chore: import upstream snapshot with attribution

docs/GettingStarted.md

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+# Getting Started
+
+## Installation
+
+### Environments and dependencies
+
+DiffSinger requires Python 3.10 or later. We strongly recommend you create a virtual environment via Conda, venv or uv before installing dependencies.
+
+1. Install The latest PyTorch following the [official instructions](https://pytorch.org/get-started/locally/) according to your OS and hardware. We recommend using the latest stable release that is >= 2.4.0.
+
+2. Install other dependencies via the following command:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+### Concepts and materials
+
+Before you proceed, it is necessary to understand some fundamental concepts in this repository and prepare some materials and assets. See [fundamental concepts and materials](BestPractices.md#fundamental-concepts-and-materials) for detailed information.
+
+## Configuration
+
+Every model needs a configuration file to run preprocessing, training, inference and deployment. Templates of configurations files are in [configs/templates](../configs/templates). Please **copy** the templates to your own data directory before you edit them.
+
+Before you continue, it is highly recommended to read through [Best Practices](BestPractices.md), which is a more detailed tutorial on how to configure your experiments.
+
+For more details about configurable parameters, see [Configuration Schemas](ConfigurationSchemas.md).
+
+> Tips: to see which parameters are required or recommended to be edited, you can search by _customizability_ in the configuration schemas.
+
+## Preprocessing
+
+Raw data pieces and transcriptions should be binarized into dataset files before training. Before doing this step, please ensure all required configurations like `raw_data_dir` and `binary_data_dir` are set properly, and all your desired functionalities and features are enabled and configured.
+
+Assume that you have a configuration file called `my_config.yaml`. Run:
+
+```bash
+python scripts/binarize.py --config my_config.yaml
+```
+
+Preprocessing can be accelerated through multiprocessing. See [binarization_args.num_workers](ConfigurationSchemas.md#binarization_args.num_workers) for more explanations.
+
+## Training
+
+Assume that you have a configuration file called `my_config.yaml` and the name of your model is `my_experiment`. Run:
+
+```bash
+python scripts/train.py --config my_config.yaml --exp_name my_experiment --reset
+```
+
+Checkpoints will be saved at the `checkpoints/my_experiment/` directory. When interrupting the program and running the above command again, the training resumes automatically from the latest checkpoint.
+
+For more suggestions related to training performance, see [performance tuning](BestPractices.md#performance-tuning).
+
+### TensorBoard
+
+Run the following command to start the TensorBoard:
+
+```bash
+tensorboard --logdir checkpoints/
+```
+
+> NOTICE
+> 
+> If you are training a model with multiple GPUs (DDP), please add `--reload_multifile=true` option when launching TensorBoard, otherwise it may not update properly.
+
+## Inference
+
+Inference of DiffSinger is based on DS files. Assume that you have a DS file named `my_song.ds` and your model is named `my_experiment`.
+
+If your model is a variance model, run:
+
+```bash
+python scripts/infer.py variance my_song.ds --exp my_experiment
+```
+
+or run
+
+```bash
+python scripts/infer.py variance --help
+```
+
+for more configurable options.
+
+If your model is an acoustic model, run:
+
+```bash
+python scripts/infer.py acoustic my_song.ds --exp my_experiment
+```
+
+or run
+
+```bash
+python scripts/infer.py acoustic --help
+```
+
+for more configurable options.
+
+## Deployment
+
+DiffSinger uses [ONNX](https://onnx.ai/) as the deployment format.
+
+Assume that you have a model named `my_experiment`.
+
+If your model is a variance model, run:
+
+```bash
+python scripts/export.py variance --exp my_experiment
+```
+
+or run
+
+```bash
+python scripts/export.py variance --help
+```
+
+for more configurable options.
+
+If your model is an acoustic model, run:
+
+```bash
+python scripts/export.py acoustic --exp my_experiment
+```
+
+or run
+
+```bash
+python scripts/export.py acoustic --help
+```
+
+for more configurable options.
+
+To export an NSF-HiFiGAN vocoder checkpoint, run:
+
+```bash
+python scripts/export.py nsf-hifigan --config CONFIG --ckpt CKPT
+```
+
+where `CONFIG` is a configuration file that has configured the same mel parameters as the vocoder (can be configs/acoustic.yaml for most cases) and `CKPT` is the path of the checkpoint to be exported.
+
+For more configurable options, run
+
+```bash
+python scripts/export.py nsf-hifigan --help
+```
+
+## Other utilities
+
+There are other useful CLI tools in the [scripts/](../scripts) directory not mentioned above:
+
+- drop_spk.py - delete speaker embeddings from checkpoints (for data security reasons when distributing models)
+- vocoder.py - bypass the acoustic model and only run the vocoder on given mel-spectrograms