| | import soundfile as sf
|
| | import torch, pdb, os, warnings, librosa
|
| | import numpy as np
|
| | import onnxruntime as ort
|
| | from tqdm import tqdm
|
| | import torch
|
| |
|
| | dim_c = 4
|
| |
|
| |
|
| | class Conv_TDF_net_trim:
|
| | def __init__(
|
| | self, device, model_name, target_name, L, dim_f, dim_t, n_fft, hop=1024
|
| | ):
|
| | super(Conv_TDF_net_trim, 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
|
| |
|
| | out_c = dim_c * 4 if target_name == "*" else 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, 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 Conv_TDF_net_trim(
|
| | device=device,
|
| | model_name="Conv-TDF",
|
| | target_name="vocals",
|
| | L=11,
|
| | dim_f=dim_f,
|
| | dim_t=dim_t,
|
| | n_fft=n_fft,
|
| | )
|
| |
|
| |
|
| | warnings.filterwarnings("ignore")
|
| | cpu = torch.device("cpu")
|
| | if torch.cuda.is_available():
|
| | device = torch.device("cuda:0")
|
| | elif torch.backends.mps.is_available():
|
| | device = torch.device("mps")
|
| | else:
|
| | device = torch.device("cpu")
|
| |
|
| |
|
| | class Predictor:
|
| | def __init__(self, args):
|
| | 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", "CPUExecutionProvider"],
|
| | )
|
| | print("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)
|
| |
|
| | 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)
|
| | if os.path.exists(path_vocal):
|
| | os.system(
|
| | "ffmpeg -i %s -vn %s -q:a 2 -y"
|
| | % (path_vocal, path_vocal[:-4] + ".%s" % format)
|
| | )
|
| | if os.path.exists(path_other):
|
| | os.system(
|
| | "ffmpeg -i %s -vn %s -q:a 2 -y"
|
| | % (path_other, path_other[:-4] + ".%s" % format)
|
| | )
|
| |
|
| |
|
| | class MDXNetDereverb:
|
| | def __init__(self, chunks):
|
| | self.onnx = "uvr5_weights/onnx_dereverb_By_FoxJoy"
|
| | self.shifts = 10
|
| | self.mixing = "min_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)
|
| |
|
| | def _path_audio_(self, input, vocal_root, others_root, format):
|
| | self.pred.prediction(input, vocal_root, others_root, format)
|
| |
|
| |
|
| | if __name__ == "__main__":
|
| | dereverb = MDXNetDereverb(15)
|
| | from time import time as ttime
|
| |
|
| | t0 = ttime()
|
| | dereverb._path_audio_(
|
| | "雪雪伴奏对消HP5.wav",
|
| | "vocal",
|
| | "others",
|
| | )
|
| | t1 = ttime()
|
| | print(t1 - t0)
|
| |
|
| |
|
| | """
|
| |
|
| | runtime\python.exe MDXNet.py
|
| |
|
| | 6G:
|
| | 15/9:0.8G->6.8G
|
| | 14:0.8G->6.5G
|
| | 25:炸
|
| |
|
| | half15:0.7G->6.6G,22.69s
|
| | fp32-15:0.7G->6.6G,20.85s
|
| |
|
| | """
|
| |
|
