Transcribe audio with multiple channels

This page describes how to use Cloud Speech-to-Text to transcribe audio files that include more than one channel. Multi-channel recognition is available for all audio encodings supported by Cloud STT for up to eight channels.

If you are using AutoDetectDecodingConfig, you don't have to specify how many audio channels the file has. It's automatically determined. You must specify audio channel count when using ExplicitDecodingConfig.

Audio data usually includes a channel for each speaker present on the recording. For example, audio of two people talking over the phone might contain two channels, where each line is recorded separately.

When you send a request with multiple channels, Cloud STT returns a result to you that identifies the different channels present in the audio, labeling the alternatives for each result with the channel_tag field.

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. Set up a Google Cloud console project.

    Set up a project

    Click to:

    • Create or select a project.
    • Enable the Speech-to-Text API for that project.

    You can view and manage these resources at any time in the Google Cloud console.

  3. Install the Google Cloud CLI.

  4. To initialize the gcloud CLI, run the following command: 

    gcloud init

  5. Set up a Google Cloud console project.

    Set up a project

    Click to:

    • Create or select a project.
    • Enable the Speech-to-Text API for that project.

    You can view and manage these resources at any time in the Google Cloud console.

  6. Install the Google Cloud CLI.

  7. To initialize the gcloud CLI, run the following command: 

    gcloud init

    8. Client libraries can use Application Default Credentials to easily authenticate with Google APIs and send requests to those APIs. With Application Default Credentials, you can test your application locally and deploy it without changing the underlying code. For more information, see Authenticate for using client libraries.

  8. If you're using a local shell, then create local authentication credentials for your user account: 

    gcloud auth application-default login

    You don't need to do this if you're using Cloud Shell.

    If an authentication error is returned, and you are using an external identity provider (IdP), confirm that you have signed in to the gcloud CLI with your federated identity.

Also ensure you have installed the client library.

Perform synchronous speech recognition on a multichannel file

Here is an example of performing synchronous speech recognition on a local multichannel audio file:

Python

import os

from google.cloud.speech_v2 import SpeechClient
from google.cloud.speech_v2.types import cloud_speech

PROJECT_ID = os.environ["GOOGLE_CLOUD_PROJECT"]


def transcribe_multichannel_v2(
    audio_file: str,
) -> cloud_speech.RecognizeResponse:
    """Transcribe the given audio file synchronously with multichannel.
    Args:
        audio_file (str): Path to the local audio file to be transcribed.
            Example: "resources/two_channel_16k.wav"
    Returns:
        cloud_speech.RecognizeResponse: The full response object which includes the transcription results.
    """
    # Instantiates a client
    client = SpeechClient()

    # Reads a file as bytes
    with open(audio_file, "rb") as f:
        audio_content = f.read()

    config = cloud_speech.RecognitionConfig(
        auto_decoding_config=cloud_speech.AutoDetectDecodingConfig(),
        language_codes=["en-US"],
        model="long",
        features=cloud_speech.RecognitionFeatures(
            multi_channel_mode=cloud_speech.RecognitionFeatures.MultiChannelMode.SEPARATE_RECOGNITION_PER_CHANNEL,
        ),
    )

    request = cloud_speech.RecognizeRequest(
        recognizer=f"projects/{PROJECT_ID}/locations/global/recognizers/_",
        config=config,
        content=audio_content,
    )

    # Transcribes the audio into text
    response = client.recognize(request=request)

    for result in response.results:
        print(f"Transcript: {result.alternatives[0].transcript}")
        print(f"Channel tag: {result.channel_tag}")

    return response

Clean up

To avoid incurring charges to your Google Cloud account for the resources used on this page, follow these steps.

  1. Optional: Revoke the authentication credentials that you created, and delete the local credential file. 

    gcloud auth application-default revoke

  2. Optional: Revoke credentials from the gcloud CLI. 

    gcloud auth revoke

Console

  • In the Google Cloud console, go to the Manage resources page.

    Go to Manage resources

  • In the project list, select the project that you want to delete, and then click Delete.
  • In the dialog, type the project ID, and then click Shut down to delete the project.

    • gcloud

    Delete a Google Cloud project:

    gcloud projects delete PROJECT_ID

    What's next