Fix vc_fn

app.py

@@ -24,27 +24,25 @@ def audio_postprocess(self, y):
     return gr_processing_utils.encode_url_or_file_to_base64(data["name"])
 
 gr.Audio.postprocess = audio_postprocess
-def create_vc_fn(model, sid):
-    def vc_fn(input_audio, vc_transform, auto_f0):
-        if input_audio is None:
-            return "You need to upload an audio", None
-        sampling_rate, audio = input_audio
-        duration = audio.shape[0] / sampling_rate
-        if duration > 20 and limitation:
-            return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
-        audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-        if len(audio.shape) > 1:
-            audio = librosa.to_mono(audio.transpose(1, 0))
-        if sampling_rate != 16000:
-            audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-        raw_path = io.BytesIO()
-        soundfile.write(raw_path, audio, 16000, format="wav")
-        raw_path.seek(0)
-        out_audio, out_sr = model.infer(sid, vc_transform, raw_path,
-                                       auto_predict_f0=auto_f0,
-                                       )
-        return "Success", (44100, out_audio.cpu().numpy())
-    return vc_fn
+def vc_fn(input_audio, vc_transform, auto_f0):
+    if input_audio is None:
+        return "You need to upload an audio", None
+    sampling_rate, audio = input_audio
+    duration = audio.shape[0] / sampling_rate
+    if duration > 20 and limitation:
+        return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
+    audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
+    if len(audio.shape) > 1:
+        audio = librosa.to_mono(audio.transpose(1, 0))
+    if sampling_rate != 16000:
+        audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
+    raw_path = io.BytesIO()
+    soundfile.write(raw_path, audio, 16000, format="wav")
+    raw_path.seek(0)
+    out_audio, out_sr = model.infer(sid, vc_transform, raw_path,
+                                   auto_predict_f0=auto_f0,
+                                   )
+    return "Success", (44100, out_audio.cpu().numpy())
 
 def get_speakers():
   speakers = []