BlueV3 — PyTorch Weights

Multilingual neural TTS (Hebrew-first) based on flow matching in a compressed latent space.

TTS version: v1.7.3 · Sample rate: 44.1 kHz · Checkpoint: ckpt_step_767000

Contents

Path	Description
checkpoints/text2latent/ckpt_step_767000.pt	Text encoder + vector-field estimator + reference encoder
checkpoints/duration_predictor/duration_predictor_final.pt	Utterance duration predictor
stats_multilingual.pt	Latent mean / std for normalization
configs/tts.json	Model / AE / DP config

Not included: the autoencoder / vocoder codec (ae_*.pt). Use your local AE checkpoint (e.g. checkpoints/44k_decoder/ae_541000.pt) or the ONNX vocoder from notmax123/BlueV3-onnx.

Companion repos

• ONNX runtime bundle (incl. vocoder): notmax123/BlueV3-onnx

Quick start (PyTorch)

# from the BlueV3 code repo
hf download notmax123/BlueV3 --local-dir ./hf_bluev3

# place weights where run_pt_inference / synth scripts expect them, e.g.:
mkdir -p checkpoints/text2latent checkpoints/duration_predictor
cp hf_bluev3/checkpoints/text2latent/ckpt_step_767000.pt checkpoints/text2latent/
cp hf_bluev3/checkpoints/duration_predictor/duration_predictor_final.pt checkpoints/duration_predictor/
cp hf_bluev3/stats_multilingual.pt .
cp hf_bluev3/configs/tts.json configs/tts.json

Example synthesis (IPA / phonemes, Hebrew):

uv run python run_pt_inference.py \
  --text "metsujˈan. vetatˈus levˈad?" \
  --lang he \
  --style_json voice_styles/Rotem.json \
  --steps 8 \
  --cfg 3.0 \
  --out out.wav

You still need:

1. Code from this project’s Git repo
2. An AE codec checkpoint for waveform decode (or use ONNX vocoder)
3. A voice style JSON (style_ttl + style_dp), e.g. export via export_ref_latent.py / reference WAV encoding

Model notes

• Latent: 24-dim AE → compressed 144 channels (chunk_compress_factor=6)
• Flow matching with classifier-free guidance (cfg_scale typically 3.0)
• Universal IPA vocab (size 256)
• Duration is predicted in seconds, then mapped to latent frames

License

MIT (see frontmatter).