Kokoro-82M — Core AI

hexgrad/Kokoro-82M (Apache-2.0), a tiny high-quality StyleTTS2 + iSTFTNet text-to-speech model (82M params, 24 kHz), converted to Apple Core AI (.aimodel, iOS 27 / macOS 27) — the CoreAI-Model-Zoo's first TTS.

Non-autoregressive: phonemes + a voice/style vector → a waveform in one pass. Runs fully on-device, English-first, with grapheme→phoneme on the host.

Use it

⚡ One line — run the kit's task op on this model (import CoreAIOps; no session, no model plumbing, downloads on first use):

let audio = try await CoreAI.speak(text, options: .model("kokoro-82m"))

Every op, one shape — Cookbook.

▶️ Run it (source) — the Speak runner (GUI + CLI, one app for every text-to-speech model in the catalog):

git clone https://github.com/john-rocky/coreai-kit
open coreai-kit/Examples/Speak/Speak.xcodeproj
# → Run, then pick "Kokoro 82M" in the model picker

# agents / headless (macOS):
cd coreai-kit/Examples/Speak
swift run speak-cli --model kokoro-82m --text "Hello from Core AI." --output hello.wav

💻 Build with it — complete; the glue is kit API, copy-paste runs:

import CoreAIKit

let speaker = try await KitSpeaker(catalog: "kokoro-82m")
let audio = try await speaker.synthesize(text)
// audio.samples: 24 kHz mono PCM in [-1, 1] — play it or write a WAV

The take-home is Examples/Speak/Sources/QuickStart.swift — this exact code as one typed function, no UI; the CLI is an argument shell over it, and the GUI drives the same KitSpeaker(catalog:) and plays the samples. English-first: G2P is a dictionary over the bundled misaki lexicons (~180k words); out-of-dictionary words are letter-spelled (no neural fallback). 28 voices ride the download — af_heart is the default; the underlying KokoroTTS takes a voice: label. Streaming? synthesizeStreaming(_:onChunk:) hands you a chunk per sentence.

Integration checklist

SPM: https://github.com/john-rocky/coreai-kit → product CoreAIKit
Info.plist: none needed
Entitlements: none needed
First run downloads the model — 0.3 GB (Mac) — then it loads from the local cache (Application Support; progress via the downloadProgress callback)
Measure in Release — Debug is ~3× slower on per-token host work

Bundles

The acoustic graph has one data-dependent length (the duration→alignment expansion), so it is cut into three voice-independent .aimodel bundles with two cheap host steps between them:

file	in → out
`kokoro_predictor.aimodel`	`input_ids[1,128]` i32, `ref_s[1,256]`, `attn_mask[1,128]` → `duration`, `d`, `t_en`
`kokoro_prosody.aimodel`	`d`, `t_en`, `aln[1,128,512]`, `ref_s`, `frame_mask[1,512]` → `asr`, `F0`, `N`
`kokoro_vocoder.aimodel`	`asr`, `F0`, `N`, `har`, `ref_s`, `frame_mask` → `audio[1, L·600]`

voices/*.pt — the 28 English voice packs (Apache-2.0). The voice is the ref_s input: ref_s = pack[len(ids)−1]. Quality leaders: af_heart, af_bella, af_nicole, bf_emma.

Token length T and frame length L are fixed buckets (128 / 512); the host left-pads to the bucket and trims the output. Longer text is split into sentences host-side. Run on the Core AI CPU compute unit. ~0.75 s / utterance on M4 Max, ~335 MB total (fp32).

Host steps

text ──(misaki G2P)──▶ ids ──▶ predictor ──▶ [build alignment] ──▶ prosody
     ──▶ [har = STFT(SineGen(f0_upsamp(F0)))] ──▶ vocoder ──▶ [trim] ──▶ 24 kHz audio

G2P is misaki (misaki[en], no espeak for English); on-device MisakiSwift gives the same English phonemes. har (the hn-nsf source's STFT) is a windowed FFT computed on the host — the one piece that must stay off the engine (its atan2 phase flips 2π at the F0→0 pad boundary under fp32).

Quality

The hn-nsf source phase is arbitrary (stock Kokoro randomizes it), so the gate is spectral: magnitude-spectrogram correlation 0.999 vs the PyTorch reference (af_heart, multiple sentences). Raw waveform correlation ~0.98 — the bounded, inaudible effect of the bucket pad boundary.

Convert / re-bucket

conversion/export_kokoro.py (python export_kokoro.py --out-dir out; --verify runs the engine-vs-torch spectral gate; --token-bucket / --frame-bucket to re-size). Card + the full port write-up: zoo/kokoro-82m.md.