File size: 6,741 Bytes
d2453a6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
"""

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)  #