proxectonos/stt_gl_conformer_ctc_large_v1.0

Conformer-CTC Large for Galician (v1.0)

This model performs automatic speech recognition (ASR) for Galician.
It is a fine-tuned version of NVIDIA's Conformer-CTC Large model, adapted to Galician using public speech datasets.

• Input: WAV mono 16 kHz
• Output: Galician transcription (lowercase)
• Architecture: Conformer-CTC (non-autoregressive)

Installation

pip install --upgrade pip
pip install "nemo_toolkit[asr]" torch torchaudio --index-url https://download.pytorch.org/whl/cu121

Quick Usage (ASR)

import nemo.collections.asr as nemo_asr

MODEL_ID = "your-username/stt-gl-conformer-large-ctc-v1.0"
asr = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(MODEL_ID)

preds = asr.transcribe(["/path/to/audio.wav"])  # list of wav paths
print(preds[0])

Forced Alignment (word-level timestamps)

1) Clone and install NeMo

git clone https://github.com/NVIDIA/NeMo
cd NeMo
pip install -r requirements/requirements_asr.txt
pip install -e .

2) Export the model to .nemo (if needed)

import nemo.collections.asr as nemo_asr
m = nemo_asr.models.EncDecCTCModelBPE.from_pretrained("your-username/stt-gl-conformer-large-ctc-v1.0")
m.save_to("stt-gl-conformer-large-ctc-v1.0.nemo")

3) Create a minimal manifest manifest.jsonl

{"audio_filepath": "/path/audio.wav", "text": "reference transcription in galician", "duration": 3.2}

4) Run the aligner

python tools/nemo_forced_aligner/align.py \
  model_path="stt-gl-conformer-large-ctc-v1.0.nemo" \
  manifest_filepath="manifest.jsonl" \
  output_dir="align_out" \
  batch_size=1 \
  transcribe_device="cuda" \
  viterbi_device="cuda" \
  align_using_pred_text=false \
  use_local_attention=true \
  save_output_file_formats='["ctm"]'

Outputs are saved as align_out/*.ctm.

Training Data

Dataset	Source
Common Voice v22 GL	Mozilla (downloaded directly)
Nos ParlAspeech GL	proxectonos/Nos_Parlaspeech-GL (Zenodo)
OpenHQ-SpeechT GL-EN	juanjucm/OpenHQ-SpeechT-GL-EN
FLEURS GL-EN	juanjucm/FLEURS-SpeechT-GL-EN

Model Details

• Base model: NVIDIA Conformer-CTC Large
• Framework: NeMo Toolkit
• Tokenizer: SentencePiece BPE packaged inside the .nemo

Forced Alignment Performance (Word-Level)

The model was also evaluated as a word-level forced aligner on the Nos ParlAspeech (GL) corpus.
The following metrics measure the timing accuracy of predicted word boundaries relative to ground-truth annotations:

Metric	Value
Mean Word Boundary Error	47.0 ms
Standard Deviation	55.5 ms
P90 (90th percentile error)	90.0 ms
Word Boundary Precision ≤ 50 ms	64.6%
Word Boundary Precision ≤ 100 ms	92.8%

This indicates that a majority of predicted word start/end timestamps fall within 50–100 ms of the manually annotated reference boundaries, making the model suitable for segmentation, subtitle alignment, and prosody/linguistic analysis workflows.

Limitations

Performance may degrade on highly technical domains, noisy audio, or speakers not represented in training data.

Contact information

For further information, send an email to proxecto.nos@usc.gal

Licensing information

Apache License, Version 2.0

Acknowledgements

This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project Desarrollo de Modelos ALIA. (Esta publicación del proyecto Desarrollo de Modelos ALIA está financiada por el Ministerio para la Transformación Digital y de la Función Pública y por el Plan de Recuperación, Transformación y Resiliencia – Financiado por la Unión Europea – NextGenerationEU).

Thanks also to Balidea for the technical development of this model.

Citation

@misc{proxectenos2026stt_gl_conformer_ctc_large_v1.0,
  author       = {{Proxecto Nós}},
  title        = {{Conformer-CTC Large for Galician} (v1.0)},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/proxectonos/stt_gl_conformer_ctc_large_v1.0}},
}