# Training ## Overview We have put a large emphasis on making training as fast as possible. Consequently, some pre-processing steps are required. Namely, before starting any training, we 1. Encode training audios into spectrograms and then with VAE into mean/std 2. Extract CLIP and synchronization features from videos 3. Extract CLIP features from text (captions) 4. Encode all extracted features into [MemoryMappedTensors](https://pytorch.org/tensordict/main/reference/generated/tensordict.MemoryMappedTensor.html) with [TensorDict](https://pytorch.org/tensordict/main/reference/tensordict.html) **NOTE:** for maximum training speed (e.g., when training the base model with 2*H100s), you would need around 3~5 GB/s of random read speed. Spinning disks would not be able to catch up and most consumer-grade SSDs would struggle. In my experience, the best bet is to have a large enough system memory such that the OS can cache the data. This way, the data is read from RAM instead of disk. The current training script does not support `_v2` training. ## Prerequisites Install [av-benchmark](https://github.com/hkchengrex/av-benchmark). We use this library to automatically evaluate on the validation set during training, and on test set after training. You will also need ffmpeg for video frames extraction. Note that `torchaudio` imposes a maximum version limit (`ffmpeg<7`). You can install it as follows: ```bash conda install -c conda-forge 'ffmpeg<7' ``` Download the corresponding VAE (`v1-16.pth` for 16kHz training, and `v1-44.pth` for 44.1kHz training), vocoder models (`best_netG.pt` for 16kHz training; the vocoder for 44.1kHz training will be downloaded automatically), the [empty string encoding](https://github.com/hkchengrex/MMAudio/releases/download/v0.1/empty_string.pth), and Synchformer weights from [MODELS.md](https://github.com/hkchengrex/MMAudio/blob/main/docs/MODELS.md) place them in `ext_weights/`. ## Preparing Audio-Video-Text Features We have prepared some example data in `training/example_videos`. Running the `training/extract_video_training_latents.py` script will extract the audio, video, and text features and save them as a `TensorDict` with a `.tsv` file containing metadata on disk. To run this script, use the `torchrun` utility: ```bash torchrun --standalone training/extract_video_training_latents.py ``` You can run this with multiple GPUs (with `--nproc_per_node=`) to speed up extraction. Check the top of the script to switch between 16kHz/44.1kHz extraction and data path definitions. Arguments: - `latent_dir` -- where intermediate latent outputs are saved. It is safe to delete this directory afterwards. - `output_dir` -- where TensorDict and the metadata file are saved. ## Preparing Audio-Text Features We have prepared some example data in `training/example_audios`. We first need to run `training/partition_clips` to partition each audio file into clips. Then, we run the `training/extract_audio_training_latents.py` script, which will extract the audio and text features and save them as a `TensorDict` with a `.tsv` file containing metadata on the disk. To run this script: ```bash python training/partition_clips.py ``` Arguments: - `data_path` -- path to the audio files (`.flac` or `.wav`) - `output_dir` -- path to the output `.csv` file - `start` -- optional; useful when you need to run multiple processes to speed up processing -- this defines the beginning of the chunk to be processed - `end` -- optional; useful when you need to run multiple processes to speed up processing -- this defines the end of the chunk to be processed Then, run the `extract_audio_training_latents.py` with `torchrun`: ```bash torchrun --standalone training/extract_audio_training_latents.py ``` You can run this with multiple GPUs (with `--nproc_per_node=`) to speed up extraction. Check the top of the script to switch between 16kHz/44.1kHz extraction. Arguments: - `data_dir` -- path to the audio files (`.flac` or `.wav`), same as the previous step - `captions_tsv` -- path to the captions file, a csv file at least with columns `id` and `caption` - `clips_tsv` -- path to the clips file, generated in the last step - `latent_dir` -- where intermediate latent outputs are saved. It is safe to delete this directory afterwards. - `output_dir` -- where TensorDict and the metadata file are saved. ## Training We use Distributed Data Parallel (DDP) for training. First, specify the data path in `config/data/base.yaml`. If you used the default parameters in the scripts above to extract features for the example data, the `Example_video` and `Example_audio` items should already be correct. To run training on the example data, use the following command: ```bash OMP_NUM_THREADS=4 torchrun --standalone --nproc_per_node=1 train.py exp_id=debug compile=False debug=True example_train=True batch_size=1 ``` This will not train a useful model, but it will check if everything is set up correctly. For full training on the base model with two GPUs, use the following command: ```bash OMP_NUM_THREADS=4 torchrun --standalone --nproc_per_node=2 train.py exp_id=exp_1 model=small_16k ``` Any outputs from training will be stored in `output/`. More configuration options can be found in `config/base_config.yaml` and `config/train_config.yaml`. ## Checkpoints Model checkpoints, including optimizer states and the latest EMA weights, are available here: https://huggingface.co/hkchengrex/MMAudio --- Godspeed!