---
license: mit
datasets:
  - meldynamics/liepa-3
language:
  - lt
metrics:
  - wer
base_model:
  - openai/whisper-large-v3-turbo
pipeline_tag: automatic-speech-recognition
tags:
  - whisper
  - asr
  - lithuanian
---

# agirdi

`sachmatkris/agirdi` is a Lithuanian automatic speech recognition (ASR) model fine-tuned from [`openai/whisper-large-v3-turbo`](https://huggingface.co/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**](https://huggingface.co/datasets/meldynamics/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 speech
- `spon` — 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

```python
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`](https://huggingface.co/openai/whisper-large-v3-turbo) using data from the [LIEPA-3](https://huggingface.co/datasets/meldynamics/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