license: apache-2.0
language:
- ja
base_model:
- hexgrad/Kokoro-82M
pipeline_tag: text-to-speech
tags:
- coreml
- kokoro
- apple-silicon
- ane
Kokoro ANE v3 — Japanese
Japanese speech with jf_alpha, using a hybrid ANE + GPU pipeline on
macOS 15 / iOS 18 or newer. Albert and masked decoder request CPU+ANE; native source
and STFT use CPU Accelerate; the optimized generator requests CPU+GPU.
The five new model bundles reuse the exact English v1.0 weights. Japanese
uses its own voice, phonemes and vocabulary. validation/shared-english-models.json
records per-file equality with English. The pinned base dependency is ANE.
The base model and pretrained weights are hexgrad/Kokoro-82M, created by hexgrad. This variant converts and optimizes those weights for Core ML.
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.
Initial standalone conversion checks
Two short real inputs with 2.025 / 2.425 seconds of audio: warm medians 24.83 / 27.72 ms, versus 114.88 / 137.52 ms for the published baseline. Apple M5 Pro, 24 GiB, macOS 27.0; optimized Swift; three discarded warmups, five measurements per input/model, rotating serial order. Tokens/style to PCM; G2P, model loading and file IO excluded. This is a bounded local development check, not a corpus or cross-device benchmark.
The Swift integration and numerical checks pass. With reference decoder
features, native source plus generator relative waveform RMSE is 0.06084 / 0.05854.
Full-chain phase-sensitive waveform statistics are reported separately and
are not a perceptual equivalence claim. A pinned multilingual Whisper base
model recovered both sentences from baseline and candidate audio, allowing
punctuation and Chinese orthographic variation; no transcript prompt was used.
See validation/ for raw measurements, transcripts and fidelity results.
Download and run
Set KOKORO_REVISION to the 40-character Hugging Face commit containing this
bundle. The helper verifies its manifest and downloads the pinned seven-stage
base dependency from the same repository.
python tools/download-fast-pipeline.py --revision "$KOKORO_REVISION" \
--language ja --output-dir assets
swift run --package-path runtime -c release kokoro-fast-check \
assets/base assets/fast assets/decoder
python tools/validate-fast-pipeline.py --models-dir assets/fast
Install the conversion/ project's multilingual extra to prepare arbitrary
text with tools/prepare-fast-input.py --assets-dir assets --text 'TEXT' --output input.json.
The output supplies ids and a 256-value style for
KokoroFastPipeline.synthesize(inputIds:style:speed:). Voice data is little-endian
Float32 [510,1,256]; the helper selects row len(phonemes)-1. Misaki 0.9.4 supplies
G2P; Japanese uses UniDic-lite 1.0.8. Split long passages into sentences. The initial standalone checks used the listed default voice; later SDK checks
cover alternate voices. Mixed Mandarin/English text is outside these checks.
Both .mlpackage and .mlmodelc are included. Use downloader --format mlpackage
to recompile on the deployment platform; compiled bundles were built on macOS
27.0. Above 64 tokens, the runtime uses dynamic Albert. Above 200 acoustic
frames (5 seconds), it uses the original vocoder. Output is 24 kHz PCM; inputs
are limited to 512 tokens and 2,000 predicted acoustic frames.
The package contains full conversion/runtime sources, pinned vocabulary and
voice, validation fixtures and actual runtime WAVs in samples/. See
conversion/docs/multilingual-fast-pipeline.md for reproduction and scope.
Provenance
Checkpoint/voice pin is in language.json; all release files are hashed in
manifest.json. Base models use FluidInference/kokoro-82m-coreml revision
006395f65025af251858b1ab0a7178a6a1e73f9f. The optimized graph derives from
mattmireles/kokoro-coreml
(Apache-2.0), adapted to the selected checkpoint. Kokoro weights are Apache-2.0;
the original conversion is MIT. Retain NOTICE, mattmireles-LICENSE and
conversion-LICENSE when redistributing.