Paradis-TTS — provenance notices

paradis_tts/onnx_models.py adapts the single-sentence text forward and
convolutional STFT from Paradee training/scripts/export_paradee.py,
Sahil Mahendrakar, commit 9c8b4d7504cbee7e64de2d0341bb690f0b0ab708,
Apache-2.0. Changes: French A_full_phase, three explicit deterministic ONNX
stages, host excitation and host postprocessing, CPU parity checks and
portable FP32/INT8-weight bundles. See LICENSE and docs/PHASE9_ONNX.md.

The portable NumPy phase filter in paradis_tts/audio.py implements the same
Paradee relative-phase correction described below; existing training sources
and their licenses are unchanged.

Paradis-TTS — phase 0 French feasibility audit

The teacher extraction in scripts/test_teacher_fr.py is adapted from:
Paradee, Sahil Mahendrakar
https://github.com/sahilmahendrakar/paradee
Commit: 9c8b4d7504cbee7e64de2d0341bb690f0b0ab708
File: training/scripts/gen_teacher.py
License: Apache-2.0 (see LICENSE)
Changes: French ff_siwis, pinned assets, CPU determinism, strict vocabulary
validation, hooks for independent parity checks, logs and test artifacts.

Kokoro-82M and Kokoro / Misaki software: hexgrad, Apache-2.0.
https://huggingface.co/hexgrad/Kokoro-82M
https://github.com/hexgrad/kokoro
https://github.com/hexgrad/misaki

French teacher voice: ff_siwis. Kokoro attributes its source data to:
Yamagishi, Junichi; Honnet, Pierre-Edouard; Garner, Philip; Lazaridis, Alexandros.
(2017). The SIWIS French Speech Synthesis Database, 2016 [dataset].
University of Edinburgh, School of Informatics, Centre for Speech Technology Research.
https://doi.org/10.7488/ds/1705
Source-data license: https://creativecommons.org/licenses/by/4.0/
The included WAVs are synthetic Kokoro outputs from original test sentences;
they are not recordings downloaded from SIWIS.

No WikiText or Wikipedia text is redistributed in phase 0 evidence.
Phase 1 includes French Wikipedia sentence excerpts in data/:
Contributors to French Wikipedia; article and history URLs in metadata.jsonl.
Source: wikimedia/wikipedia, 20231101.fr,
revision b04c8d1ceb2f5cd4588862100d08de323dccfbaa.
Original source-declared license: CC BY-SA 3.0.
Adapted corpus / original coverage sentences: CC BY-SA 4.0.
See data/README.md and docs/LICENSING.md for notices and changes.

Phase 2 extraction adapts Paradee training/scripts/gen_teacher.py and
training/scripts/regen_audio.py at the same audited Paradee commit.
Changes include French corpus/voice, stable per-ID seeds, atomic tensor shards,
checked resume, and a CUDA decoder with original CPU excitation and FFT math.
Generated teacher audio is kept in tensor shards without additional WAV exports.
Dependency licenses, including eSpeak NG's GPL, remain separate from this
repository's source-code license; see docs/LICENSING.md.

Phases 3/4 include unmodified Paradee training/scripts/student.py and
student_decoder.py in training/vendor/, under Apache-2.0 at the audited commit.
training/batches.py and losses.py adapt its baseline collators and loss formulas.
Changes: French split-aware smoke subsets, deterministic CPU reflection padding,
small batches/accumulation, portable RNG/optimizer/scaler checkpoints and Colab tools.
No English Paradee model weights or English corpus are used for French training.

Phase 6 includes unmodified Paradee training/scripts/discriminators.py at the
same audited commit. training/decoder_training.py and decoder_gan.py adapt
train_decoder.py: French split-aware data, spectral/GAN stages, CPU reflection
padding, exact global STFT convergence across accumulated microbatches, full
optimizer/discriminator/RNG resume, Drive backups and tensor/HTML listening.

paradis_tts/phase_lock.py adapts Paradee training/scripts/phase_lock.py at the
same audited Apache-2.0 commit. It is initially exposed in a CPU listening A/B
test; the raw student inference and trained checkpoints remain the references.
tests/fixtures/upstream_phase_lock.py contains the original smooth/lock
function bodies for a standalone parity test, under the same Apache-2.0 license.

The optional high-frequency loss in training/decoder_hf.py adapts the
--hf-weight objective from Paradee training/scripts/train_decoder.py at
commit 9c8b4d7504cbee7e64de2d0341bb690f0b0ab708 (Apache-2.0).
Evaluation uses the non-personalized DNSMOS P.835 windowing/calibration method
from Microsoft DNS-Challenge, revision 591184a9fcb2cbdec02520fed81a32bbbf9d73ff,
DNSMOS/dnsmos_local.py. Source repository license: CC BY 4.0.
https://github.com/microsoft/DNS-Challenge
https://creativecommons.org/licenses/by/4.0/
Adaptation: P.835 only (no P.808), SciPy polyphase resampling, explicit CPU thread
limits, resumable per-sentence logs and integration with held-out TTS evaluation.
DNSMOS model files are downloaded for evaluation and are not redistributed in
the Python package or inference archives. This attribution does not imply
Microsoft endorses Paradis-TTS.