668 lines
21 KiB
Python
668 lines
21 KiB
Python
import os, librosa
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import numpy as np
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import soundfile as sf
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from tqdm import tqdm
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import json, math, hashlib
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def crop_center(h1, h2):
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h1_shape = h1.size()
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h2_shape = h2.size()
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if h1_shape[3] == h2_shape[3]:
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return h1
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elif h1_shape[3] < h2_shape[3]:
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raise ValueError("h1_shape[3] must be greater than h2_shape[3]")
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# s_freq = (h2_shape[2] - h1_shape[2]) // 2
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# e_freq = s_freq + h1_shape[2]
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s_time = (h1_shape[3] - h2_shape[3]) // 2
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e_time = s_time + h2_shape[3]
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h1 = h1[:, :, :, s_time:e_time]
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return h1
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def wave_to_spectrogram(
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wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False
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):
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if reverse:
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wave_left = np.flip(np.asfortranarray(wave[0]))
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wave_right = np.flip(np.asfortranarray(wave[1]))
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elif mid_side:
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wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
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wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
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elif mid_side_b2:
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wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
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wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
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else:
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wave_left = np.asfortranarray(wave[0])
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wave_right = np.asfortranarray(wave[1])
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spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
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spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
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spec = np.asfortranarray([spec_left, spec_right])
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return spec
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def wave_to_spectrogram_mt(
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wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False
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):
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import threading
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if reverse:
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wave_left = np.flip(np.asfortranarray(wave[0]))
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wave_right = np.flip(np.asfortranarray(wave[1]))
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elif mid_side:
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wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
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wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
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elif mid_side_b2:
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wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
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wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
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else:
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wave_left = np.asfortranarray(wave[0])
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wave_right = np.asfortranarray(wave[1])
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def run_thread(**kwargs):
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global spec_left
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spec_left = librosa.stft(**kwargs)
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thread = threading.Thread(
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target=run_thread,
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kwargs={"y": wave_left, "n_fft": n_fft, "hop_length": hop_length},
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)
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thread.start()
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spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
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thread.join()
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spec = np.asfortranarray([spec_left, spec_right])
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return spec
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def combine_spectrograms(specs, mp):
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l = min([specs[i].shape[2] for i in specs])
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spec_c = np.zeros(shape=(2, mp.param["bins"] + 1, l), dtype=np.complex64)
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offset = 0
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bands_n = len(mp.param["band"])
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for d in range(1, bands_n + 1):
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h = mp.param["band"][d]["crop_stop"] - mp.param["band"][d]["crop_start"]
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spec_c[:, offset : offset + h, :l] = specs[d][
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:, mp.param["band"][d]["crop_start"] : mp.param["band"][d]["crop_stop"], :l
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]
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offset += h
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if offset > mp.param["bins"]:
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raise ValueError("Too much bins")
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# lowpass fiter
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if (
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mp.param["pre_filter_start"] > 0
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): # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']:
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if bands_n == 1:
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spec_c = fft_lp_filter(
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spec_c, mp.param["pre_filter_start"], mp.param["pre_filter_stop"]
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)
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else:
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gp = 1
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for b in range(
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mp.param["pre_filter_start"] + 1, mp.param["pre_filter_stop"]
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):
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g = math.pow(
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10, -(b - mp.param["pre_filter_start"]) * (3.5 - gp) / 20.0
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)
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gp = g
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spec_c[:, b, :] *= g
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return np.asfortranarray(spec_c)
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def spectrogram_to_image(spec, mode="magnitude"):
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if mode == "magnitude":
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if np.iscomplexobj(spec):
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y = np.abs(spec)
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else:
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y = spec
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y = np.log10(y**2 + 1e-8)
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elif mode == "phase":
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if np.iscomplexobj(spec):
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y = np.angle(spec)
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else:
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y = spec
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y -= y.min()
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y *= 255 / y.max()
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img = np.uint8(y)
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if y.ndim == 3:
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img = img.transpose(1, 2, 0)
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img = np.concatenate([np.max(img, axis=2, keepdims=True), img], axis=2)
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return img
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def reduce_vocal_aggressively(X, y, softmask):
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v = X - y
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y_mag_tmp = np.abs(y)
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v_mag_tmp = np.abs(v)
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v_mask = v_mag_tmp > y_mag_tmp
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y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf)
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return y_mag * np.exp(1.0j * np.angle(y))
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def mask_silence(mag, ref, thres=0.2, min_range=64, fade_size=32):
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if min_range < fade_size * 2:
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raise ValueError("min_range must be >= fade_area * 2")
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mag = mag.copy()
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idx = np.where(ref.mean(axis=(0, 1)) < thres)[0]
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starts = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
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ends = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
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uninformative = np.where(ends - starts > min_range)[0]
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if len(uninformative) > 0:
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starts = starts[uninformative]
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ends = ends[uninformative]
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old_e = None
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for s, e in zip(starts, ends):
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if old_e is not None and s - old_e < fade_size:
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s = old_e - fade_size * 2
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if s != 0:
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weight = np.linspace(0, 1, fade_size)
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mag[:, :, s : s + fade_size] += weight * ref[:, :, s : s + fade_size]
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else:
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s -= fade_size
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if e != mag.shape[2]:
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weight = np.linspace(1, 0, fade_size)
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mag[:, :, e - fade_size : e] += weight * ref[:, :, e - fade_size : e]
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else:
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e += fade_size
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mag[:, :, s + fade_size : e - fade_size] += ref[
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:, :, s + fade_size : e - fade_size
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]
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old_e = e
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return mag
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def align_wave_head_and_tail(a, b):
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l = min([a[0].size, b[0].size])
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return a[:l, :l], b[:l, :l]
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def cache_or_load(mix_path, inst_path, mp):
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mix_basename = os.path.splitext(os.path.basename(mix_path))[0]
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inst_basename = os.path.splitext(os.path.basename(inst_path))[0]
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cache_dir = "mph{}".format(
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hashlib.sha1(json.dumps(mp.param, sort_keys=True).encode("utf-8")).hexdigest()
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)
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mix_cache_dir = os.path.join("cache", cache_dir)
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inst_cache_dir = os.path.join("cache", cache_dir)
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os.makedirs(mix_cache_dir, exist_ok=True)
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os.makedirs(inst_cache_dir, exist_ok=True)
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mix_cache_path = os.path.join(mix_cache_dir, mix_basename + ".npy")
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inst_cache_path = os.path.join(inst_cache_dir, inst_basename + ".npy")
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if os.path.exists(mix_cache_path) and os.path.exists(inst_cache_path):
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X_spec_m = np.load(mix_cache_path)
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y_spec_m = np.load(inst_cache_path)
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else:
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X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
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for d in range(len(mp.param["band"]), 0, -1):
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bp = mp.param["band"][d]
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if d == len(mp.param["band"]): # high-end band
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X_wave[d], _ = librosa.load(
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mix_path, bp["sr"], False, dtype=np.float32, res_type=bp["res_type"]
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)
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y_wave[d], _ = librosa.load(
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inst_path,
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bp["sr"],
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False,
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dtype=np.float32,
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res_type=bp["res_type"],
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)
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else: # lower bands
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X_wave[d] = librosa.resample(
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X_wave[d + 1],
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mp.param["band"][d + 1]["sr"],
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bp["sr"],
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res_type=bp["res_type"],
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)
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y_wave[d] = librosa.resample(
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y_wave[d + 1],
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mp.param["band"][d + 1]["sr"],
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bp["sr"],
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res_type=bp["res_type"],
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)
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X_wave[d], y_wave[d] = align_wave_head_and_tail(X_wave[d], y_wave[d])
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X_spec_s[d] = wave_to_spectrogram(
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X_wave[d],
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bp["hl"],
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bp["n_fft"],
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mp.param["mid_side"],
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mp.param["mid_side_b2"],
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mp.param["reverse"],
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)
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y_spec_s[d] = wave_to_spectrogram(
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y_wave[d],
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bp["hl"],
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bp["n_fft"],
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mp.param["mid_side"],
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mp.param["mid_side_b2"],
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mp.param["reverse"],
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)
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del X_wave, y_wave
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X_spec_m = combine_spectrograms(X_spec_s, mp)
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y_spec_m = combine_spectrograms(y_spec_s, mp)
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if X_spec_m.shape != y_spec_m.shape:
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raise ValueError("The combined spectrograms are different: " + mix_path)
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_, ext = os.path.splitext(mix_path)
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np.save(mix_cache_path, X_spec_m)
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np.save(inst_cache_path, y_spec_m)
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return X_spec_m, y_spec_m
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def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse):
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spec_left = np.asfortranarray(spec[0])
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spec_right = np.asfortranarray(spec[1])
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wave_left = librosa.istft(spec_left, hop_length=hop_length)
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wave_right = librosa.istft(spec_right, hop_length=hop_length)
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if reverse:
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return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
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elif mid_side:
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return np.asfortranarray(
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[np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]
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)
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elif mid_side_b2:
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return np.asfortranarray(
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[
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np.add(wave_right / 1.25, 0.4 * wave_left),
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np.subtract(wave_left / 1.25, 0.4 * wave_right),
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]
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)
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else:
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return np.asfortranarray([wave_left, wave_right])
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def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse, mid_side_b2):
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import threading
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spec_left = np.asfortranarray(spec[0])
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spec_right = np.asfortranarray(spec[1])
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def run_thread(**kwargs):
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global wave_left
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wave_left = librosa.istft(**kwargs)
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thread = threading.Thread(
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target=run_thread, kwargs={"stft_matrix": spec_left, "hop_length": hop_length}
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)
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thread.start()
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wave_right = librosa.istft(spec_right, hop_length=hop_length)
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thread.join()
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if reverse:
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return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
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elif mid_side:
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return np.asfortranarray(
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[np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]
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)
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elif mid_side_b2:
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return np.asfortranarray(
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[
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np.add(wave_right / 1.25, 0.4 * wave_left),
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np.subtract(wave_left / 1.25, 0.4 * wave_right),
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]
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)
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else:
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return np.asfortranarray([wave_left, wave_right])
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def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
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wave_band = {}
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bands_n = len(mp.param["band"])
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offset = 0
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for d in range(1, bands_n + 1):
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bp = mp.param["band"][d]
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spec_s = np.ndarray(
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shape=(2, bp["n_fft"] // 2 + 1, spec_m.shape[2]), dtype=complex
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)
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h = bp["crop_stop"] - bp["crop_start"]
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spec_s[:, bp["crop_start"] : bp["crop_stop"], :] = spec_m[
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:, offset : offset + h, :
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]
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offset += h
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if d == bands_n: # higher
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if extra_bins_h: # if --high_end_process bypass
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max_bin = bp["n_fft"] // 2
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spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[
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:, :extra_bins_h, :
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]
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if bp["hpf_start"] > 0:
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spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
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if bands_n == 1:
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wave = spectrogram_to_wave(
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spec_s,
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bp["hl"],
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mp.param["mid_side"],
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mp.param["mid_side_b2"],
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mp.param["reverse"],
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)
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else:
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wave = np.add(
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wave,
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spectrogram_to_wave(
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spec_s,
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bp["hl"],
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mp.param["mid_side"],
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mp.param["mid_side_b2"],
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mp.param["reverse"],
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),
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)
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else:
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sr = mp.param["band"][d + 1]["sr"]
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if d == 1: # lower
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spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])
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wave = librosa.resample(
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spectrogram_to_wave(
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spec_s,
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bp["hl"],
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mp.param["mid_side"],
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mp.param["mid_side_b2"],
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mp.param["reverse"],
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),
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bp["sr"],
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sr,
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res_type="sinc_fastest",
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)
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else: # mid
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spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
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spec_s = fft_lp_filter(spec_s, bp["lpf_start"], bp["lpf_stop"])
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wave2 = np.add(
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wave,
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spectrogram_to_wave(
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spec_s,
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bp["hl"],
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mp.param["mid_side"],
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mp.param["mid_side_b2"],
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mp.param["reverse"],
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),
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)
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# wave = librosa.core.resample(wave2, bp['sr'], sr, res_type="sinc_fastest")
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wave = librosa.core.resample(wave2, bp["sr"], sr, res_type="scipy")
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return wave.T
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def fft_lp_filter(spec, bin_start, bin_stop):
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g = 1.0
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for b in range(bin_start, bin_stop):
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g -= 1 / (bin_stop - bin_start)
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spec[:, b, :] = g * spec[:, b, :]
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spec[:, bin_stop:, :] *= 0
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return spec
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def fft_hp_filter(spec, bin_start, bin_stop):
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g = 1.0
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for b in range(bin_start, bin_stop, -1):
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g -= 1 / (bin_start - bin_stop)
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spec[:, b, :] = g * spec[:, b, :]
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spec[:, 0 : bin_stop + 1, :] *= 0
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return spec
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def mirroring(a, spec_m, input_high_end, mp):
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if "mirroring" == a:
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mirror = np.flip(
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np.abs(
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spec_m[
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:,
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mp.param["pre_filter_start"]
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- 10
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- input_high_end.shape[1] : mp.param["pre_filter_start"]
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- 10,
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:,
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]
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),
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1,
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)
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mirror = mirror * np.exp(1.0j * np.angle(input_high_end))
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return np.where(
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np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror
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)
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if "mirroring2" == a:
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mirror = np.flip(
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np.abs(
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spec_m[
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:,
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mp.param["pre_filter_start"]
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- 10
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- input_high_end.shape[1] : mp.param["pre_filter_start"]
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- 10,
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:,
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]
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),
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1,
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)
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mi = np.multiply(mirror, input_high_end * 1.7)
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|
|
|
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, nfft, hop_length=hl)
|
|
spec_right = librosa.stft(wave_right, 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 cv2
|
|
import sys
|
|
import time
|
|
import argparse
|
|
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],
|
|
bp["sr"],
|
|
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],
|
|
mp.param["band"][d + 1]["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))
|