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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()
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