247 lines
8.0 KiB
Python
247 lines
8.0 KiB
Python
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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# del self.model
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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 # 'Predict with randomised equivariant stabilisation'
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self.mixing = "min_mag" # ['default','min_mag','max_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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