Select a transcription model

This page describes how to use a specific machine learning model for audio transcription requests to Speech-to-Text.

Transcription models

Speech-to-Text detects words in an audio clip by comparing input to one of many machine learning models. Each model has been trained by analyzing millions of examples—in this case, many, many audio recordings of people speaking.

Speech-to-Text has specialized models trained from audio from specific sources, for example phone calls or videos. Because of this training process, these specialized models provide better results when applied towards similar kinds of audio data.

For example, Speech-to-Text has a transcription model trained to recognize speech recorded over the phone. When Speech-to-Text uses the telephony or telephony_short model to transcribe phone audio, it produces more accurate transcription results than if it had transcribed phone audio using the latest_short or latest_long models.

The following table shows the transcriptions models available for use with Speech-to-Text.

Model name	Description
latest_long	Use this model for any kind of long form content such as media or spontaneous speech and conversations. Consider using this model in place of the video model, especially if the video model is not available in your target language. You can also use this in place of the default model.
latest_short	Use this model for short utterances that are a few seconds in length. It is useful for trying to capture commands or other single shot directed speech use cases. Consider using this model instead of the command and search model.
telephony	Improved version of the "phone_call" model, best for audio that originated from a phone call, typically recorded at an 8kHz sampling rate.
telephony_short	Dedicated version of the modern "telephony" model for short or even single-word utterances for audio that originated from a phone call, typically recorded at an 8kHz sampling rate.
medical_dictation	Use this model to transcribe notes dictated by a medical professional. This is a premium model that costs more than the standard rate. See the pricing page for more details.
medical_conversation	Use this model to transcribe a conversation between a medical professional and a patient. This is a premium model that costs more than the standard rate. See the pricing page for more details.
The following models are mostly based on classic non-conformer architectures and are primarily kept for legacy and backwards-compatibility reasons.
command_and_search	Best for short or single-word utterances like voice commands or voice search.
default	Best for audio that does not fit the other audio models, like long-form audio or dictation. The default model will produce transcription results for any type of audio, including audio such as video clips that have a separate model specifically tailored to it. However, recognizing video clip audio using the default model will likely yield lower-quality results than using the video model. Ideally the audio is high-fidelity, recorded at a 16kHz or greater sampling rate.
phone_call	Best for audio that originated from a phone call (typically recorded at an 8kHz sampling rate).
video	Best for audio from video clips or other sources (such as podcasts) that have multiple speakers. This model is also often the best choice for audio that was recorded with a high-quality microphone or that has lots of background noise. For best results, provide audio recorded at 16,000Hz or greater sampling rate.

Note: If you don't specify a model to use for speech recognition, Speech-to-Text attempts to select the model that best fits the settings in the RecognitionConfig of your request.

Select a model for audio transcription

To specify a specific model to use for audio transcription, you must set the model field to one of the allowed values—such as latest_long, latest_short, telephony, or telephony_short—in the RecognitionConfig parameters for the request. Speech-to-Text supports model selection for all speech recognition methods: speech:recognize, speech:longrunningrecognize, and Streaming.

Perform transcription of a local audio file

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",
        "sampleRateHertz": 16000,
        "languageCode": "en-US",
        "model": "video"
    },
    "audio": {
        "uri": "gs://cloud-samples-tests/speech/Google_Gnome.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": "OK Google stream stranger things from
            Netflix to my TV okay stranger things from
            Netflix playing on TV from the people that brought you
            Google home comes the next evolution of the smart home
            and it's just outside your window me Google know hi
            how can I help okay no what's the weather like outside
            the weather outside is sunny and 76 degrees he's right
            okay no turn on the hose I'm holding sure okay no I'm can
            I eat this lemon tree leaf yes what about this Daisy yes
            but I wouldn't recommend it but I could eat it okay
            Nomad milk to my shopping list I'm sorry that sounds like
            an indoor request I keep doing that sorry you do keep
            doing that okay no is this compost really we're all
            compost if you think about it pretty much everything is
            made up of organic matter and will return",
          "confidence": 0.9251011
        }
      ]
    }
  ]
}

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 modelSelection(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 := ioutil.ReadFile("../testdata/Google_Gnome.wav")
	if err != nil {
		return fmt.Errorf("ReadFile: %w", err)
	}

	req := &speechpb.RecognizeRequest{
		Config: &speechpb.RecognitionConfig{
			Encoding:        speechpb.RecognitionConfig_LINEAR16,
			SampleRateHertz: 16000,
			LanguageCode:    "en-US",
			Model:           "video",
		},
		Audio: &speechpb.RecognitionAudio{
			AudioSource: &speechpb.RecognitionAudio_Content{Content: data},
		},
	}

	resp, err := client.Recognize(ctx, req)
	if err != nil {
		return fmt.Errorf("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.

/**
 * Performs transcription of the given audio file synchronously with the selected model.
 *
 * @param fileName the path to a audio file to transcribe
 */
public static void transcribeModelSelection(String fileName) throws Exception {
  Path path = Paths.get(fileName);
  byte[] content = Files.readAllBytes(path);

  try (SpeechClient speech = SpeechClient.create()) {
    // Configure request with video media type
    RecognitionConfig recConfig =
        RecognitionConfig.newBuilder()
            // encoding may either be omitted or must match the value in the file header
            .setEncoding(AudioEncoding.LINEAR16)
            .setLanguageCode("en-US")
            // sample rate hertz may be either be omitted or must match the value in the file
            // header
            .setSampleRateHertz(16000)
            .setModel("video")
            .build();

    RecognitionAudio recognitionAudio =
        RecognitionAudio.newBuilder().setContent(ByteString.copyFrom(content)).build();

    RecognizeResponse recognizeResponse = speech.recognize(recConfig, recognitionAudio);
    // Just print the first result here.
    SpeechRecognitionResult result = recognizeResponse.getResultsList().get(0);
    // There can be several alternative transcripts for a given chunk of speech. Just use the
    // first (most likely) one here.
    SpeechRecognitionAlternative alternative = result.getAlternativesList().get(0);
    System.out.printf("Transcript : %s\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 model = 'Model to use, e.g. phone_call, video, default';
// 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,
  sampleRateHertz: sampleRateHertz,
  languageCode: languageCode,
  model: model,
};
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);
const transcription = response.results
  .map(result => result.alternatives[0].transcript)
  .join('\n');
console.log('Transcription: ', transcription);

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

# Instantiates a client
client = speech.SpeechClient()
# Reads a file as bytes
with open("resources/Google_Gnome.wav", "rb") as f:
    audio_content = f.read()

audio = speech.RecognitionAudio(content=audio_content)

config = speech.RecognitionConfig(
    encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
    sample_rate_hertz=16000,
    language_code="en-US",
    model="video",  # Chosen model
)

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}")

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.

Perform transcription of a Cloud Storage audio file

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.

import (
	"context"
	"fmt"
	"io"
	"strings"

	speech "cloud.google.com/go/speech/apiv1"
	"cloud.google.com/go/speech/apiv1/speechpb"
)

// transcribe_model_selection_gcs Transcribes the given audio file asynchronously with
// the selected model.
func transcribe_model_selection_gcs(w io.Writer) error {
	ctx := context.Background()

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

	audio := &speechpb.RecognitionAudio{
		AudioSource: &speechpb.RecognitionAudio_Uri{Uri: "gs://cloud-samples-tests/speech/Google_Gnome.wav"},
	}

	// The speech recognition model to use
	// See, https://cloud.google.com/speech-to-text/docs/speech-to-text-requests#select-model
	recognitionConfig := &speechpb.RecognitionConfig{
		Encoding:        speechpb.RecognitionConfig_LINEAR16,
		SampleRateHertz: 16000,
		LanguageCode:    "en-US",
		Model:           "video",
	}

	longRunningRecognizeRequest := &speechpb.LongRunningRecognizeRequest{
		Config: recognitionConfig,
		Audio:  audio,
	}

	operation, err := client.LongRunningRecognize(ctx, longRunningRecognizeRequest)
	if err != nil {
		return fmt.Errorf("error running recognize %w", err)
	}

	response, err := operation.Wait(ctx)
	if err != nil {
		return err
	}
	for i, result := range response.Results {
		alternative := result.Alternatives[0]
		fmt.Fprintf(w, "%s\n", strings.Repeat("-", 20))
		fmt.Fprintf(w, "First alternative of result %d", i)
		fmt.Fprintf(w, "Transcript: %s", 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.

/**
 * Performs transcription of the remote audio file asynchronously with the selected model.
 *
 * @param gcsUri the path to the remote audio file to transcribe.
 */
public static void transcribeModelSelectionGcs(String gcsUri) throws Exception {
  try (SpeechClient speech = SpeechClient.create()) {

    // Configure request with video media type
    RecognitionConfig config =
        RecognitionConfig.newBuilder()
            // encoding may either be omitted or must match the value in the file header
            .setEncoding(AudioEncoding.LINEAR16)
            .setLanguageCode("en-US")
            // sample rate hertz may be either be omitted or must match the value in the file
            // header
            .setSampleRateHertz(16000)
            .setModel("video")
            .build();

    RecognitionAudio audio = RecognitionAudio.newBuilder().setUri(gcsUri).build();

    // Use non-blocking call for getting file transcription
    OperationFuture<LongRunningRecognizeResponse, LongRunningRecognizeMetadata> response =
        speech.longRunningRecognizeAsync(config, audio);

    while (!response.isDone()) {
      System.out.println("Waiting for response...");
      Thread.sleep(10000);
    }

    List<SpeechRecognitionResult> results = response.get().getResultsList();

    // Just print the first result here.
    SpeechRecognitionResult result = results.get(0);
    // There can be several alternative transcripts for a given chunk of speech. Just use the
    // first (most likely) one here.
    SpeechRecognitionAlternative alternative = result.getAlternativesList().get(0);
    System.out.printf("Transcript : %s\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;

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

/**
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const gcsUri = 'gs://my-bucket/audio.raw';
// const model = 'Model to use, e.g. phone_call, video, default';
// 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,
  sampleRateHertz: sampleRateHertz,
  languageCode: languageCode,
  model: model,
};
const audio = {
  uri: gcsUri,
};

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

// Detects speech in the audio file.
const [response] = await client.recognize(request);
const transcription = response.results
  .map(result => result.alternatives[0].transcript)
  .join('\n');
console.log('Transcription: ', transcription);

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.