docs/seld233_implementation.md

SELD233 Spatial Implementation Guide

Purpose

This document is the implementation guide for the independent SELD233 spatial modality.

It answers four questions:

• which classes and functions were implemented for the core path
• which classes are already scaffolded and should be reused as-is
• what each interface accepts and returns
• what concrete code is still deferred outside the current scope

This guide should be read together with:

• docs/spatial_encoder_plan.md
• docs/seld233_spatial_scaffold.md

Fixed Constraints

• Do not modify the DCASE baseline repository.
• Do not modify the original QwenOmni main classes for this feature; use the new spatial subclasses.
• A batch must be either all mono or all FOA.
• Mono batch:
  • must contain exactly one <|AUDIO|>
  • must not contain <|spatial|>
  • only the original audio path is active
• FOA batch:
  • must contain exactly one <|AUDIO|>
  • must contain exactly one <|spatial|>
  • both audio and spatial paths are active
• Audio is clipped to 0-20s.
• Sample rate is 16kHz.
• Spatial token rate target is 2.5 Hz.
• Full 20s limits:
  • max samples: 320000
  • max feature frames: 1000
  • max SELD frames: 200
  • max spatial tokens: 50

Status Summary

Already scaffolded and reusable

• qwen2_5_omni_spur/spatial_seld233_utils.py
• qwen2_5_omni_spur/modules/seldnet233_spatial_adapter.py
• qwen2_5_omni_spur/modules/spatial_token_projector.py
• qwen2_5_omni_spur/processing_qwen2_5_omni_spatial.py
• qwen2_5_omni_spur/modeling_qwen2_5_omni_spatial.py
• config additions in qwen2_5_omni_spur/configuration_qwen2_5_omni.py

Must still be implemented

• training script migration to the new spatial subclasses
• inference script migration to the new spatial subclasses

Explicitly deferred for now

• image + spatial joint RoPE
• use_audio_in_video=True
• return_audio=True talker path with spatial modality

Implementation Inventory

Priority	File	Symbol	Current State	Required Action
P0	qwen2_5_omni_spur/modules/seldnet233_feature_bridge.py	SeldNet233FeatureBridge._extract_online_features	implemented	no further action for the current scope
P0	qwen2_5_omni_spur/modules/seldnet233_backbone.py	SeldNet233Backbone._run_seldnet_backbone	implemented	no further action for the current scope
P1	training-qwen-omni/train_spur_spatial_only_hf.py	main() and config wiring	legacy	migrate only if this script is needed for training runs
P1	qwen-omni-inference.py	script body	legacy	migrate only if this script is needed for ad hoc inference
P2	qwen2_5_omni_spur/modeling_qwen2_5_omni_spatial.py	get_rope_index	partial	only needed later if image + spatial is required
P2	qwen2_5_omni_spur/modeling_qwen2_5_omni_spatial.py	generate(return_audio=True) path	partial	only needed if talker output is required later

Detailed Interface Guide

1. SeldNet233FeatureBridge

File:

• qwen2_5_omni_spur/modules/seldnet233_feature_bridge.py

Class:

• SeldNet233FeatureBridge

Already implemented:

• constructor
• input validation
• waveform mask -> valid length conversion
• waveform length -> feature length conversion
• output mask bookkeeping
• online baseline-aligned STFT / log-mel / intensity-vector extraction
• baseline foa_wts normalization

Input contract:

• spatial_audio: torch.Tensor, shape [B, T_audio_max, 4]
• spatial_audio_lengths: torch.LongTensor, shape [B]
• feature_lengths: torch.LongTensor, shape [B]
• feature_attention_mask: torch.BoolTensor, shape [B, T_feat_max]

Output contract:

• SeldNet233FeatureBridgeOutput.features: [B, 7, T_feat_max, 64]
• SeldNet233FeatureBridgeOutput.feature_attention_mask: [B, T_feat_max]
• SeldNet233FeatureBridgeOutput.feature_lengths: [B]

Required implementation content:

1. Load or derive the exact task-233 feature configuration.
2. Reproduce the baseline feature math for FOA:
   • 4ch log-mel
   • 3ch FOA intensity vector
3. Use the task-233 normalization weights.
4. Keep output frame count aligned with feature_lengths.
5. Zero-fill padded tail frames only after valid frames are computed.

Recommended private helpers to add inside this file:

• _load_task233_feature_config()
• _load_feature_stats()
• _compute_stft(...)
• _compute_logmel_channels(...)
• _compute_intensity_vector_channels(...)
• _normalize_feature_tensor(...)

Important shape transitions:

• raw FOA waveform: [B, T_audio_max, 4]
• internal channels-first view: [B, 4, T_audio_max]
• mel-like features before stacking: [B, 4, T_feat_max, 64]
• intensity-vector features: [B, 3, T_feat_max, 64]
• final baseline tensor: [B, 7, T_feat_max, 64]

Validation checklist:

• same sample length gives same T_feat as samples_to_feature_frames
• values match offline baseline extraction on the same clip
• normalization path is identical to baseline task 233

2. SeldNet233Backbone

File:

• qwen2_5_omni_spur/modules/seldnet233_backbone.py

Class:

• SeldNet233Backbone

Already implemented:

• constructor
• input validation
• feature length -> SELD length conversion
• output mask bookkeeping
• dynamic baseline loading
• checkpoint restore with shape-compatible filtering
• last MHSA LayerNorm hidden capture

Input contract:

• seld233_features: torch.Tensor, shape [B, 7, T_feat_max, 64]
• seld233_feature_lengths: torch.LongTensor, shape [B]
• hidden_lengths: torch.LongTensor, shape [B]
• hidden_attention_mask: torch.BoolTensor, shape [B, T_seld_max]

Output contract:

• SeldNet233BackboneOutput.hidden_states: [B, T_seld_max, 128]
• SeldNet233BackboneOutput.hidden_attention_mask: [B, T_seld_max]
• SeldNet233BackboneOutput.hidden_lengths: [B]

Required implementation content:

1. Dynamically load parameters.py and seldnet_model.py from the baseline repo path.
2. Build the task-233 baseline model.
3. Restore the pretrained checkpoint from seld233_checkpoint_path.
4. Freeze the backbone if seld233_freeze_backbone=True.
5. Register a hook at the final MHSA shared representation.
6. Feed the baseline-compatible features into the model.
7. Return the captured hidden tensor, not accdoa heads and not sed logits.

Recommended private helpers to add inside this file:

• _load_baseline_modules()
• _build_seld233_model()
• _load_checkpoint_weights()
• _register_hidden_hook()
• _prepare_baseline_input_layout(...)

Important shape transitions:

• model input for this wrapper: [B, 7, T_feat_max, 64]
• likely baseline internal flattened layout: [B, T_feat_max, 448]
• captured MHSA hidden: [B, T_seld_max, 128]

Validation checklist:

• checkpoint path exists
• hook fires exactly once per forward
• hidden dim is exactly 128
• hidden length matches feature_frames_to_seld_frames

3. SeldNet233SpatialAdapter

File:

• qwen2_5_omni_spur/modules/seldnet233_spatial_adapter.py

Class:

• SeldNet233SpatialAdapter

Status:

• already usable once feature bridge and backbone are implemented

Input modes already supported:

• raw audio path:
  • spatial_audio [B, T_audio_max, 4]
• offline feature path:
  • seld233_features [B, 7, T_feat_max, 64]
• direct hidden path:
  • seld233_hidden_states [B, T_seld_max, 128]

Output:

• spatial_tokens [B, T_spat_max, 256]
• spatial_token_attention_mask [B, T_spat_max]
• spatial_token_lengths [B]

No new implementation is required here unless you want to change:

• downsampling rule
• token MLP architecture
• token dimension

Current downsampling rule:

• T_spat = ceil(T_seld / 4)
• effective rate: 10 Hz -> 2.5 Hz

4. SpatialTokenProjector

File:

• qwen2_5_omni_spur/modules/spatial_token_projector.py

Class:

• SpatialTokenProjector

Status:

• already implemented

Input:

• spatial_tokens [B, T_spat, D_in]

Output:

• projected tokens [B, T_spat, D_llm]

No further code is required here unless model capacity needs tuning.

5. Qwen2_5OmniSpatialProcessor

File:

• qwen2_5_omni_spur/processing_qwen2_5_omni_spatial.py

Class:

• Qwen2_5OmniSpatialProcessor

Status:

• implemented scaffold
• should be reused, not rewritten

Important public interfaces:

• __call__(...)
• sync_spatial_tokenizer_with_model(model)

Input behavior:

• mono batch:
  • prompt must include one <|AUDIO|>
  • prompt must not include <|spatial|>
• FOA batch:
  • prompt must include one <|AUDIO|>
  • prompt must include one <|spatial|>

Output keys already supported:

• input_ids
• input_features
• feature_attention_mask
• spatial_audio
• spatial_audio_attention_mask
• spatial_audio_lengths
• spatial_tokens
• seld233_features
• seld233_feature_attention_mask
• seld233_feature_lengths
• spatial_token_lengths

No core algorithm is missing here.

What still needs to happen outside this file:

• training script must actually instantiate this processor
• inference script must actually instantiate this processor
• both scripts must call processor.sync_spatial_tokenizer_with_model(model)

6. Qwen2_5OmniSpatialThinkerForConditionalGeneration

File:

• qwen2_5_omni_spur/modeling_qwen2_5_omni_spatial.py

Class:

• Qwen2_5OmniSpatialThinkerForConditionalGeneration

Status:

• spatial injection path is implemented
• tokenizer sync helper is implemented
• prefill spatial routing is implemented
• video + audio + spatial RoPE is implemented for use_audio_in_video=False

Important public interfaces:

• forward(...)
• prepare_inputs_for_generation(...)
• sync_spatial_tokenizer(...)

Input contract:

• normal Qwen text/audio inputs
• optional spatial inputs:
  • spatial_audio [B, T_audio_max, 4]
  • seld233_features [B, 7, T_feat_max, 64]
  • spatial_tokens [B, T_spat_max, D_spat]
  • matching length or mask tensors

Output behavior:

• if no spatial input is provided, it falls back to the base thinker
• if spatial input is provided, it injects projected spatial embeddings at <|spatial|> positions

Still missing only if scope expands later:

• true multimodal RoPE for image + spatial
• talker/audio-output path

7. Qwen2_5OmniSpatialForConditionalGeneration

File:

• qwen2_5_omni_spur/modeling_qwen2_5_omni_spatial.py

Class:

• Qwen2_5OmniSpatialForConditionalGeneration

Status:

• implemented scaffold

Important public interfaces:

• sync_spatial_tokenizer(...)
• generate(...)

No core code is missing for text-only generation with audio/spatial input.

Still deferred:

• return_audio=True path

Script-Level Work

8. Training Script

File:

• training-qwen-omni/train_spur_spatial_only_hf.py

Current issue:

• it still uses the old SPUR audio-fusion line

Minimum required changes:

1. Replace imports:
   • old:
     • Qwen2_5OmniProcessor
     • Qwen2_5OmniForConditionalGeneration
   • new:
     • Qwen2_5OmniSpatialProcessor
     • Qwen2_5OmniSpatialForConditionalGeneration
2. Replace old SPUR config overrides with new seld233_* thinker config fields.
3. After loading processor and model, call:
   • processor.sync_spatial_tokenizer_with_model(model)
4. Update prompt construction:
   • mono batch prompt: includes <|AUDIO|> only
   • FOA batch prompt: includes <|AUDIO|><|spatial|>
5. Ensure collator preserves mono or FOA batch homogeneity.
6. Ensure audio clips are clipped to 20s.
7. Ensure training batch uses the new processor output keys.

Functions that most likely need modification:

• apply_spur_config_overrides(...)
• main()

9. Inference Script

File:

• qwen-omni-inference.py

Current issue:

• it still uses the old processor/model path

Minimum required changes:

1. Replace imports with the new spatial subclasses.
2. Load FOA audio without collapsing channels.
3. Build prompt with canonical tokens:
   • FOA: <|AUDIO|><|spatial|> ...
   • mono: <|AUDIO|> ...
4. Call:
   • processor.sync_spatial_tokenizer_with_model(model)
5. Print or assert:
   • spatial_token_lengths
   • number of <|spatial|> token positions after tokenization
6. Run one prefill/generation sanity check.

There is no reusable function boundary in the current script; the top-level script body itself needs to be migrated.

Suggested Implementation Order

1. Implement SeldNet233FeatureBridge._extract_online_features.
2. Implement SeldNet233Backbone._run_seldnet_backbone.
3. Run a direct module test:
   • spatial_audio -> feature bridge -> backbone -> adapter
4. Migrate training-qwen-omni/train_spur_spatial_only_hf.py.
5. Migrate qwen-omni-inference.py.
6. Only after that, consider optional RoPE/talker extensions.

Required Checks After Each Stage

After feature bridge

• same clip gives matching online vs offline task-233 features
• feature tensor is [B, 7, T_feat_max, 64]
• feature mask matches feature_lengths

After backbone

• checkpoint loads successfully
• captured hidden is [B, T_seld_max, 128]
• hidden length matches derived T_seld

After end-to-end spatial adapter

• spatial_tokens is [B, T_spat_max, 256]
• spatial_token_lengths equals ceil(T_seld / 4)
• no placeholder count mismatch

After training/inference migration

• <|spatial|> is present in tokenizer vocab
• config.thinker_config.spatial_token_index is set
• thinker embedding size matches tokenizer size
• mono batch runs without spatial injection
• FOA batch runs with spatial injection

Out of Scope For The Next Coding Stage

• support mixed mono/FOA batches
• support image/video + spatial at the same time
• support talker audio generation with spatial tokens
• change the task-233 feature definition or baseline architecture