Paradee-8M Core ML

Core ML conversions of Paradee-8M by Sahil Mahendrakar: Kokoro-82M distilled into ~8M parameters, English TTS, 24 kHz. Paper: arXiv 2610.06817.

Used by FluidAudio (ParadeeManager, v1.2 by default). Conversion code: mobius models/tts/paradee/coreml.

v1.2

Same two-model layout as v1.0 (below), plus a voice input on both models:

ParadeeText      input_ids [1,T] int32, voice [1] int32 -> duration [1,T], d [1,224,T], asr_tok [1,512,T]
ParadeeAcoustic  en [1,224,F], asr [1,512,F], noise [1,1,600F], voice [1] int32 -> audio [1,600F], 24 kHz

• voice: 0 af_heart, 1 bf_isabella, 2 am_puck, 3 bm_fable (v1.2/config.json).
• Phonemes are misaki's, American for af_/am_ and British for bf_/bm_ (en.G2P(british=True)), as upstream. FluidAudio reads the British voices through gb_lexicon_cache.json in kokoro-82m-coreml.
• The decoder end is v1.1's frame-rate source-filter head, rewritten for Core ML (GEMM STFT/iSTFT, int32 fixed-point harmonic phase, closed-form sine bank). The noise is an input, so the graphs are deterministic.
• CPU_ONLY or CPU_AND_NE only; ALL / CPU_AND_GPU abort on the LSTMs.

Results (M5 Pro, macOS 27; 10 inputs × 4 voices):

• 0 duration differences vs PyTorch with the same noise; log-mel L1 to the released fp32 ONNX ≤ 0.012.
• Whisper large-v3-turbo WER: Core ML fp32 3.39 % = upstream ONNX fp32; Core ML int8 3.28 % = upstream int8 ONNX.
• ~200× real time on the CPU for text side + acoustic side (v1.0: 150×).
• FluidAudio tts-benchmark --corpus minimax-english (Parakeet round trip, includes G2P), int8: WER 0.93 % (af_heart), 0.71 % (bf_isabella), 1.00 % (am_puck), 0.84 % (bm_fable), ~100× real time.

samples/v1.2/: upstream's five sample sentences in each voice, rendered by v1.2/int8/.

Files (v1.0)

Pipeline (v1.0)

text -> misaki-style en-US phonemes -> ids = [0, ...vocab ids..., 0]       (<= 512 total)
ParadeeText      input_ids [1,T] int32 -> duration [1,T], d [1,224,T], asr_tok [1,512,T]
host             n_i = max(1, round(duration_i / speed));  F = sum(n)
                 en  = d       with column i repeated n_i times   [1,224,F]
                 asr = asr_tok with column i repeated n_i times   [1,512,F]
                 noise ~ N(0,1)                                    [1,1,600F]
ParadeeAcoustic  en, asr, noise -> audio [1,600F] float32, 24 kHz

• F may be 1…4000 (100 s at speed 1). Split longer input into chunks of ≤ 510 phonemes.
• noise is the harmonic source's Gaussian noise, passed in so the graph is deterministic. It is equivalent to the upstream in-graph noise (9 per-harmonic channels mixed by a linear layer = one scaled channel).
• Phonemes must be written the way misaki writes them (Kokoro's G2P); that is all Paradee saw in training. That includes misaki's last step, flap ɾ → T and glottal stop ʔ → t: lexicon-only frontends that skip it garble words like "kittens" and "satellite".
• Use CPU_ONLY or CPU_AND_NE. Do not use ALL / CPU_AND_GPU: the LSTMs abort in MPSGraph (GPURNNOps … JIT not supported), the same failure Kokoro's prosody stage has.

Accuracy (v1.0, M5 Pro, macOS 27)

10 inputs: the README quick-start line, the five held-out sentences, a numbers/abbreviation sentence, a 28.6 s paragraph, and one sentence at speed 0.8 and 1.3.

• fp32 vs PyTorch with the same noise: 0 duration rounding differences, identical lengths. Log-mel L1 against the upstream ONNX equals the ONNX's own run-to-run difference in every case (e.g. 0.123 vs 0.123).
• Whisper large-v3-turbo transcripts of Core ML fp32 and upstream ONNX fp32 are identical (WER 3.81 %; every miss is text normalization: "Ia", "favourite", "Zzyzx").
• int8: log-mel L1 to fp32 ONNX 0.18–0.20 vs 0.21–0.31 for upstream paradee_int8.onnx; WER 3.81 % vs 2.97 % (one word of ~236).

FluidAudio tts-benchmark --corpus minimax-english (100 phrases, Parakeet TDT round trip, Swift, CPU, includes the English G2P frontend): WER 1.20 % / CER 0.14 % for both int8 and fp32, RTFx ~100, 77 ms median per phrase.

Conversion notes: the 1024-point phase-lock STFT is a gather + matmul + overlap-add instead of strided convolutions (the conv form ran ~40× slower on the Core ML CPU), and kokoro's random harmonic start phases are omitted because the upstream graph never reads them.

Changelog

License

Apache-2.0, same as Paradee and Kokoro-82M. Paradee © Sahil Mahendrakar.

@misc{mahendrakar2026paradee,
  title  = {Paradee: Distilling Kokoro-82M into an 8M-Parameter Single-Voice Text-to-Speech Model},
  author = {Mahendrakar, Sahil},
  year   = {2026},
  url    = {https://huggingface.co/sahilmahendrakar/Paradee-8M-v1.0}
}