Ekko PnC v2 — Danish punctuation restoration and truecasing

Ekko PnC v2 restores punctuation and capitalization in Danish speech transcripts. It adds commas, full stops, question marks, and exclamation marks while keeping the original words in the same order. This makes it suitable for transcripts with word timestamps. The int8 ONNX model runs locally. Ekko v1 Tiny is a separate speech recognizer; PnC v2 can process its output.

Input: hej mit navn er emil hvordan går det i dag jeg har været på kontoret og bagefter skal jeg hjem

Output: Hej, mit navn er Emil. Hvordan går det i dag? Jeg har været på kontoret, og bagefter skal jeg hjem.

Quickstart

With uv and Python 3.11+:

uvx --from 'ekko-stt[pnc]' ekko pnc "hej mit navn er emil hvordan går det i dag"

Expected output: Hej, mit navn er Emil. Hvordan går det i dag?

The command installs the Ekko STT package. The first PnC run downloads the pinned int8 model from Hugging Face; later runs use the cache. For longer transcripts, the runtime uses 128-token windows with 12 words of overlap and keeps every input word in order. Pass continuous text when possible so PnC can use context across speech recognition chunks.

Python with ONNX

Install ekko-stt[pnc] in your Python environment. This loads the int8 ONNX export and applies the same windowing as the command above:

from ekko import Punctuator
from ekko.artifacts import ensure_pnc_directory

pnc = Punctuator(ensure_pnc_directory())
print(pnc("hej mit navn er emil hvordan går det i dag"))

Install transformers and torch to use the PyTorch weights directly. This short-input example maps predictions from the first token of each word back to the original words:

import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer

repo = "RyeAI/ekko-pnc"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForTokenClassification.from_pretrained(repo).eval()
words = "hej mit navn er emil hvordan går det i dag".split()
inputs = tokenizer(words, is_split_into_words=True, return_tensors="pt")

with torch.inference_mode():
    predictions = model(**inputs).logits.argmax(dim=-1)[0].tolist()

output, seen, capitalize_next = [], set(), True
for word_id, prediction in zip(inputs.word_ids(), predictions):
    if word_id is None or word_id in seen:
        continue
    seen.add(word_id)
    label = model.config.id2label[prediction]
    mark = label.removesuffix("|U")
    word = words[word_id]
    if capitalize_next or label.endswith("|U"):
        word = word[:1].upper() + word[1:]
    output.append(word + ("" if mark == "O" else mark))
    capitalize_next = mark in {".", "?", "!"}

print(" ".join(output))

For longer transcripts, use the ONNX example above; it preserves all words across overlapping windows.

Version 2 is tuned for continuous speech such as dictation and meetings. The earlier version often missed sentence endings when given a long transcript; it had mainly learned from short, isolated utterances. Version 2 was trained on longer stretches of conversation and is substantially better at finding sentence boundaries in that setting.

Results

The main evaluation used 88 clips from 8 meetings (4,322 words), with punctuation reviewed by a person. Both versions received the same correct words, so these scores measure punctuation and casing rather than speech recognition. The numbers below use the FP32 ONNX model. F1 combines precision and recall; higher is better.

The v2 casing score includes a simple rule that capitalizes the word after a predicted sentence ending. Without that rule, its casing F1 was 0.653.

On 55 held-out CoRal conversations, v2 improved sentence boundary F1 from 0.095 to 0.474. The punctuation reference for this set was machine-generated and checked to preserve every word, so this result measures agreement with that reference. This earlier comparison used the settings max_length=256, overlap_words=24. In a later runtime comparison on the same conversations, the same v2 int8 weights scored 0.471 with 256/24 and 0.599 with the recommended 128/12. The set has been used in earlier experiments, so this is guidance for runtime configuration rather than an independent evaluation of new model weights.

Very short clips are a trade-off: on isolated 3–4 second utterances, boundary F1 decreased from 0.865 (v1) to 0.818 (v2). Version 2 may add extra full stops on such inputs.

Speed

1,000 Danish words in about 0.09 seconds on an Apple M1 Pro CPU, after model loading. The int8 ONNX benchmark includes tokenization, inference, and rendering; it excludes download, speech recognition, and browser WASM. Speed varies by device and input length.

Files and use

On the meeting evaluation, int8 was behind FP32 by 0.006 boundary F1, 0.022 question F1, and 0.016 casing F1.

The int8 runtime pins revision d3129544e4e0155093e192cac58a9e69a4dea1ff. SHA-256 checksums:

pnc.int8.onnx  46bb979d409731872f10b6cb71298594595f16368d2aec2fcaf94d87b4266527
tokenizer.json a1e0d43ee307ad7a1ccc23e6e5398d877a68ca7f576067ac507625a0d50e9502
config.json    dc655c46f69eeb15dd43786ad58fb5862cc794b36cc9d2c426ab5bc4b01f23ca

The files in this repository also have checksums in SHA256SUMS. Version 1 remains available through the v1 tag.

Training and limitations

Ekko PnC builds on jonfd/electra-small-nordic. Its Danish training text included Wikipedia, NST, CoRal, and FT Speech. Training combined human-punctuated source text with machine-generated punctuation labels. The main meeting evaluation above uses human-reviewed punctuation; the CoRal conversation evaluation uses machine-generated punctuation references.

• Punctuation comes from text alone; the model cannot hear pauses or intonation. Question marks remain its weakest common punctuation class.
• It handles capitalization as an initial uppercase letter. It cannot create mixed-case words such as iPhone from lowercase input; existing capitals are preserved by the Ekko runtimes.
• The human-reviewed meeting set is small: 8 meetings and 324 reference full stops. Results on other speaking styles may differ.

License and data terms

Ekko PnC v2 is released under CC BY 4.0. See MODEL_LICENSE.md, NOTICE, and LICENSES/ for the terms and source notices. CoRal's use restrictions also apply to this trained model. The FT Speech and NST notices are included in the license bundle.