File size: 8,643 Bytes
1cd928a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 |
import numpy as np
import torch
import torch.nn.functional as F
from torchaudio.transforms import Resample
from .unit2control import Unit2ControlFacV5A
from .core import upsample
from torch import nn
CREPE_RESAMPLE_KERNEL = {}
F0_KERNEL = {}
class F0_Extractor:
def __init__(self, f0_extractor, sample_rate = 44100, hop_size = 512, f0_min = 65, f0_max = 800):
self.f0_extractor = f0_extractor
self.sample_rate = sample_rate
self.hop_size = hop_size
self.f0_min = f0_min
self.f0_max = f0_max
if f0_extractor == 'crepe':
key_str = str(sample_rate)
if key_str not in CREPE_RESAMPLE_KERNEL:
CREPE_RESAMPLE_KERNEL[key_str] = Resample(sample_rate, 16000, lowpass_filter_width = 128)
self.resample_kernel = CREPE_RESAMPLE_KERNEL[key_str]
if f0_extractor == 'rmvpe':
if 'rmvpe' not in F0_KERNEL :
from rmvpe import RMVPE
F0_KERNEL['rmvpe'] = RMVPE('pretrain/rmvpe/model.pt', hop_length=160)
self.rmvpe = F0_KERNEL['rmvpe']
def extract(self, audio, uv_interp = False, device = None, silence_front = 0): # audio: 1d numpy array
# extractor start time
n_frames = int(len(audio) // self.hop_size) + 1
start_frame = int(silence_front * self.sample_rate / self.hop_size)
real_silence_front = start_frame * self.hop_size / self.sample_rate
audio = audio[int(np.round(real_silence_front * self.sample_rate)) : ]
# extract f0 using rmvpe
if self.f0_extractor == "rmvpe":
f0 = self.rmvpe.infer_from_audio(audio, self.sample_rate, device=device, thred=0.03, use_viterbi=False)
uv = f0 == 0
if len(f0[~uv]) > 0:
f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
origin_time = 0.01 * np.arange(len(f0))
target_time = self.hop_size / self.sample_rate * np.arange(n_frames - start_frame)
f0 = np.interp(target_time, origin_time, f0)
uv = np.interp(target_time, origin_time, uv.astype(float)) > 0.5
f0[uv] = 0
f0 = np.pad(f0, (start_frame, 0))
else:
raise ValueError(f" [x] Unknown f0 extractor: {self.f0_extractor}")
# interpolate the unvoiced f0
if uv_interp:
uv = f0 == 0 # unvoiced frames bool, e.g. [True, False, False, True, False, True]
if len(f0[~uv]) > 0: # if there are voiced frames
f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
f0[f0 < self.f0_min] = self.f0_min
return f0
def batch_extract(self, audios, uv_interp=False, device=None, silence_front=0):
processed_f0s = []
for audio in audios:
# Extract f0 using rmvpe
if self.f0_extractor == "rmvpe":
f0 = self.rmvpe.infer_from_audio(audio, self.sample_rate, device=device, thred=0.03, use_viterbi=False)
f0 = torch.tensor(f0, dtype=torch.float32, device=device) # Convert to torch tensor
n_frames = int(len(audio) // self.hop_size) + 1
start_frame = int(silence_front * self.sample_rate / self.hop_size)
real_silence_front = start_frame * self.hop_size / self.sample_rate
audio = audio[int(np.round(real_silence_front * self.sample_rate)):]
target_time = self.hop_size / self.sample_rate * torch.arange(n_frames - start_frame, device=device)
f0 = F.interpolate(f0.unsqueeze(0).unsqueeze(0), size=n_frames - start_frame, mode='linear').squeeze()
else:
raise ValueError(f"Unknown f0 extractor: {self.f0_extractor}")
processed_f0s.append(f0)
processed_f0s = torch.stack(processed_f0s, 0) # Convert list of tensors to tensor
return processed_f0s
class Volume_Extractor:
def __init__(self, hop_size = 512):
self.hop_size = hop_size
def extract(self, audio): # audio: 1d numpy array
n_frames = int(len(audio) // self.hop_size) + 1
audio2 = audio ** 2
audio2 = np.pad(audio2, (int(self.hop_size // 2), int((self.hop_size + 1) // 2)), mode = 'reflect')
volume = np.array([np.mean(audio2[int(n * self.hop_size) : int((n + 1) * self.hop_size)]) for n in range(n_frames)])
volume = np.sqrt(volume)
return volume
class DotDict(dict):
def __getattr__(*args):
val = dict.get(*args)
return DotDict(val) if type(val) is dict else val
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
class CombSubFastFacV5A(torch.nn.Module):
def __init__(self,
sampling_rate,
block_size,
n_unit=256,
use_pitch_aug=False,
use_tfm=False,
pcmer_norm=False,
mode=None):
super().__init__()
print(' [LOAD] HQ-SVC Model ...')
# params
self.register_buffer("sampling_rate", torch.tensor(sampling_rate))
self.register_buffer("block_size", torch.tensor(block_size))
self.register_buffer("window", torch.sqrt(torch.hann_window(2 * block_size)))
#Unit2Control
split_map = {
'harmonic_magnitude': block_size + 1,
'harmonic_phase': block_size + 1,
'noise_magnitude': block_size + 1
}
self.unit2ctrl = Unit2ControlFacV5A(
input_channel=n_unit,
output_splits=split_map,
use_pitch_aug=use_pitch_aug,
pcmer_norm=pcmer_norm
)
self.mode = mode
def forward(self, units_frames, f0_frames, volume_frames, spk, spk_id=None, aug_shift=None, initial_phase=None, infer=True, **kwargs):
# '''
# units_frames: B x n_frames x n_unit
# f0_frames: B x n_frames x 1
# volume_frames: B x n_frames x 1
# spk: B x 256
# '''
# exciter phase
# reshape
f0_frames = f0_frames.unsqueeze(2)
volume_frames = volume_frames.unsqueeze(2)
f0 = upsample(f0_frames, self.block_size)
if infer:
x = torch.cumsum(f0.double() / self.sampling_rate, axis=1)
else:
x = torch.cumsum(f0 / self.sampling_rate, axis=1)
if initial_phase is not None:
x += initial_phase.to(x) / 2 / np.pi
x = x - torch.round(x)
x = x.to(f0)
phase_frames = 2 * np.pi * x[:, ::self.block_size, :]
outputs = self.unit2ctrl(units_frames, f0_frames, phase_frames, volume_frames, spk, spk_id, aug_shift=aug_shift, is_infer=infer)
ctrls, hidden, timbre = outputs
src_filter = torch.exp(ctrls['harmonic_magnitude'] + 1.j * np.pi * ctrls['harmonic_phase'])
src_filter = torch.cat((src_filter, src_filter[:,-1:,:]), 1)
noise_filter= torch.exp(ctrls['noise_magnitude']) / 128
noise_filter = torch.cat((noise_filter, noise_filter[:,-1:,:]), 1)
# combtooth exciter signal
combtooth = torch.sinc(self.sampling_rate * x / (f0 + 1e-3))
combtooth = combtooth.squeeze(-1)
combtooth_frames = F.pad(combtooth, (self.block_size, self.block_size)).unfold(1, 2 * self.block_size, self.block_size)
combtooth_frames = combtooth_frames * self.window
combtooth_fft = torch.fft.rfft(combtooth_frames, 2 * self.block_size)
# noise exciter signal
noise = torch.rand_like(combtooth) * 2 - 1
noise_frames = F.pad(noise, (self.block_size, self.block_size)).unfold(1, 2 * self.block_size, self.block_size)
noise_frames = noise_frames * self.window
noise_fft = torch.fft.rfft(noise_frames, 2 * self.block_size)
# apply the filters
signal_fft = combtooth_fft * src_filter + noise_fft * noise_filter
# take the ifft to resynthesize audio.
signal_frames_out = torch.fft.irfft(signal_fft, 2 * self.block_size) * self.window
# overlap add
fold = torch.nn.Fold(output_size=(1, (signal_frames_out.size(1) + 1) * self.block_size), kernel_size=(1, 2 * self.block_size), stride=(1, self.block_size))
signal = fold(signal_frames_out.transpose(1, 2))[:, 0, 0, self.block_size : -self.block_size]
if 'adaln_mlp' in self.mode:
return signal, hidden, timbre_f0, timbre, style
else:
return signal, hidden, timbre |