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Kokoro-82M CoreML

End-to-end CoreML export of hexgrad/Kokoro-82M at FP16, optimized for Apple Neural Engine. Requires iOS 18+ / macOS 15+.

A single kokoro_5s.mlmodelc runs the full pipeline (BERT β†’ duration prediction β†’ fixed-shape alignment β†’ prosody β†’ decoder) in one CoreML call. G2P (grapheme-to-phoneme) is a separate pair of CoreML models.

Looking for a smaller variant? See aufklarer/Kokoro-82M-CoreML-INT8 β€” INT8 k-means palettized, 83 MB vs 325 MB here, with log-spec distance 0.42 vs this FP16 reference on a validation utterance.

Model

Parameter Value
Parameters 82M
Precision FP16
Max audio length 5 s (200 frames @ 40 fps)
Sample rate 24 kHz
Style dimension 256
Max phonemes per pass 128

Files

File Size Description
kokoro_5s.mlmodelc 325 MB Pre-compiled E2E model (pre-compiled, loads directly on-device)
G2PEncoder.mlmodelc 0.7 MB Grapheme-to-phoneme encoder
G2PDecoder.mlmodelc 0.8 MB Grapheme-to-phoneme decoder
voices/ 0.5 MB 54 preset voice embeddings (10 languages)
vocab_index.json 4 KB Phoneme vocabulary
g2p_vocab.json 4 KB G2P vocabulary
us_gold.json, us_silver.json 6 MB English pronunciation dictionaries
pipeline_config.json 4 KB Swift pipeline config

Voices

54 preset voices across 10 languages: English (US/UK), Spanish, French, Hindi, Italian, Japanese, Korean, Portuguese, Chinese.

Usage

Add speech-swift to Package.swift:

.package(url: "https://github.com/soniqo/speech-swift", branch: "main")

Then synthesize:

import KokoroTTS

let tts = try await KokoroTTSModel.fromPretrained(
    modelId: "aufklarer/Kokoro-82M-CoreML"
)
let audio = try await tts.synthesize(
    "Hello world, this is a Kokoro test.",
    voice: "af_heart"
)

CLI:

swift run audio kokoro "Hello world" --voice af_heart --output out.wav

Source

License

  • Model weights: Apache-2.0
  • CoreML conversion: Apache-2.0

Links

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