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Kn90688/FireRedASR2-AED-int8-batch-onnx
FireRedASR2-AED-int8-batch-onnx is a machine learning model from Kn90688. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Batch-capable re-export of FireRedASR2-AED for sherpa-onnx, int8-quantized.
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Updated Aug 3, 2026
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From the Hugging Face model README
Batch-capable re-export of FireRedASR2-AED for sherpa-onnx, int8-quantized.
Supports Mandarin, English, and 20+ Chinese dialects/accents.
tokens/offset to batch=1)enc_mask encoder output and
cross_mask decoder inputRequires the fireredasr-batch-decoding branch of
this sherpa-onnx fork (batched
DecodeStreams + length bucketing + adaptive KV cache).
sherpa-onnx-offline \
--num-threads=2 \
--fire-red-asr-encoder=./encoder.int8.onnx \
--fire-red-asr-decoder=./decoder.int8.onnx \
--tokens=./tokens.txt \
./test.wav
Multiple files on the command line are decoded as length-bucketed batches.
encoder.qdqconv.int8.onnx is a drop-in replacement for
encoder.int8.onnx where every Conv weight is additionally quantized
to per-output-channel int8 (weight-only QDQ; compute stays fp32, so it
is arm64-safe).
encoder.int8.onnx on the test setsherpa-onnx-offline \
--num-threads=2 \
--fire-red-asr-encoder=./encoder.qdqconv.int8.onnx \
--fire-red-asr-decoder=./decoder.int8.onnx \
--tokens=./tokens.txt \
./test.wav
Export/quantization scripts and full optimization report:
scripts/fire-red-asr/ and docs/fireredasr-optimization.md in the fork.