docs/spatial_encoder_plan.md

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
4. New SELD spatial adapter computes:
   • SELD MHSA output
   • 2.5 Hz spatial tokens
5. Spatial projector maps tokens to LLM hidden dimension.
6. Thinker injects projected spatial embeddings into inputs_embeds.
7. 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