Parakeet-TDT-0.6B Slovenian

Fine-tuned NVIDIA Parakeet-TDT-0.6B-v3 for Slovenian automatic speech recognition, augmented with TTS-generated synthetic data.

This model is part of the paper Synthetic speech augmentation for low-resource Estonian and Slovenian ASR: Comparing Parakeet-TDT and Whisper, published in Machine Learning with Applications 26 (2026) 101043.

Model Description

Architecture: FastConformer encoder + Token-and-Duration Transducer (TDT) decoder
Parameters: 0.6B
Tokenizer: 8,192-token SentencePiece BPE
Base model: nvidia/parakeet-tdt-0.6b-v3
Fine-tuning data: CommonVoice 17.0 Slovenian + ~5,850 synthetic sentences (LLM-generated text + OpenAI TTS)
Training config: CV + Synth Unfiltered (full synthetic corpus without quality filtering)

Evaluation Results

Raw WER/CER (no text normalization)

Test Set	WER	CER
CommonVoice 17 Test	11.40	2.83
CommonVoice 17 Val	10.63	2.45
FLEURS Test	34.89	9.88

Normalized WER/CER (lowercase + punctuation removal)

Test Set	WER	CER
CommonVoice 17 Test	8.81	2.39
CommonVoice 17 Val	8.50	2.08
FLEURS Test	17.74	6.22

Improvement over baselines

Comparison	CV17 Test (WER)	FLEURS Test (WER)
vs. Zero-shot	-38.83 pp	-5.28 pp
vs. CV-only fine-tuning	-2.68 pp	-3.68 pp

All improvements are statistically significant (paired bootstrap, p < 0.001, n = 100,000).

Usage

import nemo.collections.asr as nemo_asr

# Load model
model = nemo_asr.models.ASRModel.from_pretrained("yuriyvnv/parakeet-tdt-0.6b-slovenian")

# Transcribe
transcriptions = model.transcribe(["audio.wav"])
print(transcriptions[0].text)

Training Details

Optimizer: AdamW (lr=5e-5, betas=[0.9, 0.98], weight_decay=0.001)
Schedule: Cosine annealing with 10% linear warmup
Batch size: 32
Early stopping: patience 10 epochs on val_wer
Best epoch: 74 (val_wer = 0.1085)
Precision: bf16-mixed
Seed: 42

Synthetic Data Augmentation

The synthetic training data was generated using a three-stage pipeline:

Text generation: GPT-5-mini generates diverse sentences across paraphrase, domain expansion, and morphological categories
LLM-as-judge validation: Each sentence validated for grammaticality, naturalness, and language purity
Speech synthesis: OpenAI gpt-4o-mini-tts with 11-voice rotation

Dataset: yuriyvnv/synthetic_asr_et_sl

Acknowledgments

Base model: NVIDIA Parakeet-TDT-0.6B-v3
Training data: Mozilla Common Voice 17.0
Evaluation: Google FLEURS

Citation

@article{PEREZHOHIN2026101043,
  title   = {Synthetic speech augmentation for low-resource Estonian and Slovenian ASR: Comparing Parakeet-TDT and Whisper},
  journal = {Machine Learning with Applications},
  volume  = {26},
  pages   = {101043},
  year    = {2026},
  issn    = {2666-8270},
  doi     = {10.1016/j.mlwa.2026.101043},
  url     = {https://www.sciencedirect.com/science/article/pii/S2666827026002082},
  author  = {Yuriy Perezhohin and Mauro Castelli and Aleš Popovič},
}

yuriyvnv/parakeet-tdt-0.6b-slovenian