| import hashlib
|
| import json
|
| import math
|
| import os
|
|
|
| import librosa
|
| import numpy as np
|
| import soundfile as sf
|
| from tqdm import tqdm
|
|
|
|
|
| def crop_center(h1, h2):
|
| h1_shape = h1.size()
|
| h2_shape = h2.size()
|
|
|
| if h1_shape[3] == h2_shape[3]:
|
| return h1
|
| elif h1_shape[3] < h2_shape[3]:
|
| raise ValueError("h1_shape[3] must be greater than h2_shape[3]")
|
|
|
|
|
|
|
| s_time = (h1_shape[3] - h2_shape[3]) // 2
|
| e_time = s_time + h2_shape[3]
|
| h1 = h1[:, :, :, s_time:e_time]
|
|
|
| return h1
|
|
|
|
|
| def wave_to_spectrogram(
|
| wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False
|
| ):
|
| if reverse:
|
| wave_left = np.flip(np.asfortranarray(wave[0]))
|
| wave_right = np.flip(np.asfortranarray(wave[1]))
|
| elif mid_side:
|
| wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
|
| wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
|
| elif mid_side_b2:
|
| wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
|
| wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
|
| else:
|
| wave_left = np.asfortranarray(wave[0])
|
| wave_right = np.asfortranarray(wave[1])
|
|
|
| spec_left = librosa.stft(wave_left, n_fft=n_fft, hop_length=hop_length)
|
| spec_right = librosa.stft(wave_right, n_fft=n_fft, hop_length=hop_length)
|
|
|
| spec = np.asfortranarray([spec_left, spec_right])
|
|
|
| return spec
|
|
|
|
|
| def wave_to_spectrogram_mt(
|
| wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False
|
| ):
|
| import threading
|
|
|
| if reverse:
|
| wave_left = np.flip(np.asfortranarray(wave[0]))
|
| wave_right = np.flip(np.asfortranarray(wave[1]))
|
| elif mid_side:
|
| wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
|
| wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
|
| elif mid_side_b2:
|
| wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
|
| wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
|
| else:
|
| wave_left = np.asfortranarray(wave[0])
|
| wave_right = np.asfortranarray(wave[1])
|
|
|
| def run_thread(**kwargs):
|
| global spec_left
|
| spec_left = librosa.stft(**kwargs)
|
|
|
| thread = threading.Thread(
|
| target=run_thread,
|
| kwargs={"y": wave_left, "n_fft": n_fft, "hop_length": hop_length},
|
| )
|
| thread.start()
|
| spec_right = librosa.stft(wave_right, n_fft=n_fft, hop_length=hop_length)
|
| thread.join()
|
|
|
| spec = np.asfortranarray([spec_left, spec_right])
|
|
|
| return spec
|
|
|
|
|
| def combine_spectrograms(specs, mp):
|
| l = min([specs[i].shape[2] for i in specs])
|
| spec_c = np.zeros(shape=(2, mp.param["bins"] + 1, l), dtype=np.complex64)
|
| offset = 0
|
| bands_n = len(mp.param["band"])
|
|
|
| for d in range(1, bands_n + 1):
|
| h = mp.param["band"][d]["crop_stop"] - mp.param["band"][d]["crop_start"]
|
| spec_c[:, offset : offset + h, :l] = specs[d][
|
| :, mp.param["band"][d]["crop_start"] : mp.param["band"][d]["crop_stop"], :l
|
| ]
|
| offset += h
|
|
|
| if offset > mp.param["bins"]:
|
| raise ValueError("Too much bins")
|
|
|
|
|
| if (
|
| mp.param["pre_filter_start"] > 0
|
| ):
|
| if bands_n == 1:
|
| spec_c = fft_lp_filter(
|
| spec_c, mp.param["pre_filter_start"], mp.param["pre_filter_stop"]
|
| )
|
| else:
|
| gp = 1
|
| for b in range(
|
| mp.param["pre_filter_start"] + 1, mp.param["pre_filter_stop"]
|
| ):
|
| g = math.pow(
|
| 10, -(b - mp.param["pre_filter_start"]) * (3.5 - gp) / 20.0
|
| )
|
| gp = g
|
| spec_c[:, b, :] *= g
|
|
|
| return np.asfortranarray(spec_c)
|
|
|
|
|
| def spectrogram_to_image(spec, mode="magnitude"):
|
| if mode == "magnitude":
|
| if np.iscomplexobj(spec):
|
| y = np.abs(spec)
|
| else:
|
| y = spec
|
| y = np.log10(y**2 + 1e-8)
|
| elif mode == "phase":
|
| if np.iscomplexobj(spec):
|
| y = np.angle(spec)
|
| else:
|
| y = spec
|
|
|
| y -= y.min()
|
| y *= 255 / y.max()
|
| img = np.uint8(y)
|
|
|
| if y.ndim == 3:
|
| img = img.transpose(1, 2, 0)
|
| img = np.concatenate([np.max(img, axis=2, keepdims=True), img], axis=2)
|
|
|
| return img
|
|
|
|
|
| def reduce_vocal_aggressively(X, y, softmask):
|
| v = X - y
|
| y_mag_tmp = np.abs(y)
|
| v_mag_tmp = np.abs(v)
|
|
|
| v_mask = v_mag_tmp > y_mag_tmp
|
| y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf)
|
|
|
| return y_mag * np.exp(1.0j * np.angle(y))
|
|
|
|
|
| def mask_silence(mag, ref, thres=0.2, min_range=64, fade_size=32):
|
| if min_range < fade_size * 2:
|
| raise ValueError("min_range must be >= fade_area * 2")
|
|
|
| mag = mag.copy()
|
|
|
| idx = np.where(ref.mean(axis=(0, 1)) < thres)[0]
|
| starts = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
|
| ends = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
|
| uninformative = np.where(ends - starts > min_range)[0]
|
| if len(uninformative) > 0:
|
| starts = starts[uninformative]
|
| ends = ends[uninformative]
|
| old_e = None
|
| for s, e in zip(starts, ends):
|
| if old_e is not None and s - old_e < fade_size:
|
| s = old_e - fade_size * 2
|
|
|
| if s != 0:
|
| weight = np.linspace(0, 1, fade_size)
|
| mag[:, :, s : s + fade_size] += weight * ref[:, :, s : s + fade_size]
|
| else:
|
| s -= fade_size
|
|
|
| if e != mag.shape[2]:
|
| weight = np.linspace(1, 0, fade_size)
|
| mag[:, :, e - fade_size : e] += weight * ref[:, :, e - fade_size : e]
|
| else:
|
| e += fade_size
|
|
|
| mag[:, :, s + fade_size : e - fade_size] += ref[
|
| :, :, s + fade_size : e - fade_size
|
| ]
|
| old_e = e
|
|
|
| return mag
|
|
|
|
|
| def align_wave_head_and_tail(a, b):
|
| l = min([a[0].size, b[0].size])
|
|
|
| return a[:l, :l], b[:l, :l]
|
|
|
|
|
| def cache_or_load(mix_path, inst_path, mp):
|
| mix_basename = os.path.splitext(os.path.basename(mix_path))[0]
|
| inst_basename = os.path.splitext(os.path.basename(inst_path))[0]
|
|
|
| cache_dir = "mph{}".format(
|
| hashlib.sha1(json.dumps(mp.param, sort_keys=True).encode("utf-8")).hexdigest()
|
| )
|
| mix_cache_dir = os.path.join("cache", cache_dir)
|
| inst_cache_dir = os.path.join("cache", cache_dir)
|
|
|
| os.makedirs(mix_cache_dir, exist_ok=True)
|
| os.makedirs(inst_cache_dir, exist_ok=True)
|
|
|
| mix_cache_path = os.path.join(mix_cache_dir, mix_basename + ".npy")
|
| inst_cache_path = os.path.join(inst_cache_dir, inst_basename + ".npy")
|
|
|
| if os.path.exists(mix_cache_path) and os.path.exists(inst_cache_path):
|
| X_spec_m = np.load(mix_cache_path)
|
| y_spec_m = np.load(inst_cache_path)
|
| else:
|
| X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
|
|
|
| for d in range(len(mp.param["band"]), 0, -1):
|
| bp = mp.param["band"][d]
|
|
|
| if d == len(mp.param["band"]):
|
| X_wave[d], _ = librosa.load(
|
| mix_path,
|
| sr=bp["sr"],
|
| mono=False,
|
| dtype=np.float32,
|
| res_type=bp["res_type"]
|
| )
|
| y_wave[d], _ = librosa.load(
|
| inst_path,
|
| sr=bp["sr"],
|
| mono=False,
|
| dtype=np.float32,
|
| res_type=bp["res_type"],
|
| )
|
| else:
|
| X_wave[d] = librosa.resample(
|
| X_wave[d + 1],
|
| orig_sr=mp.param["band"][d + 1]["sr"],
|
| target_sr=bp["sr"],
|
| res_type=bp["res_type"],
|
| )
|
| y_wave[d] = librosa.resample(
|
| y_wave[d + 1],
|
| orig_sr=mp.param["band"][d + 1]["sr"],
|
| target_sr=bp["sr"],
|
| res_type=bp["res_type"],
|
| )
|
|
|
| X_wave[d], y_wave[d] = align_wave_head_and_tail(X_wave[d], y_wave[d])
|
|
|
| X_spec_s[d] = wave_to_spectrogram(
|
| X_wave[d],
|
| bp["hl"],
|
| bp["n_fft"],
|
| mp.param["mid_side"],
|
| mp.param["mid_side_b2"],
|
| mp.param["reverse"],
|
| )
|
| y_spec_s[d] = wave_to_spectrogram(
|
| y_wave[d],
|
| bp["hl"],
|
| bp["n_fft"],
|
| mp.param["mid_side"],
|
| mp.param["mid_side_b2"],
|
| mp.param["reverse"],
|
| )
|
|
|
| del X_wave, y_wave
|
|
|
| X_spec_m = combine_spectrograms(X_spec_s, mp)
|
| y_spec_m = combine_spectrograms(y_spec_s, mp)
|
|
|
| if X_spec_m.shape != y_spec_m.shape:
|
| raise ValueError("The combined spectrograms are different: " + mix_path)
|
|
|
| _, ext = os.path.splitext(mix_path)
|
|
|
| np.save(mix_cache_path, X_spec_m)
|
| np.save(inst_cache_path, y_spec_m)
|
|
|
| return X_spec_m, y_spec_m
|
|
|
|
|
| def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse):
|
| spec_left = np.asfortranarray(spec[0])
|
| spec_right = np.asfortranarray(spec[1])
|
|
|
| wave_left = librosa.istft(spec_left, hop_length=hop_length)
|
| wave_right = librosa.istft(spec_right, hop_length=hop_length)
|
|
|
| if reverse:
|
| return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
|
| elif mid_side:
|
| return np.asfortranarray(
|
| [np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]
|
| )
|
| elif mid_side_b2:
|
| return np.asfortranarray(
|
| [
|
| np.add(wave_right / 1.25, 0.4 * wave_left),
|
| np.subtract(wave_left / 1.25, 0.4 * wave_right),
|
| ]
|
| )
|
| else:
|
| return np.asfortranarray([wave_left, wave_right])
|
|
|
|
|
| def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse, mid_side_b2):
|
| import threading
|
|
|
| spec_left = np.asfortranarray(spec[0])
|
| spec_right = np.asfortranarray(spec[1])
|
|
|
| def run_thread(**kwargs):
|
| global wave_left
|
| wave_left = librosa.istft(**kwargs)
|
|
|
| thread = threading.Thread(
|
| target=run_thread, kwargs={"stft_matrix": spec_left, "hop_length": hop_length}
|
| )
|
| thread.start()
|
| wave_right = librosa.istft(spec_right, hop_length=hop_length)
|
| thread.join()
|
|
|
| if reverse:
|
| return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
|
| elif mid_side:
|
| return np.asfortranarray(
|
| [np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]
|
| )
|
| elif mid_side_b2:
|
| return np.asfortranarray(
|
| [
|
| np.add(wave_right / 1.25, 0.4 * wave_left),
|
| np.subtract(wave_left / 1.25, 0.4 * wave_right),
|
| ]
|
| )
|
| else:
|
| return np.asfortranarray([wave_left, wave_right])
|
|
|
|
|
| def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
|
| wave_band = {}
|
| bands_n = len(mp.param["band"])
|
| offset = 0
|
|
|
| for d in range(1, bands_n + 1):
|
| bp = mp.param["band"][d]
|
| spec_s = np.ndarray(
|
| shape=(2, bp["n_fft"] // 2 + 1, spec_m.shape[2]), dtype=complex
|
| )
|
| h = bp["crop_stop"] - bp["crop_start"]
|
| spec_s[:, bp["crop_start"] : bp["crop_stop"], :] = spec_m[
|
| :, offset : offset + h, :
|
| ]
|
|
|
| offset += h
|
| if d == bands_n:
|
| if extra_bins_h:
|
| max_bin = bp["n_fft"] // 2
|
| spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[
|
| :, :extra_bins_h, :
|
| ]
|
| if bp["hpf_start"] > 0:
|
| spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
|
| if bands_n == 1:
|
| wave = spectrogram_to_wave(
|
| spec_s,
|
| bp["hl"],
|
| mp.param["mid_side"],
|
| mp.param["mid_side_b2"],
|
| mp.param["reverse"],
|
| )
|
| else:
|
| wave = np.add(
|
| wave,
|
| spectrogram_to_wave(
|
| spec_s,
|
| bp["hl"],
|
| mp.param["mid_side"],
|
| mp.param["mid_side_b2"],
|
| mp.param["reverse"],
|
| ),
|
| )
|
| else:
|
| sr = mp.param["band"][d + 1]["sr"]
|
| if d == 1:
|
| spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])
|
| wave = librosa.resample(
|
| spectrogram_to_wave(
|
| spec_s,
|
| bp["hl"],
|
| mp.param["mid_side"],
|
| mp.param["mid_side_b2"],
|
| mp.param["reverse"],
|
| ),
|
| orig_sr=bp["sr"],
|
| target_sr=sr,
|
| res_type="sinc_fastest",
|
| )
|
| else:
|
| spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
|
| spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])
|
| wave2 = np.add(
|
| wave,
|
| spectrogram_to_wave(
|
| spec_s,
|
| bp["hl"],
|
| mp.param["mid_side"],
|
| mp.param["mid_side_b2"],
|
| mp.param["reverse"],
|
| ),
|
| )
|
|
|
| wave = librosa.resample(wave2, orig_sr=bp["sr"], target_sr=sr, res_type="scipy")
|
|
|
| return wave.T
|
|
|
|
|
| def fft_lp_filter(spec, bin_start, bin_stop):
|
| g = 1.0
|
| for b in range(bin_start, bin_stop):
|
| g -= 1 / (bin_stop - bin_start)
|
| spec[:, b, :] = g * spec[:, b, :]
|
|
|
| spec[:, bin_stop:, :] *= 0
|
|
|
| return spec
|
|
|
|
|
| def fft_hp_filter(spec, bin_start, bin_stop):
|
| g = 1.0
|
| for b in range(bin_start, bin_stop, -1):
|
| g -= 1 / (bin_start - bin_stop)
|
| spec[:, b, :] = g * spec[:, b, :]
|
|
|
| spec[:, 0 : bin_stop + 1, :] *= 0
|
|
|
| return spec
|
|
|
|
|
| def mirroring(a, spec_m, input_high_end, mp):
|
| if "mirroring" == a:
|
| mirror = np.flip(
|
| np.abs(
|
| spec_m[
|
| :,
|
| mp.param["pre_filter_start"]
|
| - 10
|
| - input_high_end.shape[1] : mp.param["pre_filter_start"]
|
| - 10,
|
| :,
|
| ]
|
| ),
|
| 1,
|
| )
|
| mirror = mirror * np.exp(1.0j * np.angle(input_high_end))
|
|
|
| return np.where(
|
| np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror
|
| )
|
|
|
| if "mirroring2" == a:
|
| mirror = np.flip(
|
| np.abs(
|
| spec_m[
|
| :,
|
| mp.param["pre_filter_start"]
|
| - 10
|
| - input_high_end.shape[1] : mp.param["pre_filter_start"]
|
| - 10,
|
| :,
|
| ]
|
| ),
|
| 1,
|
| )
|
| mi = np.multiply(mirror, input_high_end * 1.7)
|
|
|
| return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)
|
|
|
|
|
| def ensembling(a, specs):
|
| for i in range(1, len(specs)):
|
| if i == 1:
|
| spec = specs[0]
|
|
|
| ln = min([spec.shape[2], specs[i].shape[2]])
|
| spec = spec[:, :, :ln]
|
| specs[i] = specs[i][:, :, :ln]
|
|
|
| if "min_mag" == a:
|
| spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
|
| if "max_mag" == a:
|
| spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec)
|
|
|
| return spec
|
|
|
|
|
| def stft(wave, nfft, hl):
|
| wave_left = np.asfortranarray(wave[0])
|
| wave_right = np.asfortranarray(wave[1])
|
| spec_left = librosa.stft(wave_left, n_fft=nfft, hop_length=hl)
|
| spec_right = librosa.stft(wave_right, n_fft=nfft, hop_length=hl)
|
| spec = np.asfortranarray([spec_left, spec_right])
|
|
|
| return spec
|
|
|
|
|
| def istft(spec, hl):
|
| spec_left = np.asfortranarray(spec[0])
|
| spec_right = np.asfortranarray(spec[1])
|
|
|
| wave_left = librosa.istft(spec_left, hop_length=hl)
|
| wave_right = librosa.istft(spec_right, hop_length=hl)
|
| wave = np.asfortranarray([wave_left, wave_right])
|
|
|
|
|
| if __name__ == "__main__":
|
| import argparse
|
| import sys
|
| import time
|
|
|
| import cv2
|
| from model_param_init import ModelParameters
|
|
|
| p = argparse.ArgumentParser()
|
| p.add_argument(
|
| "--algorithm",
|
| "-a",
|
| type=str,
|
| choices=["invert", "invert_p", "min_mag", "max_mag", "deep", "align"],
|
| default="min_mag",
|
| )
|
| p.add_argument(
|
| "--model_params",
|
| "-m",
|
| type=str,
|
| default=os.path.join("modelparams", "1band_sr44100_hl512.json"),
|
| )
|
| p.add_argument("--output_name", "-o", type=str, default="output")
|
| p.add_argument("--vocals_only", "-v", action="store_true")
|
| p.add_argument("input", nargs="+")
|
| args = p.parse_args()
|
|
|
| start_time = time.time()
|
|
|
| if args.algorithm.startswith("invert") and len(args.input) != 2:
|
| raise ValueError("There should be two input files.")
|
|
|
| if not args.algorithm.startswith("invert") and len(args.input) < 2:
|
| raise ValueError("There must be at least two input files.")
|
|
|
| wave, specs = {}, {}
|
| mp = ModelParameters(args.model_params)
|
|
|
| for i in range(len(args.input)):
|
| spec = {}
|
|
|
| for d in range(len(mp.param["band"]), 0, -1):
|
| bp = mp.param["band"][d]
|
|
|
| if d == len(mp.param["band"]):
|
| wave[d], _ = librosa.load(
|
| args.input[i],
|
| sr=bp["sr"],
|
| mono=False,
|
| dtype=np.float32,
|
| res_type=bp["res_type"],
|
| )
|
|
|
| if len(wave[d].shape) == 1:
|
| wave[d] = np.array([wave[d], wave[d]])
|
| else:
|
| wave[d] = librosa.resample(
|
| wave[d + 1],
|
| orig_sr=mp.param["band"][d + 1]["sr"],
|
| target_sr=bp["sr"],
|
| res_type=bp["res_type"],
|
| )
|
|
|
| spec[d] = wave_to_spectrogram(
|
| wave[d],
|
| bp["hl"],
|
| bp["n_fft"],
|
| mp.param["mid_side"],
|
| mp.param["mid_side_b2"],
|
| mp.param["reverse"],
|
| )
|
|
|
| specs[i] = combine_spectrograms(spec, mp)
|
|
|
| del wave
|
|
|
| if args.algorithm == "deep":
|
| d_spec = np.where(np.abs(specs[0]) <= np.abs(spec[1]), specs[0], spec[1])
|
| v_spec = d_spec - specs[1]
|
| sf.write(
|
| os.path.join("{}.wav".format(args.output_name)),
|
| cmb_spectrogram_to_wave(v_spec, mp),
|
| mp.param["sr"],
|
| )
|
|
|
| if args.algorithm.startswith("invert"):
|
| ln = min([specs[0].shape[2], specs[1].shape[2]])
|
| specs[0] = specs[0][:, :, :ln]
|
| specs[1] = specs[1][:, :, :ln]
|
|
|
| if "invert_p" == args.algorithm:
|
| X_mag = np.abs(specs[0])
|
| y_mag = np.abs(specs[1])
|
| max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
|
| v_spec = specs[1] - max_mag * np.exp(1.0j * np.angle(specs[0]))
|
| else:
|
| specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)
|
| v_spec = specs[0] - specs[1]
|
|
|
| if not args.vocals_only:
|
| X_mag = np.abs(specs[0])
|
| y_mag = np.abs(specs[1])
|
| v_mag = np.abs(v_spec)
|
|
|
| X_image = spectrogram_to_image(X_mag)
|
| y_image = spectrogram_to_image(y_mag)
|
| v_image = spectrogram_to_image(v_mag)
|
|
|
| cv2.imwrite("{}_X.png".format(args.output_name), X_image)
|
| cv2.imwrite("{}_y.png".format(args.output_name), y_image)
|
| cv2.imwrite("{}_v.png".format(args.output_name), v_image)
|
|
|
| sf.write(
|
| "{}_X.wav".format(args.output_name),
|
| cmb_spectrogram_to_wave(specs[0], mp),
|
| mp.param["sr"],
|
| )
|
| sf.write(
|
| "{}_y.wav".format(args.output_name),
|
| cmb_spectrogram_to_wave(specs[1], mp),
|
| mp.param["sr"],
|
| )
|
|
|
| sf.write(
|
| "{}_v.wav".format(args.output_name),
|
| cmb_spectrogram_to_wave(v_spec, mp),
|
| mp.param["sr"],
|
| )
|
| else:
|
| if not args.algorithm == "deep":
|
| sf.write(
|
| os.path.join("ensembled", "{}.wav".format(args.output_name)),
|
| cmb_spectrogram_to_wave(ensembling(args.algorithm, specs), mp),
|
| mp.param["sr"],
|
| )
|
|
|
| if args.algorithm == "align":
|
| trackalignment = [
|
| {
|
| "file1": '"{}"'.format(args.input[0]),
|
| "file2": '"{}"'.format(args.input[1]),
|
| }
|
| ]
|
|
|
| for i, e in tqdm(enumerate(trackalignment), desc="Performing Alignment..."):
|
| os.system(f"python lib/align_tracks.py {e['file1']} {e['file2']}")
|
|
|
|
|
|
|
