RVC_HF / infer /modules /train /extract /extract_f0_print.py
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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()