Using enhanced models with data logging

This page describes how to request an enhanced speech recognition model when you send a transcription request to Cloud Speech-to-Text.

Google creates and improves enhanced models based upon data collected through data logging. If you opt-in to data logging, you can help Google improve these models and also enjoy a discount on your usage.

There is currently only one enhanced model, which is used for processing phone call audio. When you request an enhanced model for your phone call transcription requests, you can receive higher quality results. Using the improved phone call model, Cloud Speech-to-Text can more accurately recognize speech captured from phone audio and therefore produce more accurate transcription of the audio data.

To use the enhanced recognition models, set the useEnhanced field to true, and then set the model field to your selected enhanced model in the RecognitionConfig parameters for the request. Cloud Speech-to-Text supports enhanced models for all speech recognition methods: speech:recognize, speech:longrunningrecognize, and StreamingRecognizeRequest.

The following code samples demonstrate how to request to use an enhanced model for a transcription request.

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 access token for a service account set up for the project using the Google Cloud Platform Cloud SDK. For instructions on installing the Cloud SDK, setting up a project with a service account, and obtaining an access token, 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"
    }
 ]
}

C#

View on GitHub Feedback

static object SyncRecognizeEnhancedModel(string filePath)
{
    var speech = SpeechClient.Create();
    var response = speech.Recognize(new RecognitionConfig()
    {
        Encoding = RecognitionConfig.Types.AudioEncoding.Linear16,
        SampleRateHertz = 8000,
        LanguageCode = "en-US",
        UseEnhanced = true,
        // A model must be specified to use an enhanced model.
        // Currently, on 'phone_call' is supported as an enhanced model.
        Model = "phone_call",
    }, RecognitionAudio.FromFile(filePath));
    foreach (var result in response.Results)
    {
        foreach (var alternative in result.Alternatives)
        {
            Console.WriteLine(alternative.Transcript);
        }
    }
    return 0;
}

Go

View on GitHub Feedback

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

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

	// path = "../testdata/commercial_mono.wav"
	data, err := ioutil.ReadFile(path)
	if err != nil {
		return fmt.Errorf("ReadFile: %v", 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("Recognize: %v", 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
}

Python

View on GitHub Feedback

from google.cloud import speech_v1p1beta1 as speech
client = speech.SpeechClient()

speech_file = 'resources/commercial_mono.wav'

with io.open(speech_file, 'rb') as audio_file:
    content = audio_file.read()

audio = speech.types.RecognitionAudio(content=content)
config = speech.types.RecognitionConfig(
    encoding=speech.enums.RecognitionConfig.AudioEncoding.LINEAR16,
    sample_rate_hertz=8000,
    language_code='en-US',
    # Enhanced models are only available to projects that
    # opt in for audio data collection.
    use_enhanced=True,
    # A model must be specified to use enhanced model.
    model='phone_call')

response = client.recognize(config, audio)

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

Java

View on GitHub Feedback

/**
 * 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

View on GitHub Feedback

// 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);
});

What's next

Review how to make synchronous transcription requests.

Was this page helpful? Let us know how we did:

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see our Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated May 14, 2019.

Using enhanced models with data logging

Protocol

C#

Go

Python

Java

Node.js

What's next

Send feedback about...