import os
import sys
import traceback
import logging
logger = logging.getLogger(__name__)
from functools import lru_cache
from time import time as ttime
from torch import Tensor
import faiss
import librosa
import numpy as np
import parselmouth
import pyworld
import torch
import torch.nn.functional as F
import torchcrepe
from scipy import signal
from tqdm import tqdm
import random
now_dir = os.getcwd()
sys.path.append(now_dir)
import re
from functools import partial
bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
input_audio_path2wav = {}
from LazyImport import lazyload
torchcrepe = lazyload("torchcrepe") # Fork Feature. Crepe algo for training and preprocess
torch = lazyload("torch")
from infer.lib.rmvpe import RMVPE
@lru_cache
def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
audio = input_audio_path2wav[input_audio_path]
f0, t = pyworld.harvest(
audio,
fs=fs,
f0_ceil=f0max,
f0_floor=f0min,
frame_period=frame_period,
)
f0 = pyworld.stonemask(audio, f0, t, fs)
return f0
def change_rms(data1, sr1, data2, sr2, rate): # 1是输入音频，2是输出音频,rate是2的占比
# print(data1.max(),data2.max())
rms1 = librosa.feature.rms(
y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
) # 每半秒一个点
rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
rms1 = torch.from_numpy(rms1)
rms1 = F.interpolate(
rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
).squeeze()
rms2 = torch.from_numpy(rms2)
rms2 = F.interpolate(
rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
).squeeze()
rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
data2 *= (
torch.pow(rms1, torch.tensor(1 - rate))
* torch.pow(rms2, torch.tensor(rate - 1))
).numpy()
return data2
class Pipeline(object):
def __init__(self, tgt_sr, config):
self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
config.x_pad,
config.x_query,
config.x_center,
config.x_max,
config.is_half,
)
self.sr = 16000 # hubert输入采样率
self.window = 160 # 每帧点数
self.t_pad = self.sr * self.x_pad # 每条前后pad时间
self.t_pad_tgt = tgt_sr * self.x_pad
self.t_pad2 = self.t_pad * 2
self.t_query = self.sr * self.x_query # 查询切点前后查询时间
self.t_center = self.sr * self.x_center # 查询切点位置
self.t_max = self.sr * self.x_max # 免查询时长阈值
self.device = config.device
self.model_rmvpe = RMVPE("%s/rmvpe.pt" % os.environ["rmvpe_root"], is_half=self.is_half, device=self.device)
self.f0_method_dict = {
"pm": self.get_pm,
"harvest": self.get_harvest,
"dio": self.get_dio,
"rmvpe": self.get_rmvpe,
"rmvpe+": self.get_pitch_dependant_rmvpe,
"crepe": self.get_f0_official_crepe_computation,
"crepe-tiny": partial(self.get_f0_official_crepe_computation, model='model'),
"mangio-crepe": self.get_f0_crepe_computation,
"mangio-crepe-tiny": partial(self.get_f0_crepe_computation, model='model'),
}
self.note_dict = [
65.41, 69.30, 73.42, 77.78, 82.41, 87.31,
92.50, 98.00, 103.83, 110.00, 116.54, 123.47,
130.81, 138.59, 146.83, 155.56, 164.81, 174.61,
185.00, 196.00, 207.65, 220.00, 233.08, 246.94,
261.63, 277.18, 293.66, 311.13, 329.63, 349.23,
369.99, 392.00, 415.30, 440.00, 466.16, 493.88,
523.25, 554.37, 587.33, 622.25, 659.25, 698.46,
739.99, 783.99, 830.61, 880.00, 932.33, 987.77,
1046.50, 1108.73, 1174.66, 1244.51, 1318.51, 1396.91,
1479.98, 1567.98, 1661.22, 1760.00, 1864.66, 1975.53,
2093.00, 2217.46, 2349.32, 2489.02, 2637.02, 2793.83,
2959.96, 3135.96, 3322.44, 3520.00, 3729.31, 3951.07
]
# Fork Feature: Get the best torch device to use for f0 algorithms that require a torch device. Will return the type (torch.device)
def get_optimal_torch_device(self, index: int = 0) -> torch.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")
return torch.device("cpu")
# Fork Feature: Compute f0 with the crepe method
def get_f0_crepe_computation(
self,
x,
f0_min,
f0_max,
p_len,
*args, # 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.
**kwargs, # 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 = self.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()
hop_length = kwargs.get('crepe_hop_length', 160)
model = kwargs.get('model', 'full')
print("Initiating prediction with a crepe_hop_length of: " + str(hop_length))
pitch: Tensor = torchcrepe.predict(
audio,
self.sr,
hop_length,
f0_min,
f0_max,
model,
batch_size=hop_length * 2,
device=torch_device,
pad=True,
)
p_len = p_len or 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)
return f0 # Resized f0
def get_f0_official_crepe_computation(
self,
x,
f0_min,
f0_max,
*args,
**kwargs
):
# Pick a batch size that doesn't cause memory errors on your gpu
batch_size = 512
# Compute pitch using first gpu
audio = torch.tensor(np.copy(x))[None].float()
model = kwargs.get('model', 'full')
f0, pd = torchcrepe.predict(
audio,
self.sr,
self.window,
f0_min,
f0_max,
model,
batch_size=batch_size,
device=self.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()
return f0
# Fork Feature: Compute pYIN f0 method
def get_f0_pyin_computation(self, x, f0_min, f0_max):
y, sr = librosa.load("saudio/Sidney.wav", self.sr, mono=True)
f0, _, _ = librosa.pyin(y, sr=self.sr, fmin=f0_min, fmax=f0_max)
f0 = f0[1:] # Get rid of extra first frame
return f0
def get_pm(self, x, p_len, *args, **kwargs):
f0 = parselmouth.Sound(x, self.sr).to_pitch_ac(
time_step=160 / 16000,
voicing_threshold=0.6,
pitch_floor=kwargs.get('f0_min'),
pitch_ceiling=kwargs.get('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, *args, **kwargs):
f0_spectral = pyworld.harvest(
x.astype(np.double),
fs=self.sr,
f0_ceil=kwargs.get('f0_max'),
f0_floor=kwargs.get('f0_min'),
frame_period=1000 * kwargs.get('hop_length', 160) / self.sr,
)
return pyworld.stonemask(x.astype(np.double), *f0_spectral, self.sr)
def get_dio(self, x, *args, **kwargs):
f0_spectral = pyworld.dio(
x.astype(np.double),
fs=self.sr,
f0_ceil=kwargs.get('f0_max'),
f0_floor=kwargs.get('f0_min'),
frame_period=1000 * kwargs.get('hop_length', 160) / self.sr,
)
return pyworld.stonemask(x.astype(np.double), *f0_spectral, self.sr)
def get_rmvpe(self, x, *args, **kwargs):
if not hasattr(self, "model_rmvpe"):
from infer.lib.rmvpe import RMVPE
logger.info(
"Loading rmvpe model,%s" % "%s/rmvpe.pt" % os.environ["rmvpe_root"]
)
self.model_rmvpe = RMVPE(
"%s/rmvpe.pt" % os.environ["rmvpe_root"],
is_half=self.is_half,
device=self.device,
)
f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
return f0
def get_pitch_dependant_rmvpe(self, x, f0_min=1, f0_max=40000, *args, **kwargs):
return self.model_rmvpe.infer_from_audio_with_pitch(x, thred=0.03, f0_min=f0_min, f0_max=f0_max)
def autotune_f0(self, f0):
autotuned_f0 = []
for freq in f0:
closest_notes = [x for x in self.note_dict if abs(x - freq) == min(abs(n - freq) for n in self.note_dict)]
autotuned_f0.append(random.choice(closest_notes))
return np.array(autotuned_f0, np.float64)
# Fork Feature: Acquire median hybrid f0 estimation calculation
def get_f0_hybrid_computation(
self,
methods_str,
input_audio_path,
x,
f0_min,
f0_max,
p_len,
filter_radius,
crepe_hop_length,
time_step
):
# Get various f0 methods from input to use in the computation stack
params = {'x': x, 'p_len': p_len, 'f0_min': f0_min,
'f0_max': f0_max, 'time_step': time_step, 'filter_radius': filter_radius,
'crepe_hop_length': crepe_hop_length, 'model': "full"
}
methods_str = re.search('hybrid\[(.+)\]', methods_str)
if methods_str: # Ensure a match was found
methods = [method.strip() for method in methods_str.group(1).split('+')]
f0_computation_stack = []
print(f"Calculating f0 pitch estimations for methods: {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:
if method not in self.f0_method_dict:
print(f"Method {method} not found.")
continue
f0 = self.f0_method_dict[method](**params)
if method == 'harvest' and filter_radius > 2:
f0 = signal.medfilt(f0, 3)
f0 = f0[1:] # Get rid of first frame.
f0_computation_stack.append(f0)
for fc in f0_computation_stack:
print(len(fc))
print(f"Calculating hybrid median f0 from the stack of: {str(methods)}")
f0_median_hybrid = np.nanmedian(f0_computation_stack, axis=0)
return f0_median_hybrid
def get_f0(
self,
input_audio_path,
x,
p_len,
f0_up_key,
f0_method,
filter_radius,
crepe_hop_length,
f0_autotune,
inp_f0=None,
f0_min=50,
f0_max=1100,
):
global input_audio_path2wav
time_step = self.window / self.sr * 1000
f0_min = 50
f0_max = 1100
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
params = {'x': x, 'p_len': p_len, 'f0_up_key': f0_up_key, 'f0_min': f0_min,
'f0_max': f0_max, 'time_step': time_step, 'filter_radius': filter_radius,
'crepe_hop_length': crepe_hop_length, 'model': "full"
}
if "hybrid" in f0_method:
# Perform hybrid median pitch estimation
input_audio_path2wav[input_audio_path] = x.astype(np.double)
f0 = self.get_f0_hybrid_computation(
f0_method,+
input_audio_path,
x,
f0_min,
f0_max,
p_len,
filter_radius,
crepe_hop_length,
time_step,
)
else:
f0 = self.f0_method_dict[f0_method](**params)
if "privateuseone" in str(self.device): # clean ortruntime memory
del self.model_rmvpe.model
del self.model_rmvpe
logger.info("Cleaning ortruntime memory")
if f0_autotune:
f0 = self.autotune_f0(f0)
f0 *= pow(2, f0_up_key / 12)
# with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
tf0 = self.sr // self.window # 每秒f0点数
if inp_f0 is not None:
delta_t = np.round(
(inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
).astype("int16")
replace_f0 = np.interp(
list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
)
shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
:shape
]
# with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
f0bak = f0.copy()
f0_mel = 1127 * np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
f0_mel_max - f0_mel_min
) + 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > 255] = 255
f0_coarse = np.rint(f0_mel).astype(np.int32)
return f0_coarse, f0bak # 1-0
def vc(
self,
model,
net_g,
sid,
audio0,
pitch,
pitchf,
times,
index,
big_npy,
index_rate,
version,
protect,
): # ,file_index,file_big_npy
feats = torch.from_numpy(audio0)
if self.is_half:
feats = feats.half()
else:
feats = feats.float()
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1)
padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
inputs = {
"source": feats.to(self.device),
"padding_mask": padding_mask,
"output_layer": 9 if version == "v1" else 12,
}
t0 = ttime()
with torch.no_grad():
logits = model.extract_features(**inputs)
feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
if protect < 0.5 and pitch is not None and pitchf is not None:
feats0 = feats.clone()
if (
not isinstance(index, type(None))
and not isinstance(big_npy, type(None))
and index_rate != 0
):
npy = feats[0].cpu().numpy()
if self.is_half:
npy = npy.astype("float32")
# _, I = index.search(npy, 1)
# npy = big_npy[I.squeeze()]
score, ix = index.search(npy, k=8)
weight = np.square(1 / score)
weight /= weight.sum(axis=1, keepdims=True)
npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
if self.is_half:
npy = npy.astype("float16")
feats = (
torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
+ (1 - index_rate) * feats
)
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
if protect < 0.5 and pitch is not None and pitchf is not None:
feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
0, 2, 1
)
t1 = ttime()
p_len = audio0.shape[0] // self.window
if feats.shape[1] < p_len:
p_len = feats.shape[1]
if pitch is not None and pitchf is not None:
pitch = pitch[:, :p_len]
pitchf = pitchf[:, :p_len]
if protect < 0.5 and pitch is not None and pitchf is not None:
pitchff = pitchf.clone()
pitchff[pitchf > 0] = 1
pitchff[pitchf < 1] = protect
pitchff = pitchff.unsqueeze(-1)
feats = feats * pitchff + feats0 * (1 - pitchff)
feats = feats.to(feats0.dtype)
p_len = torch.tensor([p_len], device=self.device).long()
with torch.no_grad():
hasp = pitch is not None and pitchf is not None
arg = (feats, p_len, pitch, pitchf, sid) if hasp else (feats, p_len, sid)
audio1 = (net_g.infer(*arg)[0][0, 0]).data.cpu().float().numpy()
del hasp, arg
del feats, p_len, padding_mask
if torch.cuda.is_available():
torch.cuda.empty_cache()
t2 = ttime()
times[0] += t1 - t0
times[2] += t2 - t1
return audio1
def process_t(self, t, s, window, audio_pad, pitch, pitchf, times, index, big_npy, index_rate, version, protect, t_pad_tgt, if_f0, sid, model, net_g):
t = t // window * window
if if_f0 == 1:
return self.vc(
model,
net_g,
sid,
audio_pad[s : t + t_pad_tgt + window],
pitch[:, s // window : (t + t_pad_tgt) // window],
pitchf[:, s // window : (t + t_pad_tgt) // window],
times,
index,
big_npy,
index_rate,
version,
protect,
)[t_pad_tgt : -t_pad_tgt]
else:
return self.vc(
model,
net_g,
sid,
audio_pad[s : t + t_pad_tgt + window],
None,
None,
times,
index,
big_npy,
index_rate,
version,
protect,
)[t_pad_tgt : -t_pad_tgt]
def pipeline(
self,
model,
net_g,
sid,
audio,
input_audio_path,
times,
f0_up_key,
f0_method,
file_index,
index_rate,
if_f0,
filter_radius,
tgt_sr,
resample_sr,
rms_mix_rate,
version,
protect,
crepe_hop_length,
f0_autotune,
f0_file=None,
f0_min=50,
f0_max=1100
):
if (
file_index != ""
# and file_big_npy != ""
# and os.path.exists(file_big_npy) == True
and os.path.exists(file_index)
and index_rate != 0
):
try:
index = faiss.read_index(file_index)
# big_npy = np.load(file_big_npy)
big_npy = index.reconstruct_n(0, index.ntotal)
except:
traceback.print_exc()
index = big_npy = None
else:
index = big_npy = None
audio = signal.filtfilt(bh, ah, audio)
audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
opt_ts = []
if audio_pad.shape[0] > self.t_max:
audio_sum = np.zeros_like(audio)
for i in range(self.window):
audio_sum += audio_pad[i : i - self.window]
for t in range(self.t_center, audio.shape[0], self.t_center):
opt_ts.append(
t
- self.t_query
+ np.where(
np.abs(audio_sum[t - self.t_query : t + self.t_query])
== np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
)[0][0]
)
s = 0
audio_opt = []
t = None
t1 = ttime()
audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
p_len = audio_pad.shape[0] // self.window
inp_f0 = None
if hasattr(f0_file, "name"):
try:
with open(f0_file.name, "r") as f:
lines = f.read().strip("\n").split("\n")
inp_f0 = []
for line in lines:
inp_f0.append([float(i) for i in line.split(",")])
inp_f0 = np.array(inp_f0, dtype="float32")
except:
traceback.print_exc()
sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
pitch, pitchf = None, None
if if_f0:
pitch, pitchf = self.get_f0(
input_audio_path,
audio_pad,
p_len,
f0_up_key,
f0_method,
filter_radius,
crepe_hop_length,
f0_autotune,
inp_f0,
f0_min,
f0_max
)
pitch = pitch[:p_len]
pitchf = pitchf[:p_len]
if self.device == "mps" or "xpu" in self.device:
pitchf = pitchf.astype(np.float32)
pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
t2 = ttime()
times[1] += t2 - t1
with tqdm(total=len(opt_ts), desc="Processing", unit="window") as pbar:
for i, t in enumerate(opt_ts):
t = t // self.window * self.window
start = s
end = t + self.t_pad2 + self.window
audio_slice = audio_pad[start:end]
pitch_slice = pitch[:, start // self.window:end // self.window] if if_f0 else None
pitchf_slice = pitchf[:, start // self.window:end // self.window] if if_f0 else None
audio_opt.append(self.vc(model, net_g, sid, audio_slice, pitch_slice, pitchf_slice, times, index, big_npy, index_rate, version, protect)[self.t_pad_tgt : -self.t_pad_tgt])
s = t
pbar.update(1)
pbar.refresh()
audio_slice = audio_pad[t:]
pitch_slice = pitch[:, t // self.window:] if if_f0 and t is not None else pitch
pitchf_slice = pitchf[:, t // self.window:] if if_f0 and t is not None else pitchf
audio_opt.append(self.vc(model, net_g, sid, audio_slice, pitch_slice, pitchf_slice, times, index, big_npy, index_rate, version, protect)[self.t_pad_tgt : -self.t_pad_tgt])
audio_opt = np.concatenate(audio_opt)
if rms_mix_rate != 1:
audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
if tgt_sr != resample_sr >= 16000:
audio_opt = librosa.resample(
audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
)
audio_max = np.abs(audio_opt).max() / 0.99
max_int16 = 32768
if audio_max > 1:
max_int16 /= audio_max
audio_opt = (audio_opt * max_int16).astype(np.int16)
del pitch, pitchf, sid
if torch.cuda.is_available():
torch.cuda.empty_cache()
print("Returning completed audio...")
print("-------------------")
return audio_opt