Transcrever áudios com tempos limite de atividade de voz

Este exemplo demonstra como transcrever o áudio de um arquivo com tempos limite de atividade de voz. Ele usa a API Speech-to-Text para transcrever o áudio e exibe essa transcrição no console. O exemplo também exibe eventos de atividade de fala, como quando uma fala começa e termina.

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 time import sleep

from google.cloud.speech_v2 import SpeechClient
from google.cloud.speech_v2.types import cloud_speech
from google.protobuf import duration_pb2  # type: ignore

PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")


def transcribe_streaming_voice_activity_timeouts(
    speech_start_timeout: int,
    speech_end_timeout: int,
    audio_file: str,
) -> cloud_speech.StreamingRecognizeResponse:
    """Transcribes audio from audio file to text.
    Args:
        speech_start_timeout: The timeout in seconds for speech start.
        speech_end_timeout: The timeout in seconds for speech end.
        audio_file: Path to the local audio file to be transcribed.
            Example: "resources/audio_silence_padding.wav"
    Returns:
        The streaming response containing the transcript.
    """
    # 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) // 20
    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 and timeout
    speech_start_timeout = duration_pb2.Duration(seconds=speech_start_timeout)
    speech_end_timeout = duration_pb2.Duration(seconds=speech_end_timeout)
    voice_activity_timeout = (
        cloud_speech.StreamingRecognitionFeatures.VoiceActivityTimeout(
            speech_start_timeout=speech_start_timeout,
            speech_end_timeout=speech_end_timeout,
        )
    )
    streaming_features = cloud_speech.StreamingRecognitionFeatures(
        enable_voice_activity_events=True, voice_activity_timeout=voice_activity_timeout
    )

    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
        for message in audio:
            sleep(0.5)
            yield message

    # 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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