A newer model is available — please use syvai/hviske-v5.3 instead. v5.3 is the current recommended Danish ASR model from this family and reaches 13.91% strict WER on the CoRal v3 full test set (beam=5). This v5.1 checkpoint is kept as the base for downstream fine-tunes (v5.2, v5.3) and for reproducibility.

hviske-v5.1

Danish ASR model — a 2B-parameter Conformer encoder-decoder trained on 3.5M samples (16k hours) of Danish speech from syvai/danish-asr-unified.

Results on CoRal v3 test

Split	Baseline WER	Baseline CER	v5.1 WER	v5.1 CER	ElevenLabs scribe_v2 WER	ElevenLabs scribe_v2 CER	OpenAI gpt-4o-transcribe WER	OpenAI gpt-4o-transcribe CER
read_aloud	104.73%	60.05%	19.45%	7.24%	18.62%	7.60%	26.34%	11.31%
conversation	126.12%	99.84%	25.46%	14.08%	31.38%	19.57%	55.24%	43.63%

WER drop of 85 pp on read-aloud and 101 pp on conversational speech.

ElevenLabs scribe_v2 evaluated via the public /v1/speech-to-text API and OpenAI gpt-4o-transcribe via /v1/audio/transcriptions — both on the full CoRal v3 test splits (n=17,560) with strict normalization (lowercase + punctuation strip + Danish digit-to-word via num2words(lang="da")).

Usage

Setup

pip install transformers==4.57.6 torch soundfile librosa

Note: this model uses native CohereAsr/Whisper classes from transformers 4.57.6. It is not compatible with transformers ≥5.0.

import torch, numpy as np, soundfile as sf
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq

processor = AutoProcessor.from_pretrained("syvai/hviske-v5.1", trust_remote_code=True)
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    "syvai/hviske-v5.1", trust_remote_code=True, dtype=torch.bfloat16
).to("cuda").eval()

audio, sr = sf.read("your_audio.wav")
audio = np.asarray(audio, dtype=np.float32)

hyp = model.transcribe(
    processor=processor,
    language="da",
    audio_arrays=[audio],
    sample_rates=[sr],
)[0]
print(hyp)

Audio > 35 s is automatically chunked. Input is resampled to 16 kHz internally.

Training details

• Architecture: 2.06B-parameter Conformer encoder-decoder, full fine-tune
• Data: syvai/danish-asr-unified pre-shuffled into 200 shards (3.41M rows) with voxpopuli, ftspeech, coral_read_aloud, coral_conversation, nst_da, nota, cv17 sources
• Epochs: 1
• Batch: 16 micro × 8 grad-accum = 128 effective batch
• Optimizer: bnb AdamW8bit, LR 5e-5 peak, 500-step warmup, cosine decay
• Augmentation: SpecAugment (2 freq × 27 bins, 2 time × 100 frames)
• Max audio: 31 s (recovers 86% of VoxPopuli long-audio samples)
• Precision: bf16 on NVIDIA RTX PRO 6000 Blackwell Max-Q
• Wall time: ~47 h

License

This model is released under Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0).

• Permitted: non-commercial use including research, education, evaluation, and personal projects, with attribution.
• Not permitted without a separate commercial license: any use by or for a commercial entity, integration into a commercial product or service, or use to generate revenue (directly or indirectly).
• Commercial licensing: contact mads@syv.ai.