""" 对源特征进行检索 """ import os import logging logger = logging.getLogger(__name__) import parselmouth import torch os.environ["CUDA_VISIBLE_DEVICES"] = "0" # import torchcrepe from time import time as ttime # import pyworld import librosa import numpy as np import soundfile as sf import torch.nn.functional as F from fairseq import checkpoint_utils # from models import SynthesizerTrn256#hifigan_nonsf # from lib.infer_pack.models import SynthesizerTrn256NSF as SynthesizerTrn256#hifigan_nsf from infer.lib.infer_pack.models import ( SynthesizerTrnMs256NSFsid as SynthesizerTrn256, ) # hifigan_nsf from scipy.io import wavfile # from lib.infer_pack.models import SynthesizerTrnMs256NSFsid_sim as SynthesizerTrn256#hifigan_nsf # from models import SynthesizerTrn256NSFsim as SynthesizerTrn256#hifigan_nsf # from models import SynthesizerTrn256NSFsimFlow as SynthesizerTrn256#hifigan_nsf device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model_path = r"E:\codes\py39\vits_vc_gpu_train\assets\hubert\hubert_base.pt" # logger.info("Load model(s) from {}".format(model_path)) models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( [model_path], suffix="", ) model = models[0] model = model.to(device) model = model.half() model.eval() # net_g = SynthesizerTrn256(1025,32,192,192,768,2,6,3,0.1,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,10,2,2],512,[16,16,4,4],183,256,is_half=True)#hifigan#512#256 # net_g = SynthesizerTrn256(1025,32,192,192,768,2,6,3,0.1,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,10,2,2],512,[16,16,4,4],109,256,is_half=True)#hifigan#512#256 net_g = SynthesizerTrn256( 1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4], 183, 256, is_half=True, ) # hifigan#512#256#no_dropout # net_g = SynthesizerTrn256(1025,32,192,192,768,2,3,3,0.1,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,10,2,2],512,[16,16,4,4],0)#ts3 # net_g = SynthesizerTrn256(1025,32,192,192,768,2,6,3,0.1,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,10,2],512,[16,16,4],0)#hifigan-ps-sr # # net_g = SynthesizerTrn(1025, 32, 192, 192, 768, 2, 6, 3, 0.1, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [5,5], 512, [15,15], 0)#ms # net_g = SynthesizerTrn(1025, 32, 192, 192, 768, 2, 6, 3, 0.1, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,10], 512, [16,16], 0)#idwt2 # weights=torch.load("infer/ft-mi_1k-noD.pt") # weights=torch.load("infer/ft-mi-freeze-vocoder-flow-enc_q_1k.pt") # weights=torch.load("infer/ft-mi-freeze-vocoder_true_1k.pt") # weights=torch.load("infer/ft-mi-sim1k.pt") weights = torch.load("infer/ft-mi-no_opt-no_dropout.pt") logger.debug(net_g.load_state_dict(weights, strict=True)) net_g.eval().to(device) net_g.half() def get_f0(x, p_len, f0_up_key=0): time_step = 160 / 16000 * 1000 f0_min = 50 f0_max = 1100 f0_mel_min = 1127 * np.log(1 + f0_min / 700) f0_mel_max = 1127 * np.log(1 + f0_max / 700) f0 = ( parselmouth.Sound(x, 16000) .to_pitch_ac( time_step=time_step / 1000, voicing_threshold=0.6, pitch_floor=f0_min, pitch_ceiling=f0_max, ) .selected_array["frequency"] ) pad_size = (p_len - len(f0) + 1) // 2 if pad_size > 0 or p_len - len(f0) - pad_size > 0: f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant") f0 *= pow(2, f0_up_key / 12) f0bak = f0.copy() f0_mel = 1127 * np.log(1 + f0 / 700) f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / ( f0_mel_max - f0_mel_min ) + 1 f0_mel[f0_mel <= 1] = 1 f0_mel[f0_mel > 255] = 255 # f0_mel[f0_mel > 188] = 188 f0_coarse = np.rint(f0_mel).astype(np.int32) return f0_coarse, f0bak import faiss index = faiss.read_index("infer/added_IVF512_Flat_mi_baseline_src_feat.index") big_npy = np.load("infer/big_src_feature_mi.npy") ta0 = ta1 = ta2 = 0 for idx, name in enumerate( [ "冬之花clip1.wav", ] ): ## wav_path = "todo-songs/%s" % name # f0_up_key = -2 # audio, sampling_rate = sf.read(wav_path) 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) feats = torch.from_numpy(audio).float() if feats.dim() == 2: # double channels feats = feats.mean(-1) assert feats.dim() == 1, feats.dim() feats = feats.view(1, -1) padding_mask = torch.BoolTensor(feats.shape).fill_(False) inputs = { "source": feats.half().to(device), "padding_mask": padding_mask.to(device), "output_layer": 9, # layer 9 } if torch.cuda.is_available(): torch.cuda.synchronize() t0 = ttime() with torch.no_grad(): logits = model.extract_features(**inputs) feats = model.final_proj(logits[0]) ####索引优化 npy = feats[0].cpu().numpy().astype("float32") D, I = index.search(npy, 1) feats = ( torch.from_numpy(big_npy[I.squeeze()].astype("float16")).unsqueeze(0).to(device) ) feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1) if torch.cuda.is_available(): torch.cuda.synchronize() t1 = ttime() # p_len = min(feats.shape[1],10000,pitch.shape[0])#太大了爆显存 p_len = min(feats.shape[1], 10000) # pitch, pitchf = get_f0(audio, p_len, f0_up_key) p_len = min(feats.shape[1], 10000, pitch.shape[0]) # 太大了爆显存 if torch.cuda.is_available(): torch.cuda.synchronize() t2 = ttime() feats = feats[:, :p_len, :] pitch = pitch[:p_len] pitchf = pitchf[:p_len] p_len = torch.LongTensor([p_len]).to(device) pitch = torch.LongTensor(pitch).unsqueeze(0).to(device) sid = torch.LongTensor([0]).to(device) pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device) with torch.no_grad(): audio = ( net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] .data.cpu() .float() .numpy() ) # nsf if torch.cuda.is_available(): torch.cuda.synchronize() t3 = ttime() ta0 += t1 - t0 ta1 += t2 - t1 ta2 += t3 - t2 # wavfile.write("ft-mi_1k-index256-noD-%s.wav"%name, 40000, audio)## # wavfile.write("ft-mi-freeze-vocoder-flow-enc_q_1k-%s.wav"%name, 40000, audio)## # wavfile.write("ft-mi-sim1k-%s.wav"%name, 40000, audio)## wavfile.write("ft-mi-no_opt-no_dropout-%s.wav" % name, 40000, audio) ## logger.debug("%.2fs %.2fs %.2fs", ta0, ta1, ta2) #