---

license: apache-2.0

language:

• ja

pipeline_tag: automatic-speech-recognition

tags:

• audio
• speech-recognition
• transcription
• ggml
• gguf
• parakeet
• ctc
• fastconformer
• japanese

library_name: ggml

base_model: grider-transwithai/parakeet-ctc-1.1b-ja

---

Parakeet CTC 1.1B (Japanese) — GGUF

GGUF / ggml conversions of grider-transwithai/parakeet-ctc-1.1b-ja for use with the crispasr CLI from CrispStrobe/CrispASR.

A 1.1 B-parameter Japanese ASR model:

• FastConformer-CTC — a 42-layer FastConformer encoder with a CTC decoder (greedy CTC at inference; one linear head over the SentencePiece vocabulary, no RNNT/TDT predictor).
• Fine-tuned from NVIDIA's English nvidia/parakeet-ctc-1.1b on Japanese data.
• 80-mel front-end, 16 kHz mono, 8× temporal subsampling (50 → 12.5 fps).
• Apache-2.0 licence (the NVIDIA base architecture is CC-BY-4.0).

Files

| File | Size | Notes |

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

| parakeet-ctc-1.1b-ja-f16.gguf | 2.13 GB | F16 — highest fidelity, closest to the NeMo reference |

| parakeet-ctc-1.1b-ja-q8_0.gguf | 1.26 GB | Q8_0 — default download, near-F16 quality |

| parakeet-ctc-1.1b-ja-q4_k.gguf | 795 MB | Q4_K — smallest; some accuracy loss, fine for quick checks |

For a CTC model the Q8_0 quant is robust (CTC is far less sensitive to

quantisation noise than the small JA TDT decoder, which can loop). Use

Q8_0 for general transcription and F16 when you want the closest

match to the NeMo Python pipeline.

Quick start

# 1. Build the runtime
git clone https://github.com/CrispStrobe/CrispASR
cd CrispASR
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j$(nproc) --target crispasr

# 2. Download the Q8_0 (default) — or swap the filename for the F16 / Q4_K
huggingface-cli download cstr/parakeet-ctc-1.1b-ja-GGUF \
    parakeet-ctc-1.1b-ja-q8_0.gguf --local-dir .

# 3. Transcribe a 16 kHz mono WAV
./build/bin/crispasr \
    -m parakeet-ctc-1.1b-ja-q8_0.gguf -f your-japanese-audio.wav -t 8

> Backend: this is a CTC model — let crispasr auto-detect it (as

> above, no --backend) or pass --backend fastconformer-ctc explicitly.

> Do not pass --backend parakeet: that is the RNN-T/TDT transducer

> runtime and it will reject a CTC model with *"required tensor

> 'decoder.embed.weight' not found"*.

crispasr can also fetch the model for you by its registry name:

./build/bin/crispasr -m parakeet-ctc-1.1b-ja \
    --auto-download -f your-japanese-audio.wav

Long-form audio

For clips longer than ~15 s, prefer VAD-bounded chunking — Japanese

FastConformer models drift on long single-pass windows (the safe

single-pass window is ~12 s):

./build/bin/crispasr -m parakeet-ctc-1.1b-ja-q8_0.gguf \
    -f long-japanese-audio.wav --vad -t 8

Model architecture

| Component | Details |

| --- | --- |

| Encoder | 42-layer FastConformer, d_model 1024 |

| Subsampling | Conv2d dw_striding stack, 8× temporal (50 → 12.5 fps) |

| Decoder | CTC — single linear head over the SentencePiece vocab, greedy decode |

| Audio | 16 kHz mono, 80 mel bins, n_fft=512, hop=160, win=400 |

| Parameters | ~1.1 B |

How this was made

1. The source .nemo checkpoint is the GAL checkpoint

(parakeet-ja-gal.nemo) from

grider-transwithai/parakeet-ctc-1.1b-ja.

The non-GAL checkpoint in that repo has corrupt F32 weights in

encoder layers 26–28 (NaN / values > 1e38) and is not usable —

the GAL checkpoint is the converted one.

1. Architecture hyperparameters are read from the checkpoint's

model_config.yaml and cross-checked against the actual tensor

shapes; the mel filterbank and Hann window are baked into the GGUF

so the runtime reproduces NeMo's front-end exactly.

1. NeMo state-dict keys are remapped to ggml-friendly names — matmul

tensors as F16, norms / biases / mel filterbank as F32 — and the

F16 GGUF is quantised to Q8_0 and Q4_K.

1. The GGUF carries the canary-ctc architecture tag; inference runs

through the shared FastConformer-CTC runtime (`--backend

fastconformer-ctc`, auto-detected from the filename), not the

RNN-T parakeet transducer backend.

Licence

Apache-2.0, inherited from the

grider-transwithai/parakeet-ctc-1.1b-ja

fine-tune. The underlying NVIDIA NeMo FastConformer-CTC architecture

(nvidia/parakeet-ctc-1.1b)

is CC-BY-4.0.

Provenance and EU AI Act Art. 53 note

• Upstream model: grider-transwithai/parakeet-ctc-1.1b-ja — published by grider-transwithai.
• Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository. No training-content summary was found on the upstream model card at the time of writing; that documentation gap is upstream's and is not filled here.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.