Trascrizione di un file locale con metadati di riconoscimento (beta)

Trascrivi un file audio locale, inclusi i metadati di riconoscimento nella risposta.

Esempio di codice

Java

Per scoprire come installare e utilizzare la libreria client per Cloud STT, consulta Librerie client di Cloud STT. Per saperne di più, consulta la documentazione di riferimento dell'JavaAPI Cloud STT.

Per eseguire l'autenticazione in Cloud STT, configura le credenziali predefinite dell'applicazione. Per saperne di più, consulta Configura l'autenticazione per un ambiente di sviluppo locale.

/**
 * Transcribe the given audio file and include recognition metadata in the request.
 *
 * @param fileName the path to an audio file.
 */
public static void transcribeFileWithMetadata(String fileName) throws Exception {
  Path path = Paths.get(fileName);
  byte[] content = Files.readAllBytes(path);

  try (SpeechClient speechClient = SpeechClient.create()) {
    // Get the contents of the local audio file
    RecognitionAudio recognitionAudio =
        RecognitionAudio.newBuilder().setContent(ByteString.copyFrom(content)).build();

    // Construct a recognition metadata object.
    // Most metadata fields are specified as enums that can be found
    // in speech.enums.RecognitionMetadata
    RecognitionMetadata metadata =
        RecognitionMetadata.newBuilder()
            .setInteractionType(InteractionType.DISCUSSION)
            .setMicrophoneDistance(MicrophoneDistance.NEARFIELD)
            .setRecordingDeviceType(RecordingDeviceType.SMARTPHONE)
            .setRecordingDeviceName("Pixel 2 XL") // Some metadata fields are free form strings
            // And some are integers, for instance the 6 digit NAICS code
            // https://www.naics.com/search/
            .setIndustryNaicsCodeOfAudio(519190)
            .build();

    // Configure request to enable enhanced models
    RecognitionConfig config =
        RecognitionConfig.newBuilder()
            .setEncoding(AudioEncoding.LINEAR16)
            .setLanguageCode("en-US")
            .setSampleRateHertz(8000)
            .setMetadata(metadata) // Add the metadata to the config
            .build();

    // Perform the transcription request
    RecognizeResponse recognizeResponse = speechClient.recognize(config, recognitionAudio);

    // Print out the results
    for (SpeechRecognitionResult result : recognizeResponse.getResultsList()) {
      // There can be several alternative transcripts for a given chunk of speech. Just use the
      // first (most likely) one here.
      SpeechRecognitionAlternative alternative = result.getAlternatives(0);
      System.out.format("Transcript: %s\n\n", alternative.getTranscript());
    }
  }
}

Node.js

Per scoprire come installare e utilizzare la libreria client per Cloud STT, consulta Librerie client di Cloud STT. Per saperne di più, consulta la documentazione di riferimento dell'APINode.js Cloud STT.

Per eseguire l'autenticazione in Cloud STT, configura le credenziali predefinite dell'applicazione. Per saperne di più, consulta Configura l'autenticazione per un ambiente di sviluppo locale.

// Imports the Google Cloud client library for Beta API
/**
 * TODO(developer): Update client library import to use new
 * version of API when desired features become available
 */
const speech = require('@google-cloud/speech').v1p1beta1;
const fs = require('fs');

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

async function syncRecognizeWithMetaData() {
  /**
   * 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 recognitionMetadata = {
    interactionType: 'DISCUSSION',
    microphoneDistance: 'NEARFIELD',
    recordingDeviceType: 'SMARTPHONE',
    recordingDeviceName: 'Pixel 2 XL',
    industryNaicsCodeOfAudio: 519190,
  };

  const config = {
    encoding: encoding,
    sampleRateHertz: sampleRateHertz,
    languageCode: languageCode,
    metadata: recognitionMetadata,
  };

  const audio = {
    content: fs.readFileSync(filename).toString('base64'),
  };

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

  // Detects speech in the audio file
  const [response] = await client.recognize(request);
  response.results.forEach(result => {
    const alternative = result.alternatives[0];
    console.log(alternative.transcript);
  });

Python

Per scoprire come installare e utilizzare la libreria client per Cloud STT, consulta Librerie client di Cloud STT. Per saperne di più, consulta la documentazione di riferimento dell'APIPython Cloud STT.

Per eseguire l'autenticazione in Cloud STT, configura le credenziali predefinite dell'applicazione. Per saperne di più, consulta Configura l'autenticazione per un ambiente di sviluppo locale.

from google.cloud import speech_v1p1beta1 as speech

client = speech.SpeechClient()

speech_file = "resources/commercial_mono.wav"

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

# Here we construct a recognition metadata object.
# Most metadata fields are specified as enums that can be found
# in speech.enums.RecognitionMetadata
metadata = speech.RecognitionMetadata()
metadata.interaction_type = speech.RecognitionMetadata.InteractionType.DISCUSSION
metadata.microphone_distance = (
    speech.RecognitionMetadata.MicrophoneDistance.NEARFIELD
)
metadata.recording_device_type = (
    speech.RecognitionMetadata.RecordingDeviceType.SMARTPHONE
)

# Some metadata fields are free form strings
metadata.recording_device_name = "Pixel 2 XL"
# And some are integers, for instance the 6 digit NAICS code
# https://www.naics.com/search/
metadata.industry_naics_code_of_audio = 519190

audio = speech.RecognitionAudio(content=content)
config = speech.RecognitionConfig(
    encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
    sample_rate_hertz=8000,
    language_code="en-US",
    # Add this in the request to send metadata.
    metadata=metadata,
)

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

for i, result in enumerate(response.results):
    alternative = result.alternatives[0]
    print("-" * 20)
    print(f"First alternative of result {i}")
    print(f"Transcript: {alternative.transcript}")

return response.results

Passaggi successivi

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