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danish-foundation-models
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Model Details
Model Provider
danish-foundation-models
Model Tree
openai/whisper-large-v3-turbo
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import pipeline asr = pipeline("automatic-speech-recognition", model="danish-foundation-models/edda-v0.2", device="cuda", chunk_length_s=30) # clips longer than 30 s are transcribed in 30 s windowsprint(asr("clip.wav")["text"]) # optional: previous text or domain vocabulary in Whisper's prompt slot (faster-whisper: initial_prompt / hotwords)prompt_ids = asr.tokenizer.get_prompt_ids("Folketinget, finansloven, Dansk Industri", return_tensors="pt").to(asr.device)print(asr("clip.wav", generate_kwargs={"prompt_ids": prompt_ids})["text"])The generation config defaults to Danish transcription (language="da", task="transcribe") and to beam search with
5 beams, so neither needs to be passed. Recordings longer than 30 s are not truncated: with chunk_length_s=30 they are
transcribed in consecutive 30 s windows and joined, which is also how the leaderboard scores long clips. Any sample rate the
pipeline can read is accepted; audio is resampled to 16 kHz. Output is cased and punctuated in the style of the training
corpora: FTSpeech is lower-case without punctuation, the other corpora are cased and mostly punctuated.
Prompting. Edda v0.2 is trained to use Whisper's previous-text prompt. Half of the training clips were conditioned on
the same speaker's preceding utterance in the <|startofprev|> slot, so the decoder treats a prompt as context: the previous
segment's transcript helps, an unrelated prompt costs little, and neither derails it. This is the slot that initial_prompt
and hotwords fill in faster-whisper, and that its default condition_on_previous_text=True fills with the previous window's
output. Measured on the five leaderboard test sets with our harness, 5 beams:
| prompt | Edda v0.2 | Edda v0.1 |
|---|---|---|
| none | 8.76 | 9.15 |
the previous clip's own transcript (condition_on_previous_text=True) | 8.74 | 211 — 95 % of clips loop |
the same unrelated sentence on every clip (initial_prompt) | 8.83 | 126 — 30 % of clips loop |
v0.2's repetition-loop rate stays at 0.2–0.3 % of clips in all three settings. v0.1's prompted runs were decoded greedily; its collapse is not a decoding artefact but the untrained slot. All of this was measured through the transformers prompt path; a CTranslate2 conversion for faster-whisper has not been tested by us.
Decoding. generation_config.json sets num_beams=5, the setting every number on this card was measured with; the
transformers speech-recognition pipeline and faster-whisper use 5 beams by default anyway, but direct model.generate() calls
and older transformers releases would otherwise decode greedily. Greedy decoding is about 0.3 WER worse and 1.3–1.7× faster.
Six public corpora, training splits only, gold transcripts only (no pseudo-labels), 1.62 M clips / 2,605 h after filtering:
| corpus | source | clips | hours | share of training steps |
|---|---|---|---|---|
| FTSpeech (parliament) | alexandrainst/ftspeech train | 983,989 | 1,690 | 43 % |
| CoRal-v3 read-aloud | CoRal-project/coral-v3 read_aloud/train | 299,253 | 521 | 24 % |
| NST Danish | alexandrainst/nst-da train¹ |
¹ NST's own test split was held out even though it is not a benchmark set.
Prompt conditioning. For 50 % of training clips the transcript of the same speaker's preceding utterance (manifest order,
which is recording order for CoRal and FTSpeech) is placed in Whisper's <|startofprev|> slot before the task tokens, truncated
to its last 223 tokens; the loss is not computed on the prompt. The other half of the clips, and clips with no preceding
utterance by the same speaker, are seen without a prompt, so the model is equally at home with an empty slot.
Full fine-tune of encoder and decoder from openai/whisper-large-v3-turbo, unmodified architecture (learned absolute
positions, fixed 30 s windows). One long constant-learning-rate run with four exits, each cooled down separately, then averaged.
| base run | 80,000 steps × 256 clips at a constant 1e-5 after a 500-step warmup; full training state saved at steps 20,000, 40,000, 60,000 and 80,000 (8 nodes × 8 MI250X GCDs × 4 clips; 20 h) |
| cooldown branches | from each saved state, 10,000 further steps with the learning rate decayed linearly to 1e-6 (4 nodes × 8 GCDs × 8 clips, 3 h 50 m each) |
| released weights | the four branch endpoints (EMA 0.9998 weights at steps 30,000, 50,000, 70,000 and 90,000) averaged with equal weights in fp32, stored in fp16 |
| optimizer | AdamW, β (0.9, 0.98), weight decay 0.01, gradient clipping 1.0, bf16 autocast |
| loss | cross-entropy with label smoothing 0.05 on `< |
| SpecAugment | time masks p 0.05 × 10 frames, feature masks p 0.05 × 10 bins |
| audio augmentation (per clip) | speed 0.9–1.1 (p 0.5); additive coloured noise at 0–20 dB SNR (p 0.6); 0.5–2 s trailing silence (p 0.25); 2.5 % of clips replaced by low-level noise with an empty transcript |
The single branch endpoints score 9.21, 9.02, 8.98 and 9.22 mean WER with greedy decoding on our harness; averaging the four and decoding with 5 beams gives the released 8.79.
The model weights are released under the Apache License 2.0 by the Alexandra Institute, which is also the licensor of the
CoRal-v3 dataset. The base model openai/whisper-large-v3-turbo
is MIT-licensed; the training corpora carry their own licenses — see the respective dataset cards. Trained by the Alexandra
Institute within the CoRal project and released by Danish Foundation Models.
| 182,575 |
| 239 |
| 16 % |
| CoRal-v3 conversation | CoRal-project/coral-v3 conversation/train | 147,184 | 144 | 12 % |
| FLEURS da_dk | google/fleurs train | 2,463 | 7.5 | 3 % |
| Common Voice 17 Danish | mozilla-foundation/common_voice_17_0 train | 3,484 | 4.1 | 2 % |