Send a transcription request to Cloud Speech-to-Text On-Prem

Prerequisites

  1. Complete all required steps in the before you begin quickstart.
  2. Deploy the API.
  3. Query the API to make sure it's working.

Install dependencies

  1. Clone python-speech and change the directory to the sample directory.

    $ git clone https://github.com/googleapis/python-speech.git
$ cd python-speech/samples/snippets

  2. Install pip and virtualenv if you have not already done so. Refer to the Google Cloud Platform Python Development Environment Setup Guide for more information.

  3. Create a virtualenv. The samples below are compatible with Python 2.7 and 3.4+.

    $ virtualenv env
$ source env/bin/activate

  4. Install the dependencies needed to run the samples.

    $ pip install -r requirements.txt

Code sample

The code sample below uses the google-cloud-speech library. You can use GitHub to browse the source and report issues.

Transcribe an audio file

You can use the code sample below to transcribe an audio file using either a public IP or cluster level IP. For more information on IP types, see the documentation on querying the API.

Public IP:

    # Using a Public IP
    $ python transcribe_onprem.py --file_path="../resources/two_channel_16k.wav" --api_endpoint=${PUBLIC_IP}:443

Cluster level IP:

    # Using a cluster level IP
    $ kubectl port-forward -n $NAMESPACE $POD 10000:10000
    $ python transcribe_onprem.py --file_path="../resources/two_channel_16k.wav" --api_endpoint="0.0.0.0:10000"

Python

To authenticate to Speech-to-Text, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

def transcribe_onprem(
    local_file_path: str,
    api_endpoint: str,
) -> speech_v1p1beta1.RecognizeResponse:
    """
    Transcribe a short audio file using synchronous speech recognition on-prem

    Args:
      local_file_path: The path to local audio file, e.g. /path/audio.wav
      api_endpoint: Endpoint to call for speech recognition, e.g. 0.0.0.0:10000

    Returns:
      The speech recognition response
          {
    """
    # api_endpoint = '0.0.0.0:10000'
    # local_file_path = '../resources/two_channel_16k.raw'

    # Create a gRPC channel to your server
    channel = grpc.insecure_channel(target=api_endpoint)
    transport = speech_v1p1beta1.services.speech.transports.SpeechGrpcTransport(
        channel=channel
    )

    client = speech_v1p1beta1.SpeechClient(transport=transport)

    # The language of the supplied audio
    language_code = "en-US"

    # Sample rate in Hertz of the audio data sent
    sample_rate_hertz = 16000

    # Encoding of audio data sent. This sample sets this explicitly.
    # This field is optional for FLAC and WAV audio formats.
    encoding = speech_v1p1beta1.RecognitionConfig.AudioEncoding.LINEAR16
    config = {
        "encoding": encoding,
        "language_code": language_code,
        "sample_rate_hertz": sample_rate_hertz,
    }
    with io.open(local_file_path, "rb") as f:
        content = f.read()
    audio = {"content": content}

    response = client.recognize(request={"config": config, "audio": audio})
    for result in response.results:
        # First alternative is the most probable result
        alternative = result.alternatives[0]
        print(f"Transcript: {alternative.transcript}")

    return response