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Transcribe audio files with the ML.TRANSCRIBE function

This document describes how to use the ML.TRANSCRIBE function with a remote model to transcribe audio files from an object table.

Supported locations

You must create the remote model used in this procedure in one of the following locations:

You must run the ML.TRANSCRIBE function in the same region as the remote model.

Required roles

To create a remote model and transcribe audio files, you need the following Identity and Access Management (IAM) roles at the project level:

These predefined roles contain the permissions required to perform the tasks in this document. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

You might also be able to get these permissions with custom roles or other predefined roles.

Before you begin

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

  2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Roles required to select or create a project

    • Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
    • Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.
    Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

    Go to project selector

  3. Verify that billing is enabled for your Google Cloud project.

  4. Enable the BigQuery, BigQuery Connection API, and Speech-to-Text APIs.

    Roles required to enable APIs

    To enable APIs, you need the serviceusage.services.enable permission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.

    Enable the APIs

  5. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Roles required to select or create a project

    • Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
    • Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.
    Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

    Go to project selector

  6. Verify that billing is enabled for your Google Cloud project.

  7. Enable the BigQuery, BigQuery Connection API, and Speech-to-Text APIs.

    Roles required to enable APIs

    To enable APIs, you need the serviceusage.services.enable permission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.

    Enable the APIs

Create a recognizer

Speech-to-Text supports resources called recognizers. Recognizers represent stored and reusable recognition configurations. You can create a recognizer to logically group together transcriptions or traffic for your application.

Creating a speech recognizer is optional. If you choose to create a speech recognizer, note the project ID, location, and recognizer ID of the recognizer for use in the CREATE MODEL statement, as described in SPEECH_RECOGNIZER. If you choose not to create a speech recognizer, you must specify a value for the recognition_config argument of the ML.TRANSCRIBE function.

You can only use the chirp transcription model in the speech recognizer or recognition_config value that you provide.

Create a dataset

Create a BigQuery dataset to contain your resources:

Console

  1. In the Google Cloud console, go to the BigQuery page.

    Go to BigQuery

  2. In the left pane, click Explorer:

    Highlighted button for the Explorer pane. [Highlighted button for the Explorer pane.]

    If you don't see the left pane, click Expand left pane to open the pane.

  3. In the Explorer pane, click your project name.

  4. Click View actions > Create dataset.

  5. On the Create dataset page, do the following:

    1. For Dataset ID, type a name for the dataset.

    2. For Location type, select Region or Multi-region.

      • If you selected Region, then select a location from the Region list.
      • If you selected Multi-region, then select US or Europe from the Multi-region list.

    3. Click Create dataset.

bq

  1. To create a new dataset, use the bq mk command with the --location flag:

    bq --location=LOCATION mk -d DATASET_ID

    Replace the following:

    • LOCATION: the dataset's location.
    • DATASET_ID: the ID of the dataset that you're creating.

  2. Confirm that the dataset was created:

    bq ls

Create a connection

Create a Cloud resource connection and get the connection's service account. Create the connection in the same location as the dataset you created in the previous step.

You can skip this step if you either have a default connection configured, or you have the BigQuery Admin role.

Select one of the following options:

Console

  1. Go to the BigQuery page.

    Go to BigQuery

  2. In the left pane, click Explorer:

    Highlighted button for the Explorer pane. [Highlighted button for the Explorer pane.]

    If you don't see the left pane, click Expand left pane to open the pane.

  3. In the Explorer pane, expand your project name, and then click Connections.

  4. On the Connections page, click Create connection.

  5. For Connection type, choose Vertex AI remote models, remote functions, BigLake and Spanner (Cloud Resource).

  6. In the Connection ID field, enter a name for your connection.

  7. For Location type, select a location for your connection. The connection should be colocated with your other resources such as datasets.

  8. Click Create connection.

  9. Click Go to connection.

  10. In the Connection info pane, copy the service account ID for use in a later step.

SQL

Use the CREATE CONNECTION statement:

  1. In the Google Cloud console, go to the BigQuery page.

    Go to BigQuery

  2. In the query editor, enter the following statement:

    CREATE CONNECTION [IF NOT EXISTS] `CONNECTION_NAME`
OPTIONS (
  connection_type = "CLOUD_RESOURCE",
  friendly_name = "FRIENDLY_NAME",
  description = "DESCRIPTION"
  );

    Replace the following:

    • CONNECTION_NAME: the name of the connection in either the PROJECT_ID.LOCATION.CONNECTION_ID, LOCATION.CONNECTION_ID, or CONNECTION_ID format. If the project or location are omitted, then they are inferred from the project and location where the statement is run.
    • FRIENDLY_NAME (optional): a descriptive name for the connection.
    • DESCRIPTION (optional): a description of the connection.

  3. Click Run.

For more information about how to run queries, see Run an interactive query.

bq

  1. In a command-line environment, create a connection:

    bq mk --connection --location=REGION --project_id=PROJECT_ID \
    --connection_type=CLOUD_RESOURCE CONNECTION_ID

    The --project_id parameter overrides the default project.

    Replace the following:

    • REGION: your connection region
    • PROJECT_ID: your Google Cloud project ID
    • CONNECTION_ID: an ID for your connection

    When you create a connection resource, BigQuery creates a unique system service account and associates it with the connection.

    Troubleshooting: If you get the following connection error, update the Google Cloud SDK:

    Flags parsing error: flag --connection_type=CLOUD_RESOURCE: value should be one of...

  2. Retrieve and copy the service account ID for use in a later step:

    bq show --connection PROJECT_ID.REGION.CONNECTION_ID

    The output is similar to the following:

    name                          properties
1234.REGION.CONNECTION_ID     {"serviceAccountId": "connection-1234-9u56h9@gcp-sa-bigquery-condel.iam.gserviceaccount.com"}

Python

Before trying this sample, follow the Python setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Python API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries. 

import google.api_core.exceptions
from google.cloud import bigquery_connection_v1

client = bigquery_connection_v1.ConnectionServiceClient()


def create_connection(
    project_id: str,
    location: str,
    connection_id: str,
):
    """Creates a BigQuery connection to a Cloud Resource.

    Cloud Resource connection creates a service account which can then be
    granted access to other Google Cloud resources for federated queries.

    Args:
        project_id: The Google Cloud project ID.
        location: The location of the connection (for example, "us-central1").
        connection_id: The ID of the connection to create.
    """

    parent = client.common_location_path(project_id, location)

    connection = bigquery_connection_v1.Connection(
        friendly_name="Example Connection",
        description="A sample connection for a Cloud Resource.",
        cloud_resource=bigquery_connection_v1.CloudResourceProperties(),
    )

    try:
        created_connection = client.create_connection(
            parent=parent, connection_id=connection_id, connection=connection
        )
        print(f"Successfully created connection: {created_connection.name}")
        print(f"Friendly name: {created_connection.friendly_name}")
        print(
            f"Service Account: {created_connection.cloud_resource.service_account_id}"
        )

    except google.api_core.exceptions.AlreadyExists:
        print(f"Connection with ID '{connection_id}' already exists.")
        print("Please use a different connection ID.")
    except Exception as e:
        print(f"An unexpected error occurred while creating the connection: {e}")

Node.js

Before trying this sample, follow the Node.js setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Node.js API reference documentation.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries. 

const {ConnectionServiceClient} =
  require('@google-cloud/bigquery-connection').v1;
const {status} = require('@grpc/grpc-js');

const client = new ConnectionServiceClient();

/**
 * Creates a new BigQuery connection to a Cloud Resource.
 *
 * A Cloud Resource connection creates a service account that can be granted access
 * to other Google Cloud resources.
 *
 * @param {string} projectId The Google Cloud project ID. for example, 'example-project-id'
 * @param {string} location The location of the project to create the connection in. for example, 'us-central1'
 * @param {string} connectionId The ID of the connection to create. for example, 'example-connection-id'
 */
async function createConnection(projectId, location, connectionId) {
  const parent = client.locationPath(projectId, location);

  const connection = {
    friendlyName: 'Example Connection',
    description: 'A sample connection for a Cloud Resource',
    // The service account for this cloudResource will be created by the API.
    // Its ID will be available in the response.
    cloudResource: {},
  };

  const request = {
    parent,
    connectionId,
    connection,
  };

  try {
    const [response] = await client.createConnection(request);

    console.log(`Successfully created connection: ${response.name}`);
    console.log(`Friendly name: ${response.friendlyName}`);

    console.log(`Service Account: ${response.cloudResource.serviceAccountId}`);
  } catch (err) {
    if (err.code === status.ALREADY_EXISTS) {
      console.log(`Connection '${connectionId}' already exists.`);
    } else {
      console.error(`Error creating connection: ${err.message}`);
    }
  }
}

Terraform

Use the google_bigquery_connection resource.

Note: To create BigQuery objects using Terraform, you must enable the Cloud Resource Manager API.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.

The following example creates a Cloud resource connection named my_cloud_resource_connection in the US region:


# This queries the provider for project information.
data "google_project" "default" {}

# This creates a cloud resource connection in the US region named my_cloud_resource_connection.
# Note: The cloud resource nested object has only one output field - serviceAccountId.
resource "google_bigquery_connection" "default" {
  connection_id = "my_cloud_resource_connection"
  project       = data.google_project.default.project_id
  location      = "US"
  cloud_resource {}
}

To apply your Terraform configuration in a Google Cloud project, complete the steps in the following sections.

Prepare Cloud Shell

  1. Launch Cloud Shell.

  2. Set the default Google Cloud project where you want to apply your Terraform configurations.

    You only need to run this command once per project, and you can run it in any directory.

    export GOOGLE_CLOUD_PROJECT=PROJECT_ID

    Environment variables are overridden if you set explicit values in the Terraform configuration file.

Prepare the directory

Each Terraform configuration file must have its own directory (also called a root module).

  1. In Cloud Shell, create a directory and a new file within that directory. The filename must have the .tf extension—for example main.tf. In this tutorial, the file is referred to as main.tf. 
    mkdir DIRECTORY && cd DIRECTORY && touch main.tf

  2. If you are following a tutorial, you can copy the sample code in each section or step.

    Copy the sample code into the newly created main.tf.

    Optionally, copy the code from GitHub. This is recommended when the Terraform snippet is part of an end-to-end solution.

  3. Review and modify the sample parameters to apply to your environment.
  4. Save your changes.
  5. Initialize Terraform. You only need to do this once per directory. 
    terraform init

    Optionally, to use the latest Google provider version, include the -upgrade option: 

    terraform init -upgrade

Apply the changes

  1. Review the configuration and verify that the resources that Terraform is going to create or update match your expectations: 
    terraform plan

    Make corrections to the configuration as necessary.

  2. Apply the Terraform configuration by running the following command and entering yes at the prompt: 
    terraform apply

    Wait until Terraform displays the "Apply complete!" message.

  3. Open your Google Cloud project to view the results. In the Google Cloud console, navigate to your resources in the UI to make sure that Terraform has created or updated them.
Note: Terraform samples typically assume that the required APIs are enabled in your Google Cloud project.

Grant access to the service account

Select one of the following options:

Console

  1. Go to the IAM & Admin page.

    Go to IAM & Admin

  2. Click Grant Access.

    The Add principals dialog opens.

  3. In the New principals field, enter the service account ID that you copied earlier.

  4. Click the Select a role field and then type Cloud Speech Client in Filter.

  5. Click Add another role.

  6. In the Select a role field, select Cloud Storage, and then select Storage Object Viewer.

  7. Click Save.

gcloud

Use the gcloud projects add-iam-policy-binding command:

gcloud projects add-iam-policy-binding 'PROJECT_NUMBER' --member='serviceAccount:MEMBER' --role='roles/speech.client' --condition=None
gcloud projects add-iam-policy-binding 'PROJECT_NUMBER' --member='serviceAccount:MEMBER' --role='roles/storage.objectViewer' --condition=None

Replace the following:

Failure to grant the permission results in a Permission denied error.

Note: If you create the recognizer in a different project than the Cloud Storage bucket used by the object table, grant the service account Identity and Access Management (IAM) roles as follows:

Create an object table

Create an object table over a set of audio files in Cloud Storage. The audio files in the object table must be of a supported type.

The Cloud Storage bucket used by the object table should be in the same project where you plan to create the model and call the ML.TRANSCRIBE function. If you want to call the ML.TRANSCRIBE function in a different project than the one that contains the Cloud Storage bucket used by the object table, you must grant the Storage Admin role at the bucket level to the service-A@gcp-sa-aiplatform.iam.gserviceaccount.com service account.

Create a model

Create a remote model with a REMOTE_SERVICE_TYPE of CLOUD_AI_SPEECH_TO_TEXT_V2:

CREATE OR REPLACE MODEL
`PROJECT_ID.DATASET_ID.MODEL_NAME`
REMOTE WITH CONNECTION {DEFAULT | `PROJECT_ID.REGION.CONNECTION_ID`}
OPTIONS (
  REMOTE_SERVICE_TYPE = 'CLOUD_AI_SPEECH_TO_TEXT_V2',
  SPEECH_RECOGNIZER = 'projects/PROJECT_NUMBER/locations/LOCATION/recognizers/RECOGNIZER_ID'
);

Replace the following:

Important: You must specify the project ID for the connection even if the connection is in the default project.

Transcribe audio files

Transcribe audio files with the ML.TRANSCRIBE function:

SELECT *
FROM ML.TRANSCRIBE(
  MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
  TABLE `PROJECT_ID.DATASET_ID.OBJECT_TABLE_NAME`,
  RECOGNITION_CONFIG => ( JSON 'recognition_config')
);

Replace the following:

Examples

Example 1

The following example transcribes the audio files represented by the audio table without overriding the recognizer's default configuration:

SELECT *
FROM ML.TRANSCRIBE(
  MODEL `myproject.mydataset.transcribe_model`,
  TABLE `myproject.mydataset.audio`
);

The following example transcribes the audio files represented by the audio table and provides a configuration for the default recognizer:

SELECT *
FROM ML.TRANSCRIBE(
  MODEL `myproject.mydataset.transcribe_model`,
  TABLE `myproject.mydataset.audio`,
  recognition_config => ( JSON '{"language_codes": ["en-US" ],"model": "chirp","auto_decoding_config": {}}')
);

What's next

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Last updated 2026-08-11 UTC.

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