Transcribe phone audio with enhanced models

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This tutorial shows how to transcribe the audio recorded from a phone using Speech-to-Text.

Audio files can come from many different sources. Audio data can come from a phone (like voicemail) or a soundtrack included in a video file.

Speech-to-Text can use one of several machine learning models to transcribe your audio file, to best match the original source of the audio. You can get better results from your speech transcription by specifying the source of the original audio. This allows the Speech-to-Text to process your audio files using a machine learning model trained for data similar to your audio file.

Caution: This tutorial uses the enhanced speech recognition model. Speech-to-Text has two types of phone call model that you can use for speech recognition, a standard model and an enhanced model. The enhanced model can provide better results at a higher price (although you can reduce the price by opting into data logging).

Objectives

• Send a audio transcription request for audio recorded from a phone (like voicemail) to Speech-to-Text.
• Specify an enhanced speech recognition model for an audio transcription request.

Costs

This tutorial uses billable components of Cloud Platform, including:

• Speech-to-Text

Use the Pricing Calculator to generate a cost estimate based on your projected usage. New Cloud Platform users might be eligible for a free trial.

Before you begin

This tutorial has several prerequisites:

• You've set up a Speech-to-Text project in the Google Cloud console.
• You've set up your environment using Application Default Credentials in the Google Cloud console.
• You have set up the development environment for your chosen programming language.
• You've installed the Google Cloud Client Library for your chosen programming language.

Send a request

To best transcribe audio captured on a phone, like a phone call or voicemail, you can set the model field in your RecognitionConfig payload to phone_call. The model field tells Speech-to-Text API which speech recognition model to use for the transcription request.

Note: See the language support page to see which models are available for your language.

You can improve the results of phone audio transcription by using an enhanced model. To use an enhanced model, you set the useEnhanced field to true in your RecognitionConfig payload.

Note: If you do not want to use the enhanced model in this tutorial, you can remove the useEnhanced field from your request to the Speech-to-Text.

The following code samples demonstrate how to select a specific transcription model when calling Speech-to-Text.

Protocol

Refer to the speech:recognize API endpoint for complete details.

To perform synchronous speech recognition, make a POST request and provide the appropriate request body. The following shows an example of a POST request using curl. The example uses the Google Cloud CLI to generate an access token. For instructions on installing the gcloud CLI, see the quickstart.

curl -s -H "Content-Type: application/json" \
    -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
    https://speech.googleapis.com/v1/speech:recognize \
    --data '{
    "config": {
        "encoding": "LINEAR16",
        "languageCode": "en-US",
        "enableWordTimeOffsets": false,
        "enableAutomaticPunctuation": true,
        "model": "phone_call",
        "useEnhanced": true
    },
    "audio": {
        "uri": "gs://cloud-samples-tests/speech/commercial_mono.wav"
    }
}'

See the RecognitionConfig reference documentation for more information on configuring the request body.

If the request is successful, the server returns a 200 OK HTTP status code and the response in JSON format:

{
  "results": [
    {
      "alternatives": [
        {
          "transcript": "Hi, I'd like to buy a Chromecast. I was wondering whether you could help me with that.",
          "confidence": 0.8930228
        }
      ],
      "resultEndTime": "5.640s"
    },
    {
      "alternatives": [
        {
          "transcript": " Certainly, which color would you like? We are blue black and red.",
          "confidence": 0.9101991
        }
      ],
      "resultEndTime": "10.220s"
    },
    {
      "alternatives": [
        {
          "transcript": " Let's go with the black one.",
          "confidence": 0.8818244
        }
      ],
      "resultEndTime": "13.870s"
    },
    {
      "alternatives": [
        {
          "transcript": " Would you like the new Chromecast Ultra model or the regular Chromecast?",
          "confidence": 0.94733626
        }
      ],
      "resultEndTime": "18.460s"
    },
    {
      "alternatives": [
        {
          "transcript": " Regular Chromecast is fine. Thank you. Okay. Sure. Would you like to ship it regular or Express?",
          "confidence": 0.9519095
        }
      ],
      "resultEndTime": "25.930s"
    },
    {
      "alternatives": [
        {
          "transcript": " Express, please.",
          "confidence": 0.9101229
        }
      ],
      "resultEndTime": "28.260s"
    },
    {
      "alternatives": [
        {
          "transcript": " Terrific. It's on the way. Thank you. Thank you very much. Bye.",
          "confidence": 0.9321616
        }
      ],
      "resultEndTime": "34.150s"
    }
 ]
}

Go

To learn how to install and use the client library for Speech-to-Text, see Speech-to-Text client libraries. For more information, see the Speech-to-Text Go API reference documentation.

To authenticate to Speech-to-Text, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

func enhancedModel(w io.Writer) error {
	ctx := context.Background()

	client, err := speech.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %w", err)
	}
	defer client.Close()

	data, err := os.ReadFile("../testdata/commercial_mono.wav")
	if err != nil {
		return fmt.Errorf("ReadFile: %w", err)
	}

	resp, err := client.Recognize(ctx, &speechpb.RecognizeRequest{
		Config: &speechpb.RecognitionConfig{
			Encoding:        speechpb.RecognitionConfig_LINEAR16,
			SampleRateHertz: 8000,
			LanguageCode:    "en-US",
			UseEnhanced:     true,
			// A model must be specified to use enhanced model.
			Model: "phone_call",
		},
		Audio: &speechpb.RecognitionAudio{
			AudioSource: &speechpb.RecognitionAudio_Content{Content: data},
		},
	})
	if err != nil {
		return fmt.Errorf("client.Recognize: %w", err)
	}

	for i, result := range resp.Results {
		fmt.Fprintf(w, "%s\n", strings.Repeat("-", 20))
		fmt.Fprintf(w, "Result %d\n", i+1)
		for j, alternative := range result.Alternatives {
			fmt.Fprintf(w, "Alternative %d: %s\n", j+1, alternative.Transcript)
		}
	}
	return nil
}

Java

To learn how to install and use the client library for Speech-to-Text, see Speech-to-Text client libraries. For more information, see the Speech-to-Text Java API reference documentation.

To authenticate to Speech-to-Text, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

/**
 * Transcribe the given audio file using an enhanced model.
 *
 * @param fileName the path to an audio file.
 */
public static void transcribeFileWithEnhancedModel(String fileName) throws Exception {
  Path path = Paths.get(fileName);
  byte[] content = Files.readAllBytes(path);

  try (SpeechClient speechClient = SpeechClient.create()) {
    // Get the contents of the local audio file
    RecognitionAudio recognitionAudio =
        RecognitionAudio.newBuilder().setContent(ByteString.copyFrom(content)).build();

    // Configure request to enable enhanced models
    RecognitionConfig config =
        RecognitionConfig.newBuilder()
            .setEncoding(AudioEncoding.LINEAR16)
            .setLanguageCode("en-US")
            .setSampleRateHertz(8000)
            .setUseEnhanced(true)
            // A model must be specified to use enhanced model.
            .setModel("phone_call")
            .build();

    // Perform the transcription request
    RecognizeResponse recognizeResponse = speechClient.recognize(config, recognitionAudio);

    // Print out the results
    for (SpeechRecognitionResult result : recognizeResponse.getResultsList()) {
      // There can be several alternative transcripts for a given chunk of speech. Just use the
      // first (most likely) one here.
      SpeechRecognitionAlternative alternative = result.getAlternatives(0);
      System.out.format("Transcript: %s\n\n", alternative.getTranscript());
    }
  }
}

Node.js

To learn how to install and use the client library for Speech-to-Text, see Speech-to-Text client libraries. For more information, see the Speech-to-Text Node.js API reference documentation.

To authenticate to Speech-to-Text, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

// Imports the Google Cloud client library for Beta API
/**
 * TODO(developer): Update client library import to use new
 * version of API when desired features become available
 */
const speech = require('@google-cloud/speech').v1p1beta1;
const fs = require('fs');

// Creates a client
const client = new speech.SpeechClient();

/**
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const filename = 'Local path to audio file, e.g. /path/to/audio.raw';
// const encoding = 'Encoding of the audio file, e.g. LINEAR16';
// const sampleRateHertz = 16000;
// const languageCode = 'BCP-47 language code, e.g. en-US';

const config = {
  encoding: encoding,
  languageCode: languageCode,
  useEnhanced: true,
  model: 'phone_call',
};
const audio = {
  content: fs.readFileSync(filename).toString('base64'),
};

const request = {
  config: config,
  audio: audio,
};

// Detects speech in the audio file
const [response] = await client.recognize(request);
response.results.forEach(result => {
  const alternative = result.alternatives[0];
  console.log(alternative.transcript);
});

Python

To learn how to install and use the client library for Speech-to-Text, see Speech-to-Text client libraries. For more information, see the Speech-to-Text Python API reference documentation.

To authenticate to Speech-to-Text, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

from google.cloud import speech


def transcribe_file_with_enhanced_model(audio_file: str) -> speech.RecognizeResponse:
    """Transcribe the given audio file using an enhanced model.
    Args:
        audio_file (str): Path to the local audio file to be transcribed.
            Example: "resources/commercial_mono.wav"
    Returns:
        speech.RecognizeResponse: The response containing the transcription results.
    """

    client = speech.SpeechClient()

    # audio_file = 'resources/commercial_mono.wav'
    with open(audio_file, "rb") as f:
        audio_content = f.read()

    audio = speech.RecognitionAudio(content=audio_content)
    config = speech.RecognitionConfig(
        encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
        sample_rate_hertz=8000,
        language_code="en-US",
        use_enhanced=True,
        # A model must be specified to use enhanced model.
        model="phone_call",
    )

    response = client.recognize(config=config, audio=audio)

    for i, result in enumerate(response.results):
        alternative = result.alternatives[0]
        print("-" * 20)
        print(f"First alternative of result {i}")
        print(f"Transcript: {alternative.transcript}")

    return response

Additional languages

C#: Please follow the C# setup instructions on the client libraries page and then visit the Speech-to-Text reference documentation for .NET.

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the Speech-to-Text reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the Speech-to-Text reference documentation for Ruby.

Caution: Enhanced models cost more than standard models. Review the pricing for more details.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

Delete the project

The easiest way to eliminate billing is to delete the project that you created for the tutorial.

To delete the project:

Caution: Deleting a project has the following effects:

• Everything in the project is deleted. If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.

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

   Go to Manage resources

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

Delete instances

To delete a Compute Engine instance:

1. In the Google Cloud console, go to the VM instances page.

   Go to VM instances

2. Select the checkbox for the instance that you want to delete.
3. To delete the instance, click More actions, click Delete, and then follow the instructions.

Delete firewall rules for the default network

To delete a firewall rule:

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

   Go to Firewall

2. Select the checkbox for the firewall rule that you want to delete.
3. To delete the firewall rule, click Delete.