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Data extraction from audio and text

Author(s): @jerjou ,   Published: 2017-05-23

Google Cloud Community tutorials submitted from the community do not represent official Google Cloud product documentation.

Oftentimes, the raw data you've gathered is not in a form that is directly explorable using the data exploration tools at your disposal. Making it usable may require converting the format, extracting the information type you're seeking, or adding metadata to further structure the data.

Google Cloud Platform makes it easy to write specialized functions to transform your data and chain them into a pipeline, and provides a number of Machine Learning APIs that enable you to transcribe audio; identify faces, landmarks, and text in images; translate between languages; and provide structure to prose.

In this tutorial, you'll write several functions to perform various transformations and extraction to turn raw audio files into structured, queryable data. These functions can then be easily combined together into a reusable data ingestion pipeline, as described in the preprocessing tutorial.


This data extraction pipeline example can be described as a series of discrete steps:

  • Unzipping a collection of mp3 audio files
  • Converting the mp3s to raw LINEAR16 audio
  • Uploading the raw audio files to Google Cloud Storage
  • Extract transcription of speech from the audio files
  • Process the transcription to identify the entities.
  • Save the results


Download the tutorial data

For this tutorial, we'll extract data from readings of Aesop's Fables from LibriVox for demonstration purposes. For convenience, we've cached a copy of the zip files in a Google Cloud Storage bucket:

  • Download the first audio file, to use while writing and testing your preprocessing functions.
  • You can also browse the whole collection of audio files here.

Convert the file

The file as downloaded from LibriVox is in a zip archive, which we'll need to convert into a format that the API accepts. In order to do this, we'll first define a function that unzips the archive, producing each file successively:

def unzip(filename):
    """Generator that yields files in the given zip archive."""
    with zipfile.ZipFile(filename, 'r') as archive:
        for zipinfo in archive.infolist():
            yield archive.open(zipinfo, 'r'), {
                'name': zipinfo.filename,

We'll also define a main function to verify the function works as expected:

def main(filenames):
    for filename in filenames:
        for srcfile, metadata in unzip(filename):
            with open(metadata['name'], 'w') as f:

You can find the complete file here. Running it produces:

$ python unzip.py aesop_fables_volume_one_librivox_64kb_mp3.zip
$ ls

which confirms that our unzipping process works as expected.

Now that we have the mp3 contents of the zip archive, we must transcode that to a format the API accepts. Currently, for audio longer than 1 minute, the audio must be in raw, monoaural, 16-bit little-endian format. We'll also make an attempt to preserve the original sample rate.

def mp3_to_raw(data, metadata):
    # Write the data to a tmpfile, for conversion
    with tempdir() as dirname:
        src = os.path.join(dirname, metadata['name'])
        dest = '{}.raw'.format(src[:src.rindex('.')])
        with open(src, 'wb') as f:
        audio = pydub.AudioSegment.from_mp3(src)
        # Convert to the format expected
        audio = audio.set_channels(1).set_sample_width(2)

        if audio.frame_rate < 8000:
            raise ValueError('Sample width must be above 8kHz')
        elif audio.frame_rate > 48000:

        audio.export(dest, format='raw')

        with open(dest, 'r') as f:
            raw_data = f.read()

    return raw_data, {
        'name': os.path.basename(dest),
        'size': len(raw_data),
        'rate': audio.frame_rate,

Again, we can define a main function to verify the function works as expected:

def main(mp3_filenames):
    for mp3_filename in mp3_filenames:
        print('Converting {}'.format(mp3_filename))
        with open(mp3_filename, 'r') as f:
            raw_data, metadata = mp3_to_raw(
                f.read(), {'name': mp3_filename})
        with open(metadata['name'], 'w') as f:


You can find the complete file here. Running it produces:

$ python convert_audio.py fables_01_02_aesop_64kb.mp3
Converting fables_01_02_aesop_64kb.mp3
{'rate': 24000, 'name': 'fables_01_02_aesop_64kb.raw', 'size': 3214080}

We've now processed our source data into a format ready to be consumed by the Speech API.

Transcribe the audio using the Speech API

To extract text data from our prepared audio file, we issue an asynchronous request to the Google Cloud Speech API, then poll the API until it finishes transcribing the file.

Upload the audio file to Google Cloud Storage

Because the audio we're transcribing is longer than a minute in length, we must first upload the raw audio files to Cloud Storage, so the Speech API can access it asynchronously. We could use the gsutil tool to do this manually, or we could do it programatically from our code. Because we'd like to eventually automate this process in a pipeline, we'll do this in code:

def stage_audio(data, metadata, destination_bucket=DESTINATION_BUCKET):
    client = storage.Client()
    blob = client.bucket(destination_bucket).blob(metadata['name'])
    blob.upload_from_string(data, content_type='application/octet-stream')

    return destination_bucket, metadata['name'], metadata

You can find the complete file here. Running it produces:

$ python stage_raw.py fables_01_02_aesop_64kb.raw --bucket=your-bucket
Uploading fables_01_02_aesop_64kb.raw
(u'your-bucket', u'fables_01_02_aesop_64kb.raw', {'name': 'fables_01_02_aesop_64kb.raw'})

Make the Speech API call

All calls to the Speech API must be authenticated, so make sure you've set up your service account correctly, as mentioned in the prerequisites. In the following code, we'll use the API's client library to create an authenticated service object, which we'll use to make the API call.

def transcribe(bucket, path, metadata):
    client = speech.Client()

    sample = client.sample(source_uri='gs://{}/{}'.format(bucket, path),
    operation = sample.long_running_recognize(
    file_size_megs = metadata['size'] * 2**-20
    operation = _poll(operation, file_size_megs)

    if operation.error:
        logging.error('Error transcribing gs://{}/{}: {}'.format(
            bucket, path, operation.error))
        raise TranscriptionError(operation.error)
        best_transcriptions = [r.alternatives[0] for r in operation.results
                               if r.alternatives]
        return best_transcriptions, metadata

Since the audio files are longer than a minute, we make the call asynchronously, and must poll the API for the result:

def _poll(operation, upper_bounds):
    n = 0
    while not operation.complete:
        sleep_secs = random.triangular(
            1, 1 + upper_bounds,
            1 + (.99**n) * upper_bounds)
        n += 1

        logging.debug('Sleeping for %s', sleep_secs)


    return operation

You can find the complete file here. Running it produces:

$ python transcribe.py --rate=24000 gs://data-science-getting-started/fables_01_02_aesop_64kb.raw --size=3214080
(0.982679188251): this is a LibriVox recording all LibriVox recordings are in the public domain for more information or to volunteer please visit librivox.org

(0.950583994389):  Aesop's Fables the goose that laid the golden egg

(0.941175699234):  a man and his wife had the Good Fortune to possessive which laid the golden egg everyday lucky though they were they soon begin to think that they were not getting rich fast and and Imagining the bird must be made of gold inside they decided to kill it in order to secure the whole store of precious metal at 1 but when they cut it open a found it was just like any other Goose this thing either got rich all at once as they had hoped you enjoyed any longer the daily addition to their well much once more and Luther

(0.80675303936):  Inns of the goose that laid the golden egg

Analyze the syntax

A text transcription of audio is fine and good, but natural language is hard to glean meaningful insight from, since it's difficult for machines to glean its structure. For this, we can leverage the Cloud Natural Language API to extract the syntax from the text.

With the Natural Language API, parsing the syntax of the text is a simple API call:

def extract_syntax(transcriptions, metadata):
    """Extracts tokens in transcriptions using the GCP Natural Language API."""
    client = language.Client()

    document = client.document_from_text(
            '\n'.join(transcriptions), language='en',
    # Only extracting tokens here, but the API also provides these other things
    sentences, tokens, sentiment, entities, lang = document.annotate_text(
            include_syntax=True, include_entities=False,

    return tokens, metadata

We can write a short main function to confirm it works:

def main(input_files):
    for line in fileinput.input(input_files):
        logging.info('Analyzing "{}"'.format(line))

        tokens, metadata = extract_syntax([line], {})
        for token in tokens:
            print('{}: {}'.format(token.text_content, token.part_of_speech))

You can find the complete file here. Running it on a sample phrase produces:

$ python sentence_structure.py - <<EOF
Everyone shall sit under their own vine and fig tree, and no one shall make them afraid.

Everyone: NOUN
shall: VERB
sit: VERB
under: ADP
their: PRON
own: ADJ
vine: NOUN
and: CONJ
fig: NOUN
tree: NOUN
and: CONJ
no: DET
one: NOUN
shall: VERB
make: VERB
them: PRON
afraid: ADJ

We've now gone from a stream of spoken prose, transformed it into a form readable by our tools, and came out with a structured catalog of its contents. In this form, we can unleash our exploratory tools, as described in the Exploratory Queries article.

But first, it's imperative that we go from manually transforming this data with a series of scripts, to automating this process.

API Documentation & other resources

We've only touched on a couple of the capabilities of the APIs we've used here. Take a look at the API documentation, and experiment with the other features.

Also, take a look at the Structuring Unstructured text demo for another example of using the Natural Language API.

What's next

The astute among you may notice that the return values of each of these steps feeds right into the arguments to the next. Indeed - it would make sense to tie all these functions together into a pipeline that can be automated, depositing the results into a database for later querying.

In fact, the next step describes how to tie functions like these together into a preprocessing pipeline, using the Google Cloud Dataflow service.

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