Sped_ParakeetRomanian_110M_TDT-CTC

SpeD_ParakeetRo_110M_TDT-CTC is a Romanian Automatic Speech Recognition (ASR) model based on the FastConformer Hybrid TDT-CTC 110M architecture from NVIDIA NeMo.
The model is adapted from an English pre-trained checkpoint to Romanian through transfer learning, leveraging both speech and text data to achieve strong performance on a low-resource language.

---

🧠 Model Architecture

• Base model: Parakeet Hybrid TDT-CTC 110M (NVIDIA NeMo)
• Pre-training: Self-Supervised Learning (SSL) on LibriLight + supervised fine-tuning on 36k hours of English
• Tokenizer: SentencePiece, 1024 BPE tokens (max subword length 5)
• Romanian alphabet: 31 characters + hyphen (“-”)

---

🗣️ Romanian Adaptation

• Training speech: 2,636 hours Romanian speech (manually and automatically annotated)
• Text corpus: 24.6M cleaned Romanian sentences (news domain + speech transcriptions)
• Tokenizer: Rebuilt on Romanian text corpus using SentencePiece

---

🧪 Data Augmentation

• Noise: MUSAN (6h Freesound subset), SNR 10–30, prob. 0.2
• Speed perturbation: 0.9–1.1, prob. 0.4
• SpecAugment + SpecCutout

---

⚙️ Training Details

This script was used for training and the base training configuration.

• Optimizer: AdamW (lr=2.0, weight_decay=1e−3)
• Scheduler: Noam Annealing with 10k warmup steps
• CTC loss weight: 0.3
• Epochs: 30
• Batch size: 32 (grad accumulation 8)
• Precision: BFloat16
• Hardware: NVIDIA RTX 4090 24GB
• Epoch time: ~5.5 hours
• Final model: Checkpoint averaging of top 10 validation WER

---

Inference

In order to run your own experiment, navigate to this repository :

cd examples/asr

python3 speech_to_text_eval.py \
dataset_manifest=../../manifests/SSC-eval1_manifest.json \
model_path=... \
output_filename=... \
decoder_type=ctc \
ctc_decoding.strategy=beam \
ctc_decoding.beam.kenlm_path=... \
ctc_decoding.beam.beam_alpha=... \
ctc_decoding.beam.beam_beta=... \
ctc_decoding.beam.beam_size=...

Results

The model can be used with two different decoders and multiple decoding strategies. In order to use the model with the external N-gram model, check this model card. Values for the beam parameters used with the 6-gram tokens model are: beam_size=32, beam_alpha=0.9, beam_beta=2.

📄 Citation

Citation

If you use this model, please cite:

@misc{pirlogeanu2025opensourcestateoftheartsolution,
      title={Open Source State-Of-the-Art Solution for Romanian Speech Recognition}, 
      author={Gabriel Pirlogeanu and Alexandru-Lucian Georgescu and Horia Cucu},
      year={2025},
      eprint={2511.03361},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2511.03361}, 
}

Also consider citing the original NVIDIA NeMo framework and KenLM:

@article{kuchaiev2019nemo,
  title={NeMo: a toolkit for building AI applications using Neural Modules},
  author={Kuchaiev, Oleksii and Ginsburg, Boris and others},
  journal={arXiv preprint arXiv:1909.09577},
  year={2019}
}

@inproceedings{heafield-2011-kenlm,
    title = "{K}en{LM}: Faster and Smaller Language Model Queries",
    author = "Heafield, Kenneth",
    editor = "Callison-Burch, Chris  and
      Koehn, Philipp  and
      Monz, Christof  and
      Zaidan, Omar F.",
    booktitle = "Proceedings of the Sixth Workshop on Statistical Machine Translation",
    month = jul,
    year = "2011",
    address = "Edinburgh, Scotland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W11-2123/",
    pages = "187--197"
}

Contact

For questions or collaborations: [email protected]

license: apache-2.0