Function asyncRecognizeGCSWords

speech/recognize.js:275–336

(
  gcsUri,
  encoding,
  sampleRateHertz,
  languageCode
)

}

async function asyncRecognizeGCSWords(
  gcsUri,
  encoding,
  sampleRateHertz,
  languageCode
) {
  // [START speech_transcribe_async_word_time_offsets_gcs]
  // 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`);