ECHO-G models

Generate full-body Unitree G1 motion from speech and text. All models output 39-channel robot motion at 30 FPS.

Code v0.2.0 · Dataset · Inference guide

Models

Model Weights Guide
Audio + text best.pt Quick start below
Audio only audio_only/best.pt Audio-only
Text only text_only/best.pt Text-only
HumanRetarget Three checkpoints in human_retarget/ Inference only

Matching configurations are in configs/; the shared FGD encoder is in evaluation/. Use revision v0.2.0 for reproducible downloads.

Quick start

Use Python 3.10 or 3.11 with a compatible PyTorch installation:

git clone --branch v0.2.0 https://github.com/Mondo-Robotics/ECHO-G.git
cd ECHO-G
python -m pip install -e . huggingface_hub
hf download gaopusen/ECHO-G --revision v0.2.0 --local-dir weights \
  --include 'best.pt' 'configs/sgdit_audio_text.yaml' 'evaluation/*' \
            'SHA256SUMS' 'LICENSE*' 'NOTICE' 'README.md'

Download and extract the dataset to data/echo-g, then generate the validation split:

echo-g-sample --checkpoint weights/best.pt \
  --config configs/sgdit_audio_text.yaml --data-root data/echo-g \
  --output-dir results/audio_text --split val --seeds 0 --device cuda

Predictions are saved in results/audio_text/seed_000/. Optional file check: (cd weights && sha256sum --ignore-missing -c SHA256SUMS).

Inputs and evaluation

The released dataset includes ready-to-use frozen audio/text features. New inputs support up to 20 seconds / 600 frames and 256 text tokens; word timestamps are relative to the clip start. Longer inputs must be segmented before processing.

Other model variants · Benchmark · MuJoCo visualization

License and attribution

Project weights and documentation: CC BY-NC 4.0. Code and configurations: PolyForm Noncommercial 1.0.0. Third-party materials retain their own terms; see NOTICE.

Copyright (c) 2026 SOAR-LAB, School of Intelligence Science and Technology, Nanjing University. Developed by Dr. Hao Xu's team with sponsorship from Mondo Robotics (妙动科技). Licensing contacts: Dr. Hao Xu and Dr. Shuo Yang. Citation.

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Dataset used to train gaopusen/ECHO-G