optimize(vc): use np.multiply instead of general *

configs/config.py

@@ -32,7 +32,7 @@ version_config_list = [
 
 def singleton_variable(func):
     def wrapper(*args, **kwargs):
-        if not wrapper.instance:
+        if wrapper.instance is None:
             wrapper.instance = func(*args, **kwargs)
         return wrapper.instance
 

infer-web.py

@@ -6,6 +6,10 @@ now_dir = os.getcwd()
 sys.path.append(now_dir)
 load_dotenv()
 load_dotenv("sha256.env")
+
+if sys.platform == "darwin":
+    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+
 from infer.modules.vc import VC
 from infer.modules.uvr5.modules import uvr
 from infer.lib.train.process_ckpt import (

infer/modules/vc/modules.py

@@ -201,7 +201,7 @@ class VC:
                 self.version,
                 protect,
                 f0_file,
-            )
+            ).astype(np.int16)
             if self.tgt_sr != resample_sr >= 16000:
                 tgt_sr = resample_sr
             else:

infer/modules/vc/pipeline.py

@@ -288,7 +288,7 @@ class Pipeline(object):
             hasp = pitch is not None and pitchf is not None
             arg = (feats, p_len, pitch, pitchf, sid) if hasp else (feats, p_len, sid)
             audio1 = (net_g.infer(*arg)[0][0, 0]).data.cpu().float().numpy()
-            del hasp, arg
+            del arg
         del feats, p_len, padding_mask
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
@@ -469,7 +469,7 @@ class Pipeline(object):
         max_int16 = 32768
         if audio_max > 1:
             max_int16 /= audio_max
-        audio_opt = (audio_opt * max_int16).astype(np.int16)
+        np.multiply(audio_opt, max_int16, audio_opt)
         del pitch, pitchf, sid
         if torch.cuda.is_available():
             torch.cuda.empty_cache()