THIRD_PARTY_NOTICES.md

Third-party notices

Irodori-TTS

• v4.1 source: https://huggingface.co/Aratako/Irodori-TTS-v4.1-Small (revision 2b28324dc263ed5e6638b3cf3dd94c82ead07b4b)
• v4 implementation: https://github.com/Aratako/Irodori-TTS (revision 8224dafb46d0aba89209a8f905f1cb7e3299d9c1); shared backbone export and tensor-based attention mask handling for dynamic ONNX inputs.
• Copyright: Chihiro Arata and contributors
• Sources: https://huggingface.co/Aratako/Irodori-TTS-500M-v3 and https://huggingface.co/Aratako/Irodori-TTS-600M-v3-VoiceDesign
• License: MIT (LICENSES/MIT.txt)
• Changes: converted from PyTorch checkpoints to ONNX; split static conditioning projection into context_kv.onnx; fixed export dimensions for the iOS runtime.

The responsible-use restrictions and disclaimers from the upstream model cards are reproduced in this repository's README.

Semantic-DACVAE-Japanese-32dim

• Copyright: Chihiro Arata and contributors
• Source: https://huggingface.co/Aratako/Semantic-DACVAE-Japanese-32dim
• License: MIT (LICENSES/MIT.txt)
• Changes: converted encoder and decoder to ONNX for the iOS runtime.

llm-jp-3-150m tokenizer

• Copyright: LLM-jp / National Institute of Informatics contributors
• Source: https://huggingface.co/llm-jp/llm-jp-3-150m
• License: Apache License 2.0 (LICENSES/Apache-2.0.txt)
• Files redistributed: tokenizer JSON and tokenizer configuration metadata.

ONNX export code

• Source: https://github.com/Aratako/Irodori-TTS
• License: MIT (LICENSES/MIT.txt)

ModernBERT-ja-310m (v4.1 encoder and tokenizer)

• Source: https://huggingface.co/sbintuitions/modernbert-ja-310m
• Copyright: SB Intuitions and contributors
• License: MIT
• The fine-tuned encoder and exact tokenizer are redistributed as part of the Irodori-TTS-v4.1-Small conversion. Text and caption share one encoder graph.

DACVAE / Descript Audio Codec ancestry (ONNX and Core ML)

• Semantic-DACVAE-Japanese-32dim source revision: 47376ee24834d7a05a48ebabfe3cde29b3c5e214 (MIT as declared by its model card).
• Base weights: https://huggingface.co/facebook/dacvae-watermarked (model metadata: Apache-2.0).
• DACVAE implementation: https://github.com/facebookresearch/dacvae (Apache License 2.0; LICENSES/Apache-2.0.txt). Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
• DAC architecture: https://github.com/descriptinc/descript-audio-codec (MIT; LICENSES/Descript-MIT.txt). Copyright (c) 2023-present, Descript.
• The base model README also contains a conflicting “SAM License” sentence; the linked DACVAE implementation LICENSE is Apache-2.0. This distribution preserves the Apache-2.0 license and does not relicense upstream components as MIT.
• Changes by the Onsei conversion: exported the decoder to a float32 Core ML ML Program using coremltools 9.0, folded weight normalization, froze constant expressions, and bounded batch/time input dimensions. No quantization or retraining. Watermarking remains bypassed as instructed by the Japanese model card. The converted model is not a watermarked-audio guarantee.
• native_decoder/manifest.json records the source ONNX digest, converted file digests and numerical verification. Core ML conversion does not grant any additional rights to upstream weights.