add atten_lim + dry/wet mix sliders

app.py

@@ -46,16 +46,20 @@ def to_wav(audio_path):
     return out
 
 
-def denoise(audio_path):
+def denoise(audio_path, atten_lim, mix):
     if not audio_path:
         return None
     from df.enhance import enhance, load_audio, save_audio
 
     model, df_state = get_model()
     audio, _ = load_audio(to_wav(audio_path), sr=df_state.sr())
-    enhanced = enhance(model, df_state, audio)
+    lim = None if atten_lim == 0 else atten_lim
+    enhanced = enhance(model, df_state, audio, atten_lim_db=lim)
+    # 混回原声：mix=1 为纯降噪结果，调低可找回被吃掉的细节
+    n = min(enhanced.shape[-1], audio.shape[-1])
+    out_audio = enhanced[..., :n] * mix + audio[..., :n] * (1.0 - mix)
     out = "/tmp/enhanced.wav"
-    save_audio(out, enhanced, df_state.sr())
+    save_audio(out, out_audio, df_state.sr())
     return out
 
 
@@ -66,9 +70,19 @@ with gr.Blocks(title="音频降噪增强") as demo:
         "适合口播、讲座这类以人声为主的素材。CPU 推理，长音频请按时长耐心等。"
     )
     inp = gr.Audio(label="原始音频", type="filepath")
+    atten = gr.Slider(
+        0, 40, value=20, step=1,
+        label="最大衰减上限 (dB)",
+        info="限制模型对每个频点的抑制强度：数值越小越温和、损失越小；0 = 不限制（最强降噪）。默认 20。",
+    )
+    mix = gr.Slider(
+        0, 1, value=1.0, step=0.05,
+        label="降噪结果占比",
+        info="1 = 纯降噪结果；调低可按比例混回原声，找回被吃掉的细节（相声里的笑声、掌声也适用）。",
+    )
     btn = gr.Button("开始降噪", variant="primary")
     out = gr.Audio(label="降噪后", type="filepath")
-    btn.click(denoise, inputs=inp, outputs=out)
+    btn.click(denoise, inputs=[inp, atten, mix], outputs=out)
 
 
 if __name__ == "__main__":