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Nemotron 3.5 Asr Streaming Slovenian

nvidia/nemotron-3.5-asr-streaming-0.6b (FastConformer-CacheAware RNNT, 638M) fine-tuned for Slovenian on ARTUR (858 h) + Common Voice — a low-latency streaming model that works across read and spontaneous speech....

TipoOtherModelo basenvidia/nemotron-3.5-asr-streaming-0.6bVersiónRevision c51324fBcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
938 MB
Modelo base
nvidia/nemotron-3.5-asr-streaming-0.6b
Versión
vRevision c51324f
Actualizado
26 sept 2026
Descargas
234
Fuente
Hugging Face

Descripción de la fuente

Slovenian streaming ASR — Nemotron-3.5 fine-tune (v3)

nvidia/nemotron-3.5-asr-streaming-0.6b (FastConformer-CacheAware RNNT, 638M) fine-tuned for Slovenian on ARTUR (858 h) + Common Voice — a low-latency streaming model that works across read and spontaneous speech. Runs on GPU via NeMo or on CPU via parakeet.cpp (GGUF).

Results (WER, normalized, held-out)

Test setWER
Common Voice sl25.07%
ARTUR multi-domain23.42%
ARTUR parliamentary (spontaneous)21.66%
FLEURS sl33.07%

Comparison — vs yuriyvnv/parakeet-tdt-0.6b-slovenian

Different design goals; honest numbers (normalized WER):

Test setThis model (streaming)parakeet-tdt-0.6b-slovenian (non-streaming)
FLEURS sl33.07%17.74%
Common Voice sl25.07%8.81%
ARTUR parliamentary (spontaneous)21.66%not reported
  • parakeet-tdt is better on clean read speech — stronger non-streaming base (parakeet-tdt-0.6b-v3), trained on read data (Common Voice + synthetic TTS).
  • This model is a streaming (real-time, low-latency, CPU-deployable) model trained on 858 h ARTUR + Common Voice, so it stays robust on spontaneous/conversational speech (parliamentary ~22%) — a domain read-only models don't target. (CV test differs slightly: CV26 here vs CV17 there.)

Pick by use case: read-speech accuracy → parakeet-tdt; real-time streaming + spontaneous speech → this model.

Files

  • nemotron_sl.nemo — NeMo checkpoint (GPU inference / further fine-tuning)
  • asr_sl_v3.gguf (q8_0) — parakeet.cpp CPU inference

How to run

# CPU, streaming, via parakeet.cpp — use lang tag sl-SI, 16 kHz mono
parakeet-cli --model asr_sl_v3.gguf --lang sl-SI audio.wav
# GPU via NeMo
from nemo.collections.asr.models import ASRModel
m = ASRModel.restore_from("nemotron_sl.nemo")
m.transcribe(["audio.wav"])   # 16 kHz mono; manifest lang/target_lang = "sl-SI"

License & data provenance

Released CC BY-SA 4.0 (to honor the ARTUR ShareAlike terms).

InputLicense
Base nvidia/nemotron-3.5-asr-streaming-0.6bOpenMDW-1.1 (retain NOTICE)
ARTUR 1.0 (CLARIN.SI, 858 h)CC BY-SA 4.0 (attribution + ShareAlike)
Common Voice slCC0

Credit CLARIN.SI (ARTUR: hdl.handle.net/11356/1776 audio, /11356/1772 transcriptions), NVIDIA, and Mozilla Common Voice.

Citation

Cite this work (Tadej Fius, MediaAtlas Ltd):

@misc{fius2026asr,
  title        = {Nemotron-3.5 Slovenian Streaming ASR},
  author       = {Fius, Tadej},
  year         = {2026},
  publisher    = {MediaAtlas Ltd},
  howpublished = {Hugging Face},
  url          = {https://huggingface.co/texdata/nemotron-3.5-asr-streaming-slovenian}
}

Upstream / source citations:

@misc{nemotron_asr, title={Nemotron-3.5 ASR Streaming 0.6B (FastConformer-CacheAware RNNT)}, author={{NVIDIA}}, url={https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b}}
@article{kuchaiev2019nemo, title={NeMo: a toolkit for building AI applications using Neural Modules}, author={Kuchaiev, O. and others}, journal={arXiv:1909.09577}, year={2019}}
@misc{artur, title={ARTUR: Speech Database of Slovene}, author={{CLARIN.SI}}, note={audio hdl:11356/1776, transcriptions hdl:11356/1772}, url={http://hdl.handle.net/11356/1776}}

Base under OpenMDW-1.1 (retain NOTICE). Data: ARTUR (CLARIN.SI); credit NVIDIA + CLARIN.SI.

Fuentes

1 fuente
Hugging FacePrincipalVersión c51324f6ee5387186655be88fd29b8963ea30b49
huggingface.co/texdata/nemotron-3.5-asr-streaming-slovenian/tree/c51324f6ee5387186655be88fd29b8963ea30b49

Verificado 6 sept

Artefactos del modelo

1 artefacto

asr_sl_v3.gguf

gguf · 938 MB · SHA-256 a78c3472c5c2…f93c · Hugging Face

Descargar

Extractos de fuentes

2 extractos

--- language: - sl license: cc-by-sa-4.0 library_name: nemo pipeline_tag: automatic-speech-recognition base_model: nvidia/nemotron-3.5-asr-streaming-0.6b tags: - automatic-speech-recognition - slovenian - nemo - fastconformer - rnnt - streaming - parakeet metrics: - wer --- # Slovenian streaming ASR — Nemotron-3.5 fine-tune (v3) `nvidia/nemotron-3.5-asr-streaming-0.6b` (FastConformer-CacheAware RNNT, 638M) fine-tuned for **Slovenian** on **ARTUR (858 h) + Common Voice** — a low-latency **streaming** model that works across read *and* spontaneous speech. Runs on GPU via NeMo or **on CPU** via parakeet.cpp (GGUF). ## Results (WER, normalized, held-out) | Test set | WER | |---|---| | Common Voice sl | 25.07% | | ARTUR multi-domain | 23.42% | | ARTUR parliamentary (spontaneous) | 21.66% | | FLEURS sl | 33.07% | ## Comparison — vs `yuriyvnv/parakeet-tdt-0.6b-slovenian` Different design goals; honest numbers (normalized WER): | Test set | This model (**streaming**) | parakeet-tdt-0.6b-slovenian (non-streaming) | |---|---|---| | FLEURS sl | 33.07% | **17.74%** | | Common Voice sl | 25.07% | **8.81%** | | ARTUR parliamentary (spontaneous) | **21.66%** | not reported | - **`parakeet-tdt`** is better on clean **read** speech — stronger non-streaming base (`parakeet-tdt-0.6b-v3`), trained on read data (Common Voice + synthetic TTS). - **This model** is a **streaming** (real-time, low-latency, CPU-deployable) model trained on 858 h ARTUR + Common Voice, so it stays robust on **spontaneous/conversational** speech (parliamentary ~22%) — a domain read-only models don't target. (CV test differs slightly: CV26 here vs CV17 there.) Pick by use case: **read-speech accuracy → parakeet-tdt**; **real-time streaming + spontaneous speech → this model**. ## Files - `nemotron_sl.nemo` — NeMo checkpoint (GPU inference / further fine-tuning) - `asr_sl_v3.gguf` (q8_0) — parakeet.cpp CPU inference ## How to run ```bash # CPU, streaming, via parakeet.cpp — use lang tag sl-SI, 16 kHz mono parakeet-cli --model asr_sl_v3.gguf --lang sl-SI audio.wav ``` ```python # GPU via NeMo from nemo.collections.asr.models import ASRModel m = ASRModel.restore_from("nemotron_sl.nemo") m.transcribe(["audio.wav"]) # 16 kHz mono; manifest lang/target_lang = "sl-SI" ``` ## License & data provenance Released **CC BY-SA 4.0** (to honor the ARTUR ShareAlike terms). | Input | License | |---|---| |...

Source context: 234 downloads · 3 likes · Pipeline automatic-speech-recognition · Library nemo · Repo texdata/nemotron-3.5-asr-streaming-slovenian