Support On-the-fly Features Extraction

[RMSnow]

✨ Description

Support on-the-fly features extraction for the large-scale data preprocessing. Its strengths can be summarized as:

• Save the disk space of features storage
• Avoid to be stuck by the features extraction due to some computational platform issues
• Simplify the preprocessing pipeline and make users focus on training

How to use?

Under the on-the-fly features extraction, the workflow for the future Amphion model is:

1. Data Preprocess like before
   • For train/val dataset split
   • For getting the metadata file (.json) like before. The utt["Path"] and utt["Duration"] are the two key elements.
   • For getting the medata statistics information like before.
2. Features Preprocess (No features preprocess any more!)
3. Training
   • For config file, you need to set preprocess.features_extraction_mode as online
   • Implement your [Task]OnlineDataset and [Model]Trainer
4. Inference like before

Currently, I have supported DiffWaveNetSVC with on-the-fly features extraction. You can see the two main classes: SVCOnlineDataset and DiffusionTrainer.

👨‍💻 Main Changes

1. model.base.base_dataset.py:
   • Rename the original BaseDataset and BaseCollator to BaseOfflineDataset and BaseOfflineDataset and BaseOfflineCollator
   • Implement the BaseOnlineDataset and BaseOnlineCollator. The __getitem__ function will get the minimum elements (such as the raw waveform and its duration)
2. processors.audio_features_extractor.py:
   • In Amphion's latest technical report, we formulate the audio generation tasks into three categories: Text to Waveform, Descriptive Text to Waveform, and Waveform to Waveform. Therefore, we can also implement three kind of features extraction: Text Features, Descriptive Text Features, and Waveform Features.
   • In audio_features_extractor.py, I have integrated the common waveform features extraction operation (such as Mel Spectrogram, F0, Energy, and Semantic Features). Note that I have not integrated some vocoder requiring features. @VocodexElysium
   • I have created text_features_extractor.py and descriptive_text_features_extractor.py for future TTS, TTA, and TTM's refactor/integration/supplement. @HeCheng0625 @lmxue @HarryHe11 @viewfinder-annn
3. Support for DiffWaveNetSVC
   • two main classes: SVCOnlineDataset and DiffusionTrainer.
4. Refactor and improve some codes
   • Such as re-organizing for config folder as Amphion/config/[Task]/[Model].json.

✅ Checklist

• [x] Code has been reviewed
• [x] Code complies with the project's code standards and best practices
• [x] Code has passed all tests
• [x] Code does not affect the normal use of existing features
• [x] Code has been commented properly
• [x] Documentation has been updated (if applicable)
• [x] Demo/checkpoint has been attached (if applicable)

[RMSnow]

The recipe should be updated to provide instructions for online feature extraction.

@lmxue Good advice. I plan to update the recipe in the future. This PR is to prepare a codebase for our recent internal research.