README.md

---
base_model: sahilmahendrakar/Paradee-8M-v1.0
language:
- en
library_name: coreml
license: apache-2.0
pipeline_tag: text-to-speech
tags:
- text-to-speech
- tts
- coreml
- kokoro
- distillation
- apple-silicon
---

# Paradee-8M Core ML

Core ML conversion of [Paradee-8M v1.0](https://huggingface.co/sahilmahendrakar/Paradee-8M-v1.0) by Sahil Mahendrakar:
Kokoro-82M distilled into an 8.07M-parameter, single-voice (`af_heart`) English TTS model, 24 kHz.
Paper: [arXiv 2610.06817](https://arxiv.org/abs/2610.06817).

Runs **~150× real time on the CPU of an M5 Pro** (93 s of audio in 0.62 s, text side + acoustic side), against 20×
for the upstream fp32 ONNX on one onnxruntime thread. The int8 build has a **12 MB weight footprint**.

Used by [FluidAudio](https://github.com/FluidInference/FluidAudio) (`ParadeeManager`).

## Files

| Path | What |
|---|---|
| `int8/` | Default. `ParadeeText.mlmodelc` (3.1 MB) + `ParadeeAcoustic.mlmodelc` (9.1 MB), int8 per-channel weights; the phase-lock filter's DFT bases stay fp32 |
| `fp32/` | Same graphs in fp32 (12 + 22 MB). Matches PyTorch to rounding |
| `*/vocab.json` | Phoneme → id map (identical to Kokoro's) |
| `mlpackage/` | Source `.mlpackage`s for both variants |
| `config.json` | Vocab, sample rate, shape limits |
| `samples/` | The model card's five held-out sentences, rendered by `int8/` |

## Pipeline

```
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](https://github.com/hexgrad/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 (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.

## License

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

```bibtex
@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}
}
```