Detect intent with audio input stream

This page shows how to stream audio input to a detect intent request using the API. Dialogflow processes the audio and converts it to text before attempting an intent match. This conversion is known as audio input, speech recognition, speech-to-text, or STT.

Before you begin

This feature is only applicable when using the API for end-user interactions. If you are using an integration, you can skip this guide.

You should do the following before reading this guide:

  1. Read Dialogflow basics.
  2. Perform setup steps.

Create an agent

If you have not already created an agent, create one now:

  1. Go to the Dialogflow ES Console.
  2. If requested, sign in to the Dialogflow Console. See Dialogflow console overview for more information.
  3. Click Create Agent in the left sidebar menu. (If you already have other agents, click the agent name, scroll to the bottom and click Create new agent.)
  4. Enter your agent's name, default language, and default time zone.
  5. If you have already created a project, enter that project. If you want to allow the Dialogflow Console to create the project, select Create a new Google project.
  6. Click the Create button.

Import the example file to your agent

The steps in this guide make assumptions about your agent, so you need to import an agent prepared for this guide. When importing, these steps use the restore option, which overwrites all agent settings, intents, and entities.

To import the file, follow these steps:

  1. Download the file.
  2. Go to the Dialogflow ES Console.
  3. Select your agent.
  4. Click the settings button next to the agent name.
  5. Select the Export and Import tab.
  6. Select Restore From Zip and follow instructions to restore the zip file that you downloaded.

Streaming basics

The Session type's streamingDetectIntent method returns a bidirectional gRPC streaming object. The available methods for this object vary by language, so see the reference documentation for your client library for details.

The streaming object is used to send and receive data concurrently. Using this object, your client streams audio content to Dialogflow, while concurrently listening for a StreamingDetectIntentResponse.

The streamingDetectIntent method has a query_input.audio_config.single_utterance parameter that affects speech recognition:

  • If false (default), speech recognition does not cease until the client closes the stream.
  • If true, Dialogflow will detect a single spoken utterance in input audio. When Dialogflow detects the audio's voice has stopped or paused, it ceases speech recognition and sends a StreamingDetectIntentResponse with a recognition result of END_OF_SINGLE_UTTERANCE to your client. Any audio sent to Dialogflow on the stream after receipt of END_OF_SINGLE_UTTERANCE is ignored by Dialogflow.

In bidirectional streaming, a client can half-close the stream object to signal to the server that it won't send more data. For example, in Java and Go, this method is called closeSend. It is important to half-close (but not abort) streams in the following situations:

  • Your client has finished sending data.
  • Your client is configured with single_utterance set to true, and it receives a StreamingDetectIntentResponse with a recognition result of END_OF_SINGLE_UTTERANCE.

After closing a stream, your client should start a new request with a new stream as needed.

Streaming detect intent

The following samples use the Session type's streamingDetectIntent method to stream audio.


func DetectIntentStream(projectID, sessionID, audioFile, languageCode string) (string, error) {
	ctx := context.Background()

	sessionClient, err := dialogflow.NewSessionsClient(ctx)
	if err != nil {
		return "", err
	defer sessionClient.Close()

	if projectID == "" || sessionID == "" {
		return "", errors.New(fmt.Sprintf("Received empty project (%s) or session (%s)", projectID, sessionID))

	sessionPath := fmt.Sprintf("projects/%s/agent/sessions/%s", projectID, sessionID)

	// In this example, we hard code the encoding and sample rate for simplicity.
	audioConfig := dialogflowpb.InputAudioConfig{AudioEncoding: dialogflowpb.AudioEncoding_AUDIO_ENCODING_LINEAR_16, SampleRateHertz: 16000, LanguageCode: languageCode}

	queryAudioInput := dialogflowpb.QueryInput_AudioConfig{AudioConfig: &audioConfig}

	queryInput := dialogflowpb.QueryInput{Input: &queryAudioInput}

	streamer, err := sessionClient.StreamingDetectIntent(ctx)
	if err != nil {
		return "", err

	f, err := os.Open(audioFile)
	if err != nil {
		return "", err

	defer f.Close()

	go func() {
		audioBytes := make([]byte, 1024)

		request := dialogflowpb.StreamingDetectIntentRequest{Session: sessionPath, QueryInput: &queryInput}
		err = streamer.Send(&request)
		if err != nil {

		for {
			_, err := f.Read(audioBytes)
			if err == io.EOF {
			if err != nil {

			request = dialogflowpb.StreamingDetectIntentRequest{InputAudio: audioBytes}
			err = streamer.Send(&request)
			if err != nil {

	var queryResult *dialogflowpb.QueryResult

	for {
		response, err := streamer.Recv()
		if err == io.EOF {
		if err != nil {

		recognitionResult := response.GetRecognitionResult()
		transcript := recognitionResult.GetTranscript()
		log.Printf("Recognition transcript: %s\n", transcript)

		queryResult = response.GetQueryResult()

	fulfillmentText := queryResult.GetFulfillmentText()
	return fulfillmentText, nil



class DetectIntentStream {

  // DialogFlow API Detect Intent sample with audio files processes as an audio stream.
  static void detectIntentStream(String projectId, String audioFilePath, String sessionId)
      throws IOException, ApiException {
    // String projectId = "YOUR_PROJECT_ID";
    // String audioFilePath = "path_to_your_audio_file";
    // Using the same `sessionId` between requests allows continuation of the conversation.
    // String sessionId = "Identifier of the DetectIntent session";

    // Instantiates a client
    try (SessionsClient sessionsClient = SessionsClient.create()) {
      // Set the session name using the sessionId (UUID) and projectID (my-project-id)
      SessionName session = SessionName.of(projectId, sessionId);

      // Instructs the speech recognizer how to process the audio content.
      // Note: hard coding audioEncoding and sampleRateHertz for simplicity.
      // Audio encoding of the audio content sent in the query request.
      InputAudioConfig inputAudioConfig =
              .setLanguageCode("en-US") // languageCode = "en-US"
              .setSampleRateHertz(16000) // sampleRateHertz = 16000

      // Build the query with the InputAudioConfig
      QueryInput queryInput = QueryInput.newBuilder().setAudioConfig(inputAudioConfig).build();

      // Create the Bidirectional stream
      BidiStream<StreamingDetectIntentRequest, StreamingDetectIntentResponse> bidiStream =

      // The first request must **only** contain the audio configuration:

      try (FileInputStream audioStream = new FileInputStream(audioFilePath)) {
        // Subsequent requests must **only** contain the audio data.
        // Following messages: audio chunks. We just read the file in fixed-size chunks. In reality
        // you would split the user input by time.
        byte[] buffer = new byte[4096];
        int bytes;
        while ((bytes = != -1) {
                  .setInputAudio(ByteString.copyFrom(buffer, 0, bytes))

      // Tell the service you are done sending data

      for (StreamingDetectIntentResponse response : bidiStream) {
        QueryResult queryResult = response.getQueryResult();
        System.out.format("Intent Display Name: %s\n", queryResult.getIntent().getDisplayName());
        System.out.format("Query Text: '%s'\n", queryResult.getQueryText());
            "Detected Intent: %s (confidence: %f)\n",
            queryResult.getIntent().getDisplayName(), queryResult.getIntentDetectionConfidence());
            "Fulfillment Text: '%s'\n",
            queryResult.getFulfillmentMessagesCount() > 0
                ? queryResult.getFulfillmentMessages(0).getText()
                : "Triggered Default Fallback Intent");


const fs = require('fs');
const util = require('util');
const {Transform, pipeline} = require('stream');
const {struct} = require('pb-util');

const pump = util.promisify(pipeline);
// Imports the Dialogflow library
const dialogflow = require('@google-cloud/dialogflow');

// Instantiates a session client
const sessionClient = new dialogflow.SessionsClient();

// The path to the local file on which to perform speech recognition, e.g.
// /path/to/audio.raw const filename = '/path/to/audio.raw';

// The encoding of the audio file, e.g. 'AUDIO_ENCODING_LINEAR_16'
// const encoding = 'AUDIO_ENCODING_LINEAR_16';

// The sample rate of the audio file in hertz, e.g. 16000
// const sampleRateHertz = 16000;

// The BCP-47 language code to use, e.g. 'en-US'
// const languageCode = 'en-US';
const sessionPath = sessionClient.projectAgentSessionPath(

const initialStreamRequest = {
  session: sessionPath,
  queryInput: {
    audioConfig: {
      audioEncoding: encoding,
      sampleRateHertz: sampleRateHertz,
      languageCode: languageCode,

// Create a stream for the streaming request.
const detectStream = sessionClient
  .on('error', console.error)
  .on('data', data => {
    if (data.recognitionResult) {
        `Intermediate transcript: ${data.recognitionResult.transcript}`
    } else {
      console.log('Detected intent:');

      const result = data.queryResult;
      // Instantiates a context client
      const contextClient = new dialogflow.ContextsClient();

      console.log(`  Query: ${result.queryText}`);
      console.log(`  Response: ${result.fulfillmentText}`);
      if (result.intent) {
        console.log(`  Intent: ${result.intent.displayName}`);
      } else {
        console.log('  No intent matched.');
      const parameters = JSON.stringify(struct.decode(result.parameters));
      console.log(`  Parameters: ${parameters}`);
      if (result.outputContexts && result.outputContexts.length) {
        console.log('  Output contexts:');
        result.outputContexts.forEach(context => {
          const contextId =
          const contextParameters = JSON.stringify(
          console.log(`    ${contextId}`);
          console.log(`      lifespan: ${context.lifespanCount}`);
          console.log(`      parameters: ${contextParameters}`);

// Write the initial stream request to config for audio input.

// Stream an audio file from disk to the Conversation API, e.g.
// "./resources/audio.raw"
await pump(
  // Format the audio stream into the request format.
  new Transform({
    objectMode: true,
    transform: (obj, _, next) => {
      next(null, {inputAudio: obj});


def detect_intent_stream(project_id, session_id, audio_file_path, language_code):
    """Returns the result of detect intent with streaming audio as input.

    Using the same `session_id` between requests allows continuation
    of the conversation."""
    from import dialogflow

    session_client = dialogflow.SessionsClient()

    # Note: hard coding audio_encoding and sample_rate_hertz for simplicity.
    audio_encoding = dialogflow.AudioEncoding.AUDIO_ENCODING_LINEAR_16
    sample_rate_hertz = 16000

    session_path = session_client.session_path(project_id, session_id)
    print("Session path: {}\n".format(session_path))

    def request_generator(audio_config, audio_file_path):
        query_input = dialogflow.QueryInput(audio_config=audio_config)

        # The first request contains the configuration.
        yield dialogflow.StreamingDetectIntentRequest(
            session=session_path, query_input=query_input

        # Here we are reading small chunks of audio data from a local
        # audio file.  In practice these chunks should come from
        # an audio input device.
        with open(audio_file_path, "rb") as audio_file:
            while True:
                chunk =
                if not chunk:
                # The later requests contains audio data.
                yield dialogflow.StreamingDetectIntentRequest(input_audio=chunk)

    audio_config = dialogflow.InputAudioConfig(

    requests = request_generator(audio_config, audio_file_path)
    responses = session_client.streaming_detect_intent(requests=requests)

    print("=" * 20)
    for response in responses:
            'Intermediate transcript: "{}".'.format(

    # Note: The result from the last response is the final transcript along
    # with the detected content.
    query_result = response.query_result

    print("=" * 20)
    print("Query text: {}".format(query_result.query_text))
        "Detected intent: {} (confidence: {})\n".format(
            query_result.intent.display_name, query_result.intent_detection_confidence
    print("Fulfillment text: {}\n".format(query_result.fulfillment_text))

Additional languages

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

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the Dialogflow reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the Dialogflow reference documentation for Ruby.


See the samples page for best practices on streaming from a browser microphone to Dialogflow.