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from __future__ import annotations

import os
from datetime import datetime

from import RecognitionAudio, RecognitionConfig

from airflow.models.dag import DAG
from import GCSCreateBucketOperator, GCSDeleteBucketOperator
from import CloudTextToSpeechSynthesizeOperator
from import CloudTranslateSpeechOperator
from airflow.utils.trigger_rule import TriggerRule

[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
[docs]PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
[docs]DAG_ID = "example_gcp_translate_speech"
[docs]BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}"
# [START howto_operator_translate_speech_gcp_filename]
[docs]FILE_NAME = f"test-translate-speech-file-{DAG_ID}-{ENV_ID}"
# [END howto_operator_translate_speech_gcp_filename] # [START howto_operator_text_to_speech_api_arguments]
[docs]INPUT = {"text": "Sample text for demo purposes"}
[docs]VOICE = {"language_code": "en-US", "ssml_gender": "FEMALE"}
[docs]AUDIO_CONFIG = {"audio_encoding": "LINEAR16"}
# [END howto_operator_text_to_speech_api_arguments] # [START howto_operator_translate_speech_arguments]
[docs]CONFIG = RecognitionConfig({"encoding": "LINEAR16", "language_code": "en_US"})
[docs]AUDIO = RecognitionAudio({"uri": f"gs://{BUCKET_NAME}/{FILE_NAME}"})
[docs]TARGET_LANGUAGE = "pl"
[docs]FORMAT = "text"
[docs]MODEL = "base"
# [END howto_operator_translate_speech_arguments] with DAG( DAG_ID, schedule="@once", # Override to match your needs start_date=datetime(2021, 1, 1), catchup=False, tags=["example"], ) as dag:
[docs] create_bucket = GCSCreateBucketOperator(task_id="create_bucket", bucket_name=BUCKET_NAME)
text_to_speech_synthesize_task = CloudTextToSpeechSynthesizeOperator( project_id=PROJECT_ID, input_data=INPUT, voice=VOICE, audio_config=AUDIO_CONFIG, target_bucket_name=BUCKET_NAME, target_filename=FILE_NAME, task_id="text_to_speech_synthesize_task", ) # [START howto_operator_translate_speech] translate_speech_task = CloudTranslateSpeechOperator( project_id=PROJECT_ID, audio=AUDIO, config=CONFIG, target_language=TARGET_LANGUAGE, format_=FORMAT, source_language=SOURCE_LANGUAGE, model=MODEL, task_id="translate_speech_task", ) translate_speech_task2 = CloudTranslateSpeechOperator( audio=AUDIO, config=CONFIG, target_language=TARGET_LANGUAGE, format_=FORMAT, source_language=SOURCE_LANGUAGE, model=MODEL, task_id="translate_speech_task2", ) # [END howto_operator_translate_speech] delete_bucket = GCSDeleteBucketOperator( task_id="delete_bucket", bucket_name=BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE ) ( # TEST SETUP create_bucket # TEST BODY >> text_to_speech_synthesize_task >> translate_speech_task >> translate_speech_task2 # TEST TEARDOWN >> delete_bucket ) from tests.system.utils.watcher import watcher # This test needs watcher in order to properly mark success/failure # when "tearDown" task with trigger rule is part of the DAG list(dag.tasks) >> watcher() from tests.system.utils import get_test_run # noqa: E402 # Needed to run the example DAG with pytest (see: tests/system/
[docs]test_run = get_test_run(dag)

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