speech-To-Text node, select Landbot.io from the list of available apps, and choose the action you need from the list of nodes within Landbot.io.
1
Google Cloud Speech-To-Text
⚙
+
2
Landbot.io
Authenticate Landbot.io
Now, click the Landbot.io node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Landbot.io settings. Authentication allows you to use Landbot.io through Latenode.
Configure the Google Cloud Speech-To-Text and Landbot.io 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 Landbot.io 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.
5
JavaScript
⚙
6
AI Anthropic Claude 3
⚙
+
7
Landbot.io
1
Trigger on Webhook
⚙
2
Google Cloud Speech-To-Text
⚙
⚙
3
Iterator
⚙
+
4
Webhook response
Save and Activate the Scenario
After configuring Google Cloud Speech-To-Text, Landbot.io, 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 Landbot.io integration works as expected. Depending on your setup, data should flow between Google Cloud Speech-To-Text and Landbot.io (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 Landbot.io
Landbot.io + Google Cloud Speech-To-Text + Google Sheets: When a new event is triggered in Landbot.io (e.g., a user submits a voice message), the audio is sent to Google Cloud Speech-To-Text for transcription. The transcribed text, along with other relevant data, is then added as a new row to a Google Sheet for analysis and storage.
Landbot.io + Google Cloud Speech-To-Text + Zendesk: When a new event with audio is triggered in Landbot.io, the audio is transcribed using Google Cloud Speech-To-Text. The resulting text is used to create a new ticket in Zendesk, capturing customer issues and feedback automatically.
Google Cloud Speech-To-Text and Landbot.io 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.
Similar apps
Related categories
About Landbot.io
Use Landbot.io in Latenode to build no-code chatbots, then connect them to your wider automation. Capture leads, qualify prospects, or provide instant support and trigger follow-up actions directly in your CRM, databases, or marketing tools. Latenode handles complex logic, scaling, and integrations without per-step fees.
Similar apps
Related categories
See how Latenode works
.svg)
.png)
FAQ Google Cloud Speech-To-Text and Landbot.io
How can I connect my Google Cloud Speech-To-Text account to Landbot.io using Latenode?
To connect your Google Cloud Speech-To-Text account to Landbot.io 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 Landbot.io accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I transcribe voice messages from Landbot.io users?
Yes, you can! Latenode allows you to automate this, sending voice notes to Google Cloud Speech-To-Text and saving the transcription back to Landbot.io, improving data analysis.
What types of tasks can I perform by integrating Google Cloud Speech-To-Text with Landbot.io?
Integrating Google Cloud Speech-To-Text with Landbot.io allows you to perform various tasks, including:
- Automatically transcribe customer voice feedback from Landbot.io.
- Analyze sentiment from transcribed voice responses in real time.
- Create summaries of conversations using AI and store them.
- Trigger specific bot flows based on spoken keywords.
- Improve chatbot accuracy by using voice input to train it.
How accurate is Google Cloud Speech-To-Text transcription within Latenode?
Google Cloud Speech-To-Text's accuracy is very high. Latenode adds retry and error handling for robust, production-ready automated transcription workflows.
Are there any limitations to the Google Cloud Speech-To-Text and Landbot.io integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Large audio files may take longer to process, depending on Google Cloud Speech-To-Text's API limits.
- Transcription accuracy can be affected by audio quality and background noise.
- Real-time transcription might not be possible for very long audio inputs.
