Seismic impedance inversion is a fundamental task in exploration geophysics. With the development of artificial intelligence, deep learning has been increasingly adopted for post-stack seismic impedance inversion. This paper proposes a frequency-aware deep hybrid framework that integrates continuous wavelet transform (CWT) representations, a transformer encoder, and atrous spatial pyramid pooling (ASPP) to improve both impedance fidelity and resolution. The CWT provides time–frequency components of seismic traces; the transformer aggregates non-local context to stabilize structural consistency; and ASPP captures local multi-scale features. In addition, we introduce a frequency-aware loss that rebalances optimization toward components that are more difficult to reconstruct, improving detail recovery in thin beds. Network training used a semi-supervised strategy. At well locations, well-log impedance values supervised the network prediction; at non-well locations, synthetic seismic data were generated by seismic forward modeling and compared with the observed seismic records. Validation on the Marmousi II model and a field dataset indicated that the proposed method achieves consistently higher accuracy than representative deep-learning baselines and a commercial post-stack inversion workflow.