Fabio
c36510c21e
2 KiB
Haas-ja-v1.0
Haas-ja-v1.0 is a
Japanese ASR model. It reuses Parakeet-TDT's FastConformer encoder and TDT joint,
replacing the LSTM prediction network with two Kimi Delta Attention blocks.
Select the haas_asr family. Its registration reuses the Parakeet runtime and
selects the KDA predictor; checkpoint variant labels do not select behavior.
./build/debug/bin/audiocpp_cli --task asr --family haas_asr \
--model models/Haas-ja-v1.0-GGUF/haas-ja-v1.0-q8_0.gguf \
--backend cuda --session-option haas_asr.matmul_weight_type=q8_0 \
--audio japanese.wav --text-out transcript.txt --log
F16 and Q8_0 packages are available. The checkpoint is Japanese-only. The encoder uses full-context attention; the family's buffered streaming interface does not make it a native cache-aware streaming encoder. Long-form splitting uses the existing Parakeet runtime policy rather than upstream's quiet-point splitting policy.
To convert the official checkpoint, download Haas-ja-v1.0.nemo from the
upstream repository, then run:
python tools/community_models/convert_haas.py \
--source Haas-ja-v1.0.nemo --staging haas-converted \
--converter build/debug/bin/audiocpp_gguf --type q8_0 \
--output haas-ja-v1.0-q8_0.gguf
Conversion requires PyTorch, PyYAML, NumPy, safetensors, SentencePiece, tokenizers, protobuf and Transformers (tested with Transformers 5.12.1 and protobuf 5.29.5). NeMo and flash-linear-attention are needed only to run the official Python implementation, not for conversion or C++ inference.
The existing haas_asr.matmul_weight_type=q8_0 session option also keeps
load-time packed and scaled projections in Q8_0. With native, projections
constructed from float32 values retain float32 storage. Omit the override when
using the F16 package if that precision is preferred.
Timestamp word boundaries follow the tokenizer’s word markers. Continuous Japanese text can therefore occupy one phrase-sized span; this does not add Japanese morphological word segmentation.