README.md

metadata

language:
  - lt
tags:
  - whisper
  - faster-whisper
  - ctranslate2
  - automatic-speech-recognition
  - lithuanian
  - medical
license: apache-2.0
base_model: openai/whisper-large-v3
library_name: ctranslate2
pipeline_tag: automatic-speech-recognition

English | Lietuvių

English

LT_AI_Medical — Lithuanian Medical Whisper

A fine-tuned Whisper large-v3 model for Lithuanian medical speech-to-text transcription, optimized for faster-whisper (CTranslate2 format).

Model Details

• Base model: openai/whisper-large-v3
• Fine-tuning method: LoRA (Low-Rank Adaptation) adapters, merged into the base model
• Format: CTranslate2 (float16 quantization)
• Language: Lithuanian (lt)
• Domain: Medical dictation (radiology, family medicine)
• Dataset: VSSA-SDSA/LT_Medical_S_corpus
• Code repository: github.com/VSSA-AtvirasKodas-LT/LT_AI_Medical

Usage

With faster-whisper

from faster_whisper import WhisperModel

model = WhisperModel("VSSA-SDSA/LT_AI_Medical", device="cuda", compute_type="float16")

segments, _ = model.transcribe(
    "audio.wav",
    language="lt",
    beam_size=5,
    vad_filter=True,
)

text = " ".join(segment.text.strip() for segment in segments)
print(text)

With the provided transcribe.py script

git clone https://github.com/VSSA-AtvirasKodas-LT/LT_AI_Medical
cd LT_AI_Medical
pip install -r requirements.txt
python transcribe.py sample.wav

Intended Use

This model is designed for transcribing Lithuanian medical speech, particularly:

• Radiology reports
• Family medicine consultations

Limitations

• Trained primarily on medical vocabulary — may not perform as well on general Lithuanian speech
• Performance may degrade on accents or dialects outside the training distribution
• Audio longer than 30 seconds may produce hallucinations without proper VAD filtering

License

This model is released under the Apache 2.0 license, inheriting from the base Whisper large-v3 license.

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Lietuvių

LT_AI_Medical — Lietuviškas medicininis Whisper modelis

Apmokytas Whisper large-v3 modelis lietuvių kalbos medicininio kalbinio teksto atpažinimui, optimizuotas faster-whisper bibliotekai (CTranslate2 formatas).

Modelio informacija

• Bazinis modelis: openai/whisper-large-v3
• Apmokymo metodas: LoRA (Low-Rank Adaptation) adapteriai, sujungti su baziniu modeliu
• Formatas: CTranslate2 (float16 kvantizacija)
• Kalba: Lietuvių (lt)
• Sritis: Medicininė diktatūra (radiologija, šeimos medicina)
• Duomenų rinkinys: VSSA-SDSA/LT_Medical_S_corpus
• Kodo saugykla: github.com/VSSA-AtvirasKodas-LT/LT_AI_Medical

Naudojimas

Su faster-whisper

from faster_whisper import WhisperModel

model = WhisperModel("VSSA-SDSA/LT_AI_Medical", device="cuda", compute_type="float16")

segments, _ = model.transcribe(
    "audio.wav",
    language="lt",
    beam_size=5,
    vad_filter=True,
)

text = " ".join(segment.text.strip() for segment in segments)
print(text)

Su pridėtu transcribe.py skriptu

git clone https://github.com/VSSA-AtvirasKodas-LT/LT_AI_Medical
cd LT_AI_Medical
pip install -r requirements.txt
python transcribe.py sample.wav

Paskirtis

Šis modelis skirtas lietuvių kalbos medicininio kalbinio teksto transkribavimui, ypač:

• Radiologijos ataskaitoms
• Šeimos medicinos konsultacijoms

Apribojimai

• Apmokytas daugiausia su medicininiu žodynu — gali prasčiau atpažinti bendros lietuvių kalbos tekstą
• Našumas gali pablogėti su akcentais ar tarmėmis, nepatenkančiomis į apmokymo duomenis
• Ilgesnis nei 30 sekundžių garsas gali sukelti haliucinacijas be tinkamo VAD filtravimo

Licencija

Šis modelis išleistas pagal Apache 2.0 licenciją, paveldėtą iš bazinio Whisper large-v3 modelio licencijos.