File size: 7,395 Bytes
85d3b29 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 |
import os
import sys
import numpy as np
import pyworld
import torchcrepe
import torch
import parselmouth
import tqdm
from multiprocessing import Process, cpu_count
current_directory = os.getcwd()
sys.path.append(current_directory)
from rvc.lib.utils import load_audio
exp_dir = sys.argv[1]
f0_method = sys.argv[2]
num_processes = cpu_count()
try:
hop_length = int(sys.argv[3])
except ValueError:
hop_length = 128
DoFormant = False
Quefrency = 1.0
Timbre = 1.0
class FeatureInput:
def __init__(self, sample_rate=16000, hop_size=160):
self.fs = sample_rate
self.hop = hop_size
self.f0_method_dict = self.get_f0_method_dict()
self.f0_bin = 256
self.f0_max = 1100.0
self.f0_min = 50.0
self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
def mncrepe(self, method, x, p_len, hop_length):
f0 = None
torch_device_index = 0
torch_device = (
torch.device(f"cuda:{torch_device_index % torch.cuda.device_count()}")
if torch.cuda.is_available()
else torch.device("mps")
if torch.backends.mps.is_available()
else torch.device("cpu")
)
audio = torch.from_numpy(x.astype(np.float32)).to(torch_device, copy=True)
audio /= torch.quantile(torch.abs(audio), 0.999)
audio = torch.unsqueeze(audio, dim=0)
if audio.ndim == 2 and audio.shape[0] > 1:
audio = torch.mean(audio, dim=0, keepdim=True).detach()
audio = audio.detach()
if method == "crepe":
pitch = torchcrepe.predict(
audio,
self.fs,
hop_length,
self.f0_min,
self.f0_max,
"full",
batch_size=hop_length * 2,
device=torch_device,
pad=True,
)
p_len = p_len or x.shape[0] // hop_length
source = np.array(pitch.squeeze(0).cpu().float().numpy())
source[source < 0.001] = np.nan
target = np.interp(
np.arange(0, len(source) * p_len, len(source)) / p_len,
np.arange(0, len(source)),
source,
)
f0 = np.nan_to_num(target)
return f0
def get_pm(self, x, p_len):
f0 = (
parselmouth.Sound(x, self.fs)
.to_pitch_ac(
time_step=160 / 16000,
voicing_threshold=0.6,
pitch_floor=self.f0_min,
pitch_ceiling=self.f0_max,
)
.selected_array["frequency"]
)
return np.pad(
f0,
[
[
max(0, (p_len - len(f0) + 1) // 2),
max(0, p_len - len(f0) - (p_len - len(f0) + 1) // 2),
]
],
mode="constant",
)
def get_harvest(self, x):
f0_spectral = pyworld.harvest(
x.astype(np.double),
fs=self.fs,
f0_ceil=self.f0_max,
f0_floor=self.f0_min,
frame_period=1000 * self.hop / self.fs,
)
return pyworld.stonemask(x.astype(np.double), *f0_spectral, self.fs)
def get_dio(self, x):
f0_spectral = pyworld.dio(
x.astype(np.double),
fs=self.fs,
f0_ceil=self.f0_max,
f0_floor=self.f0_min,
frame_period=1000 * self.hop / self.fs,
)
return pyworld.stonemask(x.astype(np.double), *f0_spectral, self.fs)
def get_rmvpe(self, x):
if not hasattr(self, "model_rmvpe"):
from rvc.lib.rmvpe import RMVPE
self.model_rmvpe = RMVPE("rmvpe.pt", is_half=False, device="cpu")
return self.model_rmvpe.infer_from_audio(x, thred=0.03)
def get_f0_method_dict(self):
return {
"pm": self.get_pm,
"harvest": self.get_harvest,
"dio": self.get_dio,
"rmvpe": self.get_rmvpe,
}
def compute_f0(self, path, f0_method, hop_length):
x = load_audio(path, self.fs)
p_len = x.shape[0] // self.hop
if f0_method in self.f0_method_dict:
f0 = (
self.f0_method_dict[f0_method](x, p_len)
if f0_method == "pm"
else self.f0_method_dict[f0_method](x)
)
elif f0_method == "crepe":
f0 = self.mncrepe(f0_method, x, p_len, hop_length)
return f0
def coarse_f0(self, f0):
f0_mel = 1127 * np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * (
self.f0_bin - 2
) / (self.f0_mel_max - self.f0_mel_min) + 1
# use 0 or 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > self.f0_bin - 1] = self.f0_bin - 1
f0_coarse = np.rint(f0_mel).astype(int)
assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (
f0_coarse.max(),
f0_coarse.min(),
)
return f0_coarse
def process_paths(self, paths, f0_method, hop_length, thread_n):
if len(paths) == 0:
print("There are no paths to process.")
return
with tqdm.tqdm(total=len(paths), leave=True, position=thread_n) as pbar:
description = f"Thread {thread_n} | Hop-Length {hop_length}"
pbar.set_description(description)
for idx, (inp_path, opt_path1, opt_path2) in enumerate(paths):
try:
if os.path.exists(opt_path1 + ".npy") and os.path.exists(
opt_path2 + ".npy"
):
pbar.update(1)
continue
feature_pit = self.compute_f0(inp_path, f0_method, hop_length)
np.save(
opt_path2,
feature_pit,
allow_pickle=False,
) # nsf
coarse_pit = self.coarse_f0(feature_pit)
np.save(
opt_path1,
coarse_pit,
allow_pickle=False,
) # ori
pbar.update(1)
except Exception as error:
print(f"f0fail-{idx}-{inp_path}-{error}")
if __name__ == "__main__":
feature_input = FeatureInput()
paths = []
input_root = f"{exp_dir}/1_16k_wavs"
output_root1 = f"{exp_dir}/2a_f0"
output_root2 = f"{exp_dir}/2b-f0nsf"
os.makedirs(output_root1, exist_ok=True)
os.makedirs(output_root2, exist_ok=True)
for name in sorted(list(os.listdir(input_root))):
input_path = f"{input_root}/{name}"
if "spec" in input_path:
continue
output_path1 = f"{output_root1}/{name}"
output_path2 = f"{output_root2}/{name}"
paths.append([input_path, output_path1, output_path2])
processes = []
print("Using f0 method: " + f0_method)
for i in range(num_processes):
p = Process(
target=feature_input.process_paths,
args=(paths[i::num_processes], f0_method, hop_length, i),
)
processes.append(p)
p.start()
for i in range(num_processes):
processes[i].join()
|