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