| # SELDNet-233 Spatial Modality Integration Design | |
| ## Document Status | |
| - Owner: Student Implementation | |
| - Reviewer: Project Maintainer | |
| - Target Repository: `/apdcephfs_cq10/share_1603164/user/schmittzhu/code/spur-qwen-2.5-omni` | |
| - Related Baseline Repository: `/apdcephfs_cq10/share_1603164/user/schmittzhu/code/DCASE2024_seld_baseline` | |
| ## 1. Background | |
| This project aims to extend the current `Qwen2.5-Omni`-based system with a new **independent spatial modality** derived from a pretrained `SELDNet` encoder. | |
| The current system already supports: | |
| - text | |
| - image | |
| - video | |
| The current SPUR-related spatial path in the Omni codebase is not a truly independent modality. Instead, it computes spatial features and fuses them back into the audio encoder branch. This is | |
| **not** the desired design for this project. | |
| - keep the **original audio encoder unchanged** | |
| - use the same FOA input audio in **two parallel paths** | |
| - inject a new set of **spatial tokens** into the LLM as a separate modality | |
| ## 2. Objective | |
| Given a 4-channel FOA audio input: | |
| 1. The **W channel** should continue to go through the original Omni mono audio pipeline. | |
| 2. The full **4-channel FOA** should go through a pretrained `SELDNet-233` encoder. | |
| 3. The `SELDNet-233` encoder should provide a sequence of **spatial tokens** derived from its **MHSA output**, not from its final localization/classification heads. | |
| 4. These spatial tokens should be projected into the LLM hidden space and injected into the thinker decoder input as a new modality `<|spatial|>`. | |
| ## 3. Non-Goals | |
| - modify the existing SELD baseline source code | |
| - modify existing SELD training or inference behavior | |
| - use SELDNet final `accdoa` or `sed_logits` as the LLM input representation | |
| ## 4. Fixed Dependencies | |
| - Path: `/apdcephfs_cq10/share_1603164/user/schmittzhu/code/DCASE2024_seld_baseline` | |
| ### 4.2 SELD checkpoint to use | |
| ### 4.3 SELD task configuration | |
| - Task ID: `233` | |
| ### 4.4 SELD model definition | |
| ## 5. Key Design Decision | |
| ### 5.1 Spatial representation source | |
| The spatial representation must come from the **shared temporal representation after the final MHSA block** in `SELDNet`, not from: | |
| - final `accdoa` | |
| - final `sed_logits` | |
| - explicit detection heads | |
| ### 5.2 Why this representation | |
| This tensor is: | |
| - temporally ordered | |
| - dense | |
| - scene-level | |
| - richer than detection output heads | |
| - better suited for LLM conditioning | |
| ## 6. Target Architecture | |
| ### 6.1 Input | |
| ### 6.2 Dual-path processing | |
| - take FOA `W channel` | |
| - convert to mono | |
| - feed into original Omni audio encoder | |
| - keep existing tokenization rate unchanged | |
| - expected token rate: about `25 Hz` | |
| Path B: New spatial path | |
| - keep all 4 FOA channels | |
| - convert to SELD baseline-compatible input features | |
| - feed into pretrained `SELDNet-233` | |
| - produce spatial token sequence | |
| - project into LLM hidden size | |
| - inject as `<|spatial|>` modality | |
| ### 6.3 Final LLM view | |
| - normal text tokens | |
| - normal audio tokens from original audio encoder | |
| - optional image/video tokens | |
| - new spatial tokens from SELDNet-233 branch | |
| ## 7. Temporal Resolution Requirements | |
| ### 7.1 Audio token frequency | |
| Keep current Omni audio token frequency unchanged: | |
| - approximately `25 Hz` | |
| ### 7.2 Spatial token frequency | |
| Spatial tokens should be lower frequency: | |
| - target `2.5 Hz` | |
| This is intentional because: | |
| - lower token rate reduces prompt length | |
| - lower token rate is sufficient for spatial reasoning | |
| Recommended first version: | |
| - `h_seld`: `[B, T_seld, 128]` | |
| - `spatial_tokens`: `[B, T_spat, 256]` | |
| - `projected_spatial`: `[B, T_spat, D_llm]` | |
| Where: | |
| - `T_spat = 5 * seconds` | |
| ## 8. Data Flow | |
| 1. Input FOA waveform enters processor. | |
| 2. Processor keeps original audio path intact: | |
| - use `W channel` for original audio encoder. | |
| 3. Processor also passes full FOA waveform as `spatial_audio`. | |
| - `spatial_audio` for new spatial path | |
| 5. New SELD spatial adapter computes: | |
| - SELD MHSA output | |
| - `2.5 Hz` spatial tokens | |
| 6. Spatial projector maps tokens to LLM hidden dimension. | |
| 8. Thinker injects projected spatial embeddings into `inputs_embeds`. | |
| 9. LLM decoder consumes them as a true multimodal prefix. | |
| ## 9. Required Code Work | |
| ### A. `qwen2_5_omni_spur/modules/seldnet233_backbone.py` | |
| Purpose: | |
| - build baseline `SeldModel` with task `233` | |
| - load pretrained checkpoint | |
| - freeze model by default | |
| - register a forward hook on the final MHSA output | |
| Expected behavior: | |
| - input: baseline-compatible SELD features | |
| - output: `h_seld [B, T_seld, D_seld]` | |
| - do not modify baseline code | |
| - use import-time wrapping or dynamic module loading | |
| - use forward hook on the last MHSA normalization output | |
| ### B. `qwen2_5_omni_spur/modules/seldnet233_feature_bridge.py` | |
| Purpose: | |
| - convert FOA waveform into the feature representation expected by SELD task `233` | |
| Expected behavior: | |
| - output: baseline feature tensor `[B, C, T_feat, F_feat]` | |
| Implementation notes: | |
| - must match SELD task `233` feature config exactly | |
| - sample rate must be `16k` | |
| - should reuse baseline feature logic where possible | |
| Purpose: | |
| - build a full SELD-based spatial token extractor | |
| Internal structure: | |
| - feature bridge | |
| - SELD backbone | |
| - spatial token head | |
| Expected behavior: | |
| - output: | |
| - `spatial_tokens [B, T_spat, D_spat]` | |
| Implementation notes: | |
| - token rate target must be `2.5 Hz` | |
| - default token dim should be `256` | |
| - token head may use: | |
| - pooling | |
| - MLP | |
| - recommended first implementation: | |
| ### D. Optional: `qwen2_5_omni_spur/modules/spatial_token_projector.py` | |
| Purpose: | |
| - project SELD spatial tokens into LLM hidden dimension | |
| ## 9.2 Existing files to modify | |
| ### A. `qwen2_5_omni_spur/configuration_qwen2_5_omni.py` | |
| Add config fields: | |
| - `spatial_token_index` | |
| - `use_seld233_spatial_modality` | |
| - `seld233_checkpoint_path` | |
| - `seld233_token_dim` | |
| - `seld233_token_rate_hz` | |
| - `seld233_projector_hidden_dim` | |
| - optional `seld233_freeze_backbone` | |
| - `attribute_map` to include `spatial_token_id` | |
| ### B. `qwen2_5_omni_spur/modules/__init__.py` | |
| Export new modules: | |
| - `SeldNet233Backbone` | |
| - optional new projector class | |
| ### C. `qwen2_5_omni_spur/processing_qwen2_5_omni.py` | |
| Add a new special token: | |
| - `<|spatial|>` | |
| - store `self.spatial_token` | |
| - optionally accept `spatial_tokens` and `spatial_token_lengths` | |
| - expand `<|spatial|>` into repeated placeholders based on spatial token length | |
| - expose spatial fields through `model_input_names` | |
| Important constraint: | |
| - original W-channel mono audio path must remain unchanged | |
| ### D. `qwen2_5_omni_spur/modeling_qwen2_5_omni.py` | |
| Required changes: | |
| - instantiate SELD spatial adapter in thinker | |
| - accept `spatial_audio` in thinker forward | |
| - compute spatial embeddings | |
| - inject them via `masked_scatter` | |
| - update multimodal rope logic | |
| - route spatial fields through generation | |
| ### E. `training-qwen-omni/train_spur_spatial_only_hf.py` | |
| Update collator: | |
| - additionally pass full FOA as `spatial_audio` | |
| ### F. `qwen-omni-inference.py` | |
| Update inference script: | |
| - register `<|spatial|>` token | |
| - construct prompt with spatial placeholder | |
| - validate both paths run together | |
| ## 10. Processor Design | |
| ### 10.1 Prompt convention | |
| - `<|spatial|>` | |
| Example: | |
| - `<|AUDIO|><|spatial|> Please answer the question using both sound content and spatial cues.` | |
| ### 10.2 Placeholder expansion behavior | |
| - `T_spat` repeated spatial token positions | |
| This mirrors current audio/image/video placeholder logic. | |
| ### 10.3 Recommended input mode | |
| - processor receives `spatial_audio` | |
| Fallback mode: | |
| - processor receives precomputed `spatial_tokens` | |
| ## 11. SELD Path Design | |
| ### 11.1 Backbone loading | |
| The SELD branch must: | |
| - use task `233` | |
| - load the fixed checkpoint above | |
| - stay frozen in the first training stage | |
| ### 11.2 Feature extraction | |
| The spatial branch must operate on: | |
| - full 4-channel FOA | |
| - `16kHz` | |
| - baseline-compatible feature format | |
| ### 11.3 Hidden representation extraction | |
| The representation to extract is: | |
| - final MHSA output | |
| - not final heads | |
| - not sed logits | |
| ### 11.4 Tokenization | |
| The spatial token head must: | |
| - preserve temporal ordering | |
| - reduce token rate to `2.5 Hz` | |
| - output a fixed hidden dim, recommended `256` | |
| ### 12.1 New thinker members | |
| Add: | |
| - optional `self.seld233_spatial_norm` | |
| ### 12.2 Forward inputs | |
| Thinker forward should support: | |
| - `spatial_audio` | |
| - `spatial_tokens` | |
| - `spatial_token_lengths` | |
| Priority: | |
| - if `spatial_tokens` provided, use them directly | |
| ### 12.3 Injection point | |
| Injection should happen in the same stage where current code merges: | |
| - text | |
| - audio | |
| - video | |
| The new logic should: | |
| 1. compute `projected_spatial` | |
| 2. build `spatial_mask = (input_ids == spatial_token_id)` | |
| 3. inject with `masked_scatter` | |
| Before scatter, verify: | |
| If not, raise an error. | |
| ### 13.1 Why needed | |
| Adding a new modality requires updating multimodal position ID construction. | |
| ### 13.2 Required additions | |
| - add `spatial_token_id` | |
| - add `spatial_seqlens` | |
| - count spatial modality occurrences | |
| - add `remain_spatials` | |
| - add spatial branch when selecting next multimodal segment | |
| ### 13.3 Spatial position assignment | |
| Spatial token positions should be assigned: | |
| - length equal to `spatial_token_lengths` | |
| - consistent with `2.5 Hz` token rate | |
| ## 14. Generation Path Changes | |
| Top-level `generate()` must forward: | |
| - `spatial_audio` | |
| - `spatial_tokens` | |
| - `spatial_token_lengths` | |
| to thinker generation. | |
| ### 14.2 Talker interaction | |
| Recommended first version: | |
| - spatial positions should be zeroed before passing thinker hidden states into talker | |
| Reason: | |
| - spatial modality is meant for reasoning | |
| - not necessarily for speech token generation conditioning | |
| ## 15. Training Strategy | |
| ### 15.1 Stage 1 | |
| Freeze: | |
| - SELD backbone | |
| Train only: | |
| - spatial token head | |
| - spatial projector | |
| - LoRA / lightweight LLM adapters if used | |
| - last spatial token head layers | |
| - projector | |
| - maybe a small part of SELD adapter | |
| ### 15.3 Stage 3 | |
| Optional advanced finetuning: | |
| - limited SELD backbone finetuning | |
| - only after full pipeline is stable | |
| ## 16. Validation Plan | |
| ### 16.1 Unit-level validation | |
| - checkpoint loads successfully | |
| - MHSA output is captured successfully | |
| - spatial token head outputs expected shape | |
| - token rate is `2.5 Hz` | |
| ### 16.2 Processor validation | |
| Validate: | |
| - spatial fields appear in batch output | |
| - original audio path is unchanged | |
| Validate: | |
| - spatial projector output matches LLM hidden size | |
| - no length mismatch | |
| - no dtype/device mismatch | |
| ### 16.4 Rope validation | |
| Validate: | |
| - generation does not crash when `<|spatial|>` is present | |
| Validate: | |
| - original audio tokens still exist | |
| - spatial tokens are injected independently | |
| - text generation completes | |
| ## 17. Risks | |
| ### 17.1 Feature mismatch risk | |
| If SELD features differ from training-time `233` config, checkpoint utility may collapse. | |
| Mitigation: | |
| - strictly reuse baseline feature logic | |
| If hook location is incorrect, extracted representation may not correspond to post-MHSA scene representation. | |
| Mitigation: | |
| - verify against baseline layer order | |
| - inspect shape and stability | |
| ### 17.3 Rope mismatch | |
| If `<|spatial|>` positions are not included in multimodal rope indexing, model behavior may degrade or fail. | |
| Mitigation: | |
| - treat spatial exactly as a fourth modality in rope construction | |
| ### 17.4 Prompt length growth | |
| Adding spatial tokens increases context length. | |
| Mitigation: | |
| ## 18. Final Requirements Checklist | |
| - [ ] Use checkpoint `233_merged29_foa_16k_sedwarmup_v1_dev_split0_multiaccdoa_foa_best_full_model.h5` | |
| - [ ] Keep original audio encoder unchanged | |
| - [ ] Audio path uses `W channel` only | |
| - [ ] Spatial path uses all 4 FOA channels | |
| - [ ] Spatial representation comes from final SELD MHSA output | |
| - [ ] Spatial token rate is `2.5 Hz` | |
| - [ ] Spatial token dimension defaults to `256` | |
| - [ ] Spatial tokens are injected as a true independent modality | |
| - [ ] `<|spatial|>` placeholder is supported in processor | |
| - [ ] RoPE logic includes spatial modality | |
| - [ ] End-to-end generate path runs successfully | |
| ## 19. Recommended Implementation Order | |
| 1. Implement `seldnet233_backbone.py` | |
| 2. Implement `seldnet233_feature_bridge.py` | |
| 3. Implement `seldnet233_spatial_adapter.py` | |
| 4. Add config fields | |
| 5. Add processor `<|spatial|>` support | |
| 6. Add thinker spatial encoder + projector | |
| 7. Add spatial `masked_scatter` | |
| 8. Update `get_rope_index()` | |
| 9. Update `generate()` | |
| 10. Update collator and inference script | |
| 11. Run shape-only validation | |
| 12. Run end-to-end generation validation | |
| ## 20. Reviewer Notes | |
| This design intentionally separates: | |
| - content understanding from the original audio encoder | |
| - spatial reasoning from the SELDNet-based encoder | |
| The design is successful only if the final model sees: | |
| - mono audio content tokens at normal resolution | |
| - spatial tokens at low resolution | |
| - both as independent modalities inside the thinker input stream | |