A soft pneumatic actuator (SPA) is one of the most prominent components in a soft robotic system. The sensing of SPAs is challenging owing to their elasticity and deformability. Sensors for specific factors are required to successfully sense an SPA. A flexible sensor is an important component for sensing the conditions of SPAs, such as the shape and deformation due to contact events during applications. Developing versatile sensors with high flexibility and tolerability for SPAs is challenging. Data-driven sensing approaches involve individual machine learning models for different actuators. In contrast, it is an enormous advantage to have versatile sensors as hardware and machine learning models as software employed on various actuators. Therefore, we propose a transferable shape estimation method based on active vibro-acoustic sensing to achieve tolerability and increase versatility. We created easily transferable sensing devices for SPAs. In addition, we employed a data-driven approach that utilizes a simple transfer learning technique on a two-dimensional convolutional neural network model. We confirm the feasibility and versatility of the proposed method through evaluation experiments. A transferable estimation method was used on SPAs to estimate the bending angle and length under various sensing and environmental conditions, and the average errors were less than 3.5 $^\circ$ and 2.1 mm, respectively.