Current Status According to Sonnet: pyproject.toml scripts/train_audio2eda.py src/configs.py src/data/audio_preprocessing.py src/data/dataloader.py src/data/datasets/hku956.py src/data/datasets/pmemo2019.py
src/data/datasets/types.py src/data/eda_preprocessing.py src/loss.py src/models/base.py src/models/decoders/cnn.py src/models/decoders/lstm.py src/models/decoders/transformer.py src/models/encoders/transformer.py src/models/encoders/wavenet.py src/models/registry.py src/optimizer.py
architect> /ask Can you do a cursory overview and tell me if you see any problems with this repo?

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ Cursory Overview of the Repository ┃ ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛

After reviewing the files, here are some potential issues and areas for improvement:

                                                                                                     1. Implementation Gaps                                                                                                         

• Placeholder Code: Many files contain placeholder comments like "Implement logic here" or "pass" without actual implementation. This is expected in a skeleton codebase, but these need to be filled in.
• Missing Files: Some files mentioned in the ending context are not present in the shared files, such as:
• src/models/audio2eda/transformer.py
• src/models/audio2eda/wavenet2eda.py
• src/logging.py
• src/checkpoint.py
• src/trainer.py

                                                                                                  2. Specific Technical Issues                                                                                                      

• DataLoaderBuilder: The build method in src/data/dataloader.py is defined as a static method but takes self as a parameter, which is inconsistent.
• Model Registry: In src/models/registry.py, the encoder and decoder types are extracted from config dictionaries using get('type'), but in src/configs.py, the ModelConfig has encoder_type and decoder_type fields directly. This
mismatch could cause errors.
• Dataset Implementation: Both dataset classes (HKU956Dataset and PMEmo2019Dataset) reference self.eda_files in their len method, but these attributes are only mentioned in comments and not actually initialized.
• Imports: Some files might be missing necessary imports (e.g., torch in some files).

                                                                                                    3. Configuration Issues                                                                                                         

• Config Validation: The TrainConfig in scripts/train_audio2eda.py is created using TrainConfig(**config_dict), which might not properly validate nested models. Consider using model_validate instead.
• DataLoader Configuration: In train_audio2eda.py, the DataLoaderBuilder.build method is called with model.encoder_params and model.decoder_params instead of audio and EDA feature configs.

                                                                                                    4. Missing Functionality                                                                                                        

• Scheduler Implementation: The OptimizerBuilder returns a scheduler, but the implementation is missing.
• Warnings: The implementation notes mention using the warn package for assumptions, but no warnings are implemented.
• S3 Integration: The dataset paths reference S3 URLs, but there's no code for S3 access or downloading.

                                                                                                    5. Architecture Concerns                                                                                                        

• Model Initialization: The model architecture seems to be split between encoders and decoders, but the specific implementations are missing. This could lead to integration issues.
• Data Preprocessing: The preprocessing functions are defined but not integrated with the datasets.

                                                                                                        Recommendations                                                                                                             

1 Complete Core Implementations: Focus on implementing the dataset classes, model architectures, and training loop.
2 Add Missing Files: Create the missing files mentioned in the ending context.
3 Fix Inconsistencies: Align the parameter names and method signatures across the codebase.
4 Add Error Handling: Implement proper error handling, especially for file operations and model initialization.
5 Add Logging: Implement the logging functionality to track training progress.
6 Add Tests: Consider adding unit tests to verify the functionality of individual components.
7 Documentation: Add docstrings and comments to explain the purpose and usage of each component.

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