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import argparse
import os
import librosa
import numpy as np
import soundfile as sf
import torch
from tqdm import tqdm
from lib import dataset
from lib import nets
from lib import spec_utils
from lib import utils
class Separator(object):
def __init__(self, model, device=None, batchsize=1, cropsize=256, postprocess=False):
self.model = model
self.offset = model.offset
self.device = device
self.batchsize = batchsize
self.cropsize = cropsize
self.postprocess = postprocess
def _postprocess(self, X_spec, mask):
if self.postprocess:
mask_mag = np.abs(mask)
mask_mag = spec_utils.merge_artifacts(mask_mag)
mask = mask_mag * np.exp(1.j * np.angle(mask))
y_spec = X_spec * mask
v_spec = X_spec - y_spec
return y_spec, v_spec
def _separate(self, X_spec_pad, roi_size):
X_dataset = []
patches = (X_spec_pad.shape[2] - 2 * self.offset) // roi_size
for i in range(patches):
start = i * roi_size
X_spec_crop = X_spec_pad[:, :, start:start + self.cropsize]
X_dataset.append(X_spec_crop)
X_dataset = np.asarray(X_dataset)
self.model.eval()
with torch.no_grad():
mask_list = []
# To reduce the overhead, dataloader is not used.
for i in tqdm(range(0, patches, self.batchsize)):
X_batch = X_dataset[i: i + self.batchsize]
X_batch = torch.from_numpy(X_batch).to(self.device)
mask = self.model.predict_mask(X_batch)
mask = mask.detach().cpu().numpy()
mask = np.concatenate(mask, axis=2)
mask_list.append(mask)
mask = np.concatenate(mask_list, axis=2)
return mask
def separate(self, X_spec):
n_frame = X_spec.shape[2]
pad_l, pad_r, roi_size = dataset.make_padding(n_frame, self.cropsize, self.offset)
X_spec_pad = np.pad(X_spec, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
X_spec_pad /= np.abs(X_spec).max()
mask = self._separate(X_spec_pad, roi_size)
mask = mask[:, :, :n_frame]
y_spec, v_spec = self._postprocess(X_spec, mask)
return y_spec, v_spec
def separate_tta(self, X_spec):
n_frame = X_spec.shape[2]
pad_l, pad_r, roi_size = dataset.make_padding(n_frame, self.cropsize, self.offset)
X_spec_pad = np.pad(X_spec, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
X_spec_pad /= X_spec_pad.max()
mask = self._separate(X_spec_pad, roi_size)
pad_l += roi_size // 2
pad_r += roi_size // 2
X_spec_pad = np.pad(X_spec, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
X_spec_pad /= X_spec_pad.max()
mask_tta = self._separate(X_spec_pad, roi_size)
mask_tta = mask_tta[:, :, roi_size // 2:]
mask = (mask[:, :, :n_frame] + mask_tta[:, :, :n_frame]) * 0.5
y_spec, v_spec = self._postprocess(X_spec, mask)
return y_spec, v_spec
def main(gpu=-1, pretrained_model='models/baseline.pth', input_file='', sr=44100, n_fft=2048,
hop_length=1024, batchsize=4, cropsize=256, output_image=False, tta=False, output_dir=""):
print('loading model...', end=' ')
device = torch.device('cpu')
if gpu >= 0:
if torch.cuda.is_available():
device = torch.device('cuda:{}'.format(gpu))
elif torch.backends.mps.is_available() and torch.backends.mps.is_built():
device = torch.device('mps')
model = nets.CascadedNet(n_fft, hop_length, 32, 128, True)
model.load_state_dict(torch.load(pretrained_model, map_location='cpu'))
model.to(device)
print('done')
print('loading wave source...', end=' ')
print('loading wave source...', end=' ')
print("Chemin du fichier audio :", input_file) # Ajoutez cette ligne pour déboguer
X, sr = librosa.load(input_file, sr=sr, mono=False, dtype=np.float32, res_type='kaiser_fast')
basename = os.path.splitext(os.path.basename(input_file))[0]
print('done')
if X.ndim == 1:
X = np.asarray([X, X])
print('stft of wave source...', end=' ')
X_spec = spec_utils.wave_to_spectrogram(X, hop_length, n_fft)
print('done')
sp = Separator(
model=model,
device=device,
batchsize=batchsize,
cropsize=cropsize,
)
if tta:
y_spec, v_spec = sp.separate_tta(X_spec)
else:
y_spec, v_spec = sp.separate(X_spec)
print('validating output directory...', end=' ')
if output_dir != "":
output_dir = output_dir.rstrip('/') + '/'
os.makedirs(output_dir, exist_ok=True)
print('done')
print('inverse stft of instruments...', end=' ')
wave = spec_utils.spectrogram_to_wave(y_spec, hop_length=hop_length)
print('done')
sf.write('{}{}_Instruments.wav'.format(output_dir, basename), wave.T, sr)
print('inverse stft of vocals...', end=' ')
wave = spec_utils.spectrogram_to_wave(v_spec, hop_length=hop_length)
print('done')
sf.write('{}{}_Vocals_finale.wav'.format(output_dir, basename), wave.T, sr)
if output_image:
image = spec_utils.spectrogram_to_image(y_spec)
utils.imwrite('{}{}_Instruments.jpg'.format(output_dir, basename), image)
image = spec_utils.spectrogram_to_image(v_spec)
utils.imwrite('{}{}_Vocals.jpg'.format(output_dir, basename), image)
import os
# Appel de la fonction avec des paramètres
main(input_file=os.getcwd()+'/audio_gnu.wav')
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