Source code for tests.system.providers.google.cloud.translate_speech.example_translate_speech
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from __future__ import annotations
import os
from datetime import datetime
from google.cloud.speech_v1 import RecognitionAudio, RecognitionConfig
from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator, GCSDeleteBucketOperator
from airflow.providers.google.cloud.operators.text_to_speech import CloudTextToSpeechSynthesizeOperator
from airflow.providers.google.cloud.operators.translate_speech 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]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}"})
# [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/README.md#run_via_pytest)
[docs]test_run = get_test_run(dag)