Leaderboard · Benchmark data · Evaluation harness
Ekko v1 Tiny — Offline Danish Speech-to-Text with Word Timestamps
Ekko v1 Tiny is a compact, 110M-parameter Danish speech recognition model with
word timestamps. It is a FastConformer RNN-T fine-tuned from
nvidia/parakeet-rnnt-110m-da-dk and runs locally. Its raw output is lowercase
text; pair it with RyeAI/ekko-pnc for capitalization and punctuation.
Best fit: clear Danish speech from one speaker, delivered at a steady pace, such as dictation or reading aloud. The published results are stronger on read and formal speech than on spontaneous conversation. Dictation itself was not evaluated as a separate test set.
Benchmark
12.37% mean WER across 26,780 rows from five Danish test sets, evaluated with the leaderboard harness. Scores use the published defaults: NFKC normalization, spoken-number folding, Danish number-word normalization, lowercase, punctuation removal, symmetric filler removal, and whitespace collapse. Lower is better.
| Test set | Speech type | WER |
|---|---|---|
| CoRal conversation | Spontaneous conversation | 24.15% |
| CoRal read aloud | Read speech | 11.87% |
| Common Voice Danish (CV 25.0 test subset) | Crowd-sourced read speech | 8.44% |
FLEURS da_dk | Read news-style speech | 9.75% |
| FTSpeech | Parliamentary speech | 7.62% |
| Equal-weight mean | 12.37% |
The NeMo checkpoint processed the benchmark at 795.8x realtime on one A100. The official run saved raw per-sample outputs, and an independent offline rescore reproduced 12.37% exactly.
Comparison with models below 800M parameters
The chart compares Tiny's Q5_0 release score with 12 open-weight models below 800M parameters in the Danish ASR Leaderboard as of 9 October 2026. Each score is an equal-weight mean WER across the same five Danish test sets (26,780 clips). Tiny scores 12.37%, 0.27 percentage points lower than Svale 600M at 12.64%. Rankings may change when new models or reruns are added.
Native GGUF quantization
The F32, F16, Q8_0, Q6_K, Q5_K, Q5_0, Q4_K, and Q4_0 files are scored on the same current 26,780-row matrix and normalization policy as the NeMo result above. All 40 variant/domain outputs completed and passed exact-row offline scoring.
| Variant | Size | Mean WER | CPU speed |
|---|---|---|---|
| F16 | 248.2 MiB | 12.315% | 74.4x |
| F32 | 430.6 MiB | 12.318% | 71.8x |
| Q8_0 | 162.7 MiB | 12.323% | 74.9x |
| Q6_K | 141.8 MiB | 12.325% | 63.0x |
| Q5_K | 129.8 MiB | 12.337% | 60.1x |
| Q5_0 | 128.5 MiB | 12.370% | 69.4x |
| Q4_K | 118.4 MiB | 12.442% | 65.4x |
| Q4_0 | 117.1 MiB | 12.467% | 72.1x |
Q5_0 remains the packaged default: it is 128.5 MiB, runs at 69.4x realtime, and is 0.047 mean-WER points behind Q8_0. Choose Q8_0 when the extra 34.2 MiB is acceptable and maximum quantized accuracy is preferred. F16 is the native reference tier.
ONNX and browser export
The model is available for sherpa-onnx and was evaluated on the same 26,780-row matrix with mandatory non-silent peak normalization to 0.95. The browser downloads the compact int8 export.
| Export | Size | Mean WER | CPU speed |
|---|---|---|---|
| FP32 ONNX | 450.6 MiB | 12.465% | 53.4x |
| INT8 ONNX | 129.0 MiB | 12.946% | 49.2x |
Both exports completed all five domains with zero runtime failures. INT8 is 71.4% smaller than FP32 and trades 0.481 mean-WER points for the smaller browser download.
Artifacts
The current NeMo, GGUF, and ONNX artifacts are pinned at Hub revision
e37e698805c538e447c25062ab13fa862462ee9e and verified by SHA-256.
Clean-cache packaged download and inference tests passed on macOS arm64 and Linux x64 with native word timestamps. The Windows x64 artifact is mapped but has not been exercised against this release.
| Format | Path | Measured mean WER |
|---|---|---|
| NeMo | ekko-v1-tiny.nemo | 12.37% |
| parakeet.cpp GGUF | gguf-parakeet.cpp/ekko-v1-tiny-{f32,f16,q8_0,q6_k,q5_k,q5_0,q4_k,q4_0}.gguf | 12.315-12.467% |
| sherpa-onnx FP32 | onnx-sherpa/{encoder,decoder,joiner}.onnx | 12.465% |
| sherpa-onnx INT8 | onnx-sherpa/{encoder,decoder,joiner}.int8.onnx | 12.946% |
The browser loads onnx-sherpa/runtime-config.json with the INT8 files and
checks its SHA-256 before using its audio and model settings.
Local Use
For pinned downloads, word timestamps, and optional PnC, see the
Ekko STT quickstart.
If you already have parakeet-cli from parakeet.cpp, you can also run the
GGUF model directly:
hf download RyeAI/ekko-v1-tiny \
gguf-parakeet.cpp/ekko-v1-tiny-q5_0.gguf \
--local-dir ./ekko-v1-tiny
parakeet-cli transcribe \
--model ./ekko-v1-tiny/gguf-parakeet.cpp/ekko-v1-tiny-q5_0.gguf \
--input audio.wav --timestamps
NeMo usage:
from nemo.collections.asr.models import EncDecRNNTBPEModel
model = EncDecRNNTBPEModel.from_pretrained("RyeAI/ekko-v1-tiny")
transcript = model.transcribe(["audio.wav"])[0]
print(transcript.text)
Training and limitations
Ekko v1 Tiny trained for 50,000 steps on an 879,083-row, 1,549.6-hour Danish manifest, including 454.1 hours of quality-gated pseudo-labelled audio.
- Danish only.
- Raw output has no punctuation or capitalization.
- The 44-token vocabulary does not contain uppercase or punctuation tokens.
- Punctuation and capitalization require a separate restoration model such as
RyeAI/ekko-pnc. - Spontaneous conversation remains the hardest benchmark domain (24.15% WER).
- Noisy audio and overlapping speakers were not evaluated separately.
License
The complete model and data terms are documented in
MODEL_LICENSE.md and the bundled LICENSES/ files.