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| """ | |
| 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) # | |