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feat(speech): Add Speech-to-Text On-Prem sample (#4223)
* feat: adds speech-to-text onprem sample * Remove client options and change to f-strings * Move to onprem folder, add resource and README Co-authored-by: Leah E. Cole <[email protected]>
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‎speech/cloud-client/resources/two_channel_16k.wav

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‎speech/cloud-client/transcribe_onprem/README.rst

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.. This file is automatically generated. Do not edit this file directly.
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Google Cloud Speech-to-Text On-Prem Python Samples
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===============================================================================
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.. warning:: This product is only available to customers that have been granted access. Please `contact us`_ to request access to the Speech-to-Text On-Prem feature.
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This directory contains samples for `Google Cloud Speech-to-Text On-Prem`_. Speech-to-Text On-Prem enables easy integration of Google speech recognition technologies into your on-prem solution.
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.. _Google Cloud Speech-to-Text On-Prem: https://cloud.google.com/speech-to-text/on-prem/priv/docs
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.. _contact us: https://cloud.google.com/contact
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.. _Google Cloud Speech-to-Text On-Prem: https://cloud.google.com/speech-to-text/on-prem/priv/docs
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Setup
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-------------------------------------------------------------------------------
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Prepare and Deploy API
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+++++++++++++++++++++++
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This sample requires you to have a Kubernetes cluster with the Speech-to-Text On-Prem service deployed. Follow the quickstart steps listed below:
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#. `Setup IAM, Kubernetes, Billing`_
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#. `Deploy the API using the UI or command line`_
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#. `Query the API to ensure it's working`_
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.. _Query the API to ensure it's working:
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https://cloud.google.com/speech-to-text/on-prem/priv/docs/query
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.. _Deploy the API using the UI or command line:
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https://cloud.google.com/speech-to-text/on-prem/priv/docs/deploy
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.. _Setup IAM, Kubernetes, Billing:
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https://cloud.google.com/speech-to-text/on-prem/priv/docs/before-you-begin
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Install Dependencies
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++++++++++++++++++++
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#. Clone python-docs-samples and change directory to the sample directory you want to use.
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.. code-block:: bash
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$ git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git
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$ cd python-doc-samples/speech/cloud-client
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#. Install `pip`_ and `virtualenv`_ if you do not already have them. You may want to refer to the `Python Development Environment Setup Guide`_ for Google Cloud Platform for instructions.
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.. _Python Development Environment Setup Guide:
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https://cloud.google.com/python/setup
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#. Create a virtualenv. Samples are compatible with Python 2.7 and 3.4+.
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.. code-block:: bash
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$ virtualenv env
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$ source env/bin/activate
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#. Install the dependencies needed to run the samples.
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.. code-block:: bash
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$ pip install -r requirements.txt
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.. _pip: https://pip.pypa.io/
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.. _virtualenv: https://virtualenv.pypa.io/
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Samples
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-------------------------------------------------------------------------------
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transcribe_onprem
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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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You can run this sample one of two ways, using a **public IP**:
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.. code-block:: bash
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# Using a Public IP
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$ python transcribe_onprem.py --file_path="../resources/two_channel_16k.wav" --api_endpoint=${PUBLIC_IP}:443
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or by using a **cluster level IP**:
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.. code-block:: bash
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# Using a cluster level IP
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$ kubectl port-forward -n $NAMESPACE $POD 10000:443
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$ python transcribe_onprem.py --file_path="../resources/two_channel_16k.wav" --api_endpoint="0.0.0.0:10000"
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The client library
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-------------------------------------------------------------------------------
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This sample uses the `Google Cloud Client Library for Python`_.
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You can read the documentation for more details on API usage and use GitHub
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to `browse the source`_ and `report issues`_.
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.. _Google Cloud Client Library for Python:
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https://googlecloudplatform.github.io/google-cloud-python/
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.. _browse the source:
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https://github.com/GoogleCloudPlatform/google-cloud-python
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.. _report issues:
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https://github.com/GoogleCloudPlatform/google-cloud-python/issues
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.. _Google Cloud SDK: https://cloud.google.com/sdk/

‎speech/cloud-client/transcribe_onprem/transcribe_onprem.py

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#!/usr/bin/env python
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# Copyright 2020, Google LLC
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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# [START speech_transcribe_onprem]
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def transcribe_onprem(local_file_path, api_endpoint):
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"""
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Transcribe a short audio file using synchronous speech recognition on-prem
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Args:
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local_file_path: The path to local audio file, e.g. /path/audio.wav
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api_endpoint: Endpoint to call for speech recognition, e.g. 0.0.0.0:10000
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"""
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from google.cloud import speech_v1p1beta1
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from google.cloud.speech_v1p1beta1 import enums
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import grpc
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import io
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# api_endpoint = '0.0.0.0:10000'
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# local_file_path = '../resources/two_channel_16k.raw'
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# Create a gRPC channel to your server
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channel = grpc.insecure_channel(target=api_endpoint)
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client = speech_v1p1beta1.SpeechClient(channel=channel)
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# The language of the supplied audio
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language_code = "en-US"
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# Sample rate in Hertz of the audio data sent
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sample_rate_hertz = 16000
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# Encoding of audio data sent. This sample sets this explicitly.
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# This field is optional for FLAC and WAV audio formats.
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encoding = enums.RecognitionConfig.AudioEncoding.LINEAR16
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config = {
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"encoding": encoding,
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"language_code": language_code,
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"sample_rate_hertz": sample_rate_hertz,
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}
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with io.open(local_file_path, "rb") as f:
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content = f.read()
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audio = {"content": content}
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response = client.recognize(config, audio)
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for result in response.results:
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# First alternative is the most probable result
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alternative = result.alternatives[0]
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print(f"Transcript: {alternative.transcript}")
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# [END speech_transcribe_onprem]
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter
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)
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parser.add_argument(
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"--file_path",
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required=True,
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help="Path to local audio file to be recognized, e.g. /path/audio.wav",
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)
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parser.add_argument(
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"--api_endpoint",
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required=True,
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help="Endpoint to call for speech recognition, e.g. 0.0.0.0:10000",
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)
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args = parser.parse_args()
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transcribe_onprem(
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local_file_path=args.file_path, api_endpoint=args.api_endpoint
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)

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