README.md

metadata

license: cc-by-nc-4.0
pipeline_tag: text-to-speech
tags:
  - text-to-speech
  - tts
  - voice-cloning
  - flow-matching
  - raw-waveform
  - english
  - chinese
  - wavtts
  - mirror

WavTTS — inference mirror for MAESTRO

Inference-ready repack of worstchan/WavTTS (Wenxi Chen et al., arXiv 2606.03455), kept by AEmotionStudio so the MAESTRO DAW can fetch the checkpoint on demand. The model is not fine-tuned, quantised below its training precision, or otherwise altered — this repo only extracts the inference weights from the upstream training checkpoint:

• model_1200000.pt (10.8 GB training checkpoint) → model.safetensors: the ema_model_state_dict EMA weights with the ema_model. prefix stripped, EMA bookkeeping (initted/step) and the training-only aux_mel_loss.* buffers dropped, floating tensors stored as float16 (the dtype upstream inference uses on CUDA).
• vocab.txt — verbatim from upstream (2556-entry pinyin/char vocab).
• config.json — pointer config updated to reference model.safetensors.

File	Purpose
model.safetensors	~0.67B DiT + CFM inference weights (fp16 EMA)
vocab.txt	Character/pinyin vocabulary (index 0 = space/unknown)
config.json	Sample rate + file pointers

WavTTS generates the raw 16 kHz waveform directly (flow matching over 160-sample waveform patches — no mel spectrogram, VAE latent or codec). Zero-shot voice cloning from a reference clip plus its transcript; English and Chinese.

License — please read

• Code: MIT (the WavTTS codebase).
• Weights (this repo): CC BY-NC 4.0 — inherited from the Emilia training dataset. Non-commercial use only; attribution required. Do not use outputs commercially without permission from the upstream authors.
• Never clone or impersonate a real person's voice without their explicit consent.

By downloading these files you accept the CC BY-NC 4.0 terms. MAESTRO shows the same terms in-app.

Citation

@article{chen2026wavtts,
  title   = {WavTTS: Towards High-Quality Zero-Shot TTS via Direct Raw Waveform Modeling},
  author  = {Chen, Wenxi and Jia, Dongya and Chen, Yushen and Niu, Zhikang and Liang, Yuzhe and Li, Xiquan and Yan, Ruiqi and Ma, Ziyang and Yang, Guanrou and Chen, Sanyuan and others},
  journal = {arXiv preprint arXiv:2606.03455},
  year    = {2026},
}