Transcribing long audio files

This page demonstrates how to transcribe long audio files (longer than 1 minute) to text using asynchronous speech recognition.

Asynchronous speech recognition starts a long running audio processing operation. Use asynchronous speech recognition to transcribe audio that is longer than 1 minute. For shorter audio, synchronous speech recognition is faster and simpler.

You can retrieve the results of the operation using the google.longrunning.Operations method. Results remain available for retrieval for 5 days (120 hours). You also have the option of uploading your results directly to a Google Cloud Storage bucket.

Audio content can be sent directly to Speech-to-Text from a local file, or the API can process audio content stored in { storage_name }. Audio files longer than 1 minute must be stored in a Cloud Storage bucket in order to be transcribed by Speech-to-Text. Performing asynchronous speech recognition on a local file longer than 1 minute will result in either an error or an incomplete transcription.

Transcribing long audio files using a Google Cloud Storage file

These samples use a Cloud Storage bucket to store the raw audio input for the long-running transcription process. For an example of a typical longrunningrecognize operation response, see the Speech-to-Text basics documentation.

Protocol

Refer to the speech:longrunningrecognize 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 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 -X POST \
     -H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
     -H "Content-Type: application/json; charset=utf-8" \
     --data "{
  'config': {
    'language_code': 'en-US'
  },
  'audio':{
    'uri':'gs://gcs-test-data/vr.flac'
  }
}" "https://speech.googleapis.com/v1/speech:longrunningrecognize"

See the RecognitionConfig and RecognitionAudio 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:

{
  "name": "7612202767953098924"
}

where name is the name of the long running operation created for the request.

Wait for processing to complete. Processing time differs depending on your source audio. In most cases, you will get results in half the length of the source audio. You can get the status of your long-running operation by making a GET request to the https://speech.googleapis.com/v1/operations/ endpoint. Replace your-operation-name with the name returned from your longrunningrecognize request. You can get the estimated progress of the request from the progressPercent field.

curl -H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
     -H "Content-Type: application/json; charset=utf-8" \
     "https://speech.googleapis.com/v1/operations/your-operation-name"

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

{
  "name": "7612202767953098924",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.speech.v1.LongRunningRecognizeMetadata",
    "progressPercent": 100,
    "startTime": "2017-07-20T16:36:55.033650Z",
    "lastUpdateTime": "2017-07-20T16:37:17.158630Z"
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.cloud.speech.v1.LongRunningRecognizeResponse",
    "results": [
      {
        "alternatives": [
          {
            "transcript": "okay so what am I doing here...(etc)...",
            "confidence": 0.96096134,
          }
        ]
      },
      {
        "alternatives": [
          {
            ...
          }
        ]
      }
    ]
  }
}

If the operation has not completed, you can poll the endpoint by repeatedly making the GET request until the done property of the response is true.

gcloud

Refer to the recognize-long-running command for complete details.

To perform asynchronous speech recognition, use the gcloud command line tool, providing the path of a local file or a Google Cloud Storage URL.

gcloud ml speech recognize-long-running \
    'gs://cloud-samples-tests/speech/brooklyn.flac' \
     --language-code='en-US' --async

If the request is successful, the server returns the ID of the long-running operation in JSON format.

{
  "name": OPERATION_ID
}

You can then get information about the operation by running the following command.

gcloud ml speech operations describe OPERATION_ID

You can also poll the operation until it completes by running the following command.

gcloud ml speech operations wait OPERATION_ID

After the operation completes, the operation returns a transcript of the audio in JSON format.

{
  "@type": "type.googleapis.com/google.cloud.speech.v1.LongRunningRecognizeResponse",
  "results": [
    {
      "alternatives": [
        {
          "confidence": 0.9840146,
          "transcript": "how old is the Brooklyn Bridge"
        }
      ]
    }
  ]
}

Go


func sendGCS(w io.Writer, client *speech.Client, gcsURI string) error {
	ctx := context.Background()

	// Send the contents of the audio file with the encoding and
	// and sample rate information to be transcripted.
	req := &speechpb.LongRunningRecognizeRequest{
		Config: &speechpb.RecognitionConfig{
			Encoding:        speechpb.RecognitionConfig_LINEAR16,
			SampleRateHertz: 16000,
			LanguageCode:    "en-US",
		},
		Audio: &speechpb.RecognitionAudio{
			AudioSource: &speechpb.RecognitionAudio_Uri{Uri: gcsURI},
		},
	}

	op, err := client.LongRunningRecognize(ctx, req)
	if err != nil {
		return err
	}
	resp, err := op.Wait(ctx)
	if err != nil {
		return err
	}

	// Print the results.
	for _, result := range resp.Results {
		for _, alt := range result.Alternatives {
			fmt.Fprintf(w, "\"%v\" (confidence=%3f)\n", alt.Transcript, alt.Confidence)
		}
	}
	return nil
}

Java

/**
 * Performs non-blocking speech recognition on remote FLAC file and prints the transcription.
 *
 * @param gcsUri the path to the remote LINEAR16 audio file to transcribe.
 */
public static void asyncRecognizeGcs(String gcsUri) throws Exception {
  // Instantiates a client with GOOGLE_APPLICATION_CREDENTIALS
  try (SpeechClient speech = SpeechClient.create()) {

    // Configure remote file request for FLAC
    RecognitionConfig config =
        RecognitionConfig.newBuilder()
            .setEncoding(AudioEncoding.FLAC)
            .setLanguageCode("en-US")
            .setSampleRateHertz(16000)
            .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();

    for (SpeechRecognitionResult result : results) {
      // 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("Transcription: %s\n", alternative.getTranscript());
    }
  }
}

Node.js

// Imports the Google Cloud client library
const speech = require('@google-cloud/speech');

// 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 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,
};

const audio = {
  uri: gcsUri,
};

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

// Detects speech in the audio file. This creates a recognition job that you
// can wait for now, or get its result later.
const [operation] = await client.longRunningRecognize(request);
// Get a Promise representation of the final result of the job
const [response] = await operation.promise();
const transcription = response.results
  .map(result => result.alternatives[0].transcript)
  .join('\n');
console.log(`Transcription: ${transcription}`);

Python

def transcribe_gcs(gcs_uri):
    """Asynchronously transcribes the audio file specified by the gcs_uri."""
    from google.cloud import speech

    client = speech.SpeechClient()

    audio = speech.RecognitionAudio(uri=gcs_uri)
    config = speech.RecognitionConfig(
        encoding=speech.RecognitionConfig.AudioEncoding.FLAC,
        sample_rate_hertz=16000,
        language_code="en-US",
    )

    operation = client.long_running_recognize(config=config, audio=audio)

    print("Waiting for operation to complete...")
    response = operation.result(timeout=90)

    # Each result is for a consecutive portion of the audio. Iterate through
    # them to get the transcripts for the entire audio file.
    for result in response.results:
        # The first alternative is the most likely one for this portion.
        print(u"Transcript: {}".format(result.alternatives[0].transcript))
        print("Confidence: {}".format(result.alternatives[0].confidence))

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.

Transcribing long audio files using a local file

These samples use a local file to store the raw audio input for the long-running transcription process. For an example of a typical longrunningrecognize operation response, see the Speech-to-Text basics documentation.

Go


func send(w io.Writer, client *speech.Client, filename string) error {
	ctx := context.Background()
	data, err := ioutil.ReadFile(filename)
	if err != nil {
		return err
	}

	// Send the contents of the audio file with the encoding and
	// and sample rate information to be transcripted.
	req := &speechpb.LongRunningRecognizeRequest{
		Config: &speechpb.RecognitionConfig{
			Encoding:        speechpb.RecognitionConfig_LINEAR16,
			SampleRateHertz: 16000,
			LanguageCode:    "en-US",
		},
		Audio: &speechpb.RecognitionAudio{
			AudioSource: &speechpb.RecognitionAudio_Content{Content: data},
		},
	}

	op, err := client.LongRunningRecognize(ctx, req)
	if err != nil {
		return err
	}
	resp, err := op.Wait(ctx)
	if err != nil {
		return err
	}

	// Print the results.
	for _, result := range resp.Results {
		for _, alt := range result.Alternatives {
			fmt.Fprintf(w, "\"%v\" (confidence=%3f)\n", alt.Transcript, alt.Confidence)
		}
	}
	return nil
}

Java

/**
 * Performs non-blocking speech recognition on raw PCM audio and prints the transcription. Note
 * that transcription is limited to 60 seconds audio.
 *
 * @param fileName the path to a PCM audio file to transcribe.
 */
public static void asyncRecognizeFile(String fileName) throws Exception {
  // Instantiates a client with GOOGLE_APPLICATION_CREDENTIALS
  try (SpeechClient speech = SpeechClient.create()) {

    Path path = Paths.get(fileName);
    byte[] data = Files.readAllBytes(path);
    ByteString audioBytes = ByteString.copyFrom(data);

    // Configure request with local raw PCM audio
    RecognitionConfig config =
        RecognitionConfig.newBuilder()
            .setEncoding(AudioEncoding.LINEAR16)
            .setLanguageCode("en-US")
            .setSampleRateHertz(16000)
            .build();
    RecognitionAudio audio = RecognitionAudio.newBuilder().setContent(audioBytes).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();

    for (SpeechRecognitionResult result : results) {
      // 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("Transcription: %s%n", alternative.getTranscript());
    }
  }
}

Node.js

// Imports the Google Cloud client library
const speech = require('@google-cloud/speech');
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,
  sampleRateHertz: sampleRateHertz,
  languageCode: languageCode,
};

/**
 * Note that transcription is limited to 60 seconds audio.
 * Use a GCS file for audio longer than 1 minute.
 */
const audio = {
  content: fs.readFileSync(filename).toString('base64'),
};

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

// Detects speech in the audio file. This creates a recognition job that you
// can wait for now, or get its result later.
const [operation] = await client.longRunningRecognize(request);

// Get a Promise representation of the final result of the job
const [response] = await operation.promise();
const transcription = response.results
  .map(result => result.alternatives[0].transcript)
  .join('\n');
console.log(`Transcription: ${transcription}`);

Python

def transcribe_file(speech_file):
    """Transcribe the given audio file asynchronously."""
    from google.cloud import speech

    client = speech.SpeechClient()

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

    """
     Note that transcription is limited to a 60 seconds audio file.
     Use a GCS file for audio longer than 1 minute.
    """
    audio = speech.RecognitionAudio(content=content)

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


    operation = client.long_running_recognize(config=config, audio=audio)

    print("Waiting for operation to complete...")
    response = operation.result(timeout=90)

    # Each result is for a consecutive portion of the audio. Iterate through
    # them to get the transcripts for the entire audio file.
    for result in response.results:
        # The first alternative is the most likely one for this portion.
        print(u"Transcript: {}".format(result.alternatives[0].transcript))
        print("Confidence: {}".format(result.alternatives[0].confidence))

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.

Uploading your transcription results to a Cloud Storage bucket

Speech-to-Text supports uploading your longrunning recognition results directly to a Cloud Storage bucket. This functionality is a Preview feature. If you implement this feature with Cloud Storage Triggers, Cloud Storage uploads can trigger notifications that call Cloud Functions and remove the need to poll Speech-to-Text for recognition results.

To have your results uploaded to a Cloud Storage bucket, provide the optional TranscriptOutputConfig output configuration in your longrunning recognition request.

  message TranscriptOutputConfig {

    oneof output_type {
      // Specifies a Cloud Storage URI for the recognition results. Must be
      // specified in the format: `gs://bucket_name/object_name`
      string gcs_uri = 1;
    }
  }

Protocol

Refer to the longrunningrecognize API endpoint for complete details.

The following example shows how to send a POST request using curl, where the body of the request specifies the path to a Cloud Storage bucket. The results are uploaded to this location as a JSON file that stores SpeechRecognitionResult.

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     --data "{
  'config': {...},
  'output_config': {
     'gcs_uri':'gs://bucket/result-output-path.json'
  },
  'audio': {
    'uri': 'gs://bucket/audio-path'
  }
}" "https://speech.googleapis.com/v1p1beta1/speech:longrunningrecognize"



The LongRunningRecognizeResponse includes the path to the GCS bucket where the upload was attempted. If the upload was unsuccessful, an output error will be returned. If a file with the same name already exists, the upload writes the results to a new file with a timestamp as the suffix.

{
  ...
  "metadata": {
    ...
    "outputConfig": {...}
  },
  ...
  "response": {
    ...
    "results": [...],
    "outputConfig": {
      "gcs_uri":"gs://bucket/result-output-path"
    },
    "outputError": {...}
  }
}

Try it for yourself

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