Take an audio recording, transcribe it to text, and then tag portions of audio based on a list of terms.
The initial use case is to find mentions of medical procedures to label the audio with procedure codes.
If available in Hex, the package can be installed
by adding audio_tagger to your list of dependencies in mix.exs:
def deps do
[
{:audio_tagger, "~> 0.1.0"}
]
endDocumentation can be generated with ExDoc and published on HexDocs. Once published, the docs can be found at https://hexdocs.pm/audio_tagger.
Audio transcription runs rather quickly. An audio file of around a minute can be transcribed in five to six seconds.
On the other hand, text-classification with a large set of labels can be quite slow:
- Within Livebook, running a single text input against the entire ICD-10 code list with around 73,000 labels doesn't complete.
- Running a single text input against 1,000 labels takes around two minutes (139.4 seconds in one case).
- Running the entire audio transcription of 14 text entries with 1,000 labels took 1793.8s (almost a half hour).
- Run with a GPU on Hugging Faces space
- Possibly consider other models that may be more performant or smaller (for example, https://huggingface.co/MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli)
We've discussed processing the text from the audio transcription before classification (by removing stop words and lemmatizing / finding the root of individual words), but with our use of a text-classification model this may not be valuable.