MS²KU-VTTS

Multi-Source Spatial Knowledge Understanding
for Immersive Visual Text-to-Speech

Shuwei He, Rui Liu^*

Inner Mongolia University    ^* Corresponding Author

(Accepted by ICASSP 2025)

Abstract

Visual Text-to-Speech (VTTS) aims to take the environmental image as the prompt to synthesize reverberant speech for the spoken content. Previous works focus on the RGB modality for global environmental modeling, overlooking the potential of multi-source spatial knowledge like depth, speaker position, and environmental semantics. To address these issues, we propose a novel multi-source spatial knowledge understanding scheme for immersive VTTS, termed MS²KU-VTTS. Specifically, we first prioritize RGB image as the dominant source and consider depth image, speaker position knowledge from object detection, and Gemini-generated semantic captions as supplementary sources. Afterwards, we propose a serial interaction mechanism to effectively integrate both dominant and supplementary sources. The resulting multi-source knowledge is dynamically integrated based on the respective contributions of each source. This enriched interaction and integration of multi-source spatial knowledge guides the speech generation model, enhancing the immersive speech experience. Experimental results demonstrate that MS²KU-VTTS surpasses existing baselines in generating immersive speech.

Overview

The proposed MS²KU-VTTS architecture consists of four components:

• Multi-source Spatial Knowledge: RGB image (dominant), depth image, speaker position, and Gemini-generated semantic captions (supplementary)
• Dominant-Supplement Serial Interaction (D-SSI): RGB-Depth Interaction, Speaker Position Enhanced Interaction, and RGB-Semantic Interaction
• Dynamic Fusion: Entropy-based dynamic weighting to aggregate multi-source spatial knowledge
• Speech Generation: ControlNet-style DiT denoiser (based on F5-TTS) with BigVGAN vocoder

Installation

git clone https://github.com/he-shuwei/MS2KU-VTTS.git
cd MS2KU-VTTS
pip install -r requirements.txt

Checkpoints & Data — download from HuggingFace:

Resource	Path	Description
MS2KU-VTTS (finetuned)	checkpoints/ms2ku_vtts/	Finetuned model for inference
Pretrain Encoder	checkpoints/pretrain_encoder/	Pretrained TTS encoder
Pretrain Decoder	checkpoints/pretrain_decoder/	Pretrained DiT decoder (ControlNet backbone)
BigVGAN v2	checkpoints/bigvgan/	Retrained vocoder (16 kHz)
Spatial environment captions	data/raw_data/captions/	Gemini-generated captions for all splits
MFA alignment results	data/processed_data/mfa/mfa_outputs.tar.gz	Pre-computed forced alignment (TextGrid)

The following third-party checkpoints are also required. Please download from their official sources:

Model	Path	Source
BERT-large-uncased	checkpoints/bert-large-uncased/	Google
ResNet-18	checkpoints/resnet-18/	Microsoft
RMVPE	checkpoints/RMVPE/rmvpe.pt	RMVPE

Data — this project uses the SoundSpaces-Speech dataset. Please follow their instructions to obtain the raw data, then run the preprocessing pipeline:

1. Download pretrained models:

   python scripts/download_bert.py
   python scripts/download_resnet18.py
   

2. ResNet18 features (RGB & depth):

   bash scripts/extract_resnet18_features/run.sh start
   

3. Caption features (Gemini + BERT):

   python scripts/generate_gemini_captions.py --api_key YOUR_KEY --image_dir data/processed_data/images --output_dir data/processed_data/captions
   bash scripts/extract_caption_features/run.sh start
   

4. Speaker position features:

   bash scripts/extract_speaker_position/run.sh start
   

5. Binarize data:

   bash scripts/binarize/run.sh start
   

Training

bash scripts/train/run.sh start

Monitor training:

bash scripts/train/run.sh log

Check status:

bash scripts/train/run.sh status

Inference

bash scripts/infer/run_infer.sh \
    --ckpt checkpoints/ms2ku_vtts/model_ckpt_best.pt \
    --outdir results/ms2ku_vtts/test_seen \
    --batch_size 16

Citation

If you find this work useful, please consider citing:

@inproceedings{he2025multi,
  title={Multi-source spatial knowledge understanding for immersive visual text-to-speech},
  author={He, Shuwei and Liu, Rui},
  booktitle={ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={1--5},
  year={2025},
  organization={IEEE}
}

Acknowledgements

This work was funded by the Young Scientists Fund (No. 62206136) and the General Program (No. 62476146) of the National Natural Science Foundation of China, the "Inner Mongolia Science and Technology Achievement Transfer and Transformation Demonstration Zone, University Collaborative Innovation Base, and University Entrepreneurship Training Base" Construction Project (Supercomputing Power Project) (No. 21300-231510).

This project builds upon several excellent open-source projects. We gratefully acknowledge:

Model Architectures & Code

• F5-TTS — Diffusion Transformer (DiT) architecture
• BigVGAN — Neural vocoder by NVIDIA
• RMVPE — Robust pitch (F0) estimation
• x-transformers — Rotary positional embeddings
• FlashAttention — Memory-efficient attention kernels

Pretrained Models

• BERT-large-uncased (Google) — Caption feature extraction
• ResNet-18 (Microsoft) — RGB and depth visual feature extraction

Datasets & Tools

• SoundSpaces-Speech (Meta Research) — Audio-visual spatial speech dataset
• Montreal Forced Aligner (MFA) — Phoneme-level forced alignment
• Google Gemini — Panoramic scene caption generation

Libraries

• PyTorch — Deep learning framework
• librosa — Audio analysis and processing
• HuggingFace Transformers — Pretrained model loading
• matplotlib — Visualization