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