Custom phrases/words are ignored by Google Speech-To-Text

I have created an audio file to recreate your scenario and I was able to improve the recognition using the model adaptation. To achieve this with this feature, I would suggest taking a look at this example and this post to better understand the adaptation model.

Now, to improve the recognition of your phrase, I performed the following:

1. I created a new audio file using the following page with the mentioned phrase.

in this lecture we’ll talk about the Burrows wheeler transform and the FM index

2. My tests were based on this code sample. This code creates a PhraseSet and CustomClass that includes the word you would like to improve, in this case the word "barrows". You can also create/update/delete the phrase set and custom class using the Speech-To-Text GUI. Below is the code I used for the improvement.

from os import pathconf_names
from google.cloud import speech_v1p1beta1 as speech
import argparse


def transcribe_with_model_adaptation(
    project_id="[PROJECT-ID]", location="global", speech_file=None, custom_class_id="[CUSTOM-CLASS-ID]", phrase_set_id="[PHRASE-SET-ID]"
):
    """
    Create`PhraseSet` and `CustomClasses` to create custom lists of similar
    items that are likely to occur in your input data.
    """
    import io

    # Create the adaptation client
    adaptation_client = speech.AdaptationClient()

    # The parent resource where the custom class and phrase set will be created.
    parent = f"projects/{project_id}/locations/{location}"

    # Create the custom class resource
    adaptation_client.create_custom_class(
        {
            "parent": parent,
            "custom_class_id": custom_class_id,
            "custom_class": {
                "items": [
                    {"value": "barrows"}
                ]
            },
        }
    )
    custom_class_name = (
        f"projects/{project_id}/locations/{location}/customClasses/{custom_class_id}"
    )
    # Create the phrase set resource
    phrase_set_response = adaptation_client.create_phrase_set(
        {
            "parent": parent,
            "phrase_set_id": phrase_set_id,
            "phrase_set": {
                "boost": 0,
                "phrases": [
                    {"value": f"${{{custom_class_name}}}", "boost": 10},
                    {"value": f"talk about the ${{{custom_class_name}}} wheeler transform", "boost": 15}
                ],
            },
        }
    )
    phrase_set_name = phrase_set_response.name
    # print(u"Phrase set name: {}".format(phrase_set_name))
 
    # The next section shows how to use the newly created custom
    # class and phrase set to send a transcription request with speech adaptation

    # Speech adaptation configuration
    speech_adaptation = speech.SpeechAdaptation(
        phrase_set_references=[phrase_set_name])

    # speech configuration object
    config = speech.RecognitionConfig(
        encoding=speech.RecognitionConfig.AudioEncoding.FLAC,
        sample_rate_hertz=24000,
        language_code="en-US",
        adaptation=speech_adaptation,
        enable_word_time_offsets=True,
        model="phone_call",
        use_enhanced=True
    )

    # The name of the audio file to transcribe
    # storage_uri URI for audio file in Cloud Storage, e.g. gs://[BUCKET]/[FILE]
    with io.open(speech_file, "rb") as audio_file:
        content = audio_file.read()

    audio = speech.RecognitionAudio(content=content)
    # audio = speech.RecognitionAudio(uri="gs://biasing-resources-test-audio/call_me_fionity_and_ionity.wav")

    # Create the speech client
    speech_client = speech.SpeechClient()

    response = speech_client.recognize(config=config, audio=audio)

    for result in response.results:
        # The first alternative is the most likely one for this portion.
        print(u"Transcript: {}".format(result.alternatives[0].transcript))

    # [END speech_transcribe_with_model_adaptation]


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
    )
    parser.add_argument("path", help="Path for audio file to be recognized")
    args = parser.parse_args()

    transcribe_with_model_adaptation(speech_file=args.path)

3. Once it runs, you will receive an improved recognition as the below; however, consider that the code tries to create a new custom class and a new phrase set when it runs, and it might throw an error with a element already exists message if try to re-create the custom class and the phrase set.

• Using the recognition without the adaptation

(python_speech2text) user@penguin:~/replication/python_speech2text$ python speech_model_adaptation_beta.py audio.flac
Transcript: in this lecture will talk about the Burrows wheeler transform and the FM index

• Using the recognition with the adaptation

(python_speech2text) user@penguin:~/replication/python_speech2text$ python speech_model_adaptation_beta.py audio.flac
Transcript: in this lecture will talk about the barrows wheeler transform and the FM index

---

Finally, I would like to add some notes about the improvement and the code I performed:

• I have used a flac audio file as it is recommended for optimal results.

• I have used the model="phone_call" and use_enhanced=True as this was the model recognized by Cloud Speech-To-Text using my own audio file. Also the enhanced model can provide better results, you can see the documentation for more details. Note that this configuration might vary from your audio file.

• Consider enable data logging to Google to collect data from your audio transcription requests. Google then uses this data to improve its machine learning models used for recognizing speech audio.

• Once I have create the custom class and the phrase set, you can use the Speech-to-Text UI to updae and perform your tests quickly. only contains the

• I have used in the phrase set the parameter boost, when you use boost, you assign a weighted value to phrase items in a PhraseSet resource. Speech-to-Text refers to this weighted value when selecting a possible transcription for words in your audio data. The higher the value, the higher the likelihood that Speech-to-Text chooses that word or phrase from the possible alternatives.

I hope this information helps you to improve your recognitions.