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Transcribir de forma asíncrona un archivo de audio con compensación horaria

Realiza una transcripción asíncrona, incluida la compensación horaria, de un archivo de audio almacenado en Cloud Storage.

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Para obtener documentación detallada que incluya este código de muestra, consulta lo siguiente:

Obtener marcas de tiempo de palabras

Código de ejemplo

Go

Para saber cómo instalar y usar la biblioteca de cliente de Cloud STT, consulta Bibliotecas de cliente de Cloud STT. Para obtener más información, consulta la documentación de referencia de la API Cloud STT Go.

Para autenticarte en Cloud STT, configura las credenciales predeterminadas de la aplicación. Para obtener más información, consulta el artículo Configurar la autenticación en un entorno de desarrollo local.


func asyncWords(client *speech.Client, out io.Writer, 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",
			EnableWordTimeOffsets: true,
		},
		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(out, "\"%v\" (confidence=%3f)\n", alt.Transcript, alt.Confidence)
			for _, w := range alt.Words {
				fmt.Fprintf(out,
					"Word: \"%v\" (startTime=%3f, endTime=%3f)\n",
					w.Word,
					float64(w.StartTime.Seconds)+float64(w.StartTime.Nanos)*1e-9,
					float64(w.EndTime.Seconds)+float64(w.EndTime.Nanos)*1e-9,
				)
			}
		}
	}
	return nil
}

Java

/**
 * Performs non-blocking speech recognition on remote FLAC file and prints the transcription as
 * well as word time offsets.
 *
 * @param gcsUri the path to the remote LINEAR16 audio file to transcribe.
 */
public static void asyncRecognizeWords(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)
            .setEnableWordTimeOffsets(true)
            .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());
      for (WordInfo wordInfo : alternative.getWordsList()) {
        System.out.println(wordInfo.getWord());
        System.out.printf(
            "\t%s.%s sec - %s.%s sec\n",
            wordInfo.getStartTime().getSeconds(),
            wordInfo.getStartTime().getNanos() / 100000000,
            wordInfo.getEndTime().getSeconds(),
            wordInfo.getEndTime().getNanos() / 100000000);
      }
    }
  }
}

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 = {
  enableWordTimeOffsets: true,
  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();
response.results.forEach(result => {
  console.log(`Transcription: ${result.alternatives[0].transcript}`);
  result.alternatives[0].words.forEach(wordInfo => {
    // NOTE: If you have a time offset exceeding 2^32 seconds, use the
    // wordInfo.{x}Time.seconds.high to calculate seconds.
    const startSecs =
      `${wordInfo.startTime.seconds}` +
      '.' +
      wordInfo.startTime.nanos / 100000000;
    const endSecs =
      `${wordInfo.endTime.seconds}` +
      '.' +
      wordInfo.endTime.nanos / 100000000;
    console.log(`Word: ${wordInfo.word}`);
    console.log(`\t ${startSecs} secs - ${endSecs} secs`);
  });
});

PHP

Para saber cómo instalar y usar la biblioteca de cliente de Cloud STT, consulta Bibliotecas de cliente de Cloud STT.


use Google\Cloud\Speech\V2\BatchRecognizeFileMetadata;
use Google\Cloud\Speech\V2\Client\SpeechClient;
use Google\Cloud\Speech\V2\BatchRecognizeRequest;
use Google\Cloud\Speech\V2\ExplicitDecodingConfig;
use Google\Cloud\Speech\V2\ExplicitDecodingConfig\AudioEncoding;
use Google\Cloud\Speech\V2\InlineOutputConfig;
use Google\Cloud\Speech\V2\RecognitionConfig;
use Google\Cloud\Speech\V2\RecognitionFeatures;
use Google\Cloud\Speech\V2\RecognitionOutputConfig;
use Google\Cloud\Speech\V2\SpeakerDiarizationConfig;

/**
 * @param string $projectId The Google Cloud project ID.
 * @param string $location The location of the recognizer.
 * @param string $recognizerId The ID of the recognizer to use. The recognizer's model must support
 *                             diarization (e.g. "chirp_3").
 * @param string $uri The Cloud Storage object to transcribe (other than global)
 *                    e.x. gs://cloud-samples-data/speech/brooklyn_bridge.raw
 */
function transcribe_async_words(string $projectId, string $location, string $recognizerId, string $uri)
{
    $apiEndpoint = $location === 'global' ? null : sprintf('%s-speech.googleapis.com', $location);
    $speech = new SpeechClient(['apiEndpoint' => $apiEndpoint]);
    $recognizerName = SpeechClient::recognizerName($projectId, $location, $recognizerId);

    // When this is enabled, we send all the words from the beginning of the audio.
    $features = new RecognitionFeatures([
        'diarization_config' => new SpeakerDiarizationConfig(),
    ]);

    $config = (new RecognitionConfig())
        ->setFeatures($features)
        // When running outside the "global" location, you can set the model to "chirp_3" in
        // RecognitionConfig instead of on the recognizer.
        // ->setModel('chirp_3')

        // Can also use {@see Google\Cloud\Speech\V2\AutoDetectDecodingConfig}
        // ->setAutoDecodingConfig(new AutoDetectDecodingConfig());

        ->setExplicitDecodingConfig(new ExplicitDecodingConfig([
            // change these variables if necessary
            'encoding' => AudioEncoding::LINEAR16,
            'sample_rate_hertz' => 16000,
            'audio_channel_count' => 1,
        ]));

    $outputConfig = (new RecognitionOutputConfig())
        ->setInlineResponseConfig(new InlineOutputConfig());

    $file = new BatchRecognizeFileMetadata();
    $file->setUri($uri);

    $request = (new BatchRecognizeRequest())
        ->setRecognizer($recognizerName)
        ->setConfig($config)
        ->setFiles([$file])
        ->setRecognitionOutputConfig($outputConfig);

    try {
        $operation = $speech->batchRecognize($request);
        $operation->pollUntilComplete();

        if ($operation->operationSucceeded()) {
            $response = $operation->getResult();
            foreach ($response->getResults() as $result) {
                if ($result->getError()) {
                    print('Error: ' . $result->getError()->getMessage());
                }
                // get the most likely transcription
                $transcript = $result->getInlineResult()->getTranscript();
                foreach ($transcript->getResults() as $transacriptResult) {
                    $alternatives = $transacriptResult->getAlternatives();
                    $mostLikely = $alternatives[0];
                    foreach ($mostLikely->getWords() as $wordInfo) {
                        $startTime = $wordInfo->getStartOffset();
                        $endTime = $wordInfo->getEndOffset();
                        printf('  Word: %s (start: %s, end: %s)' . PHP_EOL,
                            $wordInfo->getWord(),
                            $startTime?->serializeToJsonString(),
                            $endTime?->serializeToJsonString()
                        );
                    }
                }
            }
        } else {
            print_r($operation->getError());
        }
    } finally {
        $speech->close();
    }
}

Python

def transcribe_gcs_with_word_time_offsets(
    audio_uri: str,
) -> speech.RecognizeResponse:
    """Transcribe the given audio file asynchronously and output the word time
    offsets.
    Args:
        audio_uri (str): The Google Cloud Storage URI of the input audio file.
            E.g., gs://[BUCKET]/[FILE]
    Returns:
        speech.RecognizeResponse: The response containing the transcription results with word time offsets.
    """
    from google.cloud import speech

    client = speech.SpeechClient()

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

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

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

    for result in result.results:
        alternative = result.alternatives[0]
        print(f"Transcript: {alternative.transcript}")
        print(f"Confidence: {alternative.confidence}")

        for word_info in alternative.words:
            word = word_info.word
            start_time = word_info.start_time
            end_time = word_info.end_time

            print(
                f"Word: {word}, start_time: {start_time.total_seconds()}, end_time: {end_time.total_seconds()}"
            )

    return result

Ruby