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import os
import numpy as np
import torch
try:
#Fix "Torch not compiled with CUDA enabled"
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
if torch.xpu.is_available():
from lib.infer.modules.ipex import ipex_init
ipex_init()
except Exception:
pass
import torch.nn as nn
import torch.nn.functional as F
from librosa.util import normalize, pad_center, tiny
from scipy.signal import get_window
import logging
logger = logging.getLogger(__name__)
###stft codes from https://github.com/pseeth/torch-stft/blob/master/torch_stft/util.py
def window_sumsquare(
window,
n_frames,
hop_length=200,
win_length=800,
n_fft=800,
dtype=np.float32,
norm=None,
):
"""
# from librosa 0.6
Compute the sum-square envelope of a window function at a given hop length.
This is used to estimate modulation effects induced by windowing
observations in short-time fourier transforms.
Parameters
----------
window : string, tuple, number, callable, or list-like
Window specification, as in `get_window`
n_frames : int > 0
The number of analysis frames
hop_length : int > 0
The number of samples to advance between frames
win_length : [optional]
The length of the window function. By default, this matches `n_fft`.
n_fft : int > 0
The length of each analysis frame.
dtype : np.dtype
The data type of the output
Returns
-------
wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
The sum-squared envelope of the window function
"""
if win_length is None:
win_length = n_fft
n = n_fft + hop_length * (n_frames - 1)
x = np.zeros(n, dtype=dtype)
# Compute the squared window at the desired length
win_sq = get_window(window, win_length, fftbins=True)
win_sq = normalize(win_sq, norm=norm) ** 2
win_sq = pad_center(win_sq, n_fft)
# Fill the envelope
for i in range(n_frames):
sample = i * hop_length
x[sample : min(n, sample + n_fft)] += win_sq[: max(0, min(n_fft, n - sample))]
return x
class STFT(torch.nn.Module):
def __init__(
self, filter_length=1024, hop_length=512, win_length=None, window="hann"
):
"""
This module implements an STFT using 1D convolution and 1D transpose convolutions.
This is a bit tricky so there are some cases that probably won't work as working
out the same sizes before and after in all overlap add setups is tough. Right now,
this code should work with hop lengths that are half the filter length (50% overlap
between frames).
Keyword Arguments:
filter_length {int} -- Length of filters used (default: {1024})
hop_length {int} -- Hop length of STFT (restrict to 50% overlap between frames) (default: {512})
win_length {[type]} -- Length of the window function applied to each frame (if not specified, it
equals the filter length). (default: {None})
window {str} -- Type of window to use (options are bartlett, hann, hamming, blackman, blackmanharris)
(default: {'hann'})
"""
super(STFT, self).__init__()
self.filter_length = filter_length
self.hop_length = hop_length
self.win_length = win_length if win_length else filter_length
self.window = window
self.forward_transform = None
self.pad_amount = int(self.filter_length / 2)
scale = self.filter_length / self.hop_length
fourier_basis = np.fft.fft(np.eye(self.filter_length))
cutoff = int((self.filter_length / 2 + 1))
fourier_basis = np.vstack(
[np.real(fourier_basis[:cutoff, :]), np.imag(fourier_basis[:cutoff, :])]
)
forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
inverse_basis = torch.FloatTensor(
np.linalg.pinv(scale * fourier_basis).T[:, None, :]
)
assert filter_length >= self.win_length
# get window and zero center pad it to filter_length
fft_window = get_window(window, self.win_length, fftbins=True)
fft_window = pad_center(fft_window, size=filter_length)
fft_window = torch.from_numpy(fft_window).float()
# window the bases
forward_basis *= fft_window
inverse_basis *= fft_window
self.register_buffer("forward_basis", forward_basis.float())
self.register_buffer("inverse_basis", inverse_basis.float())
def transform(self, input_data):
"""Take input data (audio) to STFT domain.
Arguments:
input_data {tensor} -- Tensor of floats, with shape (num_batch, num_samples)
Returns:
magnitude {tensor} -- Magnitude of STFT with shape (num_batch,
num_frequencies, num_frames)
phase {tensor} -- Phase of STFT with shape (num_batch,
num_frequencies, num_frames)
"""
num_batches = input_data.shape[0]
num_samples = input_data.shape[-1]
self.num_samples = num_samples
# similar to librosa, reflect-pad the input
input_data = input_data.view(num_batches, 1, num_samples)
# print(1234,input_data.shape)
input_data = F.pad(
input_data.unsqueeze(1),
(self.pad_amount, self.pad_amount, 0, 0, 0, 0),
mode="reflect",
).squeeze(1)
# print(2333,input_data.shape,self.forward_basis.shape,self.hop_length)
# pdb.set_trace()
forward_transform = F.conv1d(
input_data, self.forward_basis, stride=self.hop_length, padding=0
)
cutoff = int((self.filter_length / 2) + 1)
real_part = forward_transform[:, :cutoff, :]
imag_part = forward_transform[:, cutoff:, :]
magnitude = torch.sqrt(real_part**2 + imag_part**2)
# phase = torch.atan2(imag_part.data, real_part.data)
return magnitude # , phase
def inverse(self, magnitude, phase):
"""Call the inverse STFT (iSTFT), given magnitude and phase tensors produced
by the ```transform``` function.
Arguments:
magnitude {tensor} -- Magnitude of STFT with shape (num_batch,
num_frequencies, num_frames)
phase {tensor} -- Phase of STFT with shape (num_batch,
num_frequencies, num_frames)
Returns:
inverse_transform {tensor} -- Reconstructed audio given magnitude and phase. Of
shape (num_batch, num_samples)
"""
recombine_magnitude_phase = torch.cat(
[magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1
)
inverse_transform = F.conv_transpose1d(
recombine_magnitude_phase,
self.inverse_basis,
stride=self.hop_length,
padding=0,
)
if self.window is not None:
window_sum = window_sumsquare(
self.window,
magnitude.size(-1),
hop_length=self.hop_length,
win_length=self.win_length,
n_fft=self.filter_length,
dtype=np.float32,
)
# remove modulation effects
approx_nonzero_indices = torch.from_numpy(
np.where(window_sum > tiny(window_sum))[0]
)
window_sum = torch.from_numpy(window_sum).to(inverse_transform.device)
inverse_transform[:, :, approx_nonzero_indices] /= window_sum[
approx_nonzero_indices
]
# scale by hop ratio
inverse_transform *= float(self.filter_length) / self.hop_length
inverse_transform = inverse_transform[..., self.pad_amount :]
inverse_transform = inverse_transform[..., : self.num_samples]
inverse_transform = inverse_transform.squeeze(1)
return inverse_transform
def forward(self, input_data):
"""Take input data (audio) to STFT domain and then back to audio.
Arguments:
input_data {tensor} -- Tensor of floats, with shape (num_batch, num_samples)
Returns:
reconstruction {tensor} -- Reconstructed audio given magnitude and phase. Of
shape (num_batch, num_samples)
"""
self.magnitude, self.phase = self.transform(input_data)
reconstruction = self.inverse(self.magnitude, self.phase)
return reconstruction
from time import time as ttime
class BiGRU(nn.Module):
def __init__(self, input_features, hidden_features, num_layers):
super(BiGRU, self).__init__()
self.gru = nn.GRU(
input_features,
hidden_features,
num_layers=num_layers,
batch_first=True,
bidirectional=True,
)
def forward(self, x):
return self.gru(x)[0]
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias=False,
),
nn.BatchNorm2d(out_channels, momentum=momentum),
nn.ReLU(),
nn.Conv2d(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias=False,
),
nn.BatchNorm2d(out_channels, momentum=momentum),
nn.ReLU(),
)
if in_channels != out_channels:
self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
self.is_shortcut = True
else:
self.is_shortcut = False
def forward(self, x):
if self.is_shortcut:
return self.conv(x) + self.shortcut(x)
else:
return self.conv(x) + x
class Encoder(nn.Module):
def __init__(
self,
in_channels,
in_size,
n_encoders,
kernel_size,
n_blocks,
out_channels=16,
momentum=0.01,
):
super(Encoder, self).__init__()
self.n_encoders = n_encoders
self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
self.layers = nn.ModuleList()
self.latent_channels = []
for i in range(self.n_encoders):
self.layers.append(
ResEncoderBlock(
in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
)
)
self.latent_channels.append([out_channels, in_size])
in_channels = out_channels
out_channels *= 2
in_size //= 2
self.out_size = in_size
self.out_channel = out_channels
def forward(self, x):
concat_tensors = []
x = self.bn(x)
for i in range(self.n_encoders):
_, x = self.layers[i](x)
concat_tensors.append(_)
return x, concat_tensors
class ResEncoderBlock(nn.Module):
def __init__(
self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
):
super(ResEncoderBlock, self).__init__()
self.n_blocks = n_blocks
self.conv = nn.ModuleList()
self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
for i in range(n_blocks - 1):
self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
self.kernel_size = kernel_size
if self.kernel_size is not None:
self.pool = nn.AvgPool2d(kernel_size=kernel_size)
def forward(self, x):
for i in range(self.n_blocks):
x = self.conv[i](x)
if self.kernel_size is not None:
return x, self.pool(x)
else:
return x
class Intermediate(nn.Module): #
def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
super(Intermediate, self).__init__()
self.n_inters = n_inters
self.layers = nn.ModuleList()
self.layers.append(
ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
)
for i in range(self.n_inters - 1):
self.layers.append(
ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
)
def forward(self, x):
for i in range(self.n_inters):
x = self.layers[i](x)
return x
class ResDecoderBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
super(ResDecoderBlock, self).__init__()
out_padding = (0, 1) if stride == (1, 2) else (1, 1)
self.n_blocks = n_blocks
self.conv1 = nn.Sequential(
nn.ConvTranspose2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=stride,
padding=(1, 1),
output_padding=out_padding,
bias=False,
),
nn.BatchNorm2d(out_channels, momentum=momentum),
nn.ReLU(),
)
self.conv2 = nn.ModuleList()
self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
for i in range(n_blocks - 1):
self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
def forward(self, x, concat_tensor):
x = self.conv1(x)
x = torch.cat((x, concat_tensor), dim=1)
for i in range(self.n_blocks):
x = self.conv2[i](x)
return x
class Decoder(nn.Module):
def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
super(Decoder, self).__init__()
self.layers = nn.ModuleList()
self.n_decoders = n_decoders
for i in range(self.n_decoders):
out_channels = in_channels // 2
self.layers.append(
ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
)
in_channels = out_channels
def forward(self, x, concat_tensors):
for i in range(self.n_decoders):
x = self.layers[i](x, concat_tensors[-1 - i])
return x
class DeepUnet(nn.Module):
def __init__(
self,
kernel_size,
n_blocks,
en_de_layers=5,
inter_layers=4,
in_channels=1,
en_out_channels=16,
):
super(DeepUnet, self).__init__()
self.encoder = Encoder(
in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
)
self.intermediate = Intermediate(
self.encoder.out_channel // 2,
self.encoder.out_channel,
inter_layers,
n_blocks,
)
self.decoder = Decoder(
self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
)
def forward(self, x):
x, concat_tensors = self.encoder(x)
x = self.intermediate(x)
x = self.decoder(x, concat_tensors)
return x
class E2E(nn.Module):
def __init__(
self,
n_blocks,
n_gru,
kernel_size,
en_de_layers=5,
inter_layers=4,
in_channels=1,
en_out_channels=16,
):
super(E2E, self).__init__()
self.unet = DeepUnet(
kernel_size,
n_blocks,
en_de_layers,
inter_layers,
in_channels,
en_out_channels,
)
self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
if n_gru:
self.fc = nn.Sequential(
BiGRU(3 * 128, 256, n_gru),
nn.Linear(512, 360),
nn.Dropout(0.25),
nn.Sigmoid(),
)
else:
self.fc = nn.Sequential(
nn.Linear(3 * nn.N_MELS, nn.N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
)
def forward(self, mel):
# print(mel.shape)
mel = mel.transpose(-1, -2).unsqueeze(1)
x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
x = self.fc(x)
# print(x.shape)
return x
from librosa.filters import mel
class MelSpectrogram(torch.nn.Module):
def __init__(
self,
is_half,
n_mel_channels,
sampling_rate,
win_length,
hop_length,
n_fft=None,
mel_fmin=0,
mel_fmax=None,
clamp=1e-5,
):
super().__init__()
n_fft = win_length if n_fft is None else n_fft
self.hann_window = {}
mel_basis = mel(
sr=sampling_rate,
n_fft=n_fft,
n_mels=n_mel_channels,
fmin=mel_fmin,
fmax=mel_fmax,
htk=True,
)
mel_basis = torch.from_numpy(mel_basis).float()
self.register_buffer("mel_basis", mel_basis)
self.n_fft = win_length if n_fft is None else n_fft
self.hop_length = hop_length
self.win_length = win_length
self.sampling_rate = sampling_rate
self.n_mel_channels = n_mel_channels
self.clamp = clamp
self.is_half = is_half
def forward(self, audio, keyshift=0, speed=1, center=True):
factor = 2 ** (keyshift / 12)
n_fft_new = int(np.round(self.n_fft * factor))
win_length_new = int(np.round(self.win_length * factor))
hop_length_new = int(np.round(self.hop_length * speed))
keyshift_key = str(keyshift) + "_" + str(audio.device)
if keyshift_key not in self.hann_window:
self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
# "cpu"if(audio.device.type=="privateuseone") else audio.device
audio.device
)
# fft = torch.stft(#doesn't support pytorch_dml
# # audio.cpu() if(audio.device.type=="privateuseone")else audio,
# audio,
# n_fft=n_fft_new,
# hop_length=hop_length_new,
# win_length=win_length_new,
# window=self.hann_window[keyshift_key],
# center=center,
# return_complex=True,
# )
# magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
# print(1111111111)
# print(222222222222222,audio.device,self.is_half)
if hasattr(self, "stft") == False:
# print(n_fft_new,hop_length_new,win_length_new,audio.shape)
self.stft = STFT(
filter_length=n_fft_new,
hop_length=hop_length_new,
win_length=win_length_new,
window="hann",
).to(audio.device)
magnitude = self.stft.transform(audio) # phase
# if (audio.device.type == "privateuseone"):
# magnitude=magnitude.to(audio.device)
if keyshift != 0:
size = self.n_fft // 2 + 1
resize = magnitude.size(1)
if resize < size:
magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
mel_output = torch.matmul(self.mel_basis, magnitude)
if self.is_half == True:
mel_output = mel_output.half()
log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
# print(log_mel_spec.device.type)
return log_mel_spec
class RMVPE:
def __init__(self, model_path, is_half, device=None):
self.resample_kernel = {}
self.resample_kernel = {}
self.is_half = is_half
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.device = device
self.mel_extractor = MelSpectrogram(
is_half, 128, 16000, 1024, 160, None, 30, 8000
).to(device)
if "privateuseone" in str(device):
import onnxruntime as ort
ort_session = ort.InferenceSession(
"%s/rmvpe.onnx" % os.environ["rmvpe_root"],
providers=["DmlExecutionProvider"],
)
self.model = ort_session
else:
model = E2E(4, 1, (2, 2))
ckpt = torch.load(model_path, map_location="cpu")
model.load_state_dict(ckpt)
model.eval()
if is_half == True:
model = model.half()
self.model = model
self.model = self.model.to(device)
cents_mapping = 20 * np.arange(360) + 1997.3794084376191
self.cents_mapping = np.pad(cents_mapping, (4, 4)) # 368
def mel2hidden(self, mel):
with torch.no_grad():
n_frames = mel.shape[-1]
mel = F.pad(
mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="constant"
)
if "privateuseone" in str(self.device):
onnx_input_name = self.model.get_inputs()[0].name
onnx_outputs_names = self.model.get_outputs()[0].name
hidden = self.model.run(
[onnx_outputs_names],
input_feed={onnx_input_name: mel.cpu().numpy()},
)[0]
else:
hidden = self.model(mel)
return hidden[:, :n_frames]
def decode(self, hidden, thred=0.03):
cents_pred = self.to_local_average_cents(hidden, thred=thred)
f0 = 10 * (2 ** (cents_pred / 1200))
f0[f0 == 10] = 0
# f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
return f0
def infer_from_audio(self, audio, thred=0.03):
# torch.cuda.synchronize()
t0 = ttime()
mel = self.mel_extractor(
torch.from_numpy(audio).float().to(self.device).unsqueeze(0), center=True
)
# print(123123123,mel.device.type)
# torch.cuda.synchronize()
t1 = ttime()
hidden = self.mel2hidden(mel)
# torch.cuda.synchronize()
t2 = ttime()
# print(234234,hidden.device.type)
if "privateuseone" not in str(self.device):
hidden = hidden.squeeze(0).cpu().numpy()
else:
hidden = hidden[0]
if self.is_half == True:
hidden = hidden.astype("float32")
f0 = self.decode(hidden, thred=thred)
# torch.cuda.synchronize()
t3 = ttime()
# print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
return f0
def infer_from_audio_with_pitch(self, audio, thred=0.03, f0_min=50, f0_max=1100):
t0 = ttime()
audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
mel = self.mel_extractor(audio, center=True)
t1 = ttime()
hidden = self.mel2hidden(mel)
t2 = ttime()
if "privateuseone" not in str(self.device):
hidden = hidden.squeeze(0).cpu().numpy()
else:
hidden = hidden[0]
if self.is_half == True:
hidden = hidden.astype("float32")
f0 = self.decode(hidden, thred=thred)
f0[(f0 < f0_min) | (f0 > f0_max)] = 0
t3 = ttime()
return f0
def to_local_average_cents(self, salience, thred=0.05):
# t0 = ttime()
center = np.argmax(salience, axis=1) # 帧长#index
salience = np.pad(salience, ((0, 0), (4, 4))) # 帧长,368
# t1 = ttime()
center += 4
todo_salience = []
todo_cents_mapping = []
starts = center - 4
ends = center + 5
for idx in range(salience.shape[0]):
todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
# t2 = ttime()
todo_salience = np.array(todo_salience) # 帧长,9
todo_cents_mapping = np.array(todo_cents_mapping) # 帧长,9
product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
weight_sum = np.sum(todo_salience, 1) # 帧长
devided = product_sum / weight_sum # 帧长
# t3 = ttime()
maxx = np.max(salience, axis=1) # 帧长
devided[maxx <= thred] = 0
# t4 = ttime()
# print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
return devided
if __name__ == "__main__":
import librosa
import soundfile as sf
audio, sampling_rate = sf.read(r"C:\Users\liujing04\Desktop\Z\冬之花clip1.wav")
if len(audio.shape) > 1:
audio = librosa.to_mono(audio.transpose(1, 0))
audio_bak = audio.copy()
if sampling_rate != 16000:
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
model_path = r"D:\BaiduNetdiskDownload\RVC-beta-v2-0727AMD_realtime\rmvpe.pt"
thred = 0.03 # 0.01
device = "cuda" if torch.cuda.is_available() else "cpu"
rmvpe = RMVPE(model_path, is_half=False, device=device)
t0 = ttime()
f0 = rmvpe.infer_from_audio(audio, thred=thred)
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
t1 = ttime()
logger.info("%s %.2f", f0.shape, t1 - t0)