---

license: apache-2.0

language:

• ja

pipeline_tag: automatic-speech-recognition

tags:

• audio
• speech-recognition
• transcription
• ggml
• gguf
• nemo
• rnnt
• fastconformer
• reazonspeech
• japanese

library_name: ggml

base_model: reazon-research/reazonspeech-nemo-v2

---

ReazonSpeech NeMo v2 (Japanese) — GGUF

GGUF / ggml conversions of reazon-research/reazonspeech-nemo-v2 for use with the crispasr CLI from CrispStrobe/CrispASR.

A 619 M-parameter Japanese ASR model trained on the

ReazonSpeech v2.0

corpus (~35,000 hours of Japanese audio):

• FastConformer-RNNT — pure RNN-Transducer decoder (no TDT duration head).
• Local relative-position attention (window 128 + 128, plus 1 global token), so the encoder scales to long audio without quadratic blow-up.
• 80-mel front-end, 16 kHz mono, 3000-token SentencePiece vocabulary.
• Apache-2.0 licence.

Files

| File | Size | Notes |

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

| reazonspeech-nemo-v2-f16.gguf | 1.24 GB | F16 — highest fidelity, closest to the NeMo reference |

| reazonspeech-nemo-v2-q8_0.gguf | 738 MB | Q8_0 — default download, near-F16 quality |

| reazonspeech-nemo-v2-q4_k.gguf | 477 MB | Q4_K — smallest; some accuracy loss, fine for quick checks |

Q8_0 is the recommended general-purpose quant; use F16 when you

want the closest match to the official 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/reazonspeech-nemo-v2-GGUF \
    reazonspeech-nemo-v2-q8_0.gguf --local-dir .

# 3. Transcribe a 16 kHz mono WAV
./build/bin/crispasr --backend parakeet \
    -m reazonspeech-nemo-v2-q8_0.gguf -f your-japanese-audio.wav -t 8

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

./build/bin/crispasr --backend parakeet -m reazonspeech \
    --auto-download -f your-japanese-audio.wav

(Both this RNNT model and the sibling

cstr/parakeet-ctc-1.1b-ja-GGUF

run through crispasr's parakeet backend — the runtime selects the

RNNT vs. CTC decode path from the GGUF metadata.)

Long-form audio

The local-attention encoder handles long inputs, but as with the other

Japanese FastConformer models a single long pass can drift; for clips

longer than ~15 s prefer VAD-bounded chunking:

./build/bin/crispasr --backend parakeet -m reazonspeech-nemo-v2-q8_0.gguf \
    -f long-japanese-audio.wav --vad -t 8

Model architecture

| Component | Details |

| --- | --- |

| Encoder | FastConformer with local relative-position attention (window 128+128, 1 global token) |

| Decoder | RNN-Transducer (RNNT) — LSTM predictor + joint network; no TDT durations |

| Vocab | 3000 SentencePiece tokens (Japanese) |

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

| Parameters | ~619 M |

How this was made

1. The .nemo checkpoint from

reazon-research/reazonspeech-nemo-v2

is unpacked; architecture hyperparameters (d_model, layers, local-attn

window, predictor/joint dims, vocab) are read from

model_config.yaml and cross-checked against the tensor shapes. The

mel filterbank and Hann window are baked into the GGUF so the runtime

reproduces NeMo's front-end.

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. Inference runs through src/parakeet.{h,cpp} in CrispASR, which

handles the local relative-position attention and the RNNT

predictor/joint loop.

Licence

Apache-2.0, inherited from

reazon-research/reazonspeech-nemo-v2.

Please also see the

ReazonSpeech

project for details on the training corpus.