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from functools import lru_cache | |
from time import time as ttime | |
import faiss | |
import librosa | |
import numpy as np | |
import os | |
import parselmouth | |
import pyworld | |
import sys | |
import torch | |
import torch.nn.functional as F | |
import torchcrepe | |
import traceback | |
from scipy import signal | |
from torch import Tensor | |
from common import BASE_DIR, MODELS_DIR | |
now_dir = os.path.join(BASE_DIR, "src") | |
sys.path.append(now_dir) | |
bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000) | |
input_audio_path2wav = {} | |
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 VC(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 | |
# 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: | |
# 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") | |
# Fork Feature: Compute f0 with the crepe method | |
def get_f0_crepe_computation( | |
self, | |
x, | |
f0_min, | |
f0_max, | |
p_len, | |
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 = 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() | |
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, | |
model="full", | |
): | |
# 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() | |
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 | |
# 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 | |
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.sr) | |
.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": | |
f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max) | |
f0 = f0[1:] # Get rid of extra first frame | |
elif method == "crepe-tiny": | |
f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny") | |
f0 = f0[1:] # Get rid of extra first frame | |
elif method == "mangio-crepe": | |
f0 = self.get_f0_crepe_computation( | |
x, f0_min, f0_max, p_len, crepe_hop_length | |
) | |
elif method == "mangio-crepe-tiny": | |
f0 = self.get_f0_crepe_computation( | |
x, f0_min, f0_max, p_len, crepe_hop_length, "tiny" | |
) | |
elif method == "harvest": | |
f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10) | |
if filter_radius > 2: | |
f0 = signal.medfilt(f0, 3) | |
f0 = f0[1:] # Get rid of first frame. | |
elif method == "dio": # Potentially buggy? | |
f0, t = pyworld.dio( | |
x.astype(np.double), | |
fs=self.sr, | |
f0_ceil=f0_max, | |
f0_floor=f0_min, | |
frame_period=10, | |
) | |
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr) | |
f0 = signal.medfilt(f0, 3) | |
f0 = f0[1:] | |
# elif method == "pyin": Not Working just yet | |
# f0 = self.get_f0_pyin_computation(x, f0_min, f0_max) | |
# Push method to the stack | |
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 get_f0( | |
self, | |
input_audio_path, | |
x, | |
p_len, | |
f0_up_key, | |
f0_method, | |
filter_radius, | |
crepe_hop_length, | |
inp_f0=None, | |
): | |
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) | |
if f0_method == "pm": | |
f0 = ( | |
parselmouth.Sound(x, self.sr) | |
.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 f0_method == "harvest": | |
input_audio_path2wav[input_audio_path] = x.astype(np.double) | |
f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10) | |
if filter_radius > 2: | |
f0 = signal.medfilt(f0, 3) | |
elif f0_method == "dio": # Potentially Buggy? | |
f0, t = pyworld.dio( | |
x.astype(np.double), | |
fs=self.sr, | |
f0_ceil=f0_max, | |
f0_floor=f0_min, | |
frame_period=10, | |
) | |
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr) | |
f0 = signal.medfilt(f0, 3) | |
elif f0_method == "crepe": | |
f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max) | |
elif f0_method == "crepe-tiny": | |
f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny") | |
elif f0_method == "mangio-crepe": | |
f0 = self.get_f0_crepe_computation( | |
x, f0_min, f0_max, p_len, crepe_hop_length | |
) | |
elif f0_method == "mangio-crepe-tiny": | |
f0 = self.get_f0_crepe_computation( | |
x, f0_min, f0_max, p_len, crepe_hop_length, "tiny" | |
) | |
elif f0_method == "rmvpe": | |
if hasattr(self, "model_rmvpe") == False: | |
from vc.rmvpe import RMVPE | |
self.model_rmvpe = RMVPE( | |
os.path.join(MODELS_DIR, "rvc", "rmvpe.pt"), | |
is_half=self.is_half, | |
device=self.device, | |
) | |
f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03) | |
elif "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, | |
) | |
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.int) | |
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 != None and pitchf != None: | |
feats0 = feats.clone() | |
if ( | |
isinstance(index, type(None)) == False | |
and isinstance(big_npy, type(None)) == False | |
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 != None and pitchf != 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 != None and pitchf != None: | |
pitch = pitch[:, :p_len] | |
pitchf = pitchf[:, :p_len] | |
if protect < 0.5 and pitch != None and pitchf != 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(): | |
if pitch != None and pitchf != None: | |
audio1 = ( | |
(net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]) | |
.data.cpu() | |
.float() | |
.numpy() | |
) | |
else: | |
audio1 = ( | |
(net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy() | |
) | |
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 pipeline( | |
self, | |
model, | |
net_g, | |
sid, | |
audio, | |
input_audio_path, | |
times, | |
f0_up_key, | |
f0_method, | |
file_index, | |
# file_big_npy, | |
index_rate, | |
if_f0, | |
filter_radius, | |
tgt_sr, | |
resample_sr, | |
rms_mix_rate, | |
version, | |
protect, | |
crepe_hop_length, | |
f0_file=None, | |
): | |
if ( | |
file_index != "" | |
# and file_big_npy != "" | |
# and os.path.exists(file_big_npy) == True | |
and os.path.exists(file_index) == True | |
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") == True: | |
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 == 1: | |
pitch, pitchf = self.get_f0( | |
input_audio_path, | |
audio_pad, | |
p_len, | |
f0_up_key, | |
f0_method, | |
filter_radius, | |
crepe_hop_length, | |
inp_f0, | |
) | |
pitch = pitch[:p_len] | |
pitchf = pitchf[:p_len] | |
if self.device == "mps": | |
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 | |
for t in opt_ts: | |
t = t // self.window * self.window | |
if if_f0 == 1: | |
audio_opt.append( | |
self.vc( | |
model, | |
net_g, | |
sid, | |
audio_pad[s : t + self.t_pad2 + self.window], | |
pitch[:, s // self.window : (t + self.t_pad2) // self.window], | |
pitchf[:, s // self.window : (t + self.t_pad2) // self.window], | |
times, | |
index, | |
big_npy, | |
index_rate, | |
version, | |
protect, | |
)[self.t_pad_tgt : -self.t_pad_tgt] | |
) | |
else: | |
audio_opt.append( | |
self.vc( | |
model, | |
net_g, | |
sid, | |
audio_pad[s : t + self.t_pad2 + self.window], | |
None, | |
None, | |
times, | |
index, | |
big_npy, | |
index_rate, | |
version, | |
protect, | |
)[self.t_pad_tgt : -self.t_pad_tgt] | |
) | |
s = t | |
if if_f0 == 1: | |
audio_opt.append( | |
self.vc( | |
model, | |
net_g, | |
sid, | |
audio_pad[t:], | |
pitch[:, t // self.window :] if t is not None else pitch, | |
pitchf[:, t // self.window :] if t is not None else pitchf, | |
times, | |
index, | |
big_npy, | |
index_rate, | |
version, | |
protect, | |
)[self.t_pad_tgt : -self.t_pad_tgt] | |
) | |
else: | |
audio_opt.append( | |
self.vc( | |
model, | |
net_g, | |
sid, | |
audio_pad[t:], | |
None, | |
None, | |
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 resample_sr >= 16000 and tgt_sr != resample_sr: | |
audio_opt = librosa.resample( | |
audio_opt, orig_sr=tgt_sr, target_sr=resample_sr | |
) | |
tgt_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() | |
return audio_opt, tgt_sr | |