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Configuration for CAL-MOS Hugging Face models.
CAL-MOS: Bridging Layers with Adapters for Robust MOS Prediction Across
Speech Foundation Models, Interspeech 2026.
"""
from transformers import PretrainedConfig
class CalMOSConfig(PretrainedConfig):
"""Configuration for the CAL-MOS Adapters + Mean (A+M) model."""
model_type = "calmos"
def __init__(
self,
backbone_name=None,
num_feature_layers=None,
adapter_input_dim=None,
adapter_dim=None,
mlp_input_dim=None,
mlp_hidden_dim=None,
mlp_num_layers=1,
mlp_output_size=1,
mlp_dropout=0.1,
target_sr=16000,
mos_min=1.0,
mos_max=5.0,
layer_weight_strategy="layer_adapter",
pooling_strategy="mean",
freeze_backbone=True,
**kwargs,
):
super().__init__(**kwargs)
self.backbone_name = backbone_name
self.num_feature_layers = num_feature_layers
self.adapter_input_dim = adapter_input_dim
self.adapter_dim = adapter_dim
self.mlp_input_dim = mlp_input_dim
self.mlp_hidden_dim = mlp_hidden_dim
self.mlp_num_layers = mlp_num_layers
self.mlp_output_size = mlp_output_size
self.mlp_dropout = mlp_dropout
self.target_sr = target_sr
self.mos_min = mos_min
self.mos_max = mos_max
self.layer_weight_strategy = layer_weight_strategy
self.pooling_strategy = pooling_strategy
self.freeze_backbone = freeze_backbone
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