Add Supertonic FP16 MLX export and native runner

[seyeong-han]

Summary

Add an ExecuTorch MLX implementation of Supertonic 3, Supertone's on-device multilingual TTS model.

Published ONNX models
  -> export-only Python code
  -> one FP16 ExecuTorch PTE
  -> C++ runner with MLX
  -> 44.1 kHz PCM16 WAV

The Python model files translate the published ONNX architecture and weights into an ExecuTorch PTE. They are needed only when creating the PTE; the C++ runner does not use Python or the source ONNX model files.

This PR supports one-shot, batch-1, five-step synthesis on macOS arm64. It does not include model weights, generated PTEs, WAVs, a persistent server, benchmarks, or application integration.

What to review

The 49 files do not need equal review depth.

1. Create the PTE (Python, export time only)

• examples/models/supertonic/loaders/checkpoint_loader.py
• examples/models/supertonic/model/duration_predictor.py
• examples/models/supertonic/model/text_encoder.py
• examples/models/supertonic/model/vector_estimator.py
• examples/models/supertonic/model/vocoder.py
• examples/models/supertonic/export/common.py
• examples/models/supertonic/export/export_supertonic.py
• examples/models/supertonic/source_transformations/mlx.py

These files define the four Supertonic stages in PyTorch, copy and convert the ONNX weights, apply MLX-compatible graph rewrites, and export four PTE methods: duration_predictor, text_encoder, vector_estimator, and vocoder.

Review the layer/weight mapping, equivalent outputs, dynamic bounds, FP16 conversion, metadata, and MLX delegation. The highest-signal tests are test_checkpoint_loader.py, test_stage_parity.py, and test_mlx_pipeline.py.

2. Run the PTE (C++)

• examples/models/supertonic/runtime/supertonic_runner.cpp
• examples/models/supertonic/runtime/text_processor.cpp
• examples/models/supertonic/runtime/style_loader.cpp
• examples/models/supertonic/runtime/wav_writer.cpp
• examples/models/supertonic/runtime/main.cpp

These files load the PTE, call its four methods, preprocess and chunk text, run five generation steps, trim the waveform, and write a WAV file. Review the PTE compatibility checks, Unicode handling, tensor flow, and output validation.

The highest-signal tests are examples/models/supertonic/runtime/tests/supertonic_runtime_test.cpp and examples/models/supertonic/runtime/tests/run_integration.cmake.

3. Build and CI

• examples/models/supertonic/CMakeLists.txt
• examples/models/supertonic/CMakePresets.json
• .github/workflows/mlx.yml
• examples/models/supertonic/README.md
• examples/models/supertonic/NOTICE

Review the macOS arm64 restriction, MLX runtime files, pinned-asset CI flow, usage, and licensing. The remaining unit tests, package files, and root README/Makefile entries are support changes that can be skimmed.

Runtime files

After export, synthesis needs only supertonic_runner, mlx.metallib, the exported PTE, unicode_indexer.json, and one voice-style JSON file.

Validation

• 151 Python tests and 2/2 native CTests passed, including real PTE synthesis.
• Local Apple M5 Max benchmark (batch-1 FP16, five steps): short-input median 45.9 ms, RTF 0.0244 (40.9x real time).
• ONNX FP32 vs MLX FP16 waveform cosine/Pearson: 0.999603; SQNR: 30.95 dB.
• GitHub CI: 201 successful, 30 skipped, 1 neutral, 0 failed.

Provenance

• Source: supertone-inc/supertonic at 7e2804f96016a7028cb1ed627353c61c1e9dd281 (MIT).
• Model assets: Supertone/supertonic-3 at 3cadd1ee6394adea1bd021217a0e650ede09a323 (BigScience Open RAIL-M).
• Assets are downloaded separately and are not redistributed. Exported PTEs remain subject to the model asset license.

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/22063

• 📄 Preview Python docs built from this PR

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✅ No Failures

As of commit 39c750c with merge base cff6f4d ():
💚 Looks good so far! There are no failures yet. 💚

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