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, bp['sr'], sr, res_type="sinc_fastest") 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"]): # 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))