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Running
on
Zero
| 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_freq = (h2_shape[2] - h1_shape[2]) // 2 | |
| # e_freq = s_freq + h1_shape[2] | |
| 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") | |
| # lowpass fiter | |
| if ( | |
| mp.param["pre_filter_start"] > 0 | |
| ): # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']: | |
| 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"]): # high-end 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: # lower bands | |
| 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: # higher | |
| if extra_bins_h: # if --high_end_process bypass | |
| 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: # lower | |
| 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: # mid | |
| 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.core.resample(wave2, orig_sr=bp['sr'], target_sr=sr, res_type="sinc_fastest") | |
| wave = librosa.core.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"]): # high-end 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: # mono to stereo | |
| wave[d] = np.array([wave[d], wave[d]]) | |
| else: # lower bands | |
| 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']}") | |
| # print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1)) | |