启用脏话过滤器

本页面介绍如何使用 Speech-to-Text 自动检测音频数据中的亵渎性字词，并在转录内容中予以剔除。

您可以通过在 RecognitionConfig 中设置 profanityFilter=true 来启用脏话过滤器。启用后，Speech-to-Text 将尝试检测亵渎性字词并在转录内容中仅返回第一个字母后跟星号（例如 f***）。如果此字段设置为 false 或未设置，Speech-to-Text 将不会尝试过滤脏话。

以下示例演示了如何启用脏话过滤器以识别存储在 Google Cloud Storage 存储桶中的音频。

Java

在编辑器中打开 在 GitHub 上查看 反馈

import com.google.cloud.speech.v1.RecognitionAudio;
import com.google.cloud.speech.v1.RecognitionConfig;
import com.google.cloud.speech.v1.RecognitionConfig.AudioEncoding;
import com.google.cloud.speech.v1.RecognizeResponse;
import com.google.cloud.speech.v1.SpeechClient;
import com.google.cloud.speech.v1.SpeechRecognitionAlternative;
import com.google.cloud.speech.v1.SpeechRecognitionResult;
import java.util.List;

public class SpeechProfanityFilter {

  public void speechProfanityFilter() throws Exception {
    String uriPath = "gs://cloud-samples-tests/speech/brooklyn.flac";
    speechProfanityFilter(uriPath);
  }

  /**
   * Transcribe a remote audio file with multi-channel recognition
   *
   * @param gcsUri the path to the audio file
   */
  public static void speechProfanityFilter(String gcsUri) throws Exception {
    // Instantiates a client with GOOGLE_APPLICATION_CREDENTIALS
    try (SpeechClient speech = SpeechClient.create()) {

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

      // Set the remote path for the audio file
      RecognitionAudio audio = RecognitionAudio.newBuilder().setUri(gcsUri).build();

      // Use blocking call to get audio transcript
      RecognizeResponse response = speech.recognize(config, audio);
      List<SpeechRecognitionResult> results = response.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

在编辑器中打开 在 GitHub 上查看 反馈

// Filters profanity

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const gcsUri = 'gs://my-bucket/audio.raw';

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

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

  const audio = {
    uri: gcsUri,
  };

  const config = {
    encoding: 'FLAC',
    sampleRateHertz: 16000,
    languageCode: 'en-US',
    profanityFilter: true, // set this to true
  };
  const request = {
    audio: audio,
    config: config,
  };

  // 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}`);
}
syncRecognizeWithProfanityFilter().catch(console.error);

Python

在编辑器中打开 在 GitHub 上查看 反馈

def sync_recognize_with_profanity_filter_gcs(gcs_uri):

    from google.cloud import speech

    client = speech.SpeechClient()

    audio = {"uri": gcs_uri}

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

    response = client.recognize(config=config, audio=audio)

    for i, result in enumerate(response.results):
        alternative = result.alternatives[0]
        print(u"Transcript: {}".format(alternative.transcript))