---

license: cc-by-4.0

base_model: moondream/parakeet-ultra

base_model_relation: quantized

library_name: transcribe.cpp

pipeline_tag: automatic-speech-recognition

language:

- en

- de

- fr

- es

- it

- pt

- ru

- uk

- hr

- sl

- lv

- lt

- et

- fi

- sv

- da

- nl

- pl

- cs

- sk

- hu

- ro

- bg

- el

- mt

tags:

- gguf

- transcribe.cpp

- asr

- speech-to-text

- parakeet

- conformer

- tdt

- multilingual

- long-form

transcribe_cpp:

schema_version: 2

wer_librispeech_test_clean:

f32: 1.8

f16: 1.8

q8_0: 1.8

q6_k: 1.81

q5_k_m: 1.81

q4_k_m: 1.82

wer_fleurs_en:

q8_0: 4.26

wer_fleurs_de:

q8_0: 4.58

wer_fleurs_fr:

q8_0: 4.66

wer_fleurs_es:

q8_0: 3.06

wer_fleurs_it:

q8_0: 2.34

wer_fleurs_pt:

q8_0: 4.16

wer_fleurs_ru:

q8_0: 5.63

wer_fleurs_uk:

q8_0: 5.71

wer_fleurs_hr:

q8_0: 11.3

wer_fleurs_sl:

q8_0: 18.98

wer_fleurs_lv:

q8_0: 18.04

wer_fleurs_lt:

q8_0: 17.06

wer_fleurs_et:

q8_0: 14.44

wer_fleurs_fi:

q8_0: 11.14

wer_fleurs_sv:

q8_0: 12.83

wer_fleurs_da:

q8_0: 15.98

wer_fleurs_nl:

q8_0: 6.7

wer_fleurs_pl:

q8_0: 6.16

wer_fleurs_cs:

q8_0: 11.04

wer_fleurs_sk:

q8_0: 7.83

wer_fleurs_hu:

q8_0: 13.09

wer_fleurs_ro:

q8_0: 10.12

wer_fleurs_bg:

q8_0: 11.04

wer_fleurs_el:

q8_0: 32.25

wer_fleurs_mt:

q8_0: 17.01

rtf_m4_max:

cpu: 35.03

metal: 189.12

rtf_ryzen_4750u:

cpu: 13.29

vulkan: 23.0

streaming: false

translate: false

lang_detect: true

timestamps: token

---

parakeet-ultra: transcribe.cpp GGUF

GGUF conversions of moondream/parakeet-ultra for use

with transcribe.cpp.

Ported from upstream commit

73175eb,

pinned 2026-10-04.

Validated against the kestrel 0.9.1 (Photon) reference at transcribe.cpp commit

08f9e6f0

on 2026-10-05.

Moondream's post-trained parakeet-tdt-0.6b-v3. Offline multilingual

speech-to-text over the same 25 European languages, with lower error rates

than v3 on English and on every FLEURS language we measured. A FastConformer

encoder with a TDT transducer decoder, plus a small voice-activity head that

the model uses to cut long recordings at pauses into segments of at most

30 seconds. Takes 16 kHz mono WAV and produces a punctuated, cased

transcript with optional token-level timestamps. A drop-in replacement for

v3: same size, languages and API. Not a streaming model and does not

translate.

Downloads

| Quantization | Download | Size | WER (LibriSpeech test-clean) |

| --- | --- | ---: | ---: |

| F32 | parakeet-ultra-F32.gguf | 2.51 GB | 1.80% |

| F16 | parakeet-ultra-F16.gguf | 1.26 GB | 1.80% |

| Q8_0 | parakeet-ultra-Q8_0.gguf | 740 MB | 1.80% |

| Q6_K | parakeet-ultra-Q6_K.gguf | 611 MB | 1.81% |

| Q5_K_M | parakeet-ultra-Q5_K_M.gguf | 550 MB | 1.81% |

| Q4_K_M | parakeet-ultra-Q4_K_M.gguf | 486 MB | 1.82% |

WER on the full LibriSpeech test-clean split (2,620 utterances), batch size 8, timestamps none, language hint en, decoded on cuda. Measured at transcribe.cpp 021aa17d.

Greedy transducer decoding, no external LM. The reference is Moondream's

own runtime (kestrel 0.9.1, the engine behind Photon), not transformers.

On this manifest kestrel scores 1.80% and the C++ F32 build matches it.

Moondream's card reports 1.41% on the same split, scored with the Open

ASR Leaderboard normalizer.

Long-form: on 11 full TED-LIUM 3 talks, segmented by the model's

voice-activity head exactly as kestrel does it, F32 scores 2.22%, identical

to kestrel, and every quant stays within 0.05pp.

Usage

Build transcribe.cpp from source:

git clone git@github.com:handy-computer/transcribe.cpp.git
cd transcribe.cpp
cmake -B build && cmake --build build

Run on a 16 kHz mono WAV:

build/bin/transcribe-cli \
  -m parakeet-ultra-Q8_0.gguf \
  input.wav

If your audio isn't already 16 kHz mono WAV, convert it first:

ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav

See the transcribe.cpp model page for performance

numbers, numerical validation, and reproduction steps.

License

Inherited from the base model: CC-BY-4.0. See the

upstream model card for full terms.

This repository includes the license text

(LICENSE) and a

NOTICE recording

how these files were derived from the original.

---

Original Model Card

> The section below is reproduced from

> moondream/parakeet-ultra at commit

> 73175eb for offline reference. The upstream card is the

> authoritative source.

Moondream Parakeet Ultra

A post-trained version of parakeet-tdt-0.6b-v3. Same

architecture, same tokenizer, same 0.6B parameters in full precision. It is better than the original on every

benchmark group below, by a wide margin on the 25-language FLEURS set, in background noise and on long-form audio,

and it runs faster than the original does in NeMo.

| | parakeet-tdt-0.6b-v3 | parakeet-ultra |

|---|---|---|

| Open ASR Leaderboard, 7 English sets (WER %) | 6.26 | 5.80 |

| FLEURS, 25 languages (WER %) | 11.62 | 9.55 |

| Business speech, AA-WER style (WER %) | 6.15 | 5.79 |

| Background noise, 9 MUSAN conditions (WER %) | 6.72 | 5.82 |

| TED-LIUM long-form (WER %) | 2.71 | 1.94 |

Its sibling Parakeet Redux is the ternary version of the same

architecture, 178 MB, built for CPUs and Apple silicon. Read the

release post for the story behind both models.

Usage

Run it with Photon. Every accuracy number on this page is Photon on an NVIDIA GPU.

# pip install moondream
import moondream as md

with md.photon("moondream/parakeet-ultra") as speech:
    result = speech.transcribe(audio="speech.wav")
    print(result["text"])

    # timestamps="segment": one entry per sentence, with its start
    # and end in seconds; "word" adds the start and end of every word
    result = speech.transcribe(audio="speech.wav", timestamps="word")
    for segment in result["segments"]:
        print(segment["start"], segment["end"], segment["text"])
        for word in segment["words"]:
            print(word["start"], word["end"], word["word"])

Performance

Real-time factor: seconds of audio transcribed per second of wall clock, higher is faster. One NVIDIA B200, 128

requests in flight, the same files on both sides; NeMo 3.0 runs the original checkpoint with its own transcribe

call at batch 128, Photon runs parakeet-ultra.

| | NeMo, parakeet-tdt-0.6b-v3 | Photon, parakeet-ultra |

|---|---|---|

| LibriSpeech test-clean, 2,620 utterances, 5.4 hours | 6,005× | 9,743× |

| AMI test, 12,643 utterances, 8.7 hours | 4,394× | 6,688× |

Benchmarks

Word error rate in percent, lower is better. Both models are scored on the same files with the Open ASR

Leaderboard's own pipeline as of September 2026 (its normalizers and compound-merging alignment, with the FLEURS

references prepared as the leaderboard's text column is). parakeet-ultra runs in Photon on an NVIDIA GPU, the

original in NeMo in bf16.

Open ASR Leaderboard

The seven English test sets of the Hugging Face Open ASR Leaderboard: audiobooks (LibriSpeech), meetings (AMI),

earnings calls (Earnings-22), podcasts and YouTube (GigaSpeech), financial calls (SPGISpeech) and parliament

(VoxPopuli).

| set | parakeet-tdt-0.6b-v3 | parakeet-ultra |

|---|---|---|

| LibriSpeech test-clean | 1.52 | 1.41 |

| LibriSpeech test-other | 3.13 | 2.98 |

| AMI | 10.86 | 9.77 |

| Earnings-22 | 10.75 | 9.76 |

| GigaSpeech | 8.05 | 7.71 |

| SPGISpeech | 3.63 | 3.34 |

| VoxPopuli | 5.88 | 5.65 |

| average | 6.26 | 5.80 |

FLEURS

The FLEURS test split: read Wikipedia sentences, a few hundred per language, for all 25 languages the model

supports.

| language | parakeet-tdt-0.6b-v3 | parakeet-ultra |

|---|---|---|

| Bulgarian | 11.90 | 10.09 |

| Croatian | 10.93 | 9.65 |

| Czech | 10.85 | 9.97 |

| Danish | 16.78 | 14.31 |

| Dutch | 6.18 | 5.46 |

| English | 4.25 | 3.55 |

| Estonian | 13.23 | 9.69 |

| Finnish | 11.05 | 9.19 |

| French | 4.81 | 4.32 |

| German | 4.13 | 3.61 |

| Greek | 35.71 | 32.25 |

| Hungarian | 13.65 | 10.76 |

| Italian | 2.61 | 2.00 |

| Latvian | 21.38 | 15.95 |

| Lithuanian | 21.09 | 16.36 |

| Maltese | 19.13 | 14.92 |

| Polish | 6.70 | 5.54 |

| Portuguese | 4.65 | 3.96 |

| Romanian | 11.54 | 9.18 |

| Russian | 5.91 | 5.21 |

| Slovak | 9.46 | 7.03 |

| Slovene | 21.76 | 16.64 |

| Spanish | 3.12 | 2.72 |

| Swedish | 13.75 | 11.57 |

| Ukrainian | 5.94 | 4.75 |

| average | 11.62 | 9.55 |

Business speech (AA-WER style)

The AMI and VoxPopuli test sets with the Artificial Analysis cleaning applied, and Earnings-22 scored in 30-second

chunks joined per call, the way the AA-WER benchmark does it.

| set | parakeet-tdt-0.6b-v3 | parakeet-ultra |

|---|---|---|

| AMI (cleaned) | 9.52 | 8.48 |

| VoxPopuli (cleaned) | 3.02 | 3.10 |

| Earnings-22, 30-second chunks | 5.90 | 5.78 |

| average | 6.15 | 5.79 |

Background noise

The clean sets with MUSAN noise (the half of the corpus not used for training) mixed in at a fixed signal-to-noise

ratio; 0 dB means the noise is as loud as the speech.

| set | parakeet-tdt-0.6b-v3 | parakeet-ultra |

|---|---|---|

| LibriSpeech test-other, 10 dB | 4.12 | 3.87 |

| LibriSpeech test-other, 5 dB | 5.49 | 5.10 |

| LibriSpeech test-other, 0 dB | 9.06 | 8.31 |

| FLEURS German, 10 dB | 5.78 | 4.98 |

| FLEURS German, 5 dB | 8.07 | 6.91 |

| FLEURS German, 0 dB | 14.45 | 12.08 |

| FLEURS Spanish, 10 dB | 3.99 | 3.13 |

| FLEURS Spanish, 5 dB | 4.12 | 3.38 |

| FLEURS Spanish, 0 dB | 5.44 | 4.64 |

| average | 6.72 | 5.82 |

Long-form

Eleven complete TED-LIUM 3 talks of 10-20 minutes each. parakeet-ultra transcribes them through Photon, whose

segmenter cuts each talk at pauses found by the model's VAD head into segments of at most 30 seconds; the original

runs NeMo's own long-audio path.

| set | parakeet-tdt-0.6b-v3 | parakeet-ultra |

|---|---|---|

| TED-LIUM 3, 11 full talks of 10-20 minutes | 2.71 | 1.94 |

Notes

• Based on parakeet-tdt-0.6b-v3 by NVIDIA. Languages,

tokenizer and output conventions (punctuation, casing, numerals) are the original's.

• Runs with Photon.
• Long audio is segmented by the model itself: the weights carry a small voice-activity head on the encoder's

subsampler, and Photon uses it to cut recordings at pauses into segments of at most 30 seconds. No external VAD

model is needed.

• License is CC-BY-4.0, same as the original.