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extract_f0_print.py
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extract_f0_print.py
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import os
import parselmouth
import sys
import traceback
now_dir = os.getcwd()
sys.path.append(now_dir)
from my_utils import load_audio
import pyworld
import numpy as np, logging
import torchcrepe # Fork Feature. Crepe algo for training and preprocess
import torch
from torch import Tensor # Fork Feature. Used for pitch prediction for torch crepe.
import scipy.signal as signal # Fork Feature hybrid inference
import tqdm
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+")
def printt(strr):
print(strr)
f.write("%s\n" % strr)
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
# print("EXTRACTION CREPE HOP LENGTH: " + str(extraction_crepe_hop_length))
# print("EXTRACTION CREPE HOP LENGTH TYPE: " + str(type(extraction_crepe_hop_length)))
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)
# EXPERIMENTAL. PROBABLY BUGGY
def get_f0_hybrid_computation(
self,
methods_str,
x,
f0_min,
f0_max,
p_len,
crepe_hop_length,
time_step,
):
# 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 = []
print("Calculating f0 pitch estimations for methods: %s" % str(methods))
x = x.astype(np.float32)
x /= np.quantile(np.abs(x), 0.999)
# Get f0 calculations for all methods specified
for method in methods:
f0 = None
if method == "pm":
f0 = (
parselmouth.Sound(x, self.fs)
.to_pitch_ac(
time_step=time_step / 1000,
voicing_threshold=0.6,
pitch_floor=f0_min,
pitch_ceiling=f0_max,
)
.selected_array["frequency"]
)
pad_size = (p_len - len(f0) + 1) // 2
if pad_size > 0 or p_len - len(f0) - pad_size > 0:
f0 = np.pad(
f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
)
elif method == "crepe":
# Pick a batch size that doesn't cause memory errors on your gpu
torch_device_index = 0
torch_device = None
if torch.cuda.is_available():
torch_device = torch.device(f"cuda:{torch_device_index % torch.cuda.device_count()}")
elif torch.backends.mps.is_available():
torch_device = torch.device("mps")
else:
torch_device = torch.device("cpu")
model = "full"
batch_size = 512
# Compute pitch using first gpu
audio = torch.tensor(np.copy(x))[None].float()
f0, pd = torchcrepe.predict(
audio,
self.fs,
160,
self.f0_min,
self.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
elif method == "mangio-crepe":
# print("Performing crepe pitch extraction. (EXPERIMENTAL)")
# print("CREPE PITCH EXTRACTION HOP LENGTH: " + str(crepe_hop_length))
x = x.astype(np.float32)
x /= np.quantile(np.abs(x), 0.999)
torch_device_index = 0
torch_device = None
if torch.cuda.is_available():
torch_device = torch.device(f"cuda:{torch_device_index % torch.cuda.device_count()}")
elif torch.backends.mps.is_available():
torch_device = torch.device("mps")
else:
torch_device = torch.device("cpu")
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 f0 Crepe Feature Extraction with an extraction_crepe_hop_length of: " +
# str(crepe_hop_length)
# )
# Pitch prediction for pitch extraction
pitch: 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 == "harvest":
f0, t = 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,
)
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs)
f0 = signal.medfilt(f0, 3)
f0 = f0[1:]
elif method == "dio":
f0, t = 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,
)
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs)
f0 = signal.medfilt(f0, 3)
f0 = f0[1:]
f0_computation_stack.append(f0)
for fc in f0_computation_stack:
print(len(fc))
# print("Calculating hybrid median f0 from the stack of: %s" % str(methods))
f0_median_hybrid = None
if len(f0_computation_stack) == 1:
f0_median_hybrid = f0_computation_stack[0]
else:
f0_median_hybrid = np.nanmedian(f0_computation_stack, axis=0)
return f0_median_hybrid
def compute_f0(self, path, f0_method, crepe_hop_length):
x = load_audio(path, self.fs)
p_len = x.shape[0] // self.hop
if f0_method == "pm":
time_step = 160 / 16000 * 1000
f0 = (
parselmouth.Sound(x, self.fs)
.to_pitch_ac(
time_step=time_step / 1000,
voicing_threshold=0.6,
pitch_floor=self.f0_min,
pitch_ceiling=self.f0_max,
)
.selected_array["frequency"]
)
pad_size = (p_len - len(f0) + 1) // 2
if pad_size > 0 or p_len - len(f0) - pad_size > 0:
f0 = np.pad(
f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
)
elif f0_method == "harvest":
f0, t = 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,
)
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs)
elif f0_method == "dio":
f0, t = 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,
)
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs)
elif f0_method == "crepe": # Fork Feature: Added crepe f0 for f0 feature extraction
# Pick a batch size that doesn't cause memory errors on your gpu
torch_device_index = 0
torch_device = None
if torch.cuda.is_available():
torch_device = torch.device(f"cuda:{torch_device_index % torch.cuda.device_count()}")
elif torch.backends.mps.is_available():
torch_device = torch.device("mps")
else:
torch_device = torch.device("cpu")
model = "full"
batch_size = 512
# Compute pitch using first gpu
audio = torch.tensor(np.copy(x))[None].float()
f0, pd = torchcrepe.predict(
audio,
self.fs,
160,
self.f0_min,
self.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()
elif f0_method == "mangio-crepe":
# print("Performing crepe pitch extraction. (EXPERIMENTAL)")
# print("CREPE PITCH EXTRACTION HOP LENGTH: " + str(crepe_hop_length))
x = x.astype(np.float32)
x /= np.quantile(np.abs(x), 0.999)
torch_device_index = 0
torch_device = None
if torch.cuda.is_available():
torch_device = torch.device(f"cuda:{torch_device_index % torch.cuda.device_count()}")
elif torch.backends.mps.is_available():
torch_device = torch.device("mps")
else:
torch_device = torch.device("cpu")
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 f0 Crepe Feature Extraction with an extraction_crepe_hop_length of: " +
# str(crepe_hop_length)
# )
# Pitch prediction for pitch extraction
pitch: 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 "hybrid" in f0_method: # EXPERIMENTAL
# Perform hybrid median pitch estimation
time_step = 160 / 16000 * 1000
f0 = self.get_f0_hybrid_computation(
f0_method,
x,
self.f0_min,
self.f0_max,
p_len,
crepe_hop_length,
time_step
)
# Mangio-RVC-Fork Feature: Add hybrid f0 inference to feature extraction. EXPERIMENTAL...
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")
else:
with tqdm.tqdm(total=len(paths), leave=True, position=thread_n) as pbar:
for idx, (inp_path, opt_path1, opt_path2) in enumerate(paths):
try:
pbar.set_description("thread:%s, f0ing, Hop-Length:%s" % (thread_n, crepe_hop_length))
pbar.update(1)
if (
os.path.exists(opt_path1 + ".npy") == True
and os.path.exists(opt_path2 + ".npy") == True
):
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
except:
printt("f0fail-%s-%s-%s" % (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()