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import os
import traceback
from concurrent.futures import ProcessPoolExecutor
from typing import *
import multiprocessing as mp
import numpy as np
import pyworld
import torch
import torchcrepe
from torch import Tensor
from tqdm import tqdm
from lib.rvc.utils import load_audio
def get_optimal_torch_device(index: int = 0) -> torch.device:
# Get cuda device
if torch.cuda.is_available():
return torch.device(f"cuda:{index % torch.cuda.device_count()}") # Very fast
elif torch.backends.mps.is_available():
return torch.device("mps")
# Insert an else here to grab "xla" devices if available. TO DO later. Requires the torch_xla.core.xla_model library
# Else wise return the "cpu" as a torch device,
return torch.device("cpu")
def get_f0_official_crepe_computation(
x,
sr,
f0_min,
f0_max,
model="full",
):
batch_size = 512
torch_device = get_optimal_torch_device()
audio = torch.tensor(np.copy(x))[None].float()
f0, pd = torchcrepe.predict(
audio,
sr,
160,
f0_min,
f0_max,
model,
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_f0_crepe_computation(
x,
sr,
f0_min,
f0_max,
hop_length=160, # 512 before. Hop length changes the speed that the voice jumps to a different dramatic pitch. Lower hop lengths means more pitch accuracy but longer inference time.
model="full", # Either use crepe-tiny "tiny" or crepe "full". Default is full
):
x = x.astype(np.float32) # fixes the F.conv2D exception. We needed to convert double to float.
x /= np.quantile(np.abs(x), 0.999)
torch_device = get_optimal_torch_device()
audio = torch.from_numpy(x).to(torch_device, copy=True)
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()
print("Initiating prediction with a crepe_hop_length of: " + str(hop_length))
pitch: Tensor = torchcrepe.predict(
audio,
sr,
hop_length,
f0_min,
f0_max,
model,
batch_size=hop_length * 2,
device=torch_device,
pad=True
)
p_len = x.shape[0] // hop_length
# Resize the pitch for final f0
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)
f0 = f0[1:] # Get rid of extra first frame
return f0 # Resized f0
def compute_f0(
path: str,
f0_method: str,
fs: int,
hop: int,
f0_max: float,
f0_min: float,
):
x = load_audio(path, fs)
if f0_method == "harvest":
f0, t = pyworld.harvest(
x.astype(np.double),
fs=fs,
f0_ceil=f0_max,
f0_floor=f0_min,
frame_period=1000 * hop / fs,
)
f0 = pyworld.stonemask(x.astype(np.double), f0, t, fs)
elif f0_method == "dio":
f0, t = pyworld.dio(
x.astype(np.double),
fs=fs,
f0_ceil=f0_max,
f0_floor=f0_min,
frame_period=1000 * hop / fs,
)
f0 = pyworld.stonemask(x.astype(np.double), f0, t, fs)
elif f0_method == "mangio-crepe":
f0 = get_f0_crepe_computation(x, fs, f0_min, f0_max, 160, "full")
elif f0_method == "crepe":
f0 = get_f0_official_crepe_computation(x.astype(np.double), fs, f0_min, f0_max, "full")
return f0
def coarse_f0(f0, f0_bin, f0_mel_min, f0_mel_max):
f0_mel = 1127 * np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (
f0_mel_max - f0_mel_min
) + 1
# use 0 or 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
f0_coarse = np.rint(f0_mel).astype(np.int)
assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (
f0_coarse.max(),
f0_coarse.min(),
)
return f0_coarse
def processor(paths, f0_method, samplerate=16000, hop_size=160, process_id=0):
fs = samplerate
hop = hop_size
f0_bin = 256
f0_max = 1100.0
f0_min = 50.0
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
if len(paths) != 0:
for idx, (inp_path, opt_path1, opt_path2) in enumerate(
tqdm(paths, position=1 + process_id)
):
try:
if (
os.path.exists(opt_path1 + ".npy") == True
and os.path.exists(opt_path2 + ".npy") == True
):
continue
featur_pit = compute_f0(inp_path, f0_method, fs, hop, f0_max, f0_min)
np.save(
opt_path2,
featur_pit,
allow_pickle=False,
) # nsf
coarse_pit = coarse_f0(featur_pit, f0_bin, f0_mel_min, f0_mel_max)
np.save(
opt_path1,
coarse_pit,
allow_pickle=False,
) # ori
except:
print(f"f0 failed {idx}: {inp_path} {traceback.format_exc()}")
def run(training_dir: str, num_processes: int, f0_method: str):
paths = []
dataset_dir = os.path.join(training_dir, "1_16k_wavs")
opt_dir_f0 = os.path.join(training_dir, "2a_f0")
opt_dir_f0_nsf = os.path.join(training_dir, "2b_f0nsf")
if os.path.exists(opt_dir_f0) and os.path.exists(opt_dir_f0_nsf):
return
os.makedirs(opt_dir_f0, exist_ok=True)
os.makedirs(opt_dir_f0_nsf, exist_ok=True)
names = []
for pathname in sorted(list(os.listdir(dataset_dir))):
if os.path.isdir(os.path.join(dataset_dir, pathname)):
for f in sorted(list(os.listdir(os.path.join(dataset_dir, pathname)))):
if "spec" in f:
continue
names.append(os.path.join(pathname, f))
else:
names.append(pathname)
for name in names: # dataset_dir/{05d}/file.ext
filepath = os.path.join(dataset_dir, name)
if "spec" in filepath:
continue
opt_filepath_f0 = os.path.join(opt_dir_f0, name)
opt_filepath_f0_nsf = os.path.join(opt_dir_f0_nsf, name)
paths.append([filepath, opt_filepath_f0, opt_filepath_f0_nsf])
for dir in set([(os.path.dirname(p[1]), os.path.dirname(p[2])) for p in paths]):
os.makedirs(dir[0], exist_ok=True)
os.makedirs(dir[1], exist_ok=True)
with ProcessPoolExecutor(mp_context=mp.get_context("spawn")) as executer:
for i in range(num_processes):
executer.submit(processor, paths[i::num_processes], f0_method, process_id=i)
processor(paths, f0_method)
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