Show HN: faster-enhancer.c – C library for stable real-time on-device denoising

A dependency-free C/SIMD int8 runtime for FastEnhancer-Medium at 48 kHz achieves a 0.069 real-time factor on a single Apple M2 core and 0.096 on a Galaxy S23+, with no inference framework, no heap allocation after startup, and no retraining. The library, published by developer aask1357, runs the unchanged FastEnhancer weights using six hand-written int8 GEMM kernel tiers and a 565,108-byte W8A8 weight blob, producing byte-identical output across ARM NEON, DOTPROD, and I8MM tiers.

A dependency-free C/SIMD int8 runtime for FastEnhancer-Medium at 48 kHz — streaming speech enhancement that runs in a fraction of one CPU core, with no inference framework, no heap allocation after startup, and no retraining. It runs the published FastEnhancer weights https://github.com/aask1357/fastenhancer unchanged. The architecture is untouched; only the runtime is specialized. 0.069 real-time factor on one Apple M2 core I8MM, racing 0.096 on a Galaxy S23+ Snapdragon 8 Gen 2 565,108 bytes W8A8 weight blob 162 KiB static library, macOS arm64 One fixed model, six hand-written int8 GEMM kernel tiers, one of which is selected at initialization. There is no scalar fallback: a host that does not meet the baseline ISA fails at fe init rather than silently taking a slow path. Training-free. Post-training quantization only. No QAT and no calibration set — activation ranges are recomputed from each frame. Streaming and causal. 320 samples in, 320 out, no look-ahead. The STFT contributes a fixed 704-sample 14.67 ms alignment delay. Allocation-free after init. All state lives in one 432,384-byte structure, so a long run cannot drift into allocator jitter. Byte-identical across tiers. Within an architecture family, NEON, DOTPROD and I8MM produce bit-for-bit identical output, enforced by a regression gate. The same holds for the x86 tiers. The public API is four functions. js include "fe.h" int fe init const void weights blob, int weights size ; void fe run const float in, float out ; / 320 in - 320 out / void fe reset void ; void fe free void ; A minimal streaming loop: if fe init weights, weights size = 0 return -1; while read 320 samples in buf { fe run in buf, out buf ; / in buf == out buf is fine in-place / write 320 samples out buf ; } fe free ; cmake -S . -B build -DCMAKE BUILD TYPE=Release cmake --build build -j No external dependencies. Produces libfe.a plus the runners in build/ . To try it on real audio, fetch the test clips first needs curl and ffmpeg ; the clips are downloaded from their original location rather than redistributed here : ./testaudio/fetch.sh ./build/fe run file weights/fe.q8 testaudio/street 5dB.f32 out.wav 5569 i8mm fe run file reports per-frame percentiles against the 6.667 ms frame budget. Two environment knobs are useful: FE QOS=hi|lo|off picks a QoS class, and FE PACE=1 feeds one frame per frame period instead of racing. Measured with 5569-frame clips, steady-state percentiles after discarding the first 200 frames, median over repeats. | Device | Tier | p50 RTF | p99 RTF | ms/frame | |---|---|---|---|---| | Apple M2 | I8MM | 0.069 | 0.084 | 0.458 | | Apple M2 | DOTPROD | 0.068 | 0.081 | 0.452 | | Apple M2 | NEON | 0.163 | 0.187 | 1.085 | | Galaxy S23+ | I8MM | 0.096 | 0.105 | 0.640 | | Galaxy S23+ | DOTPROD | 0.113 | 0.122 | 0.753 | | Galaxy S23+ | NEON | 0.344 | 0.365 | 2.293 | Two things worth knowing before reading those as deployment cost. A benchmark races; an audio callback does not. Feeding frames as fast as the core accepts them holds the clock at maximum. A real callback delivers one frame per 6.667 ms and lets the core idle, and the governor responds. Paced on an M2 P-core the same work costs 4.2x more per frame 0.286 RTF while using 49% less energy. Both are real-time; they answer different questions. The x86 tiers are not timed here. They build and pass the tier-equality gate, but this table is ARM only. The int8 engine is compared against the fp32 ONNX graph of the same weights, on the engine's own causal analysis grid. Scoring a streaming engine against a default centered-STFT reference measures framing phase rather than quantization error: the two grids differ by 192 samples, which is not a multiple of the 320-sample hop, so the offset does not cancel. Over the 824-utterance VoiceBank-DEMAND test set at its native 48 kHz: | PESQ | STOI | SNR | LSD | | |---|---|---|---|---| | noisy input | 1.967 | 0.9211 | 8.39 | 14.72 | | fp32 ONNX | 3.060 | 0.9512 | 19.43 | 12.56 | fe q8 | 3.054 | 0.9509 | 19.35 | 12.33 | The port tracks the fp32 model to -0.006 PESQ and -0.08 dB SNR. Quantization costs about 1.6% of what the enhancement itself gains. | ISA | Tier | Instruction | Requires | |---|---|---|---| | arm64 | NEON | vmull s8 - vmlal s8 - vpadalq s16 | baseline | | arm64 | DOTPROD | vdotq laneq s32 | FEAT DotProd | | arm64 | I8MM | vmmlaq s32 | FEAT I8MM | | x86-64 | AVX2 | vpmovsxbw + vpmaddwd | AVX2 + FMA3 + F16C | | x86-64 | AVX-VNNI | vpdpbusd ymm | Alder Lake+, Zen 4 | | x86-64 | AVX-512 VNNI | vpdpbusd zmm | Ice Lake / SPR, Zen 4 | SSE4.1 is deliberately unsupported: without FMA3 the dequantization epilogue becomes a two-step rounding that drifts until bit-identity breaks. The AVX-512 tier is verified functionally under Intel SDE; no AVX-512 host was available to time it. Bit-identity across tiers is a gate, not an aspiration: RUNNER=build/fe run file FRAMES=500 ./tools/sha256 matrix.sh Every tier within an architecture family must produce the same SHA-256 per clip. Run it before and after any non-trivial change and diff the output; an empty diff means no byte-level regression. build/fe test cross tier performs the same check in-process and also reports cross-tier SNR. weights/fe.q8 is the production blob 565,108 bytes, 511,754 parameters . To regenerate it from the upstream ONNX export: python3 tools/onnx to bin.py --onnx fastenhancer m spec.onnx \ --variant medium --out weights/fe.fp32.bin python3 tools/quantize bin.py --in weights/fe.fp32.bin --out weights/fe.q8 Weights are per-output-row symmetric int8; activations are per-tensor asymmetric uint8, recomputed every frame. Both are clamped to -127, 127 rather than the full int8 range. Discarding one code point makes int16 accumulation provably overflow-free and removes the one value that breaks cross-tier reproducibility, at a measured cost of 0.000 PESQ to three decimals. include/fe.h the only public header src/ fe pipeline.c public ABI fe engine.c per-frame pipeline fe stft.c fe fft.c causal STFT/iSTFT, tiered 1024-point real FFT nn/ conv, GRU, MHSA, activations, GEMM wrappers qgemm/{arm,x86}/ per-tier int8 GEMM kernels winograd/ F 2,3 microkernels fft/ per-tier FFT kernels tests/ fe run file, fe test cross tier, fe bench qgemm tools/ weight pipeline + regression gate docs/ architecture, optimization notes docs/architecture.md /kdrkdrkdr/faster-enhancer.c/blob/main/docs/architecture.md — layer inventory, shapes, and the per-frame dataflow. docs/optimizations.md /kdrkdrkdr/faster-enhancer.c/blob/main/docs/optimizations.md — quantization design, the six kernel tiers, fp16 cross-stage storage, op fusion, and measurement methodology. It also separates numbers backed by deposited artifacts from numbers that exist only as prose in that document. The model, its architecture and its weights are FastEnhancer, by Sunghwan Ahn et al. — see aask1357/fastenhancer https://github.com/aask1357/fastenhancer . This project is an independent runtime port and is not affiliated with the authors. Kernel patterns were informed by ggml/llama.cpp, XNNPACK, oneDNN and ARM KleidiAI; see NOTICE and the Sources table in docs/optimizations.md . Test clips are the RNNoise demo samples, fetched from their original location by testaudio/fetch.sh .