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Whispered speech lacks vocal-fold excitation and exhibits reduced energy and shifted formant frequencies, making natural and intelligible voice reconstruction highly challenging. To address this issue, we propose \emph{WhisperVC}, a three-stage framework for Mandarin whisper-to-speech (W2S) conversion. Stage~1 employs a fine-tuned Content Encoder based on the OpenAI Whisper-large~V3 model and a Conformer-based variational autoencoder with soft-DTW alignment to learn domain-invariant and temporally consistent representations. Stage~2 introduces a deterministic Length--Channel Aligner and a duration-free FastSpeech~2 model conditioned on speaker embeddings for controllable timbre and stable prosody. Stage~3 fine-tunes a HiFi-GAN vocoder on predicted mel-spectrograms to synthesize high-fidelity waveforms. Experiments on the AISHELL6-Whisper corpus demonstrate that WhisperVC achieves near ground-truth quality (\textbf{DNSMOS~3.11}, \textbf{UTMOS~2.52}, \textbf{CER~18.67\%}), while maintaining speaker similarity (\textbf{cosine~0.76}) and robust performance under whisper-only inference.