File size: 10,048 Bytes
7bc29af |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 |
import os
import sys
import traceback
import parselmouth
now_dir = os.getcwd()
sys.path.append(now_dir)
import logging
from LazyImport import lazyload
import numpy as np
import pyworld
torchcrepe = lazyload("torchcrepe") # Fork Feature. Crepe algo for training and preprocess
torch = lazyload("torch")
#from torch import Tensor # Fork Feature. Used for pitch prediction for torch crepe.
tqdm = lazyload("tqdm")
from infer.lib.audio import load_audio
logging.getLogger("numba").setLevel(logging.WARNING)
from multiprocessing import Process
exp_dir = sys.argv[1]
f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
DoFormant = False
Quefrency = 1.0
Timbre = 1.0
def printt(strr):
print(strr)
f.write(f"{strr}\n")
f.flush()
n_p = int(sys.argv[2])
f0method = sys.argv[3]
extraction_crepe_hop_length = 0
try:
extraction_crepe_hop_length = int(sys.argv[4])
except:
print("Temp Issue. echl is not being passed with argument!")
extraction_crepe_hop_length = 128
class FeatureInput(object):
def __init__(self, samplerate=16000, hop_size=160):
self.fs = samplerate
self.hop = hop_size
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, crepe_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 == 'mangio-crepe':
pitch: torch.Tensor = torchcrepe.predict(
audio,
self.fs,
crepe_hop_length,
self.f0_min,
self.f0_max,
"full",
batch_size=crepe_hop_length * 2,
device=torch_device,
pad=True,
)
p_len = p_len or x.shape[0] // crepe_hop_length
# Resize the pitch
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)
elif method == 'crepe':
batch_size = 512
audio = torch.tensor(np.copy(x))[None].float()
f0, pd = torchcrepe.predict(
audio,
self.fs,
160,
self.f0_min,
self.f0_max,
"full",
batch_size=batch_size,
device=torch_device,
return_periodicity=True,
)
pd = torchcrepe.filter.median(pd, 3)
f0 = torchcrepe.filter.mean(f0, 3)
f0[pd < 0.1] = 0
f0 = f0[0].cpu().numpy()
f0 = f0[1:] # Get rid of extra first frame
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 hasattr(self, "model_rmvpe") == False:
from infer.lib.rmvpe import RMVPE
print("Loading rmvpe model")
self.model_rmvpe = RMVPE(
"assets/rmvpe/rmvpe.pt", is_half=False, device="cpu"
)
return self.model_rmvpe.infer_from_audio(x, thred=0.03)
def get_rmvpe_dml(self, x):
...
def get_f0_method_dict(self):
return {
"pm": self.get_pm,
"harvest": self.get_harvest,
"dio": self.get_dio,
"rmvpe": self.get_rmvpe
}
def get_f0_hybrid_computation(
self,
methods_str,
x,
p_len,
crepe_hop_length,
):
# Get various f0 methods from input to use in the computation stack
s = methods_str
s = s.split("hybrid")[1]
s = s.replace("[", "").replace("]", "")
methods = s.split("+")
f0_computation_stack = []
for method in methods:
if method in self.f0_method_dict:
f0 = self.f0_method_dict[method](x, p_len) if method == 'pm' else self.f0_method_dict[method](x)
f0_computation_stack.append(f0)
elif method == 'crepe' or method == 'mangio-crepe':
self.the_other_complex_function(x, method, crepe_hop_length)
if len(f0_computation_stack) != 0:
f0_median_hybrid = np.nanmedian(f0_computation_stack, axis=0) if len(f0_computation_stack)>1 else f0_computation_stack[0]
return f0_median_hybrid
else:
raise ValueError("No valid methods were provided")
def compute_f0(self, path, f0_method, crepe_hop_length):
x = load_audio(path, self.fs, DoFormant, Quefrency, Timbre)
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 in ['crepe', 'mangio-crepe']:
f0 = self.mncrepe(f0_method, x, p_len, crepe_hop_length)
elif "hybrid" in f0_method: # EXPERIMENTAL
# Perform hybrid median pitch estimation
f0 = self.get_f0_hybrid_computation(
f0_method,
x,
p_len,
crepe_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 go(self, paths, f0_method, crepe_hop_length, thread_n):
if len(paths) == 0:
printt("no-f0-todo")
return
with tqdm.tqdm(total=len(paths), leave=True, position=thread_n) as pbar:
description = f"thread:{thread_n}, f0ing, Hop-Length:{crepe_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
featur_pit = self.compute_f0(inp_path, f0_method, crepe_hop_length)
np.save(
opt_path2,
featur_pit,
allow_pickle=False,
) # nsf
coarse_pit = self.coarse_f0(featur_pit)
np.save(
opt_path1,
coarse_pit,
allow_pickle=False,
) # ori
pbar.update(1)
except Exception as e:
printt(f"f0fail-{idx}-{inp_path}-{traceback.format_exc()}")
if __name__ == "__main__":
# exp_dir=r"E:\codes\py39\dataset\mi-test"
# n_p=16
# f = open("%s/log_extract_f0.log"%exp_dir, "w")
printt(sys.argv)
featureInput = FeatureInput()
paths = []
inp_root = "%s/1_16k_wavs" % (exp_dir)
opt_root1 = "%s/2a_f0" % (exp_dir)
opt_root2 = "%s/2b-f0nsf" % (exp_dir)
os.makedirs(opt_root1, exist_ok=True)
os.makedirs(opt_root2, exist_ok=True)
for name in sorted(list(os.listdir(inp_root))):
inp_path = "%s/%s" % (inp_root, name)
if "spec" in inp_path:
continue
opt_path1 = "%s/%s" % (opt_root1, name)
opt_path2 = "%s/%s" % (opt_root2, name)
paths.append([inp_path, opt_path1, opt_path2])
ps = []
print("Using f0 method: " + f0method)
for i in range(n_p):
p = Process(
target=featureInput.go,
args=(paths[i::n_p], f0method, extraction_crepe_hop_length, i),
)
ps.append(p)
p.start()
for i in range(n_p):
ps[i].join() |