WhisperBridge is the speech-to-text entry point in Llamatik. The library currently exposes a WAV-based transcription API.

Initialize the model#

val modelPath = WhisperBridge.getModelPath("ggml-base.en.bin")
val ok = WhisperBridge.initModel(modelPath)
check(ok)

Transcribe a file#

val text = WhisperBridge.transcribeWav(
    wavPath = "/path/to/audio.wav",
    language = "en"
)
println(text)

Language hint#

The language parameter is optional, but supplying it can improve reliability when you already know the input language.

Initial prompt#

The initialPrompt parameter primes the model before transcription begins. Use it to bias output toward specific vocabulary, domain terms, or formatting conventions.

val text = WhisperBridge.transcribeWav(
    wavPath = audioPath,
    language = "en",
    initialPrompt = "The following is a developer podcast about Kotlin Multiplatform."
)

The model uses the prompt as prior context — it does not transcribe it literally. This is useful when your audio contains technical terms or proper nouns that the model might otherwise mis-transcribe.

Recommended workflow#

1. record or obtain audio
2. convert it to WAV if needed
3. initialize the model once
4. call transcribeWav(...) for each file
5. release the model when done

Cleanup#

WhisperBridge.release()

Practical notes#

• For best results, keep input audio clear and reasonably clean.
• Reuse the initialized model if you transcribe multiple files.
• WASM support is currently not available for WhisperBridge.