Mirror lj1995/VoiceConversionWebUI @ b2c8cae96e3b — infer.py

infer.py

@@ -0,0 +1,48 @@
+import torch, pdb, os,sys,librosa,warnings,traceback
+warnings.filterwarnings("ignore")
+torch.manual_seed(114514)
+sys.path.append(os.getcwd())
+from config import inp_root,opt_root,f0_up_key,person,is_half,device
+os.makedirs(opt_root,exist_ok=True)
+import soundfile as sf
+from infer_pack.models import SynthesizerTrnMs256NSF as SynthesizerTrn256
+from scipy.io import wavfile
+from fairseq import checkpoint_utils
+import scipy.signal as signal
+from vc_infer_pipeline import VC
+
+models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"],suffix="",)
+model = models[0]
+model = model.to(device)
+if(is_half):model = model.half()
+else:model = model.float()
+model.eval()
+
+cpt=torch.load(person,map_location="cpu")
+dv=cpt["dv"]
+tgt_sr=cpt["config"][-1]
+net_g = SynthesizerTrn256(*cpt["config"],is_half=is_half)
+net_g.load_state_dict(cpt["weight"],strict=True)
+net_g.eval().to(device)
+if(is_half):net_g = net_g.half()
+else:net_g = net_g.float()
+
+vc=VC(tgt_sr,device,is_half)
+
+for name in os.listdir(inp_root):
+    try:
+        wav_path="%s\%s"%(inp_root,name)
+        print("processing %s"%wav_path)
+        audio, sampling_rate = sf.read(wav_path)
+        if len(audio.shape) > 1:
+            audio = librosa.to_mono(audio.transpose(1, 0))
+        if sampling_rate != vc.sr:
+            audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=vc.sr)
+
+        times = [0, 0, 0]
+        audio_opt=vc.pipeline(model,net_g,dv,audio,times,f0_up_key,f0_file=None)
+        wavfile.write("%s/%s"%(opt_root,name), tgt_sr, audio_opt)
+    except:
+        traceback.print_exc()
+
+print(times)