livebooks/using_audio_tagger_library.livemd

<!-- livebook:{"app_settings":{"auto_shutdown_ms":5000,"multi_session":true,"show_source":true,"slug":"transcriber"}} -->

# Tag Audio

```elixir
Mix.install(
  [
    {:audio_tagger, path: "./development/ml/audio_tagger"},
    {:kino_bumblebee, "~> 0.4.0"},
    {:exla, ">= 0.0.0"},
    {:explorer, "~> 0.7.0"},
    {:kino_explorer, "~> 0.1.11"}
  ],
  config: [
    nx: [default_backend: EXLA.Backend]
    # exla: [
    #   clients: [
    #     cuda: [
    #       platform: :cuda,
    #       lazy_transfers: :never
    #     ]
    #   ]
    # ]
  ]
)
```

## Step 1: Create Vector Embeddings for ICD-9 Codes

```elixir
# Use sentence-transformers/all-MiniLM-L6-v2 to create vectors for each medical code description
tmpfile = Path.join(System.tmp_dir(), "CMS32_DESC_LONG_SHORT_DX")

AudioTagger.Vectors.precalculate(tmpfile)
```

## Step 2: Transcribe Audio Recording

```elixir
# 1 - Prepare model and choose audio file
featurizer = AudioTagger.Transcriber.prepare_featurizer()

audio_input = Kino.Input.audio("Audio", sampling_rate: featurizer.sampling_rate)
```

```elixir
# 2 - Transcribe audio recording to text using OpenAI's Whisper model (takes approximately a minute on an M1 Max)
chosen_audio = Kino.Input.read(audio_input)

if chosen_audio == nil do
  raise "No file chosen. Please select a file in the widget above."
end

file = chosen_audio.file_ref |> Kino.Input.file_path() |> File.read!()
options = [model_name: "openai/whisper-tiny", num_channels: chosen_audio.num_channels]

transcription_df =
  AudioTagger.Transcriber.transcribe_audio(featurizer, file, options)
  |> Enum.map(&Function.identity/1)
  |> Explorer.DataFrame.new()
```

## Step 3: Tag Transcribed Audio

```elixir
labels_df =
  "#{tmpfile}.csv"
  |> Explorer.DataFrame.from_csv!(
    dtypes: [
      {"DIAGNOSIS CODE", :string},
      {"LONG DESCRIPTION", :string},
      {"SHORT DESCRIPTION", :string}
    ]
  )
  |> Explorer.DataFrame.select([0, 1, 2])
  |> Explorer.DataFrame.rename(["code", "long_description", "short_description"])

tagged_audio =
  transcription_df
  |> AudioTagger.Classifier.SemanticSearch.tag(
    labels_df,
    "#{tmpfile}.bin"
  )
```