Datasets:

X-Zhang
/

StreamTalk-Inference-Data

license: other
pretty_name: StreamTalk Inference Data
language:
  - en
tags:
  - motion-generation
  - co-speech-gesture-generation
  - streaming-generation
  - beat2
  - smpl-x
  - arxiv:2608.01643
size_categories:
  - n<1K

StreamTalk Inference Data

Ready-to-compare SMPL-X predictions from the official retrained StreamTalk CFG checkpoints. This release follows the convenient inference-data style of SemTalk: users can download generated test outputs and evaluate or visualize them without rerunning WavLM, retrieval, diffusion sampling, and SMPL-X conversion.

No BEAT2 ground-truth motion, audio, training cache, or model checkpoint is included in these archives.

Downloads

Scope	Clips	Checkpoint	CFG	Archive	SHA-256
Speaker 2 (Scott)	15	epoch 946	3	download	`859cb968c04804a797e3f8eb17aeba1f91bda29305f970564c9be481e41fa452`
All speakers	265	epoch 940	3	download	`295d933883dc40acb9df5f2d2ba02767ec62c9d83f423a78900932499fc8edb4`

wget https://huggingface.co/datasets/X-Zhang/StreamTalk-Inference-Data/resolve/main/streamtalk_speaker2_e0946_cfg3_npz.tar.gz
wget https://huggingface.co/datasets/X-Zhang/StreamTalk-Inference-Data/resolve/main/streamtalk_all_e0940_cfg3_npz.tar.gz
sha256sum -c SHA256SUMS

tar -xzf streamtalk_speaker2_e0946_cfg3_npz.tar.gz
tar -xzf streamtalk_all_e0940_cfg3_npz.tar.gz

Released FGD re-score

The downloadable NPZ bytes were independently re-scored with the StreamTalk release scorer and the pinned EMAGE/PantoMatrix AESK encoder (SHA-256 5cd9566b...c40f55). Equal-length metric batching uses batch size 16.

Scope	Archive re-score FGD ↓	Selection-time FGD ↓	Absolute difference
Speaker 2 (Scott)	0.3788789702354345	0.3788789702354416	7.1e-15
All speakers	0.21764184426140076	0.21767150775461275	2.97e-5

Both differences are below the selected engineering reporting tolerance of 1e-3. These are accelerated EMAGE/AESK measurements, not a claim of paper-exact B=1/M=1/full-window bitwise equivalence. See speaker2_fgd.json and all_fgd.json for the complete metric contract.

NPZ schema

Every archive contains one res_<BEAT2-test-id>.npz per official test ID plus a scope manifest. Load files with allow_pickle=False.

Key	Shape/type	Meaning
`poses`	`[T, 165]`, float32	local SMPL-X axis-angle rotations
`trans`	`[T, 3]`, float32	root translation
`expressions`	`[T, 100]`, float32	facial expression coefficients
`betas`	`[300]`, float32	body-shape coefficients
`gender`	string	body-model metadata
`mocap_frame_rate`	scalar	30 FPS
`model`	string	`smplx2020`

import numpy as np

with np.load("res_2_scott_0_1_1.npz", allow_pickle=False) as motion:
    poses = motion["poses"]
    expressions = motion["expressions"]
    trans = motion["trans"]

Re-score with StreamTalk

The generated files do not include BEAT2 ground truth. After obtaining BEAT2 under its own terms, use the scorer in the StreamTalk repository:

python tools/evaluate_generated_fgd.py \
  --predictions streamtalk_speaker2_e0946_cfg3_npz \
  --ground_truth /path/to/beat_english_v2.0.0/smplxflame_30 \
  --scope speaker2 --device cuda:0 --metric_batch_size 16

python tools/evaluate_generated_fgd.py \
  --predictions streamtalk_all_e0940_cfg3_npz \
  --ground_truth /path/to/beat_english_v2.0.0/smplxflame_30 \
  --scope all --device cuda:0 --metric_batch_size 16

Provenance and terms

Generator code: StreamTalk training/evaluation commit b6cac99d5bae5164a21f5db25afef72c3c30556a.
Speaker 2 checkpoint SHA-256: 07065d52e333cc48960e4e8fad5c0331f6b9e482f14aa1b78cb721904da5b075.
All-speaker checkpoint SHA-256: 3a0e3558d788fd3f11a5a87a8198b42725104b5b9b24f95a4e7c84c14395c9a5.
Seed: 0. Speaker 2 generation uses clip batch 1; all-speaker generation uses clip batch 16; both use CFG 3 and an 8-frame retrieval query.

The predictions are derived from experiments on BEAT2 and use the SMPL-X body model. Users remain responsible for complying with the licenses and terms of the BEAT2 dataset and SMPL-X. This dataset card does not redistribute BEAT2 ground truth, audio, or SMPL-X model files.