Transcrever áudios com eventos de atividade de voz

Este exemplo demonstra como transcrever o áudio de um arquivo para texto e detectar eventos de atividade de fala, como quando alguém começa ou para de falar.

Exemplo de código

Python

Para saber como instalar e usar a biblioteca de cliente da Cloud STT, consulte Bibliotecas de cliente da Cloud STT. Saiba mais na documentação de referência da API Cloud STT para Python.

Para se autenticar na Cloud STT, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

import os

from google.cloud.speech_v2 import SpeechClient
from google.cloud.speech_v2.types import cloud_speech

PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")


def transcribe_streaming_voice_activity_events(
    audio_file: str,
) -> cloud_speech.StreamingRecognizeResponse:
    """Transcribes audio from a file into text and detects voice activity
        events using Google Cloud Speech-to-Text API.
    Args:
        audio_file (str): Path to the local audio file to be transcribed.
            Example: "resources/audio.wav"
    Returns:
        list[cloud_speech.StreamingRecognizeResponse]: A list of `StreamingRecognizeResponse` objects.
    """
    # Instantiates a client
    client = SpeechClient()

    # Reads a file as bytes
    with open(audio_file, "rb") as file:
        audio_content = file.read()

    # In practice, stream should be a generator yielding chunks of audio data
    chunk_length = len(audio_content) // 5
    stream = [
        audio_content[start : start + chunk_length]
        for start in range(0, len(audio_content), chunk_length)
    ]
    audio_requests = (
        cloud_speech.StreamingRecognizeRequest(audio=audio) for audio in stream
    )

    recognition_config = cloud_speech.RecognitionConfig(
        auto_decoding_config=cloud_speech.AutoDetectDecodingConfig(),
        language_codes=["en-US"],
        model="long",
    )

    # Sets the flag to enable voice activity events
    streaming_features = cloud_speech.StreamingRecognitionFeatures(
        enable_voice_activity_events=True
    )
    streaming_config = cloud_speech.StreamingRecognitionConfig(
        config=recognition_config, streaming_features=streaming_features
    )

    config_request = cloud_speech.StreamingRecognizeRequest(
        recognizer=f"projects/{PROJECT_ID}/locations/global/recognizers/_",
        streaming_config=streaming_config,
    )

    def requests(config: cloud_speech.RecognitionConfig, audio: list) -> list:
        yield config
        yield from audio

    # Transcribes the audio into text
    responses_iterator = client.streaming_recognize(
        requests=requests(config_request, audio_requests)
    )
    responses = []
    for response in responses_iterator:
        responses.append(response)
        if (
            response.speech_event_type
            == cloud_speech.StreamingRecognizeResponse.SpeechEventType.SPEECH_ACTIVITY_BEGIN
        ):
            print("Speech started.")
        if (
            response.speech_event_type
            == cloud_speech.StreamingRecognizeResponse.SpeechEventType.SPEECH_ACTIVITY_END
        ):
            print("Speech ended.")
        for result in response.results:
            print(f"Transcript: {result.alternatives[0].transcript}")

    return responses

A seguir

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