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EVAL_SPLIT = 0.01 # Fraction of training data used for evaluation
WANDB_KEY = "<YOUR_WANDB_KEY>" # Weights and Biases API key
# -------------------- Configuration for M3 Training --------------------
M3_TRAIN_FOLDERS = [
"<YOUR_TRAINING_DATA_FOLDER>" # Directory containing training data for M3
]
M3_EVAL_FOLDERS = [
"<YOUR_EVALUATION_DATA_FOLDER>" # Directory containing evaluation data for M3 (optional)
]
PATCH_SIZE = 64 # Size of each patch
PATCH_LENGTH = 512 # Length of the patches
PATCH_NUM_LAYERS = 12 # Number of layers in the encoder
TOKEN_NUM_LAYERS = 3 # Number of layers in the decoder
M3_HIDDEN_SIZE = 768 # Size of the hidden layer
M3_NUM_EPOCH = 100 # Maximum number of epochs for training
M3_LEARNING_RATE = 1e-4 # Learning rate for the optimizer
M3_BATCH_SIZE = 16 # Batch size per GPU (single card) during training
M3_MASK_RATIO = 0.45 # Ratio of masked elements during training
M3_DETERMINISTIC = True # Ensures deterministic results with random seeds
M3_WANDB_LOG = True # Enable logging to Weights and Biases
M3_LOAD_CKPT = True # Load model weights from a checkpoint if available
M3_WEIGHTS_PATH = (
"weights_m3"+
"_h_size_" + str(M3_HIDDEN_SIZE) +
"_t_layers_" + str(TOKEN_NUM_LAYERS) +
"_p_layers_" + str(PATCH_NUM_LAYERS) +
"_p_size_" + str(PATCH_SIZE) +
"_p_length_" + str(PATCH_LENGTH) +
"_lr_" + str(M3_LEARNING_RATE) +
"_batch_" + str(M3_BATCH_SIZE) +
"_mask_" + str(M3_MASK_RATIO) + ".pth"
) # Path to store the model weights
M3_LOGS_PATH = M3_WEIGHTS_PATH.replace("weights", "logs").replace("pth", "txt") # Path to save training logs
# -------------------- Configuration for CLaMP3 Training ----------------
CLAMP3_TRAIN_JSONL = "<YOUR_TRAINING_JSONL_FILE>" # Path to the JSONL file with training data for CLaMP3
CLAMP3_EVAL_JSONL = "<YOUR_EVALUATION_JSONL_FILE>" # Path to the JSONL file with evaluation data for CLaMP3 (optional)
CLAMP3_HIDDEN_SIZE = 768 # Size of the hidden layer
TEXT_MODEL_NAME = "FacebookAI/xlm-roberta-base" # Name of the pre-trained text model
MAX_TEXT_LENGTH = 128 # Maximum allowed length for text input
AUDIO_HIDDEN_SIZE = 768 # Size of the hidden layer for audio features
AUDIO_NUM_LAYERS = 12 # Number of layers in the audio encoder
MAX_AUDIO_LENGTH = 128 # Maximum allowed length for audio input
CLAMP3_NUM_EPOCH = 100 # Maximum number of epochs for training
CLAMP3_LEARNING_RATE = 1e-5 # Learning rate for the optimizer
CLAMP3_BATCH_SIZE = 256 # Batch size per GPU (single card) during training
LOGIT_SCALE = 1 # Scaling factor for contrastive loss
FREEZE_TEXT = False # Freeze the weights of the text model and text projection layer
TEXT_DROPOUT = True # Whether to apply dropout during text processing
CLAMP3_DETERMINISTIC = True # Ensures deterministic results with random seeds
CLAMP3_LOAD_M3 = True # Load weights from the M3 model
CLAMP3_WANDB_LOG = True # Enable logging to Weights and Biases
CLAMP3_LOAD_CKPT = True # Load weights from a checkpoint if available
SAVE_EVERY = 5 # Save model weights every SAVE_EVERY epochs
CLAMP3_WEIGHTS_PATH = (
"weights_clamp3_saas" +
"_h_size_" + str(CLAMP3_HIDDEN_SIZE) +
"_t_model_" + TEXT_MODEL_NAME.replace("/", "_") +
"_t_length_" + str(MAX_TEXT_LENGTH) +
"_a_size_" + str(AUDIO_HIDDEN_SIZE) +
"_a_layers_" + str(AUDIO_NUM_LAYERS) +
"_a_length_" + str(MAX_AUDIO_LENGTH) +
"_s_size_" + str(M3_HIDDEN_SIZE) +
"_s_layers_" + str(PATCH_NUM_LAYERS) +
"_p_size_" + str(PATCH_SIZE) +
"_p_length_" + str(PATCH_LENGTH) + ".pth"
) # Path to store CLaMP3 model weights
CLAMP3_LOGS_PATH = CLAMP3_WEIGHTS_PATH.replace("weights", "logs").replace("pth", "txt") # Path to save training logs
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