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"""This module contains a Google Cloud Translate Speech operator."""
from __future__ import annotations

from typing import TYPE_CHECKING, Sequence

from google.protobuf.json_format import MessageToDict

from airflow.exceptions import AirflowException
from import CloudSpeechToTextHook
from import CloudTranslateHook
from import GoogleCloudBaseOperator
from import FileDetailsLink

    from import RecognitionAudio, RecognitionConfig

    from airflow.utils.context import Context

[docs]class CloudTranslateSpeechOperator(GoogleCloudBaseOperator): """ Recognizes speech in audio input and translates it. Note that it uses the first result from the recognition api response - the one with the highest confidence In order to see other possible results please use :ref:`howto/operator:CloudSpeechToTextRecognizeSpeechOperator` and :ref:`howto/operator:CloudTranslateTextOperator` separately .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:CloudTranslateSpeechOperator` See Execute method returns string object with the translation This is a list of dictionaries queried value. Dictionary typically contains three keys (though not all will be present in all cases). * ``detectedSourceLanguage``: The detected language (as an ISO 639-1 language code) of the text. * ``translatedText``: The translation of the text into the target language. * ``input``: The corresponding input value. * ``model``: The model used to translate the text. Dictionary is set as XCom return value. :param audio: audio data to be recognized. See more: :param config: information to the recognizer that specifies how to process the request. See more: :param target_language: The language to translate results into. This is required by the API and defaults to the target language of the current instance. Check the list of available languages here: :param format_: (Optional) One of ``text`` or ``html``, to specify if the input text is plain text or HTML. :param source_language: (Optional) The language of the text to be translated. :param model: (Optional) The model used to translate the text, such as ``'base'`` or ``'nmt'``. :param project_id: Optional, Google Cloud Project ID where the Compute Engine Instance exists. If set to None or missing, the default project_id from the Google Cloud connection is used. :param gcp_conn_id: Optional, The connection ID used to connect to Google Cloud. Defaults to 'google_cloud_default'. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """ # [START translate_speech_template_fields]
[docs] template_fields: Sequence[str] = ( "target_language", "format_", "source_language", "model", "project_id", "gcp_conn_id", "impersonation_chain", )
# [END translate_speech_template_fields] def __init__( self, *, audio: RecognitionAudio, config: RecognitionConfig, target_language: str, format_: str, source_language: str | None, model: str, project_id: str | None = None, gcp_conn_id: str = "google_cloud_default", impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) = audio self.config = config self.target_language = target_language self.format_ = format_ self.source_language = source_language self.model = model self.project_id = project_id self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context) -> dict: speech_to_text_hook = CloudSpeechToTextHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain, ) translate_hook = CloudTranslateHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain, ) recognize_result = speech_to_text_hook.recognize_speech(config=self.config, recognize_dict = MessageToDict(recognize_result._pb)"Recognition operation finished") if not recognize_dict["results"]:"No recognition results") return {} self.log.debug("Recognition result: %s", recognize_dict) try: transcript = recognize_dict["results"][0]["alternatives"][0]["transcript"] except KeyError as key: raise AirflowException( f"Wrong response '{recognize_dict}' returned - it should contain {key} field" ) try: translation = translate_hook.translate( values=transcript, target_language=self.target_language, format_=self.format_, source_language=self.source_language, model=self.model, )"Translated output: %s", translation) FileDetailsLink.persist( context=context, task_instance=self,["uri"][5:], project_id=self.project_id or translate_hook.project_id, ) return translation except ValueError as e: self.log.error("An error has been thrown from translate speech method:") self.log.error(e) raise AirflowException(e)

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