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# Copyright (c) 2023 seanghay
#
# This code is from an unliscensed repository.
#
# Note: This code has been modified to fit the context of this repository.
# This code is included in an MIT-licensed repository.
# The repository's MIT license does not apply to this code.
# This code is modified from https://github.com/seanghay/uvr-mdx-infer/blob/main/separate.py
import torch
import numpy as np
import onnxruntime as ort
from tqdm import tqdm
class ConvTDFNet:
"""
ConvTDFNet - Convolutional Temporal Frequency Domain Network.
"""
def __init__(self, target_name, L, dim_f, dim_t, n_fft, hop=1024):
"""
Initialize ConvTDFNet.
Args:
target_name (str): The target name for separation.
L (int): Number of layers.
dim_f (int): Dimension in the frequency domain.
dim_t (int): Dimension in the time domain (log2).
n_fft (int): FFT size.
hop (int, optional): Hop size. Defaults to 1024.
Returns:
None
"""
super(ConvTDFNet, self).__init__()
self.dim_c = 4
self.dim_f = dim_f
self.dim_t = 2**dim_t
self.n_fft = n_fft
self.hop = hop
self.n_bins = self.n_fft // 2 + 1
self.chunk_size = hop * (self.dim_t - 1)
self.window = torch.hann_window(window_length=self.n_fft, periodic=True)
self.target_name = target_name
out_c = self.dim_c * 4 if target_name == "*" else self.dim_c
self.freq_pad = torch.zeros([1, out_c, self.n_bins - self.dim_f, self.dim_t])
self.n = L // 2
def stft(self, x):
"""
Perform Short-Time Fourier Transform (STFT).
Args:
x (torch.Tensor): Input waveform.
Returns:
torch.Tensor: STFT of the input waveform.
"""
x = x.reshape([-1, self.chunk_size])
x = torch.stft(
x,
n_fft=self.n_fft,
hop_length=self.hop,
window=self.window,
center=True,
return_complex=True,
)
x = torch.view_as_real(x)
x = x.permute([0, 3, 1, 2])
x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape(
[-1, self.dim_c, self.n_bins, self.dim_t]
)
return x[:, :, : self.dim_f]
def istft(self, x, freq_pad=None):
"""
Perform Inverse Short-Time Fourier Transform (ISTFT).
Args:
x (torch.Tensor): Input STFT.
freq_pad (torch.Tensor, optional): Frequency padding. Defaults to None.
Returns:
torch.Tensor: Inverse STFT of the input.
"""
freq_pad = (
self.freq_pad.repeat([x.shape[0], 1, 1, 1])
if freq_pad is None
else freq_pad
)
x = torch.cat([x, freq_pad], -2)
c = 4 * 2 if self.target_name == "*" else 2
x = x.reshape([-1, c, 2, self.n_bins, self.dim_t]).reshape(
[-1, 2, self.n_bins, self.dim_t]
)
x = x.permute([0, 2, 3, 1])
x = x.contiguous()
x = torch.view_as_complex(x)
x = torch.istft(
x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True
)
return x.reshape([-1, c, self.chunk_size])
class Predictor:
"""
Predictor class for source separation using ConvTDFNet and ONNX Runtime.
"""
def __init__(self, args, device):
"""
Initialize the Predictor.
Args:
args (dict): Configuration arguments.
device (str): Device to run the model ('cuda' or 'cpu').
Returns:
None
Raises:
ValueError: If the provided device is not 'cuda' or 'cpu'.
"""
self.args = args
self.model_ = ConvTDFNet(
target_name="vocals",
L=11,
dim_f=args["dim_f"],
dim_t=args["dim_t"],
n_fft=args["n_fft"],
)
if device == "cuda":
self.model = ort.InferenceSession(
args["model_path"], providers=["CUDAExecutionProvider"]
)
elif device == "cpu":
self.model = ort.InferenceSession(
args["model_path"], providers=["CPUExecutionProvider"]
)
else:
raise ValueError("Device must be either 'cuda' or 'cpu'")
def demix(self, mix):
"""
Separate the sources from the input mix.
Args:
mix (np.ndarray): Input mixture signal.
Returns:
np.ndarray: Separated sources.
Raises:
AssertionError: If margin is zero.
"""
samples = mix.shape[-1]
margin = self.args["margin"]
chunk_size = self.args["chunks"] * 44100
assert margin != 0, "Margin cannot be zero!"
if margin > chunk_size:
margin = chunk_size
segmented_mix = {}
if self.args["chunks"] == 0 or samples < chunk_size:
chunk_size = samples
counter = -1
for skip in range(0, samples, chunk_size):
counter += 1
s_margin = 0 if counter == 0 else margin
end = min(skip + chunk_size + margin, samples)
start = skip - s_margin
segmented_mix[skip] = mix[:, start:end].copy()
if end == samples:
break
sources = self.demix_base(segmented_mix, margin_size=margin)
return sources
def demix_base(self, mixes, margin_size):
"""
Base function for source separation.
Args:
mixes (dict): Dictionary of segmented mixtures.
margin_size (int): Size of the margin.
Returns:
np.ndarray: Separated sources.
"""
chunked_sources = []
progress_bar = tqdm(total=len(mixes))
progress_bar.set_description("Source separation")
for mix in mixes:
cmix = mixes[mix]
sources = []
n_sample = cmix.shape[1]
model = self.model_
trim = model.n_fft // 2
gen_size = model.chunk_size - 2 * trim
pad = gen_size - n_sample % gen_size
mix_p = np.concatenate(
(np.zeros((2, trim)), cmix, np.zeros((2, pad)), np.zeros((2, trim))), 1
)
mix_waves = []
i = 0
while i < n_sample + pad:
waves = np.array(mix_p[:, i : i + model.chunk_size])
mix_waves.append(waves)
i += gen_size
mix_waves = torch.tensor(np.array(mix_waves), dtype=torch.float32)
with torch.no_grad():
_ort = self.model
spek = model.stft(mix_waves)
if self.args["denoise"]:
spec_pred = (
-_ort.run(None, {"input": -spek.cpu().numpy()})[0] * 0.5
+ _ort.run(None, {"input": spek.cpu().numpy()})[0] * 0.5
)
tar_waves = model.istft(torch.tensor(spec_pred))
else:
tar_waves = model.istft(
torch.tensor(_ort.run(None, {"input": spek.cpu().numpy()})[0])
)
tar_signal = (
tar_waves[:, :, trim:-trim]
.transpose(0, 1)
.reshape(2, -1)
.numpy()[:, :-pad]
)
start = 0 if mix == 0 else margin_size
end = None if mix == list(mixes.keys())[::-1][0] else -margin_size
if margin_size == 0:
end = None
sources.append(tar_signal[:, start:end])
progress_bar.update(1)
chunked_sources.append(sources)
_sources = np.concatenate(chunked_sources, axis=-1)
progress_bar.close()
return _sources
def predict(self, mix):
"""
Predict the separated sources from the input mix.
Args:
mix (np.ndarray): Input mixture signal.
Returns:
tuple: Tuple containing the mixture minus the separated sources and the separated sources.
"""
if mix.ndim == 1:
mix = np.asfortranarray([mix, mix])
tail = mix.shape[1] % (self.args["chunks"] * 44100)
if mix.shape[1] % (self.args["chunks"] * 44100) != 0:
mix = np.pad(
mix,
(
(0, 0),
(
0,
self.args["chunks"] * 44100
- mix.shape[1] % (self.args["chunks"] * 44100),
),
),
)
mix = mix.T
sources = self.demix(mix.T)
opt = sources[0].T
if tail != 0:
return ((mix - opt)[: -(self.args["chunks"] * 44100 - tail), :], opt)
else:
return ((mix - opt), opt)
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