| --- |
| 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} |
| } |
| ``` |
|
|
