Core ML conversions of hexgrad/Kokoro-82M (English and Japanese) and hexgrad/Kokoro-82M-v1.1-zh (Mandarin). The original Kokoro models and pretrained weights are by hexgrad; this repository provides Apple Core ML conversions and runtime optimizations. See FluidAudio for inference.
ANE version 3 (opt-in)
Available for English, Japanese and Mandarin, with fixed Albert/decoder buckets, native source DSP and a GPU generator adapted from Matt Mireles's conversion. Existing model paths and defaults remain available.
FluidAudio SDK v0.17.6
Use KokoroAneManager(version: .v3) for English,
KokoroAneManager(variant: .japanese, version: .v3) for Japanese, or
KokoroAneManager(variant: .mandarin, version: .v3) for Mandarin.
All three accept native text and variant voices. Existing callers retain
.legacy. See SDK usage, voices and platform limits.
Requires macOS 15 / iOS 18; physical iOS inference remains unvalidated.
The SDK pins model release 6c0750dd02ef9d981fb82f147d784a3f171755ee and base
revision 006395f65025af251858b1ab0a7178a6a1e73f9f, adopting fp32
KokoroProsody_v2 and COLA-corrected KokoroTail_v2. The bundled standalone
conversion runtime and its historical measurements below are separate from this
SDK integration. This documentation update does not change model bytes or pins.
SDK compute routing and selected measurements
Hybrid is not .all or ANE-only. Albert, PostAlbert, Alignment, Prosody and
masked decoder request .cpuAndNeuralEngine; the fast generator requests
.cpuAndGPU; native source/STFT runs on CPU/Accelerate. Fallback Vocoder requests
CPU+ANE, Noise/Tail CPU+GPU. Core ML chooses actual placement within each policy.
| Policy | English | Japanese | Mandarin |
|---|---|---|---|
| Current hybrid | 49.45 ms | 35.85 ms | 53.97 ms |
| CPU+ANE throughout | 511.40 ms | 474.45 ms | 584.19 ms |
.all throughout |
SIGABRT | SIGABRT | SIGABRT |
| CPU+GPU throughout | SIGABRT | SIGABRT | SIGABRT |
Apple M5 Pro, 24 GiB, macOS 27.0 (26A428); October 7, 2026 (Toronto).
Median of five calls after two warmups on one selected demo input per language;
3.350 / 3.050 / 3.675 seconds of output. Timer covers prepared tokens/style to
complete PCM, excluding text processing, loading and WAV IO. Identical inputs
per policy, serial isolated processes, native DSP unchanged. These are local
exploratory checks, not corpus or cross-device results. Global overrides were
applied in a local harness; the public v3 API retains its fixed hybrid routing.
Both .all and CPU+GPU aborted during Prosody on the second warmup with Apple's
GPURNNOps: JIT not supported. CPU+ANE permits CPU fallback; it is not ANE-only.
Live-demo screenshots instead time raw text through complete audio and showed 57.8–79.8 ms in selected serial runs. Their 1.88× / 5.29× / 2.25× speedups versus legacy are individual observations, not a benchmark average. Concurrent demo mode also includes shared-resource contention. See inputs, ranges, protocol and raw records.
Compute plans prefer ANE for static Albert/decoder and GPU for the generator, but plan estimates and system-wide asitop power samples do not establish actual per-process execution. See ANE profiling and telemetry limits.
Changelog
Fixed models are uploaded under a new name (_v2, …) next to the originals so older FluidAudio releases keep working and cached clients re-download. Each row links the issue, the conversion change (mobius), the Swift change (FluidAudio) and the HF commit.
| Date | Files | Change | Issue | mobius | FluidAudio | HF commit |
|---|---|---|---|---|---|---|
| 2026-11-08 | ANE*/KokoroVocoder_v2.{mlmodelc,mlpackage} |
fp32 compute: the fp16 Core ML CPU path on SME chips (A19 Pro, A20, M5) corrupted the first ~1 s of long utterances (robotic onset) | #1135 | – (model-lab kokoro-vocoder-v2-coreml) |
#1137 | 89102029 |
| 2026-10-28 | gb_lexicon_cache.json |
British lexicon cache (misaki gb_gold + gb_silver, us_lexicon_cache.json format) for Paradee's British voices |
#1072 | #114 | #1073 | 5c321779 |
| 2026-09-25 | ANE*/KokoroProsody_v2.mlmodelc |
fp32 compute: fp16 CPU/ANE corrupted F0/N at the utterance onset for T_a ≥ ~400 (quiet first words) | #947 | #108 | #963 | 9db203c5 |
| 2026-09-23 | ANE/{es,fr}_lexicon_cache.json |
Spanish / French lexicon caches | #926 | – | #950 | c9223c6c |
| 2026-09-13 | ANE-ja/g2p/* |
Japanese G2P assets (trimmed unidic-lite + Cutlet) | #914 | #90 | #915 | c9fdfdac |
| 2026-08-19 | ANE*/KokoroTail_v2.mlmodelc |
COLA-normalized iSTFT (output was 1.5× reference) | #852 | #83 | #868 | acac8811 |
| 2026-08-19 | ANE*/KokoroVocoder.mlmodelc |
rank-stable anchor for macOS 14 Espresso | #836 | #82 | – | cdaea5e2 |
Historical M4 Pro benchmark
We generated the same strings with to gerneate audio between 1s to ~300s in order to test the speed across a range of varying inputs on Pytorch CPU, MPS, and MLX pipeline, and compared it against the native Swift version with Core ML models.
Each pipeline warmed up the models by running through it once with pesudo inputs, and then comparing the raw inference time with the model already loaded. You can see that for the Core ML model, we traded lower memory and very slightly faster inference for longer initial warm-up.
Note that the Pytorch kokoro model in Pytorch has a memory leak issue: https://github.com/hexgrad/kokoro/issues/152
The following tests were ran on M4 Pro, 48GB RAM, Macbook Pro. If you have another device, please do try replicating it as well!
Kokoro-82M PyTorch (CPU)
KPipeline benchmark for voice af_heart (warm-up took 0.175s) using hexgrad/kokoro
Test Chars Output (s) Inf(s) RTFx Peak GB
1 42 2.750 0.187 14.737x 1.44
2 129 8.625 0.530 16.264x 1.85
3 254 15.525 0.923 16.814x 2.65
4 93 6.125 0.349 17.566x 2.66
5 104 7.200 0.410 17.567x 2.70
6 130 9.300 0.504 18.443x 2.72
7 197 12.850 0.726 17.711x 2.83
8 6 1.350 0.098 13.823x 2.83
9 1228 76.200 4.342 17.551x 3.19
10 567 35.200 2.069 17.014x 4.85
11 4615 286.525 17.041 16.814x 4.78
Total - 461.650 27.177 16.987x 4.85
Kokoro-82M PyTorch (MPS)
I wasn't able to run the MPS model for longer durations, even with PYTORCH_ENABLE_MPS_FALLBACK=1 enabled, it kept crashing for the longer strings.
KPipeline benchmark for voice af_heart (warm-up took 0.568s) using pip package
Test Chars Output (s) Inf(s) RTFx Peak GB
1 42 2.750 0.414 6.649x 1.41
2 129 8.625 0.729 11.839x 1.54
Total - 11.375 1.142 9.960x 1.54
Kokoro-82M MLX Pipeline
TTS benchmark for voice af_heart (warm-up took an extra 2.155s) using model prince-canuma/Kokoro-82M
Test Chars Output (s) Inf(s) RTFx Peak GB
1 42 2.750 0.347 7.932x 1.12
2 129 8.650 0.597 14.497x 2.47
3 254 15.525 0.825 18.829x 2.65
4 93 6.125 0.306 20.039x 2.65
5 104 7.200 0.343 21.001x 2.65
6 130 9.300 0.560 16.611x 2.65
7 197 12.850 0.596 21.573x 2.65
8 6 1.350 0.364 3.706x 2.65
9 1228 76.200 2.979 25.583x 3.29
10 567 35.200 1.374 25.615x 3.37
11 4615 286.500 11.112 25.783x 3.37
Total - 461.650 19.401 23.796x 3.37
Swift + Fluid Audio Core ML models
Note that it does take ~15s to compile the model on the first run, subsequent runs are shorter, we expect ~2s to load.
> swift run fluidaudio tts --benchmark
...
FluidAudio TTS benchmark for voice af_heart (warm-up took an extra 2.348s)
Test Chars Ouput (s) Inf(s) RTFx
1 42 2.825 0.440 6.424x
2 129 7.725 0.594 13.014x
3 254 13.400 0.776 17.278x
4 93 5.875 0.587 10.005x
5 104 6.675 0.613 10.889x
6 130 8.075 0.621 13.008x
7 197 10.650 0.627 16.983x
8 6 0.825 0.360 2.290x
9 1228 67.625 2.362 28.625x
10 567 33.025 1.341 24.619x
11 4269 247.600 9.087 27.248x
Total - 404.300 17.408 23.225
Peak memory usage (process-wide): 1.503 GB
Credtis
special thanks to @laishere for making kokoro ANE possible his source: https://github.com/laishere/kokoro-coreml