translate_speech

AI + Machine Learning


Providers:

Run this DAG

1. Install Astronomer CLISkip if you already have the CLI

2. Initate the project:

3. Copy and paste the code below into a file in the

dags
directory.

4. Add the following to your requirements.txt file:

5. Run the DAG:

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import os
from airflow import models
from airflow.providers.google.cloud.operators.text_to_speech import CloudTextToSpeechSynthesizeOperator
from airflow.providers.google.cloud.operators.translate_speech import CloudTranslateSpeechOperator
from airflow.utils import dates
GCP_PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "example-project")
BUCKET_NAME = os.environ.get("GCP_TRANSLATE_SPEECH_TEST_BUCKET", "INVALID BUCKET NAME")
# [START howto_operator_translate_speech_gcp_filename]
FILENAME = "gcp-speech-test-file"
# [END howto_operator_translate_speech_gcp_filename]
# [START howto_operator_text_to_speech_api_arguments]
INPUT = {"text": "Sample text for demo purposes"}
VOICE = {"language_code": "en-US", "ssml_gender": "FEMALE"}
AUDIO_CONFIG = {"audio_encoding": "LINEAR16"}
# [END howto_operator_text_to_speech_api_arguments]
# [START howto_operator_translate_speech_arguments]
CONFIG = {"encoding": "LINEAR16", "language_code": "en_US"}
AUDIO = {"uri": f"gs://{BUCKET_NAME}/{FILENAME}"}
TARGET_LANGUAGE = 'pl'
FORMAT = 'text'
MODEL = 'base'
SOURCE_LANGUAGE = None # type: None
# [END howto_operator_translate_speech_arguments]
with models.DAG(
"example_gcp_translate_speech",
schedule_interval=None, # Override to match your needs
start_date=dates.days_ago(1),
tags=['example'],
) as dag:
text_to_speech_synthesize_task = CloudTextToSpeechSynthesizeOperator(
project_id=GCP_PROJECT_ID,
input_data=INPUT,
voice=VOICE,
audio_config=AUDIO_CONFIG,
target_bucket_name=BUCKET_NAME,
target_filename=FILENAME,
task_id="text_to_speech_synthesize_task",
)
# [START howto_operator_translate_speech]
translate_speech_task = CloudTranslateSpeechOperator(
project_id=GCP_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]
text_to_speech_synthesize_task >> translate_speech_task >> translate_speech_task2