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Check out the documentation for more information.
GROW checkpoints
The three checkpoints released here correspond to rows of Table 1 in the paper.
| Folder | Table 1 row | Model | Checkpoint | Notes |
|---|---|---|---|---|
01_pretrain/ |
Row 1 | Pretrained DiTAR (DiTAR) |
model_200000.pt |
200K-step pretrain; also the frozen reference q for RL |
02_ditar_grpo_nfe10/ |
Row 3 | DiTAR-GRPO, NFE=10 (DiTAR_SDE) |
model_750.pt |
Flow-GRPO baseline, rollout NFE=10, Ξ²W2=0, lr=1e-5 |
03_grow_nfe10/ |
Row 6 | GROW, NFE=10 (DiTAR_ORW) |
model_750.pt |
Default GROW setting: rollout NFE=10, Ξ²W2=0.025, lr=2e-6 |
Reported full-test-set metrics (WER / SIM β LibriSpeech-PC Β· Seed-TTS EN Β· Seed-TTS ZH):
| Checkpoint | LS WER/SIM | Seed-EN WER/SIM | Seed-ZH WER/SIM |
|---|---|---|---|
01_pretrain |
2.373 / 0.648 | 2.406 / 0.663 | 1.269 / 0.717 |
02_ditar_grpo_nfe10 |
2.332 / 0.683 | 1.728 / 0.699 | 1.180 / 0.738 |
03_grow_nfe10 |
1.927 / 0.701 | 1.763 / 0.702 | 0.983 / 0.742 |
Each folder ships the training/inference config.yaml next to the
.pt weights. The eval loader reads model weights from model_state_dict
(RL checkpoints) or ema_model_state_dict (the pretrained checkpoint),
matching cfg.model.name.
Layout
model_ckpts/
βββ 01_pretrain/ config.yaml model_200000.pt
βββ 02_ditar_grpo_nfe10/ config.yaml model_750.pt
βββ 03_grow_nfe10/ config.yaml model_750.pt
The
.ptweights are large (4β13 GB) and are distributed via HuggingFace, not git. Download them into the matching folder before running evaluation.
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