Instructions to use 3dio-ai/svale-110M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use 3dio-ai/svale-110M with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("3dio-ai/svale-110M") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
svale-110M
Danish speech recognition for CPU. nvidia/parakeet-rnnt-110m-da-dk fine-tuned on 2,850 h of
public Danish speech (CoRal-v3, FTSpeech, Common Voice, FLEURS, YODAS; train splits only).
Lowercase, no punctuation. 16x realtime on 8 CPU threads.
WER, Danish ASR leaderboard normaliser:
| CoRal conversation | CoRal read-aloud | FTSpeech | Common Voice | FLEURS | mean |
|---|---|---|---|---|---|
| 26.74 | 11.52 | 7.33 | 9.52 | 9.63 | 12.95 |
Use
from huggingface_hub import hf_hub_download
import nemo.collections.asr as nemo_asr
model = nemo_asr.models.ASRModel.restore_from(hf_hub_download("3dio-ai/svale-110M", "svale-110M.nemo"))
print(model.transcribe(["audio.wav"]))
NeMo 2.1+, 16 kHz mono.
Licence
NVIDIA Open Model License. CoRal OpenRAIL-D use restrictions apply: no speech synthesis, no biometric identification.
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Model tree for 3dio-ai/svale-110M
Base model
nvidia/parakeet-rnnt-110m-da-dk