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import os |
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import logging |
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logger = logging.getLogger(__name__) |
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import librosa |
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import numpy as np |
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import soundfile as sf |
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import torch |
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from tqdm import tqdm |
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cpu = torch.device("cpu") |
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class ConvTDFNetTrim: |
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def __init__( |
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self, device, model_name, target_name, L, dim_f, dim_t, n_fft, hop=1024 |
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): |
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super(ConvTDFNetTrim, self).__init__() |
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self.dim_f = dim_f |
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self.dim_t = 2**dim_t |
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self.n_fft = n_fft |
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self.hop = hop |
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self.n_bins = self.n_fft // 2 + 1 |
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self.chunk_size = hop * (self.dim_t - 1) |
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self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to( |
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device |
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) |
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self.target_name = target_name |
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self.blender = "blender" in model_name |
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self.dim_c = 4 |
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out_c = self.dim_c * 4 if target_name == "*" else self.dim_c |
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self.freq_pad = torch.zeros( |
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[1, out_c, self.n_bins - self.dim_f, self.dim_t] |
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).to(device) |
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self.n = L // 2 |
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def stft(self, x): |
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x = x.reshape([-1, self.chunk_size]) |
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x = torch.stft( |
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x, |
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n_fft=self.n_fft, |
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hop_length=self.hop, |
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window=self.window, |
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center=True, |
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return_complex=True, |
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) |
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x = torch.view_as_real(x) |
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x = x.permute([0, 3, 1, 2]) |
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x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape( |
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[-1, self.dim_c, self.n_bins, self.dim_t] |
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) |
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return x[:, :, : self.dim_f] |
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def istft(self, x, freq_pad=None): |
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freq_pad = ( |
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self.freq_pad.repeat([x.shape[0], 1, 1, 1]) |
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if freq_pad is None |
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else freq_pad |
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) |
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x = torch.cat([x, freq_pad], -2) |
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c = 4 * 2 if self.target_name == "*" else 2 |
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x = x.reshape([-1, c, 2, self.n_bins, self.dim_t]).reshape( |
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[-1, 2, self.n_bins, self.dim_t] |
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) |
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x = x.permute([0, 2, 3, 1]) |
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x = x.contiguous() |
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x = torch.view_as_complex(x) |
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x = torch.istft( |
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x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True |
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) |
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return x.reshape([-1, c, self.chunk_size]) |
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def get_models(device, dim_f, dim_t, n_fft): |
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return ConvTDFNetTrim( |
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device=device, |
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model_name="Conv-TDF", |
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target_name="vocals", |
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L=11, |
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dim_f=dim_f, |
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dim_t=dim_t, |
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n_fft=n_fft, |
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) |
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class Predictor: |
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def __init__(self, args): |
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import onnxruntime as ort |
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logger.info(ort.get_available_providers()) |
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self.args = args |
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self.model_ = get_models( |
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device=cpu, dim_f=args.dim_f, dim_t=args.dim_t, n_fft=args.n_fft |
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) |
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self.model = ort.InferenceSession( |
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os.path.join(args.onnx, self.model_.target_name + ".onnx"), |
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providers=[ |
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"CUDAExecutionProvider", |
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"DmlExecutionProvider", |
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"CPUExecutionProvider", |
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], |
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) |
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logger.info("ONNX load done") |
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def demix(self, mix): |
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samples = mix.shape[-1] |
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margin = self.args.margin |
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chunk_size = self.args.chunks * 44100 |
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assert not margin == 0, "margin cannot be zero!" |
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if margin > chunk_size: |
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margin = chunk_size |
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segmented_mix = {} |
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if self.args.chunks == 0 or samples < chunk_size: |
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chunk_size = samples |
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counter = -1 |
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for skip in range(0, samples, chunk_size): |
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counter += 1 |
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s_margin = 0 if counter == 0 else margin |
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end = min(skip + chunk_size + margin, samples) |
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start = skip - s_margin |
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segmented_mix[skip] = mix[:, start:end].copy() |
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if end == samples: |
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break |
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sources = self.demix_base(segmented_mix, margin_size=margin) |
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""" |
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mix:(2,big_sample) |
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segmented_mix:offset->(2,small_sample) |
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sources:(1,2,big_sample) |
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""" |
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return sources |
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def demix_base(self, mixes, margin_size): |
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chunked_sources = [] |
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progress_bar = tqdm(total=len(mixes)) |
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progress_bar.set_description("Processing") |
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for mix in mixes: |
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cmix = mixes[mix] |
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sources = [] |
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n_sample = cmix.shape[1] |
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model = self.model_ |
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trim = model.n_fft // 2 |
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gen_size = model.chunk_size - 2 * trim |
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pad = gen_size - n_sample % gen_size |
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mix_p = np.concatenate( |
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(np.zeros((2, trim)), cmix, np.zeros((2, pad)), np.zeros((2, trim))), 1 |
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) |
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mix_waves = [] |
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i = 0 |
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while i < n_sample + pad: |
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waves = np.array(mix_p[:, i : i + model.chunk_size]) |
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mix_waves.append(waves) |
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i += gen_size |
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mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu) |
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with torch.no_grad(): |
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_ort = self.model |
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spek = model.stft(mix_waves) |
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if self.args.denoise: |
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spec_pred = ( |
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-_ort.run(None, {"input": -spek.cpu().numpy()})[0] * 0.5 |
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+ _ort.run(None, {"input": spek.cpu().numpy()})[0] * 0.5 |
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) |
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tar_waves = model.istft(torch.tensor(spec_pred)) |
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else: |
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tar_waves = model.istft( |
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torch.tensor(_ort.run(None, {"input": spek.cpu().numpy()})[0]) |
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) |
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tar_signal = ( |
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tar_waves[:, :, trim:-trim] |
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.transpose(0, 1) |
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.reshape(2, -1) |
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.numpy()[:, :-pad] |
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) |
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start = 0 if mix == 0 else margin_size |
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end = None if mix == list(mixes.keys())[::-1][0] else -margin_size |
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if margin_size == 0: |
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end = None |
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sources.append(tar_signal[:, start:end]) |
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progress_bar.update(1) |
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chunked_sources.append(sources) |
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_sources = np.concatenate(chunked_sources, axis=-1) |
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progress_bar.close() |
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return _sources |
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def prediction(self, m, vocal_root, others_root, format): |
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os.makedirs(vocal_root, exist_ok=True) |
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os.makedirs(others_root, exist_ok=True) |
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basename = os.path.basename(m) |
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mix, rate = librosa.load(m, mono=False, sr=44100) |
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if mix.ndim == 1: |
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mix = np.asfortranarray([mix, mix]) |
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mix = mix.T |
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sources = self.demix(mix.T) |
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opt = sources[0].T |
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if format in ["wav", "flac"]: |
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sf.write( |
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"%s/%s_main_vocal.%s" % (vocal_root, basename, format), mix - opt, rate |
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) |
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sf.write("%s/%s_others.%s" % (others_root, basename, format), opt, rate) |
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else: |
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path_vocal = "%s/%s_main_vocal.wav" % (vocal_root, basename) |
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path_other = "%s/%s_others.wav" % (others_root, basename) |
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sf.write(path_vocal, mix - opt, rate) |
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sf.write(path_other, opt, rate) |
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if os.path.exists(path_vocal): |
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os.system( |
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"ffmpeg -i %s -vn %s -q:a 2 -y" |
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% (path_vocal, path_vocal[:-4] + ".%s" % format) |
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) |
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if os.path.exists(path_other): |
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os.system( |
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"ffmpeg -i %s -vn %s -q:a 2 -y" |
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% (path_other, path_other[:-4] + ".%s" % format) |
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) |
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class MDXNetDereverb: |
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def __init__(self, chunks, device): |
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self.onnx = "assets/uvr5_weights/onnx_dereverb_By_FoxJoy" |
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self.shifts = 10 |
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self.mixing = "min_mag" |
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self.chunks = chunks |
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self.margin = 44100 |
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self.dim_t = 9 |
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self.dim_f = 3072 |
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self.n_fft = 6144 |
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self.denoise = True |
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self.pred = Predictor(self) |
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self.device = device |
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def path_audio(self, input, vocal_root, others_root, format): |
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self.pred.prediction(input, vocal_root, others_root, format) |
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