agirdi
sachmatkris/agirdi is a Lithuanian automatic speech recognition (ASR) model fine-tuned from openai/whisper-large-v3-turbo.
The model was fine-tuned specifically for Lithuanian speech recognition using the read (read) and spontaneous (spon) portions of the LIEPA-3 corpus.
Model Details
- Base model:
openai/whisper-large-v3-turbo - Language: Lithuanian (
lt) - Task: Automatic Speech Recognition (ASR)
- Architecture: Whisper
- License: MIT
Training Data
agirdi was fine-tuned on the LIEPA-3 (Didysis lietuvių kalbos garsynas) Lithuanian speech corpus.
Only the following LIEPA-3 subsets were used for fine-tuning:
read— read Lithuanian speechspon— spontaneous Lithuanian speech
The remaining LIEPA-3 subsets were not used for training.
LIEPA-3 is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Evaluation
The model is evaluated using Word Error Rate (WER).
| Dataset | WER ↓ |
|---|---|
| LIEPA-3 test set | 3.05% |
| FLEURS Lithuanian | 10.50% |
The LIEPA-3 test set measures performance on speech from the same corpus used for fine-tuning, while FLEURS Lithuanian provides an external evaluation set for measuring generalization beyond the training corpus.
Usage
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
model_id = "sachmatkris/agirdi"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id,
torch_dtype=torch_dtype,
low_cpu_mem_usage=True,
use_safetensors=True,
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
torch_dtype=torch_dtype,
device=device,
)
result = pipe("audio.wav")
print(result["text"])
Limitations
agirdi is specialized for Lithuanian speech recognition. Performance may vary depending on recording quality, background noise, speaker characteristics, dialect, domain-specific terminology, and other acoustic conditions.
The model may also inherit limitations and biases present in the original Whisper model and the LIEPA-3 training data.
Acknowledgements
agirdi was fine-tuned from openai/whisper-large-v3-turbo using data from the LIEPA-3 Lithuanian speech corpus.
Please refer to the LIEPA-3 dataset documentation for its license, attribution requirements, and further information about the corpus.
Citation
If you use agirdi in research, applications, or derived models, please cite/credit the model:
agirdi — Lithuanian Whisper ASR Kristijonas Raudys, 2026 https://huggingface.co/sachmatkris/agirdi