sbv2-v2/docs/MATCHA_FLOW.md

Matcha flow integration

Style-Bert-VITS2 can optionally generate its aligned prior latent with a Matcha-TTS-inspired conditional flow matching (CFM) decoder. The existing BERT/style encoder, duration predictors, VITS normalizing flow, and waveform generator are retained.

Set model.use_matcha to true for a newly trained or fine-tuned model. The template configurations enable it. Models whose configuration does not contain this key continue to use the original Gaussian prior sampling path.

Relevant settings:

• train.c_matcha: weight of the CFM velocity loss.
• model.matcha_channels: U-Net hidden width.
• model.matcha_num_heads: attention head count.
• model.matcha_dropout: transformer dropout.
• model.matcha_sigma_min: minimum flow path noise.
• model.matcha_n_timesteps: Euler steps at inference; more steps trade speed for refinement quality.

The Matcha branch has new parameters, so enabling it on an existing checkpoint requires fine-tuning before inference. It is trained on the same random latent segments used by the waveform generator to keep memory use bounded.