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:

• asia-northeast1
• asia-south1
• asia-southeast1
• australia-southeast1
• eu
• europe-west1
• europe-west2
• europe-west3
• europe-west4
• northamerica-northeast1
• us
• us-central1
• us-east1
• us-east4
• us-west1

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

Required permissions

• To work with a Speech-to-Text recognizer, you need the following roles:

  • speech.recognizers.create
  • speech.recognizers.get
  • speech.recognizers.recognize
  • speech.recognizers.update

• To create a connection, you need membership in the following role:

  • roles/bigquery.connectionAdmin

• To create the model using BigQuery ML, you need the following permissions:

  • bigquery.jobs.create
  • bigquery.models.create
  • bigquery.models.getData
  • bigquery.models.updateData
  • bigquery.models.updateMetadata

• To run inference, you need the following permissions:

  • bigquery.tables.getData on the object table
  • bigquery.models.getData on the model
  • bigquery.jobs.create

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.

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud project.

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

   Enable the APIs

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

   Go to project selector

6. Make sure that billing is enabled for your Google Cloud project.

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

   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.

Create a connection

Create a cloud resource connection and get the connection's service account.

Select one of the following options:

 ## This creates a cloud resource connection.
 ## Note: The cloud resource nested object has only one output only field - serviceAccountId.
 resource "google_bigquery_connection" "connection" {
    connection_id = "CONNECTION_ID"
    project = "PROJECT_ID"
    location = "REGION"
    cloud_resource {}
}        

Grant access to the service account

Select one of the following options:

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

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

Create a dataset

Create a dataset to contain the model and the object table.

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 `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:

• PROJECT_ID: your project ID.
• DATASET_ID: the ID of the dataset to contain the model.
• MODEL_NAME: the name of the model.
• REGION: the region used by the connection.
• CONNECTION_ID: the connection ID—for example, myconnection.

  When you view the connection details in the Google Cloud console, the connection ID is the value in the last section of the fully qualified connection ID that is shown in Connection ID—for example projects/myproject/locations/connection_location/connections/myconnection.

• PROJECT_NUMBER: the project number of the project that contains the speech recognizer. You can find this value on the Project info card in the Dashboard page of the Google Cloud console.
• LOCATION: the location used by the speech recognizer. You can find this value in the Location field on the List recognizers page of the Google Cloud console.
• RECOGNIZER_ID: the speech recognizer ID. You can find this value in the ID field on the List recognizers page of the Google Cloud console.

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.

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:

• PROJECT_ID: your project ID.
• DATASET_ID: the ID of the dataset that contains the model.
• MODEL_NAME: the name of the model.
• OBJECT_TABLE_NAME: the name of the object table that contains the URIs of the audio files to process.
• recognition_config: a RecognitionConfig resource in JSON format.

  If you specified a recognizer for the remote model SPEECH_RECOGNIZER option, you can optionally specify a recognition_config value to override the default configuration of the specified recognizer.

  You must specify this argument if you didn't specify a recognizer for the remote model.

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 overrides the recognizer's default configuration:

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

What's next

• For information about model inference in BigQuery ML, see Model inference overview.
• For information about the supported SQL statements and functions for each model type, see End-to-end user journey for each model.