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Neural Audio Codecs (NACs) achieve high-fidelity reconstruction at low bitrates but remain insensitive to fundamental frequency (F0) distortion—a critical limitation for tonal languages, covering 40%+ of the world's languages, where F0 contour marks lexical distinctions. We propose the Pitch-Injected Residual Adapter (PIRA), a lightweight plug-and-play module that restores tonal information in frozen NACs through explicit F0 and voiced/unvoiced side-information injection. PIRA processes quantized F0 through dilated convolutions to capture tone sandhi dependencies, supervised by a CREPE embedding loss for pitch-specific gradients. A confidence network gates injection per-frame, suppressing it for checked tones and unvoiced segments. With 1.25M–1.65M trainable parameters, PIRA reduces codec-induced Tone Error Rate (dTER) by 35.7% on average (macro-averaged across five codecs, each averaged over three tonal languages), while preserving English quality and adding ≤0.4 kbps overhead.