Export Speech-to-Text transcript to Cloud Storage (Beta)

This sample demonstrates how to export a speech-to-text transcript to a Cloud Storage bucket.

Code sample

Python

To learn how to install and use the client library for Speech-to-Text, see Speech-to-Text client libraries. For more information, see the Speech-to-Text Python API reference documentation.

To authenticate to Speech-to-Text, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

from google.cloud import speech
from google.cloud import storage
from google.cloud.speech_v1 import types


def export_transcript_to_storage_beta(
    input_storage_uri,
    output_storage_uri,
    encoding,
    sample_rate_hertz,
    language_code,
    bucket_name,
    object_name,
):
    # input_uri URI for audio file in Cloud Storage, e.g. gs://[BUCKET]/[FILE]
    audio = speech.RecognitionAudio(uri=input_storage_uri)

    # Pass in the URI of the Cloud Storage bucket to hold the transcription
    output_config = speech.TranscriptOutputConfig(gcs_uri=output_storage_uri)

    # Speech configuration object
    config = speech.RecognitionConfig(
        encoding=encoding,
        sample_rate_hertz=sample_rate_hertz,
        language_code=language_code,
    )

    # Compose the long-running request
    request = speech.LongRunningRecognizeRequest(
        audio=audio, config=config, output_config=output_config
    )

    # create the speech client
    speech_client = speech.SpeechClient()

    # create the storage client
    storage_client = storage.Client()

    # run the recognizer to export transcript
    operation = speech_client.long_running_recognize(request=request)

    print("Waiting for operation to complete...")
    operation.result(timeout=90)

    # get bucket with name
    bucket = storage_client.get_bucket(bucket_name)

    # get blob from bucket
    blob = bucket.get_blob(object_name)

    # get content as bytes
    results_bytes = blob.download_as_bytes()

    # get transcript exported in storage bucket
    storage_transcript = types.LongRunningRecognizeResponse.from_json(
        results_bytes, ignore_unknown_fields=True
    )

    # Each result is for a consecutive portion of the audio. Iterate through
    # them to get the transcripts for the entire audio file.
    for result in storage_transcript.results:
        # The first alternative is the most likely one for this portion.
        print(f"Transcript: {result.alternatives[0].transcript}")
        print(f"Confidence: {result.alternatives[0].confidence}")

What's next

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