import os import logging logger = logging.getLogger(__name__) import librosa import numpy as np import soundfile as sf import torch from tqdm import tqdm cpu = torch.device("cpu") class ConvTDFNetTrim: def __init__( self, device, model_name, target_name, L, dim_f, dim_t, n_fft, hop=1024 ): super(ConvTDFNetTrim, self).__init__() 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).to( device ) self.target_name = target_name self.blender = "blender" in model_name self.dim_c = 4 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] ).to(device) self.n = L // 2 def stft(self, x): 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): 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]) def get_models(device, dim_f, dim_t, n_fft): return ConvTDFNetTrim( device=device, model_name="Conv-TDF", target_name="vocals", L=11, dim_f=dim_f, dim_t=dim_t, n_fft=n_fft, ) class Predictor: def __init__(self, args): import onnxruntime as ort logger.info(ort.get_available_providers()) self.args = args self.model_ = get_models( device=cpu, dim_f=args.dim_f, dim_t=args.dim_t, n_fft=args.n_fft ) self.model = ort.InferenceSession( os.path.join(args.onnx, self.model_.target_name + ".onnx"), providers=[ "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider", ], ) logger.info("ONNX load done") def demix(self, mix): samples = mix.shape[-1] margin = self.args.margin chunk_size = self.args.chunks * 44100 assert not 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) """ mix:(2,big_sample) segmented_mix:offset->(2,small_sample) sources:(1,2,big_sample) """ return sources def demix_base(self, mixes, margin_size): chunked_sources = [] progress_bar = tqdm(total=len(mixes)) progress_bar.set_description("Processing") 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(mix_waves, dtype=torch.float32).to(cpu) 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) # del self.model progress_bar.close() return _sources def prediction(self, m, vocal_root, others_root, format): os.makedirs(vocal_root, exist_ok=True) os.makedirs(others_root, exist_ok=True) basename = os.path.basename(m) mix, rate = librosa.load(m, mono=False, sr=44100) if mix.ndim == 1: mix = np.asfortranarray([mix, mix]) mix = mix.T sources = self.demix(mix.T) opt = sources[0].T if format in ["wav", "flac"]: sf.write( "%s/%s_main_vocal.%s" % (vocal_root, basename, format), mix - opt, rate ) sf.write("%s/%s_others.%s" % (others_root, basename, format), opt, rate) else: path_vocal = "%s/%s_main_vocal.wav" % (vocal_root, basename) path_other = "%s/%s_others.wav" % (others_root, basename) sf.write(path_vocal, mix - opt, rate) sf.write(path_other, opt, rate) opt_path_vocal = path_vocal[:-4] + ".%s" % format opt_path_other = path_other[:-4] + ".%s" % format if os.path.exists(path_vocal): os.system( "ffmpeg -i '%s' -vn '%s' -q:a 2 -y" % (path_vocal, opt_path_vocal) ) if os.path.exists(opt_path_vocal): try: os.remove(path_vocal) except: pass if os.path.exists(path_other): os.system( "ffmpeg -i '%s' -vn '%s' -q:a 2 -y" % (path_other, opt_path_other) ) if os.path.exists(opt_path_other): try: os.remove(path_other) except: pass class MDXNetDereverb: def __init__(self, chunks): self.onnx = "%s/uvr5_weights/onnx_dereverb_By_FoxJoy"%os.path.dirname(os.path.abspath(__file__)) self.shifts = 10 # 'Predict with randomised equivariant stabilisation' self.mixing = "min_mag" # ['default','min_mag','max_mag'] self.chunks = chunks self.margin = 44100 self.dim_t = 9 self.dim_f = 3072 self.n_fft = 6144 self.denoise = True self.pred = Predictor(self) self.device = cpu def _path_audio_(self, input, others_root, vocal_root, format, is_hp3=False): self.pred.prediction(input, vocal_root, others_root, format)