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np.load(path) - - X, y = data['X'], data['y'] - - X_mag = np.abs(X) - y_mag = np.abs(y) - - return X_mag, y_mag - - -def make_pair(mix_dir, inst_dir): - input_exts = ['.wav', '.m4a', '.mp3', '.mp4', '.flac'] - - X_list = sorted([ - os.path.join(mix_dir, fname) - for fname in os.listdir(mix_dir) - if os.path.splitext(fname)[1] in input_exts]) - y_list = sorted([ - os.path.join(inst_dir, fname) - for fname in os.listdir(inst_dir) - if os.path.splitext(fname)[1] in input_exts]) - - filelist = list(zip(X_list, y_list)) - - return filelist - - -def train_val_split(dataset_dir, split_mode, val_rate, val_filelist): - if split_mode == 'random': - filelist = make_pair( - os.path.join(dataset_dir, 'mixtures'), - os.path.join(dataset_dir, 'instruments')) - - random.shuffle(filelist) - - if len(val_filelist) == 0: - val_size = int(len(filelist) * val_rate) - train_filelist = filelist[:-val_size] - val_filelist = filelist[-val_size:] - else: - train_filelist = [ - pair for pair in filelist - if list(pair) not in val_filelist] - elif split_mode == 'subdirs': - if len(val_filelist) != 0: - raise ValueError('The `val_filelist` option is not available in `subdirs` mode') - - train_filelist = make_pair( - os.path.join(dataset_dir, 'training/mixtures'), - os.path.join(dataset_dir, 'training/instruments')) - - val_filelist = make_pair( - os.path.join(dataset_dir, 'validation/mixtures'), - os.path.join(dataset_dir, 'validation/instruments')) - - return train_filelist, val_filelist - - -def augment(X, y, reduction_rate, reduction_mask, mixup_rate, mixup_alpha): - perm = np.random.permutation(len(X)) - for i, idx in enumerate(tqdm(perm)): - if np.random.uniform() < reduction_rate: - y[idx] = spec_utils.reduce_vocal_aggressively(X[idx], y[idx], reduction_mask) - - if np.random.uniform() < 0.5: - # swap channel - X[idx] = X[idx, ::-1] - y[idx] = y[idx, ::-1] - if np.random.uniform() < 0.02: - # mono - X[idx] = X[idx].mean(axis=0, keepdims=True) - y[idx] = y[idx].mean(axis=0, keepdims=True) - if np.random.uniform() < 0.02: - # inst - X[idx] = y[idx] - - if np.random.uniform() < mixup_rate and i < len(perm) - 1: - lam = np.random.beta(mixup_alpha, mixup_alpha) - X[idx] = lam * X[idx] + (1 - lam) * X[perm[i + 1]] - y[idx] = lam * y[idx] + (1 - lam) * y[perm[i + 1]] - - return X, y - - -def make_padding(width, cropsize, offset): - left = offset - roi_size = cropsize - left * 2 - if roi_size == 0: - roi_size = cropsize - right = roi_size - (width % roi_size) + left - - return left, right, roi_size - - -def make_training_set(filelist, cropsize, patches, sr, hop_length, n_fft, offset): - len_dataset = patches * len(filelist) - - X_dataset = np.zeros( - (len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64) - y_dataset = np.zeros( - (len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64) - - for i, (X_path, y_path) in enumerate(tqdm(filelist)): - X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft) - coef = np.max([np.abs(X).max(), np.abs(y).max()]) - X, y = X / coef, y / coef - - l, r, roi_size = make_padding(X.shape[2], cropsize, offset) - X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode='constant') - y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode='constant') - - starts = np.random.randint(0, X_pad.shape[2] - cropsize, patches) - ends = starts + cropsize - for j in range(patches): - idx = i * patches + j - X_dataset[idx] = X_pad[:, :, starts[j]:ends[j]] - y_dataset[idx] = y_pad[:, :, starts[j]:ends[j]] - - return X_dataset, y_dataset - - -def make_validation_set(filelist, cropsize, sr, hop_length, n_fft, offset): - patch_list = [] - patch_dir = 'cs{}_sr{}_hl{}_nf{}_of{}'.format(cropsize, sr, hop_length, n_fft, offset) - os.makedirs(patch_dir, exist_ok=True) - - for i, (X_path, y_path) in enumerate(tqdm(filelist)): - basename = os.path.splitext(os.path.basename(X_path))[0] - - X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft) - coef = np.max([np.abs(X).max(), np.abs(y).max()]) - X, y = X / coef, y / coef - - l, r, roi_size = make_padding(X.shape[2], cropsize, offset) - X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode='constant') - y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode='constant') - - len_dataset = int(np.ceil(X.shape[2] / roi_size)) - for j in range(len_dataset): - outpath = os.path.join(patch_dir, '{}_p{}.npz'.format(basename, j)) - start = j * roi_size - if not os.path.exists(outpath): - np.savez( - outpath, - X=X_pad[:, :, start:start + cropsize], - y=y_pad[:, :, start:start + cropsize]) - patch_list.append(outpath) - - return VocalRemoverValidationSet(patch_list) diff --git a/lib_v5/filelist.py b/lib_v5/filelist.py deleted file mode 100644 index fcfc82a..0000000 --- a/lib_v5/filelist.py +++ /dev/null @@ -1,423 +0,0 @@ -import json - -def get_vr_download_list(list): - with open("lib_v5/filelists/download_lists/vr_download_list.txt", "r") as f: - text=f.read().splitlines() - - list = text - - return list - -def get_mdx_download_list(list): - with open("lib_v5/filelists/download_lists/mdx_download_list.txt", "r") as f: - text=f.read().splitlines() - - list = text - - return list - -def get_demucs_download_list(list): - with open("lib_v5/filelists/download_lists/demucs_download_list.txt", "r") as f: - text=f.read().splitlines() - - list = text - - return list - -def get_mdx_demucs_en_list(list): - with open("lib_v5/filelists/ensemble_list/mdx_demuc_en_list.txt", "r") as f: - text=f.read().splitlines() - - list = text - - return list - -def get_vr_en_list(list): - with open("lib_v5/filelists/ensemble_list/vr_en_list.txt", "r") as f: - text=f.read().splitlines() - - list = text - - return list - -def get_download_links(links, downloads=''): - - f = open(f"lib_v5/filelists/download_lists/download_links.json") - download_links = json.load(f) - - if downloads == 'Demucs v3: mdx': - url_1 = download_links['Demucs_v3_mdx_url_1'] - url_2 = download_links['Demucs_v3_mdx_url_2'] - url_3 = download_links['Demucs_v3_mdx_url_3'] - url_4 = download_links['Demucs_v3_mdx_url_4'] - url_5 = download_links['Demucs_v3_mdx_url_5'] - - links = url_1, url_2, url_3, url_4, url_5 - - - if downloads == 'Demucs v3: mdx_q': - url_1 = download_links['Demucs_v3_mdx_q_url_1'] - url_2 = download_links['Demucs_v3_mdx_q_url_2'] - url_3 = download_links['Demucs_v3_mdx_q_url_3'] - url_4 = download_links['Demucs_v3_mdx_q_url_4'] - url_5 = download_links['Demucs_v3_mdx_q_url_5'] - - links = url_1, url_2, url_3, url_4, url_5 - - if downloads == 'Demucs v3: mdx_extra': - url_1 = download_links['Demucs_v3_mdx_extra_url_1'] - url_2 = download_links['Demucs_v3_mdx_extra_url_2'] - url_3 = download_links['Demucs_v3_mdx_extra_url_3'] - url_4 = download_links['Demucs_v3_mdx_extra_url_4'] - url_5 = download_links['Demucs_v3_mdx_extra_url_5'] - - links = url_1, url_2, url_3, url_4, url_5 - - if downloads == 'Demucs v3: mdx_extra_q': - url_1 = download_links['Demucs_v3_mdx_extra_q_url_1'] - url_2 = download_links['Demucs_v3_mdx_extra_q_url_2'] - url_3 = download_links['Demucs_v3_mdx_extra_q_url_3'] - url_4 = download_links['Demucs_v3_mdx_extra_q_url_4'] - url_5 = download_links['Demucs_v3_mdx_extra_q_url_5'] - - links = url_1, url_2, url_3, url_4, url_5 - - if downloads == 'Demucs v3: UVR Models': - url_1 = download_links['Demucs_v3_UVR_url_1'] - url_2 = download_links['Demucs_v3_UVR_url_2'] - url_3 = download_links['Demucs_v3_UVR_url_3'] - url_4 = download_links['Demucs_v3_UVR_url_4'] - url_5 = download_links['Demucs_v3_UVR_url_5'] - - links = url_1, url_2, url_3, url_4, url_5 - - if downloads == 'Demucs v2: demucs': - url_1 = download_links['Demucs_v2_demucs_url_1'] - links = url_1 - - if downloads == 'Demucs v2: demucs_extra': - url_1 = download_links['Demucs_v2_demucs_extra_url_1'] - - links = url_1 - - if downloads == 'Demucs v2: demucs48_hq': - url_1 = download_links['Demucs_v2_demucs48_hq_url_1'] - - links = url_1 - - if downloads == 'Demucs v2: tasnet': - url_1 = download_links['Demucs_v2_tasnet_url_1'] - - links = url_1 - - if downloads == 'Demucs v2: tasnet_extra': - url_1 = download_links['Demucs_v2_tasnet_extra_url_1'] - - links = url_1 - - if downloads == 'Demucs v2: demucs_unittest': - url_1 = download_links['Demucs_v2_demucs_unittest_url_1'] - - links = url_1 - - if downloads == 'Demucs v1: demucs': - url_1 = download_links['Demucs_v1_demucs_url_1'] - - links = url_1 - - if downloads == 'Demucs v1: demucs_extra': - url_1 = download_links['Demucs_v1_demucs_extra_url_1'] - - links = url_1 - - if downloads == 'Demucs v1: light': - url_1 = download_links['Demucs_v1_light_url_1'] - - links = url_1 - - if downloads == 'Demucs v1: light_extra': - url_1 = download_links['Demucs_v1_light_extra_url_1'] - - links = url_1 - - if downloads == 'Demucs v1: tasnet': - url_1 = download_links['Demucs_v1_tasnet_url_1'] - - links = url_1 - - if downloads == 'Demucs v1: tasnet_extra': - url_1 = download_links['Demucs_v1_tasnet_extra_url_1'] - - links = url_1 - - if downloads == 'model_repo': - url_1 = download_links['model_repo_url_1'] - - links = url_1 - - if downloads == 'single_model_repo': - url_1 = download_links['single_model_repo_url_1'] - - links = url_1 - - if downloads == 'exclusive': - url_1 = download_links['exclusive_url_1'] - url_2 = download_links['exclusive_url_2'] - - links = url_1, url_2, url_3 - - if downloads == 'refresh': - url_1 = download_links['refresh_url_1'] - url_2 = download_links['refresh_url_2'] - url_3 = download_links['refresh_url_3'] - - links = url_1, url_2, url_3 - - if downloads == 'app_patch': - url_1 = download_links['app_patch'] - - links = url_1 - - return links - -def provide_model_param_hash(model_hash): - #v5 Models - if model_hash == '47939caf0cfe52a0e81442b85b971dfd': - model_params_set=str('lib_v5/modelparams/4band_44100.json') - param_name=str('4band_44100') - elif model_hash == '4e4ecb9764c50a8c414fee6e10395bbe': - model_params_set=str('lib_v5/modelparams/4band_v2.json') - param_name=str('4band_v2') - elif model_hash == 'e60a1e84803ce4efc0a6551206cc4b71': - model_params_set=str('lib_v5/modelparams/4band_44100.json') - param_name=str('4band_44100') - elif model_hash == 'a82f14e75892e55e994376edbf0c8435': - model_params_set=str('lib_v5/modelparams/4band_44100.json') - param_name=str('4band_44100') - elif model_hash == '6dd9eaa6f0420af9f1d403aaafa4cc06': - model_params_set=str('lib_v5/modelparams/4band_v2_sn.json') - param_name=str('4band_v2_sn') - elif model_hash == '5c7bbca45a187e81abbbd351606164e5': - model_params_set=str('lib_v5/modelparams/3band_44100_msb2.json') - param_name=str('3band_44100_msb2') - elif model_hash == 'd6b2cb685a058a091e5e7098192d3233': - model_params_set=str('lib_v5/modelparams/3band_44100_msb2.json') - param_name=str('3band_44100_msb2') - elif model_hash == 'c1b9f38170a7c90e96f027992eb7c62b': - model_params_set=str('lib_v5/modelparams/4band_44100.json') - param_name=str('4band_44100') - elif model_hash == 'c3448ec923fa0edf3d03a19e633faa53': - model_params_set=str('lib_v5/modelparams/4band_44100.json') - param_name=str('4band_44100') - elif model_hash == '68aa2c8093d0080704b200d140f59e54': - model_params_set=str('lib_v5/modelparams/3band_44100.json') - param_name=str('3band_44100.json') - elif model_hash == 'fdc83be5b798e4bd29fe00fe6600e147': - model_params_set=str('lib_v5/modelparams/3band_44100_mid.json') - param_name=str('3band_44100_mid.json') - elif model_hash == '2ce34bc92fd57f55db16b7a4def3d745': - model_params_set=str('lib_v5/modelparams/3band_44100_mid.json') - param_name=str('3band_44100_mid.json') - elif model_hash == '52fdca89576f06cf4340b74a4730ee5f': - model_params_set=str('lib_v5/modelparams/4band_44100.json') - param_name=str('4band_44100.json') - elif model_hash == '41191165b05d38fc77f072fa9e8e8a30': - model_params_set=str('lib_v5/modelparams/4band_44100.json') - param_name=str('4band_44100.json') - elif model_hash == '89e83b511ad474592689e562d5b1f80e': - model_params_set=str('lib_v5/modelparams/2band_32000.json') - param_name=str('2band_32000.json') - elif model_hash == '0b954da81d453b716b114d6d7c95177f': - model_params_set=str('lib_v5/modelparams/2band_32000.json') - param_name=str('2band_32000.json') - - #v4 Models - - elif model_hash == '6a00461c51c2920fd68937d4609ed6c8': - model_params_set=str('lib_v5/modelparams/1band_sr16000_hl512.json') - param_name=str('1band_sr16000_hl512') - elif model_hash == '0ab504864d20f1bd378fe9c81ef37140': - model_params_set=str('lib_v5/modelparams/1band_sr32000_hl512.json') - param_name=str('1band_sr32000_hl512') - elif model_hash == '7dd21065bf91c10f7fccb57d7d83b07f': - model_params_set=str('lib_v5/modelparams/1band_sr32000_hl512.json') - param_name=str('1band_sr32000_hl512') - elif model_hash == '80ab74d65e515caa3622728d2de07d23': - model_params_set=str('lib_v5/modelparams/1band_sr32000_hl512.json') - param_name=str('1band_sr32000_hl512') - elif model_hash == 'edc115e7fc523245062200c00caa847f': - model_params_set=str('lib_v5/modelparams/1band_sr33075_hl384.json') - param_name=str('1band_sr33075_hl384') - elif model_hash == '28063e9f6ab5b341c5f6d3c67f2045b7': - model_params_set=str('lib_v5/modelparams/1band_sr33075_hl384.json') - param_name=str('1band_sr33075_hl384') - elif model_hash == 'b58090534c52cbc3e9b5104bad666ef2': - model_params_set=str('lib_v5/modelparams/1band_sr44100_hl512.json') - param_name=str('1band_sr44100_hl512') - elif model_hash == '0cdab9947f1b0928705f518f3c78ea8f': - model_params_set=str('lib_v5/modelparams/1band_sr44100_hl512.json') - param_name=str('1band_sr44100_hl512') - elif model_hash == 'ae702fed0238afb5346db8356fe25f13': - model_params_set=str('lib_v5/modelparams/1band_sr44100_hl1024.json') - param_name=str('1band_sr44100_hl1024') - else: - try: - with open(f"lib_v5/filelists/model_cache/vr_param_cache/{model_hash}.txt", "r") as f: - name = f.read() - model_params_set=str(f'lib_v5/modelparams/{name}') - param_name=str(name) - ('using text of hash worked') - except: - model_params_set=str('Not Found Using Hash') - param_name=str('Not Found Using Hash') - - model_params = model_params_set, param_name - - return model_params - -def provide_model_param_name(ModelName): - #1 Band - if '1band_sr16000_hl512' in ModelName: - model_params_set=str('lib_v5/modelparams/1band_sr16000_hl512.json') - param_name=str('1band_sr16000_hl512') - elif '1band_sr32000_hl512' in ModelName: - model_params_set=str('lib_v5/modelparams/1band_sr32000_hl512.json') - param_name=str('1band_sr32000_hl512') - elif '1band_sr33075_hl384' in ModelName: - model_params_set=str('lib_v5/modelparams/1band_sr33075_hl384.json') - param_name=str('1band_sr33075_hl384') - elif '1band_sr44100_hl256' in ModelName: - model_params_set=str('lib_v5/modelparams/1band_sr44100_hl256.json') - param_name=str('1band_sr44100_hl256') - elif '1band_sr44100_hl512' in ModelName: - model_params_set=str('lib_v5/modelparams/1band_sr44100_hl512.json') - param_name=str('1band_sr44100_hl512') - elif '1band_sr44100_hl1024' in ModelName: - model_params_set=str('lib_v5/modelparams/1band_sr44100_hl1024.json') - param_name=str('1band_sr44100_hl1024') - - #2 Band - elif '2band_44100_lofi' in ModelName: - model_params_set=str('lib_v5/modelparams/2band_44100_lofi.json') - param_name=str('2band_44100_lofi') - - #3 Band - - elif '3band_44100_mid' in ModelName: - model_params_set=str('lib_v5/modelparams/3band_44100_mid.json') - param_name=str('3band_44100_mid') - elif '3band_44100_msb2' in ModelName: - model_params_set=str('lib_v5/modelparams/3band_44100_msb2.json') - param_name=str('3band_44100_msb2') - - #4 Band - - elif '4band_44100_msb' in ModelName: - model_params_set=str('lib_v5/modelparams/4band_44100_msb.json') - param_name=str('4band_44100_msb') - elif '4band_44100_msb2' in ModelName: - model_params_set=str('lib_v5/modelparams/4band_44100_msb2.json') - param_name=str('4band_44100_msb2') - elif '4band_44100_reverse' in ModelName: - model_params_set=str('lib_v5/modelparams/4band_44100_reverse.json') - param_name=str('4band_44100_reverse') - elif 'tmodelparam' in ModelName: - model_params_set=str('lib_v5/modelparams/tmodelparam.json') - param_name=str('User Model Param Set') - else: - model_params_set=str('Not Found Using Name') - param_name=str('Not Found Using Name') - - model_params = model_params_set, param_name - - return model_params - -def provide_mdx_model_param_name(modelhash): - with open("lib_v5/filelists/hashes/mdx_original_hashes.txt", "r") as f: - mdx_original=f.read() - with open("lib_v5/filelists/hashes/mdx_new_hashes.txt", "r") as f: - mdx_new=f.read() - with open("lib_v5/filelists/hashes/mdx_new_inst_hashes.txt", "r") as f: - mdx_new_inst=f.read() - - if modelhash in mdx_original: - MDX_modeltype = 'mdx_original' - elif modelhash in mdx_new: - MDX_modeltype = 'mdx_new' - elif modelhash in mdx_new_inst: - MDX_modeltype = 'mdx_new_inst' - else: - MDX_modeltype = 'None' - - if MDX_modeltype == 'mdx_original': - modeltype = 'v' - noise_pro = 'MDX-NET_Noise_Profile_14_kHz' - stemset_n = '(Vocals)' - compensate = 1.03597672895 - source_val = 3 - n_fft_scale_set=6144 - dim_f_set=2048 - elif MDX_modeltype == 'mdx_new': - modeltype = 'v' - noise_pro = 'MDX-NET_Noise_Profile_17_kHz' - stemset_n = '(Vocals)' - compensate = 1.08 - source_val = 3 - n_fft_scale_set=7680 - dim_f_set=3072 - elif MDX_modeltype == 'mdx_new_inst': - modeltype = 'v' - noise_pro = 'MDX-NET_Noise_Profile_17_kHz' - stemset_n = '(Instrumental)' - compensate = 1.08 - source_val = 3 - n_fft_scale_set=7680 - dim_f_set=3072 - elif modelhash == '6f7eefc2e6b9d819ba88dc0578056ca5': - modeltype = 'o' - noise_pro = 'MDX-NET_Noise_Profile_Full_Band' - stemset_n = '(Other)' - compensate = 1.03597672895 - source_val = 2 - n_fft_scale_set=8192 - dim_f_set=2048 - elif modelhash == '72a27258a69b2381b60523a50982e9f1': - modeltype = 'd' - noise_pro = 'MDX-NET_Noise_Profile_Full_Band' - stemset_n = '(Drums)' - compensate = 1.03597672895 - source_val = 1 - n_fft_scale_set=4096 - dim_f_set=2048 - elif modelhash == '7051d7315c04285e94a97edcac3f2f76': - modeltype = 'b' - noise_pro = 'MDX-NET_Noise_Profile_Full_Band' - stemset_n = '(Bass)' - compensate = 1.03597672895 - source_val = 0 - n_fft_scale_set=16384 - dim_f_set=2048 - else: - try: - f = open(f"lib_v5/filelists/model_cache/mdx_model_cache/{modelhash}.json") - mdx_model_de = json.load(f) - modeltype = mdx_model_de["modeltype"] - noise_pro = mdx_model_de["noise_pro"] - stemset_n = mdx_model_de["stemset_n"] - compensate = mdx_model_de["compensate"] - source_val = mdx_model_de["source_val"] - n_fft_scale_set = mdx_model_de["n_fft_scale_set"] - dim_f_set = mdx_model_de["dim_f_set"] - except: - modeltype = 'Not Set' - noise_pro = 'Not Set' - stemset_n = 'Not Set' - compensate = 'Not Set' - source_val = 'Not Set' - n_fft_scale_set='Not Set' - dim_f_set='Not Set' - - - model_params = modeltype, noise_pro, stemset_n, compensate, source_val, n_fft_scale_set, dim_f_set - - return model_params \ No newline at end of file diff --git a/lib_v5/filelists/download_codes/temp/temp.txt b/lib_v5/filelists/download_codes/temp/temp.txt deleted file mode 100644 index 3602361..0000000 --- a/lib_v5/filelists/download_codes/temp/temp.txt +++ /dev/null @@ -1 +0,0 @@ -temp \ No newline at end of file diff --git a/lib_v5/filelists/download_codes/user_code.txt b/lib_v5/filelists/download_codes/user_code.txt deleted file mode 100644 index 8b13789..0000000 --- a/lib_v5/filelists/download_codes/user_code.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/lib_v5/filelists/download_codes/user_code_download.txt b/lib_v5/filelists/download_codes/user_code_download.txt deleted file mode 100644 index 8b13789..0000000 --- a/lib_v5/filelists/download_codes/user_code_download.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/lib_v5/filelists/download_lists/demucs_download_list.txt b/lib_v5/filelists/download_lists/demucs_download_list.txt deleted file mode 100644 index ad9df0f..0000000 --- a/lib_v5/filelists/download_lists/demucs_download_list.txt +++ /dev/null @@ -1,19 +0,0 @@ -No Model Selected -No Model Selected -Demucs v3: UVR Models -Demucs v3: mdx -Demucs v3: mdx_q -Demucs v3: mdx_extra -Demucs v3: mdx_extra_q -Demucs v2: demucs -Demucs v2: demucs_extra -Demucs v2: demucs48_hq -Demucs v2: tasnet -Demucs v2: tasnet_extra -Demucs v2: demucs_unittest -Demucs v1: demucs -Demucs v1: demucs_extra -Demucs v1: light -Demucs v1: light_extra -Demucs v1: tasnet -Demucs v1: tasnet_extra \ No newline at end of file diff --git a/lib_v5/filelists/download_lists/download_links.json b/lib_v5/filelists/download_lists/download_links.json deleted file mode 100644 index 62d8357..0000000 --- a/lib_v5/filelists/download_lists/download_links.json +++ /dev/null @@ -1,42 +0,0 @@ -{ - "Demucs_v3_mdx_url_1": "https://dl.fbaipublicfiles.com/demucs/mdx_final/0d19c1c6-0f06f20e.th", - "Demucs_v3_mdx_url_2": "https://dl.fbaipublicfiles.com/demucs/mdx_final/7ecf8ec1-70f50cc9.th", - "Demucs_v3_mdx_url_3": "https://dl.fbaipublicfiles.com/demucs/mdx_final/c511e2ab-fe698775.th", - "Demucs_v3_mdx_url_4": "https://dl.fbaipublicfiles.com/demucs/mdx_final/7d865c68-3d5dd56b.th", - "Demucs_v3_mdx_url_5": "https://raw.githubusercontent.com/facebookresearch/demucs/main/demucs/remote/mdx.yaml", - "Demucs_v3_mdx_q_url_1": "https://dl.fbaipublicfiles.com/demucs/mdx_final/6b9c2ca1-3fd82607.th", - "Demucs_v3_mdx_q_url_2": "https://dl.fbaipublicfiles.com/demucs/mdx_final/b72baf4e-8778635e.th", - "Demucs_v3_mdx_q_url_3": "https://dl.fbaipublicfiles.com/demucs/mdx_final/42e558d4-196e0e1b.th", - "Demucs_v3_mdx_q_url_4": "https://dl.fbaipublicfiles.com/demucs/mdx_final/305bc58f-18378783.th", - "Demucs_v3_mdx_q_url_5": "https://raw.githubusercontent.com/facebookresearch/demucs/main/demucs/remote/mdx_q.yaml", - "Demucs_v3_mdx_extra_url_1": "https://dl.fbaipublicfiles.com/demucs/mdx_final/e51eebcc-c1b80bdd.th", - "Demucs_v3_mdx_extra_url_2": "https://dl.fbaipublicfiles.com/demucs/mdx_final/a1d90b5c-ae9d2452.th", - "Demucs_v3_mdx_extra_url_3": "https://dl.fbaipublicfiles.com/demucs/mdx_final/5d2d6c55-db83574e.th", - "Demucs_v3_mdx_extra_url_4": "https://dl.fbaipublicfiles.com/demucs/mdx_final/cfa93e08-61801ae1.th", - "Demucs_v3_mdx_extra_url_5": "https://raw.githubusercontent.com/facebookresearch/demucs/main/demucs/remote/mdx_extra.yaml", - "Demucs_v3_mdx_extra_q_url_1": "https://dl.fbaipublicfiles.com/demucs/mdx_final/83fc094f-4a16d450.th", - "Demucs_v3_mdx_extra_q_url_2": "https://dl.fbaipublicfiles.com/demucs/mdx_final/464b36d7-e5a9386e.th", - "Demucs_v3_mdx_extra_q_url_3": "https://dl.fbaipublicfiles.com/demucs/mdx_final/14fc6a69-a89dd0ee.th", - "Demucs_v3_mdx_extra_q_url_4": "https://dl.fbaipublicfiles.com/demucs/mdx_final/7fd6ef75-a905dd85.th", - "Demucs_v3_mdx_extra_q_url_5": "https://raw.githubusercontent.com/facebookresearch/demucs/main/demucs/remote/mdx_extra_q.yaml", - "Demucs_v3_UVR_url_1": "https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/ebf34a2d.th", - "Demucs_v3_UVR_url_2": "https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/ebf34a2db.th", - "Demucs_v3_UVR_url_3": "https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/UVR_Demucs_Model_1.yaml", - "Demucs_v3_UVR_url_4": "https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/UVR_Demucs_Model_2.yaml", - "Demucs_v3_UVR_url_5": "https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/UVR_Demucs_Model_Bag.yaml", - "Demucs_v2_demucs_url_1": "https://dl.fbaipublicfiles.com/demucs/v3.0/demucs-e07c671f.th", - "Demucs_v2_demucs_extra_url_1": "https://dl.fbaipublicfiles.com/demucs/v3.0/demucs_extra-3646af93.th", - "Demucs_v2_demucs48_hq_url_1": "https://dl.fbaipublicfiles.com/demucs/v3.0/demucs48_hq-28a1282c.th", - "Demucs_v2_tasnet_url_1": "https://dl.fbaipublicfiles.com/demucs/v3.0/tasnet-beb46fac.th", - "Demucs_v2_tasnet_extra_url_1": "https://dl.fbaipublicfiles.com/demucs/v3.0/tasnet_extra-df3777b2.th", - "Demucs_v2_demucs_unittest_url_1": "https://dl.fbaipublicfiles.com/demucs/v3.0/demucs_unittest-09ebc15f.th", - "Demucs_v1_demucs_url_1": "https://dl.fbaipublicfiles.com/demucs/v2.0/demucs.th", - "Demucs_v1_demucs_extra_url_1": "https://dl.fbaipublicfiles.com/demucs/v2.0/demucs_extra.th", - "Demucs_v1_light_url_1": "https://dl.fbaipublicfiles.com/demucs/v2.0/light.th", - "Demucs_v1_light_extra_url_1": "https://dl.fbaipublicfiles.com/demucs/v2.0/light_extra.th", - "Demucs_v1_tasnet_url_1": "https://dl.fbaipublicfiles.com/demucs/v2.0/tasnet.th", - "Demucs_v1_tasnet_extra_url_1": "https://dl.fbaipublicfiles.com/demucs/v2.0/tasnet_extra.th", - "model_repo_url_1": "https://github.com/TRvlvr/model_repo/releases/download/model_pack_repo/", - "single_model_repo_url_1": "https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/", - "app_patch": "https://github.com/TRvlvr/model_repo/releases/download/uvr_update_patches/" -} \ No newline at end of file diff --git a/lib_v5/filelists/download_lists/mdx_download_list.txt b/lib_v5/filelists/download_lists/mdx_download_list.txt deleted file mode 100644 index 96fb7f2..0000000 --- a/lib_v5/filelists/download_lists/mdx_download_list.txt +++ /dev/null @@ -1,13 +0,0 @@ -No Model Selected -No Model Selected -MDX-Net Model: UVR_MDXNET_Main -MDX-Net Model: UVR_MDXNET_1_9703 -MDX-Net Model: UVR_MDXNET_2_9682 -MDX-Net Model: UVR_MDXNET_3_9662 -MDX-Net Model: UVR_MDXNET_9482 -MDX-Net Model: UVR_MDXNET_KARA -Developer Pack: voc_model_epochs_434-439 -Developer Pack: inst_model_epochs_10-17 -Developer Pack: inst_model_epochs_26-40 -Developer Pack: inst_model_epochs_51-57 -Developer Pack: inst_model_epochs_58-64 diff --git a/lib_v5/filelists/download_lists/temp/temp.txt b/lib_v5/filelists/download_lists/temp/temp.txt deleted file mode 100644 index 3602361..0000000 --- a/lib_v5/filelists/download_lists/temp/temp.txt +++ /dev/null @@ -1 +0,0 @@ -temp \ No newline at end of file diff --git a/lib_v5/filelists/download_lists/vr_download_list.txt b/lib_v5/filelists/download_lists/vr_download_list.txt deleted file mode 100644 index e3f740c..0000000 --- a/lib_v5/filelists/download_lists/vr_download_list.txt +++ /dev/null @@ -1,25 +0,0 @@ -No Model Selected -No Model Selected -VR Arch Model Pack v5: SP Models -VR Arch Model Pack v5: HP2 Models -VR Arch Model Pack v4: Main Models -VR Arch Single Model v5: 1_HP-UVR -VR Arch Single Model v5: 2_HP-UVR -VR Arch Single Model v5: 3_HP-Vocal-UVR -VR Arch Single Model v5: 4_HP-Vocal-UVR -VR Arch Single Model v5: 5_HP-Karaoke-UVR -VR Arch Single Model v5: 6_HP-Karaoke-UVR -VR Arch Single Model v5: 7_HP2-UVR -VR Arch Single Model v5: 8_HP2-UVR -VR Arch Single Model v5: 9_HP2-UVR -VR Arch Single Model v5: 10_SP-UVR-2B-32000-1 -VR Arch Single Model v5: 11_SP-UVR-2B-32000-2 -VR Arch Single Model v5: 12_SP-UVR-3B-44100 -VR Arch Single Model v5: 13_SP-UVR-4B-44100-1 -VR Arch Single Model v5: 14_SP-UVR-4B-44100-2 -VR Arch Single Model v5: 15_SP-UVR-MID-44100-1 -VR Arch Single Model v5: 16_SP-UVR-MID-44100-2 -VR Arch Single Model v4: MGM_HIGHEND_v4 -VR Arch Single Model v4: MGM_LOWEND_A_v4 -VR Arch Single Model v4: MGM_LOWEND_B_v4 -VR Arch Single Model v4: MGM_MAIN_v4 \ No newline at end of file diff --git a/lib_v5/filelists/ensemble_list/mdx_demuc_en_list.txt b/lib_v5/filelists/ensemble_list/mdx_demuc_en_list.txt deleted file mode 100644 index 132f6ee..0000000 --- a/lib_v5/filelists/ensemble_list/mdx_demuc_en_list.txt +++ /dev/null @@ -1,16 +0,0 @@ -No Model -No Model -MDX-Net: UVR-MDX-NET 1 -MDX-Net: UVR-MDX-NET 2 -MDX-Net: UVR-MDX-NET 3 -MDX-Net: UVR_MDXNET_9482 -MDX-Net: UVR-MDX-NET Karaoke -MDX-Net: bass -Demucs: UVR_Demucs_Model_1 -Demucs: UVR_Demucs_Model_2 -Demucs: UVR_Demucs_Model_Bag -Demucs: Demucs_unittest v2 -Demucs: mdx_extra -Demucs: mdx_q -Demucs: Tasnet v1 -Demucs: Tasnet_extra v1 diff --git a/lib_v5/filelists/ensemble_list/vr_en_list.txt b/lib_v5/filelists/ensemble_list/vr_en_list.txt deleted file mode 100644 index 21b2b79..0000000 --- a/lib_v5/filelists/ensemble_list/vr_en_list.txt +++ /dev/null @@ -1,13 +0,0 @@ -No Model -No Model -1_HP-UVR -2_HP-UVR -6_HP-Karaoke-UVR -11_SP-UVR-2B-32000-2 -13_SP-UVR-4B-44100-1 -MGM_HIGHEND_v4 (1) -MGM_HIGHEND_v4 -MGM_LOWEND_A_v4 -MGM_LOWEND_B_v4 -MGM_MAIN_v4 -WIP-Piano-4band-129605kb diff --git a/lib_v5/filelists/hashes/mdx_new_hashes.txt b/lib_v5/filelists/hashes/mdx_new_hashes.txt deleted file mode 100644 index 9da2909..0000000 --- a/lib_v5/filelists/hashes/mdx_new_hashes.txt +++ /dev/null @@ -1,266 +0,0 @@ -0374836ab954ccd5946b29668c36bfdd -e494001f6e38f3ff9f8db07073dbcc38 -1284cfb6ca5cbe68778a392bf92fe448 -927f5993571090980f7042a5087ad7d5 -8a7b42b609383c9bd11c093e357c8d01 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F - -from lib_v5 import spec_utils - - -class Conv2DBNActiv(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(Conv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, nout, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - bias=False), - nn.BatchNorm2d(nout), - activ() - ) - - def __call__(self, x): - return self.conv(x) - - -class SeperableConv2DBNActiv(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(SeperableConv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, nin, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - groups=nin, - bias=False), - nn.Conv2d( - nin, nout, - kernel_size=1, - bias=False), - nn.BatchNorm2d(nout), - activ() - ) - - def __call__(self, x): - return self.conv(x) - - -class Encoder(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): - super(Encoder, self).__init__() - self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) - - def __call__(self, x): - skip = self.conv1(x) - h = self.conv2(skip) - - return h, skip - - -class Decoder(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): - super(Decoder, self).__init__() - self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.dropout = nn.Dropout2d(0.1) if dropout else None - - def __call__(self, x, skip=None): - x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) - if skip is not None: - skip = spec_utils.crop_center(skip, x) - x = torch.cat([x, skip], dim=1) - h = self.conv(x) - - if self.dropout is not None: - h = self.dropout(h) - - return h - - -class ASPPModule(nn.Module): - - def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU): - super(ASPPModule, self).__init__() - self.conv1 = nn.Sequential( - nn.AdaptiveAvgPool2d((1, None)), - Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - ) - self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), - nn.Dropout2d(0.1) - ) - - def forward(self, x): - _, _, h, w = x.size() - feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True) - feat2 = self.conv2(x) - feat3 = self.conv3(x) - feat4 = self.conv4(x) - feat5 = self.conv5(x) - out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1) - bottle = self.bottleneck(out) - return bottle diff --git a/lib_v5/layers_123812KB .py b/lib_v5/layers_123812KB .py deleted file mode 100644 index e48d70b..0000000 --- a/lib_v5/layers_123812KB .py +++ /dev/null @@ -1,116 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from lib_v5 import spec_utils - - -class Conv2DBNActiv(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(Conv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, nout, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - bias=False), - nn.BatchNorm2d(nout), - activ() - ) - - def __call__(self, x): - return self.conv(x) - - -class SeperableConv2DBNActiv(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(SeperableConv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, nin, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - groups=nin, - bias=False), - nn.Conv2d( - nin, nout, - kernel_size=1, - bias=False), - nn.BatchNorm2d(nout), - activ() - ) - - def __call__(self, x): - return self.conv(x) - - -class Encoder(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): - super(Encoder, self).__init__() - self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) - - def __call__(self, x): - skip = self.conv1(x) - h = self.conv2(skip) - - return h, skip - - -class Decoder(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): - super(Decoder, self).__init__() - self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.dropout = nn.Dropout2d(0.1) if dropout else None - - def __call__(self, x, skip=None): - x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) - if skip is not None: - skip = spec_utils.crop_center(skip, x) - x = torch.cat([x, skip], dim=1) - h = self.conv(x) - - if self.dropout is not None: - h = self.dropout(h) - - return h - - -class ASPPModule(nn.Module): - - def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU): - super(ASPPModule, self).__init__() - self.conv1 = nn.Sequential( - nn.AdaptiveAvgPool2d((1, None)), - Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - ) - self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), - nn.Dropout2d(0.1) - ) - - def forward(self, x): - _, _, h, w = x.size() - feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True) - feat2 = self.conv2(x) - feat3 = self.conv3(x) - feat4 = self.conv4(x) - feat5 = self.conv5(x) - out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1) - bottle = self.bottleneck(out) - return bottle diff --git a/lib_v5/layers_123821KB.py b/lib_v5/layers_123821KB.py deleted file mode 100644 index e48d70b..0000000 --- a/lib_v5/layers_123821KB.py +++ /dev/null @@ -1,116 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from lib_v5 import spec_utils - - -class Conv2DBNActiv(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(Conv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, nout, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - bias=False), - nn.BatchNorm2d(nout), - activ() - ) - - def __call__(self, x): - return self.conv(x) - - -class SeperableConv2DBNActiv(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(SeperableConv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, nin, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - groups=nin, - bias=False), - nn.Conv2d( - nin, nout, - kernel_size=1, - bias=False), - nn.BatchNorm2d(nout), - activ() - ) - - def __call__(self, x): - return self.conv(x) - - -class Encoder(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): - super(Encoder, self).__init__() - self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) - - def __call__(self, x): - skip = self.conv1(x) - h = self.conv2(skip) - - return h, skip - - -class Decoder(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): - super(Decoder, self).__init__() - self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.dropout = nn.Dropout2d(0.1) if dropout else None - - def __call__(self, x, skip=None): - x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) - if skip is not None: - skip = spec_utils.crop_center(skip, x) - x = torch.cat([x, skip], dim=1) - h = self.conv(x) - - if self.dropout is not None: - h = self.dropout(h) - - return h - - -class ASPPModule(nn.Module): - - def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU): - super(ASPPModule, self).__init__() - self.conv1 = nn.Sequential( - nn.AdaptiveAvgPool2d((1, None)), - Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - ) - self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), - nn.Dropout2d(0.1) - ) - - def forward(self, x): - _, _, h, w = x.size() - feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True) - feat2 = self.conv2(x) - feat3 = self.conv3(x) - feat4 = self.conv4(x) - feat5 = self.conv5(x) - out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1) - bottle = self.bottleneck(out) - return bottle diff --git a/lib_v5/layers_129605KB.py b/lib_v5/layers_129605KB.py deleted file mode 100644 index 6c318de..0000000 --- a/lib_v5/layers_129605KB.py +++ /dev/null @@ -1,119 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from lib_v5 import spec_utils - - -class Conv2DBNActiv(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(Conv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, nout, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - bias=False), - nn.BatchNorm2d(nout), - activ() - ) - - def __call__(self, x): - return self.conv(x) - - -class SeperableConv2DBNActiv(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(SeperableConv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, nin, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - groups=nin, - bias=False), - nn.Conv2d( - nin, nout, - kernel_size=1, - bias=False), - nn.BatchNorm2d(nout), - activ() - ) - - def __call__(self, x): - return self.conv(x) - - -class Encoder(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): - super(Encoder, self).__init__() - self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) - - def __call__(self, x): - skip = self.conv1(x) - h = self.conv2(skip) - - return h, skip - - -class Decoder(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): - super(Decoder, self).__init__() - self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.dropout = nn.Dropout2d(0.1) if dropout else None - - def __call__(self, x, skip=None): - x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) - if skip is not None: - skip = spec_utils.crop_center(skip, x) - x = torch.cat([x, skip], dim=1) - h = self.conv(x) - - if self.dropout is not None: - h = self.dropout(h) - - return h - - -class ASPPModule(nn.Module): - - def __init__(self, nin, nout, dilations=(4, 8, 16, 32), activ=nn.ReLU): - super(ASPPModule, self).__init__() - self.conv1 = nn.Sequential( - nn.AdaptiveAvgPool2d((1, None)), - Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - ) - self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) - self.conv6 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 6, nout, 1, 1, 0, activ=activ), - nn.Dropout2d(0.1) - ) - - def forward(self, x): - _, _, h, w = x.size() - feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True) - feat2 = self.conv2(x) - feat3 = self.conv3(x) - feat4 = self.conv4(x) - feat5 = self.conv5(x) - feat6 = self.conv6(x) - out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6), dim=1) - bottle = self.bottleneck(out) - return bottle diff --git a/lib_v5/layers_33966KB.py b/lib_v5/layers_33966KB.py deleted file mode 100644 index d410a21..0000000 --- a/lib_v5/layers_33966KB.py +++ /dev/null @@ -1,122 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from lib_v5 import spec_utils - - -class Conv2DBNActiv(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(Conv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, nout, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - bias=False), - nn.BatchNorm2d(nout), - activ() - ) - - def __call__(self, x): - return self.conv(x) - - -class SeperableConv2DBNActiv(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(SeperableConv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, nin, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - groups=nin, - bias=False), - nn.Conv2d( - nin, nout, - kernel_size=1, - bias=False), - nn.BatchNorm2d(nout), - activ() - ) - - def __call__(self, x): - return self.conv(x) - - -class Encoder(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): - super(Encoder, self).__init__() - self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) - - def __call__(self, x): - skip = self.conv1(x) - h = self.conv2(skip) - - return h, skip - - -class Decoder(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): - super(Decoder, self).__init__() - self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.dropout = nn.Dropout2d(0.1) if dropout else None - - def __call__(self, x, skip=None): - x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) - if skip is not None: - skip = spec_utils.crop_center(skip, x) - x = torch.cat([x, skip], dim=1) - h = self.conv(x) - - if self.dropout is not None: - h = self.dropout(h) - - return h - - -class ASPPModule(nn.Module): - - def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU): - super(ASPPModule, self).__init__() - self.conv1 = nn.Sequential( - nn.AdaptiveAvgPool2d((1, None)), - Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - ) - self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) - self.conv6 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) - self.conv7 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), - nn.Dropout2d(0.1) - ) - - def forward(self, x): - _, _, h, w = x.size() - feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True) - feat2 = self.conv2(x) - feat3 = self.conv3(x) - feat4 = self.conv4(x) - feat5 = self.conv5(x) - feat6 = self.conv6(x) - feat7 = self.conv7(x) - out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1) - bottle = self.bottleneck(out) - return bottle diff --git a/lib_v5/layers_537227KB.py b/lib_v5/layers_537227KB.py deleted file mode 100644 index d410a21..0000000 --- a/lib_v5/layers_537227KB.py +++ /dev/null @@ -1,122 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from lib_v5 import spec_utils - - -class Conv2DBNActiv(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(Conv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, nout, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - bias=False), - nn.BatchNorm2d(nout), - activ() - ) - - def __call__(self, x): - return self.conv(x) - - -class SeperableConv2DBNActiv(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(SeperableConv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, nin, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - groups=nin, - bias=False), - nn.Conv2d( - nin, nout, - kernel_size=1, - bias=False), - nn.BatchNorm2d(nout), - activ() - ) - - def __call__(self, x): - return self.conv(x) - - -class Encoder(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): - super(Encoder, self).__init__() - self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) - - def __call__(self, x): - skip = self.conv1(x) - h = self.conv2(skip) - - return h, skip - - -class Decoder(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): - super(Decoder, self).__init__() - self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.dropout = nn.Dropout2d(0.1) if dropout else None - - def __call__(self, x, skip=None): - x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) - if skip is not None: - skip = spec_utils.crop_center(skip, x) - x = torch.cat([x, skip], dim=1) - h = self.conv(x) - - if self.dropout is not None: - h = self.dropout(h) - - return h - - -class ASPPModule(nn.Module): - - def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU): - super(ASPPModule, self).__init__() - self.conv1 = nn.Sequential( - nn.AdaptiveAvgPool2d((1, None)), - Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - ) - self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) - self.conv6 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) - self.conv7 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), - nn.Dropout2d(0.1) - ) - - def forward(self, x): - _, _, h, w = x.size() - feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True) - feat2 = self.conv2(x) - feat3 = self.conv3(x) - feat4 = self.conv4(x) - feat5 = self.conv5(x) - feat6 = self.conv6(x) - feat7 = self.conv7(x) - out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1) - bottle = self.bottleneck(out) - return bottle diff --git a/lib_v5/layers_537238KB.py b/lib_v5/layers_537238KB.py deleted file mode 100644 index d410a21..0000000 --- a/lib_v5/layers_537238KB.py +++ /dev/null @@ -1,122 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from lib_v5 import spec_utils - - -class Conv2DBNActiv(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(Conv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, nout, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - bias=False), - nn.BatchNorm2d(nout), - activ() - ) - - def __call__(self, x): - return self.conv(x) - - -class SeperableConv2DBNActiv(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(SeperableConv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, nin, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - groups=nin, - bias=False), - nn.Conv2d( - nin, nout, - kernel_size=1, - bias=False), - nn.BatchNorm2d(nout), - activ() - ) - - def __call__(self, x): - return self.conv(x) - - -class Encoder(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): - super(Encoder, self).__init__() - self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) - - def __call__(self, x): - skip = self.conv1(x) - h = self.conv2(skip) - - return h, skip - - -class Decoder(nn.Module): - - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): - super(Decoder, self).__init__() - self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.dropout = nn.Dropout2d(0.1) if dropout else None - - def __call__(self, x, skip=None): - x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) - if skip is not None: - skip = spec_utils.crop_center(skip, x) - x = torch.cat([x, skip], dim=1) - h = self.conv(x) - - if self.dropout is not None: - h = self.dropout(h) - - return h - - -class ASPPModule(nn.Module): - - def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU): - super(ASPPModule, self).__init__() - self.conv1 = nn.Sequential( - nn.AdaptiveAvgPool2d((1, None)), - Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - ) - self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) - self.conv6 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) - self.conv7 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), - nn.Dropout2d(0.1) - ) - - def forward(self, x): - _, _, h, w = x.size() - feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True) - feat2 = self.conv2(x) - feat3 = self.conv3(x) - feat4 = self.conv4(x) - feat5 = self.conv5(x) - feat6 = self.conv6(x) - feat7 = self.conv7(x) - out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1) - bottle = self.bottleneck(out) - return bottle diff --git a/lib_v5/model_param_init.py b/lib_v5/model_param_init.py deleted file mode 100644 index 0cee888..0000000 --- a/lib_v5/model_param_init.py +++ /dev/null @@ -1,60 +0,0 @@ -import json -import os -import pathlib - -default_param = {} -default_param['bins'] = 768 -default_param['unstable_bins'] = 9 # training only -default_param['reduction_bins'] = 762 # training only -default_param['sr'] = 44100 -default_param['pre_filter_start'] = 757 -default_param['pre_filter_stop'] = 768 -default_param['band'] = {} - - -default_param['band'][1] = { - 'sr': 11025, - 'hl': 128, - 'n_fft': 960, - 'crop_start': 0, - 'crop_stop': 245, - 'lpf_start': 61, # inference only - 'res_type': 'polyphase' -} - -default_param['band'][2] = { - 'sr': 44100, - 'hl': 512, - 'n_fft': 1536, - 'crop_start': 24, - 'crop_stop': 547, - 'hpf_start': 81, # inference only - 'res_type': 'sinc_best' -} - - -def int_keys(d): - r = {} - for k, v in d: - if k.isdigit(): - k = int(k) - r[k] = v - return r - - -class ModelParameters(object): - def __init__(self, config_path=''): - if '.pth' == pathlib.Path(config_path).suffix: - import zipfile - - with zipfile.ZipFile(config_path, 'r') as zip: - self.param = json.loads(zip.read('param.json'), object_pairs_hook=int_keys) - elif '.json' == pathlib.Path(config_path).suffix: - with open(config_path, 'r') as f: - self.param = json.loads(f.read(), object_pairs_hook=int_keys) - else: - self.param = default_param - - for k in ['mid_side', 'mid_side_b', 'mid_side_b2', 'stereo_w', 'stereo_n', 'reverse']: - if not k in self.param: - self.param[k] = False \ No newline at end of file diff --git a/lib_v5/modelparams/1band_sr16000_hl512.json b/lib_v5/modelparams/1band_sr16000_hl512.json deleted file mode 100644 index 72cb449..0000000 --- a/lib_v5/modelparams/1band_sr16000_hl512.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "bins": 1024, - "unstable_bins": 0, - "reduction_bins": 0, - "band": { - "1": { - "sr": 16000, - "hl": 512, - "n_fft": 2048, - "crop_start": 0, - "crop_stop": 1024, - "hpf_start": -1, - "res_type": "sinc_best" - } - }, - "sr": 16000, - "pre_filter_start": 1023, - "pre_filter_stop": 1024 -} \ No newline at end of file diff --git a/lib_v5/modelparams/1band_sr32000_hl512.json b/lib_v5/modelparams/1band_sr32000_hl512.json deleted file mode 100644 index 3c00ecf..0000000 --- a/lib_v5/modelparams/1band_sr32000_hl512.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "bins": 1024, - "unstable_bins": 0, - "reduction_bins": 0, - "band": { - "1": { - "sr": 32000, - "hl": 512, - "n_fft": 2048, - "crop_start": 0, - "crop_stop": 1024, - "hpf_start": -1, - "res_type": "kaiser_fast" - } - }, - "sr": 32000, - "pre_filter_start": 1000, - "pre_filter_stop": 1021 -} \ No newline at end of file diff --git a/lib_v5/modelparams/1band_sr33075_hl384.json b/lib_v5/modelparams/1band_sr33075_hl384.json deleted file mode 100644 index 55666ac..0000000 --- a/lib_v5/modelparams/1band_sr33075_hl384.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "bins": 1024, - "unstable_bins": 0, - "reduction_bins": 0, - "band": { - "1": { - "sr": 33075, - "hl": 384, - "n_fft": 2048, - "crop_start": 0, - "crop_stop": 1024, - "hpf_start": -1, - "res_type": "sinc_best" - } - }, - "sr": 33075, - "pre_filter_start": 1000, - "pre_filter_stop": 1021 -} \ No newline at end of file diff --git a/lib_v5/modelparams/1band_sr44100_hl1024.json b/lib_v5/modelparams/1band_sr44100_hl1024.json deleted file mode 100644 index 665abe2..0000000 --- a/lib_v5/modelparams/1band_sr44100_hl1024.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "bins": 1024, - "unstable_bins": 0, - "reduction_bins": 0, - "band": { - "1": { - "sr": 44100, - "hl": 1024, - "n_fft": 2048, - "crop_start": 0, - "crop_stop": 1024, - "hpf_start": -1, - "res_type": "sinc_best" - } - }, - "sr": 44100, - "pre_filter_start": 1023, - "pre_filter_stop": 1024 -} \ No newline at end of file diff --git a/lib_v5/modelparams/1band_sr44100_hl256.json b/lib_v5/modelparams/1band_sr44100_hl256.json deleted file mode 100644 index 0e8b16f..0000000 --- a/lib_v5/modelparams/1band_sr44100_hl256.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "bins": 256, - "unstable_bins": 0, - "reduction_bins": 0, - "band": { - "1": { - "sr": 44100, - "hl": 256, - "n_fft": 512, - "crop_start": 0, - "crop_stop": 256, - "hpf_start": -1, - "res_type": "sinc_best" - } - }, - "sr": 44100, - "pre_filter_start": 256, - "pre_filter_stop": 256 -} \ No newline at end of file diff --git a/lib_v5/modelparams/1band_sr44100_hl512.json b/lib_v5/modelparams/1band_sr44100_hl512.json deleted file mode 100644 index 3b38fca..0000000 --- a/lib_v5/modelparams/1band_sr44100_hl512.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "bins": 1024, - "unstable_bins": 0, - "reduction_bins": 0, - "band": { - "1": { - "sr": 44100, - "hl": 512, - "n_fft": 2048, - "crop_start": 0, - "crop_stop": 1024, - "hpf_start": -1, - "res_type": "sinc_best" - } - }, - "sr": 44100, - "pre_filter_start": 1023, - "pre_filter_stop": 1024 -} \ No newline at end of file diff --git a/lib_v5/modelparams/1band_sr44100_hl512_cut.json b/lib_v5/modelparams/1band_sr44100_hl512_cut.json deleted file mode 100644 index 630df35..0000000 --- a/lib_v5/modelparams/1band_sr44100_hl512_cut.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "bins": 1024, - "unstable_bins": 0, - "reduction_bins": 0, - "band": { - "1": { - "sr": 44100, - "hl": 512, - "n_fft": 2048, - "crop_start": 0, - "crop_stop": 700, - "hpf_start": -1, - "res_type": "sinc_best" - } - }, - "sr": 44100, - "pre_filter_start": 1023, - "pre_filter_stop": 700 -} \ No newline at end of file diff --git a/lib_v5/modelparams/2band_32000.json b/lib_v5/modelparams/2band_32000.json deleted file mode 100644 index ab9cf11..0000000 --- a/lib_v5/modelparams/2band_32000.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "bins": 768, - "unstable_bins": 7, - "reduction_bins": 705, - "band": { - "1": { - "sr": 6000, - "hl": 66, - "n_fft": 512, - "crop_start": 0, - "crop_stop": 240, - "lpf_start": 60, - "lpf_stop": 118, - "res_type": "sinc_fastest" - }, - "2": { - "sr": 32000, - "hl": 352, - "n_fft": 1024, - "crop_start": 22, - "crop_stop": 505, - "hpf_start": 44, - "hpf_stop": 23, - "res_type": "sinc_medium" - } - }, - "sr": 32000, - "pre_filter_start": 710, - "pre_filter_stop": 731 -} diff --git a/lib_v5/modelparams/2band_44100_lofi.json b/lib_v5/modelparams/2band_44100_lofi.json deleted file mode 100644 index 7faa216..0000000 --- a/lib_v5/modelparams/2band_44100_lofi.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "bins": 512, - "unstable_bins": 7, - "reduction_bins": 510, - "band": { - "1": { - "sr": 11025, - "hl": 160, - "n_fft": 768, - "crop_start": 0, - "crop_stop": 192, - "lpf_start": 41, - "lpf_stop": 139, - "res_type": "sinc_fastest" - }, - "2": { - "sr": 44100, - "hl": 640, - "n_fft": 1024, - "crop_start": 10, - "crop_stop": 320, - "hpf_start": 47, - "hpf_stop": 15, - "res_type": "sinc_medium" - } - }, - "sr": 44100, - "pre_filter_start": 510, - "pre_filter_stop": 512 -} diff --git a/lib_v5/modelparams/2band_48000.json b/lib_v5/modelparams/2band_48000.json deleted file mode 100644 index be075f5..0000000 --- a/lib_v5/modelparams/2band_48000.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "bins": 768, - "unstable_bins": 7, - "reduction_bins": 705, - "band": { - "1": { - "sr": 6000, - "hl": 66, - "n_fft": 512, - "crop_start": 0, - "crop_stop": 240, - "lpf_start": 60, - "lpf_stop": 240, - "res_type": "sinc_fastest" - }, - "2": { - "sr": 48000, - "hl": 528, - "n_fft": 1536, - "crop_start": 22, - "crop_stop": 505, - "hpf_start": 82, - "hpf_stop": 22, - "res_type": "sinc_medium" - } - }, - "sr": 48000, - "pre_filter_start": 710, - "pre_filter_stop": 731 -} \ No newline at end of file diff --git a/lib_v5/modelparams/3band_44100.json b/lib_v5/modelparams/3band_44100.json deleted file mode 100644 index d99e239..0000000 --- a/lib_v5/modelparams/3band_44100.json +++ /dev/null @@ -1,42 +0,0 @@ -{ - "bins": 768, - "unstable_bins": 5, - "reduction_bins": 733, - "band": { - "1": { - "sr": 11025, - "hl": 128, - "n_fft": 768, - "crop_start": 0, - "crop_stop": 278, - "lpf_start": 28, - "lpf_stop": 140, - "res_type": "polyphase" - }, - "2": { - "sr": 22050, - "hl": 256, - "n_fft": 768, - "crop_start": 14, - "crop_stop": 322, - "hpf_start": 70, - "hpf_stop": 14, - "lpf_start": 283, - "lpf_stop": 314, - "res_type": "polyphase" - }, - "3": { - "sr": 44100, - "hl": 512, - "n_fft": 768, - "crop_start": 131, - "crop_stop": 313, - "hpf_start": 154, - "hpf_stop": 141, - "res_type": "sinc_medium" - } - }, - "sr": 44100, - "pre_filter_start": 757, - "pre_filter_stop": 768 -} diff --git a/lib_v5/modelparams/3band_44100_mid.json b/lib_v5/modelparams/3band_44100_mid.json deleted file mode 100644 index fc2c487..0000000 --- a/lib_v5/modelparams/3band_44100_mid.json +++ /dev/null @@ -1,43 +0,0 @@ -{ - "mid_side": true, - "bins": 768, - "unstable_bins": 5, - "reduction_bins": 733, - "band": { - "1": { - "sr": 11025, - "hl": 128, - "n_fft": 768, - "crop_start": 0, - "crop_stop": 278, - "lpf_start": 28, - "lpf_stop": 140, - "res_type": "polyphase" - }, - "2": { - "sr": 22050, - "hl": 256, - "n_fft": 768, - "crop_start": 14, - "crop_stop": 322, - "hpf_start": 70, - "hpf_stop": 14, - "lpf_start": 283, - "lpf_stop": 314, - "res_type": "polyphase" - }, - "3": { - "sr": 44100, - "hl": 512, - "n_fft": 768, - "crop_start": 131, - "crop_stop": 313, - "hpf_start": 154, - "hpf_stop": 141, - "res_type": "sinc_medium" - } - }, - "sr": 44100, - "pre_filter_start": 757, - "pre_filter_stop": 768 -} diff --git a/lib_v5/modelparams/3band_44100_msb2.json b/lib_v5/modelparams/3band_44100_msb2.json deleted file mode 100644 index 33b0877..0000000 --- a/lib_v5/modelparams/3band_44100_msb2.json +++ /dev/null @@ -1,43 +0,0 @@ -{ - "mid_side_b2": true, - "bins": 640, - "unstable_bins": 7, - "reduction_bins": 565, - "band": { - "1": { - "sr": 11025, - "hl": 108, - "n_fft": 1024, - "crop_start": 0, - "crop_stop": 187, - "lpf_start": 92, - "lpf_stop": 186, - "res_type": "polyphase" - }, - "2": { - "sr": 22050, - "hl": 216, - "n_fft": 768, - "crop_start": 0, - "crop_stop": 212, - "hpf_start": 68, - "hpf_stop": 34, - "lpf_start": 174, - "lpf_stop": 209, - "res_type": "polyphase" - }, - "3": { - "sr": 44100, - "hl": 432, - "n_fft": 640, - "crop_start": 66, - "crop_stop": 307, - "hpf_start": 86, - "hpf_stop": 72, - "res_type": "kaiser_fast" - } - }, - "sr": 44100, - "pre_filter_start": 639, - "pre_filter_stop": 640 -} diff --git a/lib_v5/modelparams/4band_44100.json b/lib_v5/modelparams/4band_44100.json deleted file mode 100644 index 4ae850a..0000000 --- a/lib_v5/modelparams/4band_44100.json +++ /dev/null @@ -1,54 +0,0 @@ -{ - "bins": 768, - "unstable_bins": 7, - "reduction_bins": 668, - "band": { - "1": { - "sr": 11025, - "hl": 128, - "n_fft": 1024, - "crop_start": 0, - "crop_stop": 186, - "lpf_start": 37, - "lpf_stop": 73, - "res_type": "polyphase" - }, - "2": { - "sr": 11025, - "hl": 128, - "n_fft": 512, - "crop_start": 4, - "crop_stop": 185, - "hpf_start": 36, - "hpf_stop": 18, - "lpf_start": 93, - "lpf_stop": 185, - "res_type": "polyphase" - }, - "3": { - "sr": 22050, - "hl": 256, - "n_fft": 512, - "crop_start": 46, - "crop_stop": 186, - "hpf_start": 93, - "hpf_stop": 46, - "lpf_start": 164, - "lpf_stop": 186, - "res_type": "polyphase" - }, - "4": { - "sr": 44100, - "hl": 512, - "n_fft": 768, - "crop_start": 121, - "crop_stop": 382, - "hpf_start": 138, - "hpf_stop": 123, - "res_type": "sinc_medium" - } - }, - "sr": 44100, - "pre_filter_start": 740, - "pre_filter_stop": 768 -} diff --git a/lib_v5/modelparams/4band_44100_mid.json b/lib_v5/modelparams/4band_44100_mid.json deleted file mode 100644 index 6346701..0000000 --- a/lib_v5/modelparams/4band_44100_mid.json +++ /dev/null @@ -1,55 +0,0 @@ -{ - "bins": 768, - "unstable_bins": 7, - "mid_side": true, - "reduction_bins": 668, - "band": { - "1": { - "sr": 11025, - "hl": 128, - "n_fft": 1024, - "crop_start": 0, - "crop_stop": 186, - "lpf_start": 37, - "lpf_stop": 73, - "res_type": "polyphase" - }, - "2": { - "sr": 11025, - "hl": 128, - "n_fft": 512, - "crop_start": 4, - "crop_stop": 185, - "hpf_start": 36, - "hpf_stop": 18, - "lpf_start": 93, - "lpf_stop": 185, - "res_type": "polyphase" - }, - "3": { - "sr": 22050, - "hl": 256, - "n_fft": 512, - "crop_start": 46, - "crop_stop": 186, - "hpf_start": 93, - "hpf_stop": 46, - "lpf_start": 164, - "lpf_stop": 186, - "res_type": "polyphase" - }, - "4": { - "sr": 44100, - "hl": 512, - "n_fft": 768, - "crop_start": 121, - "crop_stop": 382, - "hpf_start": 138, - "hpf_stop": 123, - "res_type": "sinc_medium" - } - }, - "sr": 44100, - "pre_filter_start": 740, - "pre_filter_stop": 768 -} diff --git a/lib_v5/modelparams/4band_44100_msb.json b/lib_v5/modelparams/4band_44100_msb.json deleted file mode 100644 index 0bf4771..0000000 --- a/lib_v5/modelparams/4band_44100_msb.json +++ /dev/null @@ -1,55 +0,0 @@ -{ - "mid_side_b": true, - "bins": 768, - "unstable_bins": 7, - "reduction_bins": 668, - "band": { - "1": { - "sr": 11025, - "hl": 128, - "n_fft": 1024, - "crop_start": 0, - "crop_stop": 186, - "lpf_start": 37, - "lpf_stop": 73, - "res_type": "polyphase" - }, - "2": { - "sr": 11025, - "hl": 128, - "n_fft": 512, - "crop_start": 4, - "crop_stop": 185, - "hpf_start": 36, - "hpf_stop": 18, - "lpf_start": 93, - "lpf_stop": 185, - "res_type": "polyphase" - }, - "3": { - "sr": 22050, - "hl": 256, - "n_fft": 512, - "crop_start": 46, - "crop_stop": 186, - "hpf_start": 93, - "hpf_stop": 46, - "lpf_start": 164, - "lpf_stop": 186, - "res_type": "polyphase" - }, - "4": { - "sr": 44100, - "hl": 512, - "n_fft": 768, - "crop_start": 121, - "crop_stop": 382, - "hpf_start": 138, - "hpf_stop": 123, - "res_type": "sinc_medium" - } - }, - "sr": 44100, - "pre_filter_start": 740, - "pre_filter_stop": 768 -} \ No newline at end of file diff --git a/lib_v5/modelparams/4band_44100_msb2.json b/lib_v5/modelparams/4band_44100_msb2.json deleted file mode 100644 index 0bf4771..0000000 --- a/lib_v5/modelparams/4band_44100_msb2.json +++ /dev/null @@ -1,55 +0,0 @@ -{ - "mid_side_b": true, - "bins": 768, - "unstable_bins": 7, - "reduction_bins": 668, - "band": { - "1": { - "sr": 11025, - "hl": 128, - "n_fft": 1024, - "crop_start": 0, - "crop_stop": 186, - "lpf_start": 37, - "lpf_stop": 73, - "res_type": "polyphase" - }, - "2": { - "sr": 11025, - "hl": 128, - "n_fft": 512, - "crop_start": 4, - "crop_stop": 185, - "hpf_start": 36, - "hpf_stop": 18, - "lpf_start": 93, - "lpf_stop": 185, - "res_type": "polyphase" - }, - "3": { - "sr": 22050, - "hl": 256, - "n_fft": 512, - "crop_start": 46, - "crop_stop": 186, - "hpf_start": 93, - "hpf_stop": 46, - "lpf_start": 164, - "lpf_stop": 186, - "res_type": "polyphase" - }, - "4": { - "sr": 44100, - "hl": 512, - "n_fft": 768, - "crop_start": 121, - "crop_stop": 382, - "hpf_start": 138, - "hpf_stop": 123, - "res_type": "sinc_medium" - } - }, - "sr": 44100, - "pre_filter_start": 740, - "pre_filter_stop": 768 -} \ No newline at end of file diff --git a/lib_v5/modelparams/4band_44100_reverse.json b/lib_v5/modelparams/4band_44100_reverse.json deleted file mode 100644 index 779a1c9..0000000 --- a/lib_v5/modelparams/4band_44100_reverse.json +++ /dev/null @@ -1,55 +0,0 @@ -{ - "reverse": true, - "bins": 768, - "unstable_bins": 7, - "reduction_bins": 668, - "band": { - "1": { - "sr": 11025, - "hl": 128, - "n_fft": 1024, - "crop_start": 0, - "crop_stop": 186, - "lpf_start": 37, - "lpf_stop": 73, - "res_type": "polyphase" - }, - "2": { - "sr": 11025, - "hl": 128, - "n_fft": 512, - "crop_start": 4, - "crop_stop": 185, - "hpf_start": 36, - "hpf_stop": 18, - "lpf_start": 93, - "lpf_stop": 185, - "res_type": "polyphase" - }, - "3": { - "sr": 22050, - "hl": 256, - "n_fft": 512, - "crop_start": 46, - "crop_stop": 186, - "hpf_start": 93, - "hpf_stop": 46, - "lpf_start": 164, - "lpf_stop": 186, - "res_type": "polyphase" - }, - "4": { - "sr": 44100, - "hl": 512, - "n_fft": 768, - "crop_start": 121, - "crop_stop": 382, - "hpf_start": 138, - "hpf_stop": 123, - "res_type": "sinc_medium" - } - }, - "sr": 44100, - "pre_filter_start": 740, - "pre_filter_stop": 768 -} \ No newline at end of file diff --git a/lib_v5/modelparams/4band_44100_sw.json b/lib_v5/modelparams/4band_44100_sw.json deleted file mode 100644 index 1fefd4a..0000000 --- a/lib_v5/modelparams/4band_44100_sw.json +++ /dev/null @@ -1,55 +0,0 @@ -{ - "stereo_w": true, - "bins": 768, - "unstable_bins": 7, - "reduction_bins": 668, - "band": { - "1": { - "sr": 11025, - "hl": 128, - "n_fft": 1024, - "crop_start": 0, - "crop_stop": 186, - "lpf_start": 37, - "lpf_stop": 73, - "res_type": "polyphase" - }, - "2": { - "sr": 11025, - "hl": 128, - "n_fft": 512, - "crop_start": 4, - "crop_stop": 185, - "hpf_start": 36, - "hpf_stop": 18, - "lpf_start": 93, - "lpf_stop": 185, - "res_type": "polyphase" - }, - "3": { - "sr": 22050, - "hl": 256, - "n_fft": 512, - "crop_start": 46, - "crop_stop": 186, - "hpf_start": 93, - "hpf_stop": 46, - "lpf_start": 164, - "lpf_stop": 186, - "res_type": "polyphase" - }, - "4": { - "sr": 44100, - "hl": 512, - "n_fft": 768, - "crop_start": 121, - "crop_stop": 382, - "hpf_start": 138, - "hpf_stop": 123, - "res_type": "sinc_medium" - } - }, - "sr": 44100, - "pre_filter_start": 740, - "pre_filter_stop": 768 -} \ No newline at end of file diff --git a/lib_v5/modelparams/4band_v2.json b/lib_v5/modelparams/4band_v2.json deleted file mode 100644 index af79810..0000000 --- a/lib_v5/modelparams/4band_v2.json +++ /dev/null @@ -1,54 +0,0 @@ -{ - "bins": 672, - "unstable_bins": 8, - "reduction_bins": 637, - "band": { - "1": { - "sr": 7350, - "hl": 80, - "n_fft": 640, - "crop_start": 0, - "crop_stop": 85, - "lpf_start": 25, - "lpf_stop": 53, - "res_type": "polyphase" - }, - "2": { - "sr": 7350, - "hl": 80, - "n_fft": 320, - "crop_start": 4, - "crop_stop": 87, - "hpf_start": 25, - "hpf_stop": 12, - "lpf_start": 31, - "lpf_stop": 62, - "res_type": "polyphase" - }, - "3": { - "sr": 14700, - "hl": 160, - "n_fft": 512, - "crop_start": 17, - "crop_stop": 216, - "hpf_start": 48, - "hpf_stop": 24, - "lpf_start": 139, - "lpf_stop": 210, - "res_type": "polyphase" - }, - "4": { - "sr": 44100, - "hl": 480, - "n_fft": 960, - "crop_start": 78, - "crop_stop": 383, - "hpf_start": 130, - "hpf_stop": 86, - "res_type": "kaiser_fast" - } - }, - "sr": 44100, - "pre_filter_start": 668, - "pre_filter_stop": 672 -} \ No newline at end of file diff --git a/lib_v5/modelparams/4band_v2_sn.json b/lib_v5/modelparams/4band_v2_sn.json deleted file mode 100644 index 319b998..0000000 --- a/lib_v5/modelparams/4band_v2_sn.json +++ /dev/null @@ -1,55 +0,0 @@ -{ - "bins": 672, - "unstable_bins": 8, - "reduction_bins": 637, - "band": { - "1": { - "sr": 7350, - "hl": 80, - "n_fft": 640, - "crop_start": 0, - "crop_stop": 85, - "lpf_start": 25, - "lpf_stop": 53, - "res_type": "polyphase" - }, - "2": { - "sr": 7350, - "hl": 80, - "n_fft": 320, - "crop_start": 4, - "crop_stop": 87, - "hpf_start": 25, - "hpf_stop": 12, - "lpf_start": 31, - "lpf_stop": 62, - "res_type": "polyphase" - }, - "3": { - "sr": 14700, - "hl": 160, - "n_fft": 512, - "crop_start": 17, - "crop_stop": 216, - "hpf_start": 48, - "hpf_stop": 24, - "lpf_start": 139, - "lpf_stop": 210, - "res_type": "polyphase" - }, - "4": { - "sr": 44100, - "hl": 480, - "n_fft": 960, - "crop_start": 78, - "crop_stop": 383, - "hpf_start": 130, - "hpf_stop": 86, - "convert_channels": "stereo_n", - "res_type": "kaiser_fast" - } - }, - "sr": 44100, - "pre_filter_start": 668, - "pre_filter_stop": 672 -} \ No newline at end of file diff --git a/lib_v5/modelparams/Auto b/lib_v5/modelparams/Auto deleted file mode 100644 index cf5ff7a..0000000 --- a/lib_v5/modelparams/Auto +++ /dev/null @@ -1 +0,0 @@ -Auto \ No newline at end of file diff --git a/lib_v5/modelparams/ensemble.json b/lib_v5/modelparams/ensemble.json deleted file mode 100644 index ca96bf1..0000000 --- a/lib_v5/modelparams/ensemble.json +++ /dev/null @@ -1,43 +0,0 @@ -{ - "mid_side_b2": true, - "bins": 1280, - "unstable_bins": 7, - "reduction_bins": 565, - "band": { - "1": { - "sr": 11025, - "hl": 108, - "n_fft": 2048, - "crop_start": 0, - "crop_stop": 374, - "lpf_start": 92, - "lpf_stop": 186, - "res_type": "polyphase" - }, - "2": { - "sr": 22050, - "hl": 216, - "n_fft": 1536, - "crop_start": 0, - "crop_stop": 424, - "hpf_start": 68, - "hpf_stop": 34, - "lpf_start": 348, - "lpf_stop": 418, - "res_type": "polyphase" - }, - "3": { - "sr": 44100, - "hl": 432, - "n_fft": 1280, - "crop_start": 132, - "crop_stop": 614, - "hpf_start": 172, - "hpf_stop": 144, - "res_type": "polyphase" - } - }, - "sr": 44100, - "pre_filter_start": 1280, - "pre_filter_stop": 1280 -} \ No newline at end of file diff --git a/lib_v5/modelparamset.py b/lib_v5/modelparamset.py deleted file mode 100644 index 64c196d..0000000 --- a/lib_v5/modelparamset.py +++ /dev/null @@ -1,166 +0,0 @@ -def provide_model_param_hash(model_hash): - #v5 Models - if model_hash == '47939caf0cfe52a0e81442b85b971dfd': - model_params_set=str('lib_v5/modelparams/4band_44100.json') - param_name=str('4band_44100') - elif model_hash == '4e4ecb9764c50a8c414fee6e10395bbe': - model_params_set=str('lib_v5/modelparams/4band_v2.json') - param_name=str('4band_v2') - elif model_hash == 'e60a1e84803ce4efc0a6551206cc4b71': - model_params_set=str('lib_v5/modelparams/4band_44100.json') - param_name=str('4band_44100') - elif model_hash == 'a82f14e75892e55e994376edbf0c8435': - model_params_set=str('lib_v5/modelparams/4band_44100.json') - param_name=str('4band_44100') - elif model_hash == '6dd9eaa6f0420af9f1d403aaafa4cc06': - model_params_set=str('lib_v5/modelparams/4band_v2_sn.json') - param_name=str('4band_v2_sn') - elif model_hash == '5c7bbca45a187e81abbbd351606164e5': - model_params_set=str('lib_v5/modelparams/3band_44100_msb2.json') - param_name=str('3band_44100_msb2') - elif model_hash == 'd6b2cb685a058a091e5e7098192d3233': - model_params_set=str('lib_v5/modelparams/3band_44100_msb2.json') - param_name=str('3band_44100_msb2') - elif model_hash == 'c1b9f38170a7c90e96f027992eb7c62b': - model_params_set=str('lib_v5/modelparams/4band_44100.json') - param_name=str('4band_44100') - elif model_hash == 'c3448ec923fa0edf3d03a19e633faa53': - model_params_set=str('lib_v5/modelparams/4band_44100.json') - param_name=str('4band_44100') - elif model_hash == '68aa2c8093d0080704b200d140f59e54': - model_params_set=str('lib_v5/modelparams/3band_44100.json') - param_name=str('3band_44100.json') - elif model_hash == 'fdc83be5b798e4bd29fe00fe6600e147': - model_params_set=str('lib_v5/modelparams/3band_44100_mid.json') - param_name=str('3band_44100_mid.json') - elif model_hash == '2ce34bc92fd57f55db16b7a4def3d745': - model_params_set=str('lib_v5/modelparams/3band_44100_mid.json') - param_name=str('3band_44100_mid.json') - elif model_hash == '52fdca89576f06cf4340b74a4730ee5f': - model_params_set=str('lib_v5/modelparams/4band_44100.json') - param_name=str('4band_44100.json') - elif model_hash == '41191165b05d38fc77f072fa9e8e8a30': - model_params_set=str('lib_v5/modelparams/4band_44100.json') - param_name=str('4band_44100.json') - elif model_hash == '89e83b511ad474592689e562d5b1f80e': - model_params_set=str('lib_v5/modelparams/2band_32000.json') - param_name=str('2band_32000.json') - elif model_hash == '0b954da81d453b716b114d6d7c95177f': - model_params_set=str('lib_v5/modelparams/2band_32000.json') - param_name=str('2band_32000.json') - - #v4 Models - - elif model_hash == '6a00461c51c2920fd68937d4609ed6c8': - model_params_set=str('lib_v5/modelparams/1band_sr16000_hl512.json') - param_name=str('1band_sr16000_hl512') - elif model_hash == '0ab504864d20f1bd378fe9c81ef37140': - model_params_set=str('lib_v5/modelparams/1band_sr32000_hl512.json') - param_name=str('1band_sr32000_hl512') - elif model_hash == '7dd21065bf91c10f7fccb57d7d83b07f': - model_params_set=str('lib_v5/modelparams/1band_sr32000_hl512.json') - param_name=str('1band_sr32000_hl512') - elif model_hash == '80ab74d65e515caa3622728d2de07d23': - model_params_set=str('lib_v5/modelparams/1band_sr32000_hl512.json') - param_name=str('1band_sr32000_hl512') - elif model_hash == 'edc115e7fc523245062200c00caa847f': - model_params_set=str('lib_v5/modelparams/1band_sr33075_hl384.json') - param_name=str('1band_sr33075_hl384') - elif model_hash == '28063e9f6ab5b341c5f6d3c67f2045b7': - model_params_set=str('lib_v5/modelparams/1band_sr33075_hl384.json') - param_name=str('1band_sr33075_hl384') - elif model_hash == 'b58090534c52cbc3e9b5104bad666ef2': - model_params_set=str('lib_v5/modelparams/1band_sr44100_hl512.json') - param_name=str('1band_sr44100_hl512') - elif model_hash == '0cdab9947f1b0928705f518f3c78ea8f': - model_params_set=str('lib_v5/modelparams/1band_sr44100_hl512.json') - param_name=str('1band_sr44100_hl512') - elif model_hash == 'ae702fed0238afb5346db8356fe25f13': - model_params_set=str('lib_v5/modelparams/1band_sr44100_hl1024.json') - param_name=str('1band_sr44100_hl1024') - else: - model_params_set=str('Not Found Using Hash') - param_name=str('Not Found Using Hash') - - model_params = model_params_set, param_name - - return model_params - -def provide_model_param_name(ModelName): - #1 Band - if '1band_sr16000_hl512' in ModelName: - model_params_set=str('lib_v5/modelparams/1band_sr16000_hl512.json') - param_name=str('1band_sr16000_hl512') - elif '1band_sr32000_hl512' in ModelName: - model_params_set=str('lib_v5/modelparams/1band_sr32000_hl512.json') - param_name=str('1band_sr32000_hl512') - elif '1band_sr33075_hl384' in ModelName: - model_params_set=str('lib_v5/modelparams/1band_sr33075_hl384.json') - param_name=str('1band_sr33075_hl384') - elif '1band_sr44100_hl256' in ModelName: - model_params_set=str('lib_v5/modelparams/1band_sr44100_hl256.json') - param_name=str('1band_sr44100_hl256') - elif '1band_sr44100_hl512' in ModelName: - model_params_set=str('lib_v5/modelparams/1band_sr44100_hl512.json') - param_name=str('1band_sr44100_hl512') - elif '1band_sr44100_hl1024' in ModelName: - model_params_set=str('lib_v5/modelparams/1band_sr44100_hl1024.json') - param_name=str('1band_sr44100_hl1024') - - #2 Band - elif '2band_44100_lofi' in ModelName: - model_params_set=str('lib_v5/modelparams/2band_44100_lofi.json') - param_name=str('2band_44100_lofi') - elif '2band_32000' in ModelName: - model_params_set=str('lib_v5/modelparams/2band_32000.json') - param_name=str('2band_32000') - elif '2band_48000' in ModelName: - model_params_set=str('lib_v5/modelparams/2band_48000.json') - param_name=str('2band_48000') - - #3 Band - elif '3band_44100' in ModelName: - model_params_set=str('lib_v5/modelparams/3band_44100.json') - param_name=str('3band_44100') - elif '3band_44100_mid' in ModelName: - model_params_set=str('lib_v5/modelparams/3band_44100_mid.json') - param_name=str('3band_44100_mid') - elif '3band_44100_msb2' in ModelName: - model_params_set=str('lib_v5/modelparams/3band_44100_msb2.json') - param_name=str('3band_44100_msb2') - - #4 Band - elif '4band_44100' in ModelName: - model_params_set=str('lib_v5/modelparams/4band_44100.json') - param_name=str('4band_44100') - elif '4band_44100_mid' in ModelName: - model_params_set=str('lib_v5/modelparams/4band_44100_mid.json') - param_name=str('4band_44100_mid') - elif '4band_44100_msb' in ModelName: - model_params_set=str('lib_v5/modelparams/4band_44100_msb.json') - param_name=str('4band_44100_msb') - elif '4band_44100_msb2' in ModelName: - model_params_set=str('lib_v5/modelparams/4band_44100_msb2.json') - param_name=str('4band_44100_msb2') - elif '4band_44100_reverse' in ModelName: - model_params_set=str('lib_v5/modelparams/4band_44100_reverse.json') - param_name=str('4band_44100_reverse') - elif '4band_44100_sw' in ModelName: - model_params_set=str('lib_v5/modelparams/4band_44100_sw.json') - param_name=str('4band_44100_sw') - elif '4band_v2' in ModelName: - model_params_set=str('lib_v5/modelparams/4band_v2.json') - param_name=str('4band_v2') - elif '4band_v2_sn' in ModelName: - model_params_set=str('lib_v5/modelparams/4band_v2_sn.json') - param_name=str('4band_v2_sn') - elif 'tmodelparam' in ModelName: - model_params_set=str('lib_v5/modelparams/tmodelparam.json') - param_name=str('User Model Param Set') - else: - model_params_set=str('Not Found Using Name') - param_name=str('Not Found Using Name') - - model_params = model_params_set, param_name - - return model_params \ No newline at end of file diff --git a/lib_v5/nets.py b/lib_v5/nets.py deleted file mode 100644 index b5a8417..0000000 --- a/lib_v5/nets.py +++ /dev/null @@ -1,113 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from lib_v5 import layers -from lib_v5 import spec_utils - - -class BaseASPPNet(nn.Module): - - def __init__(self, nin, ch, dilations=(4, 8, 16)): - super(BaseASPPNet, self).__init__() - self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) - self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) - self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) - self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) - - self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) - - self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) - self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) - self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) - self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) - - def __call__(self, x): - h, e1 = self.enc1(x) - h, e2 = self.enc2(h) - h, e3 = self.enc3(h) - h, e4 = self.enc4(h) - - h = self.aspp(h) - - h = self.dec4(h, e4) - h = self.dec3(h, e3) - h = self.dec2(h, e2) - h = self.dec1(h, e1) - - return h - - -class CascadedASPPNet(nn.Module): - - def __init__(self, n_fft): - super(CascadedASPPNet, self).__init__() - self.stg1_low_band_net = BaseASPPNet(2, 16) - self.stg1_high_band_net = BaseASPPNet(2, 16) - - self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0) - self.stg2_full_band_net = BaseASPPNet(8, 16) - - self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) - self.stg3_full_band_net = BaseASPPNet(16, 32) - - self.out = nn.Conv2d(32, 2, 1, bias=False) - self.aux1_out = nn.Conv2d(16, 2, 1, bias=False) - self.aux2_out = nn.Conv2d(16, 2, 1, bias=False) - - self.max_bin = n_fft // 2 - self.output_bin = n_fft // 2 + 1 - - self.offset = 128 - - def forward(self, x, aggressiveness=None): - mix = x.detach() - x = x.clone() - - x = x[:, :, :self.max_bin] - - bandw = x.size()[2] // 2 - aux1 = torch.cat([ - self.stg1_low_band_net(x[:, :, :bandw]), - self.stg1_high_band_net(x[:, :, bandw:]) - ], dim=2) - - h = torch.cat([x, aux1], dim=1) - aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) - - h = torch.cat([x, aux1, aux2], dim=1) - h = self.stg3_full_band_net(self.stg3_bridge(h)) - - mask = torch.sigmoid(self.out(h)) - mask = F.pad( - input=mask, - pad=(0, 0, 0, self.output_bin - mask.size()[2]), - mode='replicate') - - if self.training: - aux1 = torch.sigmoid(self.aux1_out(aux1)) - aux1 = F.pad( - input=aux1, - pad=(0, 0, 0, self.output_bin - aux1.size()[2]), - mode='replicate') - aux2 = torch.sigmoid(self.aux2_out(aux2)) - aux2 = F.pad( - input=aux2, - pad=(0, 0, 0, self.output_bin - aux2.size()[2]), - mode='replicate') - return mask * mix, aux1 * mix, aux2 * mix - else: - if aggressiveness: - mask[:, :, :aggressiveness['split_bin']] = torch.pow(mask[:, :, :aggressiveness['split_bin']], 1 + aggressiveness['value'] / 3) - mask[:, :, aggressiveness['split_bin']:] = torch.pow(mask[:, :, aggressiveness['split_bin']:], 1 + aggressiveness['value']) - - return mask * mix - - def predict(self, x_mag, aggressiveness=None): - h = self.forward(x_mag, aggressiveness) - - if self.offset > 0: - h = h[:, :, :, self.offset:-self.offset] - assert h.size()[3] > 0 - - return h diff --git a/lib_v5/nets_123812KB.py b/lib_v5/nets_123812KB.py deleted file mode 100644 index d32b5c0..0000000 --- a/lib_v5/nets_123812KB.py +++ /dev/null @@ -1,112 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from lib_v5 import layers_123821KB as layers - - -class BaseASPPNet(nn.Module): - - def __init__(self, nin, ch, dilations=(4, 8, 16)): - super(BaseASPPNet, self).__init__() - self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) - self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) - self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) - self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) - - self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) - - self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) - self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) - self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) - self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) - - def __call__(self, x): - h, e1 = self.enc1(x) - h, e2 = self.enc2(h) - h, e3 = self.enc3(h) - h, e4 = self.enc4(h) - - h = self.aspp(h) - - h = self.dec4(h, e4) - h = self.dec3(h, e3) - h = self.dec2(h, e2) - h = self.dec1(h, e1) - - return h - - -class CascadedASPPNet(nn.Module): - - def __init__(self, n_fft): - super(CascadedASPPNet, self).__init__() - self.stg1_low_band_net = BaseASPPNet(2, 32) - self.stg1_high_band_net = BaseASPPNet(2, 32) - - self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) - self.stg2_full_band_net = BaseASPPNet(16, 32) - - self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0) - self.stg3_full_band_net = BaseASPPNet(32, 64) - - self.out = nn.Conv2d(64, 2, 1, bias=False) - self.aux1_out = nn.Conv2d(32, 2, 1, bias=False) - self.aux2_out = nn.Conv2d(32, 2, 1, bias=False) - - self.max_bin = n_fft // 2 - self.output_bin = n_fft // 2 + 1 - - self.offset = 128 - - def forward(self, x, aggressiveness=None): - mix = x.detach() - x = x.clone() - - x = x[:, :, :self.max_bin] - - bandw = x.size()[2] // 2 - aux1 = torch.cat([ - self.stg1_low_band_net(x[:, :, :bandw]), - self.stg1_high_band_net(x[:, :, bandw:]) - ], dim=2) - - h = torch.cat([x, aux1], dim=1) - aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) - - h = torch.cat([x, aux1, aux2], dim=1) - h = self.stg3_full_band_net(self.stg3_bridge(h)) - - mask = torch.sigmoid(self.out(h)) - mask = F.pad( - input=mask, - pad=(0, 0, 0, self.output_bin - mask.size()[2]), - mode='replicate') - - if self.training: - aux1 = torch.sigmoid(self.aux1_out(aux1)) - aux1 = F.pad( - input=aux1, - pad=(0, 0, 0, self.output_bin - aux1.size()[2]), - mode='replicate') - aux2 = torch.sigmoid(self.aux2_out(aux2)) - aux2 = F.pad( - input=aux2, - pad=(0, 0, 0, self.output_bin - aux2.size()[2]), - mode='replicate') - return mask * mix, aux1 * mix, aux2 * mix - else: - if aggressiveness: - mask[:, :, :aggressiveness['split_bin']] = torch.pow(mask[:, :, :aggressiveness['split_bin']], 1 + aggressiveness['value'] / 3) - mask[:, :, aggressiveness['split_bin']:] = torch.pow(mask[:, :, aggressiveness['split_bin']:], 1 + aggressiveness['value']) - - return mask * mix - - def predict(self, x_mag, aggressiveness=None): - h = self.forward(x_mag, aggressiveness) - - if self.offset > 0: - h = h[:, :, :, self.offset:-self.offset] - assert h.size()[3] > 0 - - return h diff --git a/lib_v5/nets_123821KB.py b/lib_v5/nets_123821KB.py deleted file mode 100644 index d32b5c0..0000000 --- a/lib_v5/nets_123821KB.py +++ /dev/null @@ -1,112 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from lib_v5 import layers_123821KB as layers - - -class BaseASPPNet(nn.Module): - - def __init__(self, nin, ch, dilations=(4, 8, 16)): - super(BaseASPPNet, self).__init__() - self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) - self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) - self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) - self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) - - self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) - - self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) - self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) - self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) - self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) - - def __call__(self, x): - h, e1 = self.enc1(x) - h, e2 = self.enc2(h) - h, e3 = self.enc3(h) - h, e4 = self.enc4(h) - - h = self.aspp(h) - - h = self.dec4(h, e4) - h = self.dec3(h, e3) - h = self.dec2(h, e2) - h = self.dec1(h, e1) - - return h - - -class CascadedASPPNet(nn.Module): - - def __init__(self, n_fft): - super(CascadedASPPNet, self).__init__() - self.stg1_low_band_net = BaseASPPNet(2, 32) - self.stg1_high_band_net = BaseASPPNet(2, 32) - - self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) - self.stg2_full_band_net = BaseASPPNet(16, 32) - - self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0) - self.stg3_full_band_net = BaseASPPNet(32, 64) - - self.out = nn.Conv2d(64, 2, 1, bias=False) - self.aux1_out = nn.Conv2d(32, 2, 1, bias=False) - self.aux2_out = nn.Conv2d(32, 2, 1, bias=False) - - self.max_bin = n_fft // 2 - self.output_bin = n_fft // 2 + 1 - - self.offset = 128 - - def forward(self, x, aggressiveness=None): - mix = x.detach() - x = x.clone() - - x = x[:, :, :self.max_bin] - - bandw = x.size()[2] // 2 - aux1 = torch.cat([ - self.stg1_low_band_net(x[:, :, :bandw]), - self.stg1_high_band_net(x[:, :, bandw:]) - ], dim=2) - - h = torch.cat([x, aux1], dim=1) - aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) - - h = torch.cat([x, aux1, aux2], dim=1) - h = self.stg3_full_band_net(self.stg3_bridge(h)) - - mask = torch.sigmoid(self.out(h)) - mask = F.pad( - input=mask, - pad=(0, 0, 0, self.output_bin - mask.size()[2]), - mode='replicate') - - if self.training: - aux1 = torch.sigmoid(self.aux1_out(aux1)) - aux1 = F.pad( - input=aux1, - pad=(0, 0, 0, self.output_bin - aux1.size()[2]), - mode='replicate') - aux2 = torch.sigmoid(self.aux2_out(aux2)) - aux2 = F.pad( - input=aux2, - pad=(0, 0, 0, self.output_bin - aux2.size()[2]), - mode='replicate') - return mask * mix, aux1 * mix, aux2 * mix - else: - if aggressiveness: - mask[:, :, :aggressiveness['split_bin']] = torch.pow(mask[:, :, :aggressiveness['split_bin']], 1 + aggressiveness['value'] / 3) - mask[:, :, aggressiveness['split_bin']:] = torch.pow(mask[:, :, aggressiveness['split_bin']:], 1 + aggressiveness['value']) - - return mask * mix - - def predict(self, x_mag, aggressiveness=None): - h = self.forward(x_mag, aggressiveness) - - if self.offset > 0: - h = h[:, :, :, self.offset:-self.offset] - assert h.size()[3] > 0 - - return h diff --git a/lib_v5/nets_129605KB.py b/lib_v5/nets_129605KB.py deleted file mode 100644 index f08a214..0000000 --- a/lib_v5/nets_129605KB.py +++ /dev/null @@ -1,116 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from lib_v5 import layers_129605KB as layers - - -class BaseASPPNet(nn.Module): - - def __init__(self, nin, ch, dilations=(4, 8, 16, 32)): - super(BaseASPPNet, self).__init__() - self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) - self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) - self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) - self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) - self.enc5 = layers.Encoder(ch * 8, ch * 16, 3, 2, 1) - - self.aspp = layers.ASPPModule(ch * 16, ch * 32, dilations) - - self.dec5 = layers.Decoder(ch * (16 + 32), ch * 16, 3, 1, 1) - self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) - self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) - self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) - self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) - - def __call__(self, x): - h, e1 = self.enc1(x) - h, e2 = self.enc2(h) - h, e3 = self.enc3(h) - h, e4 = self.enc4(h) - h, e5 = self.enc5(h) - - h = self.aspp(h) - - h = self.dec5(h, e5) - h = self.dec4(h, e4) - h = self.dec3(h, e3) - h = self.dec2(h, e2) - h = self.dec1(h, e1) - - return h - - -class CascadedASPPNet(nn.Module): - - def __init__(self, n_fft): - super(CascadedASPPNet, self).__init__() - self.stg1_low_band_net = BaseASPPNet(2, 16) - self.stg1_high_band_net = BaseASPPNet(2, 16) - - self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0) - self.stg2_full_band_net = BaseASPPNet(8, 16) - - self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) - self.stg3_full_band_net = BaseASPPNet(16, 32) - - self.out = nn.Conv2d(32, 2, 1, bias=False) - self.aux1_out = nn.Conv2d(16, 2, 1, bias=False) - self.aux2_out = nn.Conv2d(16, 2, 1, bias=False) - - self.max_bin = n_fft // 2 - self.output_bin = n_fft // 2 + 1 - - self.offset = 128 - - def forward(self, x, aggressiveness=None): - mix = x.detach() - x = x.clone() - - x = x[:, :, :self.max_bin] - - bandw = x.size()[2] // 2 - aux1 = torch.cat([ - self.stg1_low_band_net(x[:, :, :bandw]), - self.stg1_high_band_net(x[:, :, bandw:]) - ], dim=2) - - h = torch.cat([x, aux1], dim=1) - aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) - - h = torch.cat([x, aux1, aux2], dim=1) - h = self.stg3_full_band_net(self.stg3_bridge(h)) - - mask = torch.sigmoid(self.out(h)) - mask = F.pad( - input=mask, - pad=(0, 0, 0, self.output_bin - mask.size()[2]), - mode='replicate') - - if self.training: - aux1 = torch.sigmoid(self.aux1_out(aux1)) - aux1 = F.pad( - input=aux1, - pad=(0, 0, 0, self.output_bin - aux1.size()[2]), - mode='replicate') - aux2 = torch.sigmoid(self.aux2_out(aux2)) - aux2 = F.pad( - input=aux2, - pad=(0, 0, 0, self.output_bin - aux2.size()[2]), - mode='replicate') - return mask * mix, aux1 * mix, aux2 * mix - else: - if aggressiveness: - mask[:, :, :aggressiveness['split_bin']] = torch.pow(mask[:, :, :aggressiveness['split_bin']], 1 + aggressiveness['value'] / 3) - mask[:, :, aggressiveness['split_bin']:] = torch.pow(mask[:, :, aggressiveness['split_bin']:], 1 + aggressiveness['value']) - - return mask * mix - - def predict(self, x_mag, aggressiveness=None): - h = self.forward(x_mag, aggressiveness) - - if self.offset > 0: - h = h[:, :, :, self.offset:-self.offset] - assert h.size()[3] > 0 - - return h diff --git a/lib_v5/nets_33966KB.py b/lib_v5/nets_33966KB.py deleted file mode 100644 index 07e2b8c..0000000 --- a/lib_v5/nets_33966KB.py +++ /dev/null @@ -1,112 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from lib_v5 import layers_33966KB as layers - - -class BaseASPPNet(nn.Module): - - def __init__(self, nin, ch, dilations=(4, 8, 16, 32)): - super(BaseASPPNet, self).__init__() - self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) - self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) - self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) - self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) - - self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) - - self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) - self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) - self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) - self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) - - def __call__(self, x): - h, e1 = self.enc1(x) - h, e2 = self.enc2(h) - h, e3 = self.enc3(h) - h, e4 = self.enc4(h) - - h = self.aspp(h) - - h = self.dec4(h, e4) - h = self.dec3(h, e3) - h = self.dec2(h, e2) - h = self.dec1(h, e1) - - return h - - -class CascadedASPPNet(nn.Module): - - def __init__(self, n_fft): - super(CascadedASPPNet, self).__init__() - self.stg1_low_band_net = BaseASPPNet(2, 16) - self.stg1_high_band_net = BaseASPPNet(2, 16) - - self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0) - self.stg2_full_band_net = BaseASPPNet(8, 16) - - self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) - self.stg3_full_band_net = BaseASPPNet(16, 32) - - self.out = nn.Conv2d(32, 2, 1, bias=False) - self.aux1_out = nn.Conv2d(16, 2, 1, bias=False) - self.aux2_out = nn.Conv2d(16, 2, 1, bias=False) - - self.max_bin = n_fft // 2 - self.output_bin = n_fft // 2 + 1 - - self.offset = 128 - - def forward(self, x, aggressiveness=None): - mix = x.detach() - x = x.clone() - - x = x[:, :, :self.max_bin] - - bandw = x.size()[2] // 2 - aux1 = torch.cat([ - self.stg1_low_band_net(x[:, :, :bandw]), - self.stg1_high_band_net(x[:, :, bandw:]) - ], dim=2) - - h = torch.cat([x, aux1], dim=1) - aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) - - h = torch.cat([x, aux1, aux2], dim=1) - h = self.stg3_full_band_net(self.stg3_bridge(h)) - - mask = torch.sigmoid(self.out(h)) - mask = F.pad( - input=mask, - pad=(0, 0, 0, self.output_bin - mask.size()[2]), - mode='replicate') - - if self.training: - aux1 = torch.sigmoid(self.aux1_out(aux1)) - aux1 = F.pad( - input=aux1, - pad=(0, 0, 0, self.output_bin - aux1.size()[2]), - mode='replicate') - aux2 = torch.sigmoid(self.aux2_out(aux2)) - aux2 = F.pad( - input=aux2, - pad=(0, 0, 0, self.output_bin - aux2.size()[2]), - mode='replicate') - return mask * mix, aux1 * mix, aux2 * mix - else: - if aggressiveness: - mask[:, :, :aggressiveness['split_bin']] = torch.pow(mask[:, :, :aggressiveness['split_bin']], 1 + aggressiveness['value'] / 3) - mask[:, :, aggressiveness['split_bin']:] = torch.pow(mask[:, :, aggressiveness['split_bin']:], 1 + aggressiveness['value']) - - return mask * mix - - def predict(self, x_mag, aggressiveness=None): - h = self.forward(x_mag, aggressiveness) - - if self.offset > 0: - h = h[:, :, :, self.offset:-self.offset] - assert h.size()[3] > 0 - - return h diff --git a/lib_v5/nets_537227KB.py b/lib_v5/nets_537227KB.py deleted file mode 100644 index 566e3f9..0000000 --- a/lib_v5/nets_537227KB.py +++ /dev/null @@ -1,113 +0,0 @@ -import torch -import numpy as np -from torch import nn -import torch.nn.functional as F - -from lib_v5 import layers_537238KB as layers - - -class BaseASPPNet(nn.Module): - - def __init__(self, nin, ch, dilations=(4, 8, 16)): - super(BaseASPPNet, self).__init__() - self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) - self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) - self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) - self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) - - self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) - - self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) - self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) - self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) - self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) - - def __call__(self, x): - h, e1 = self.enc1(x) - h, e2 = self.enc2(h) - h, e3 = self.enc3(h) - h, e4 = self.enc4(h) - - h = self.aspp(h) - - h = self.dec4(h, e4) - h = self.dec3(h, e3) - h = self.dec2(h, e2) - h = self.dec1(h, e1) - - return h - - -class CascadedASPPNet(nn.Module): - - def __init__(self, n_fft): - super(CascadedASPPNet, self).__init__() - self.stg1_low_band_net = BaseASPPNet(2, 64) - self.stg1_high_band_net = BaseASPPNet(2, 64) - - self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0) - self.stg2_full_band_net = BaseASPPNet(32, 64) - - self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0) - self.stg3_full_band_net = BaseASPPNet(64, 128) - - self.out = nn.Conv2d(128, 2, 1, bias=False) - self.aux1_out = nn.Conv2d(64, 2, 1, bias=False) - self.aux2_out = nn.Conv2d(64, 2, 1, bias=False) - - self.max_bin = n_fft // 2 - self.output_bin = n_fft // 2 + 1 - - self.offset = 128 - - def forward(self, x, aggressiveness=None): - mix = x.detach() - x = x.clone() - - x = x[:, :, :self.max_bin] - - bandw = x.size()[2] // 2 - aux1 = torch.cat([ - self.stg1_low_band_net(x[:, :, :bandw]), - self.stg1_high_band_net(x[:, :, bandw:]) - ], dim=2) - - h = torch.cat([x, aux1], dim=1) - aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) - - h = torch.cat([x, aux1, aux2], dim=1) - h = self.stg3_full_band_net(self.stg3_bridge(h)) - - mask = torch.sigmoid(self.out(h)) - mask = F.pad( - input=mask, - pad=(0, 0, 0, self.output_bin - mask.size()[2]), - mode='replicate') - - if self.training: - aux1 = torch.sigmoid(self.aux1_out(aux1)) - aux1 = F.pad( - input=aux1, - pad=(0, 0, 0, self.output_bin - aux1.size()[2]), - mode='replicate') - aux2 = torch.sigmoid(self.aux2_out(aux2)) - aux2 = F.pad( - input=aux2, - pad=(0, 0, 0, self.output_bin - aux2.size()[2]), - mode='replicate') - return mask * mix, aux1 * mix, aux2 * mix - else: - if aggressiveness: - mask[:, :, :aggressiveness['split_bin']] = torch.pow(mask[:, :, :aggressiveness['split_bin']], 1 + aggressiveness['value'] / 3) - mask[:, :, aggressiveness['split_bin']:] = torch.pow(mask[:, :, aggressiveness['split_bin']:], 1 + aggressiveness['value']) - - return mask * mix - - def predict(self, x_mag, aggressiveness=None): - h = self.forward(x_mag, aggressiveness) - - if self.offset > 0: - h = h[:, :, :, self.offset:-self.offset] - assert h.size()[3] > 0 - - return h diff --git a/lib_v5/nets_537238KB.py b/lib_v5/nets_537238KB.py deleted file mode 100644 index 566e3f9..0000000 --- a/lib_v5/nets_537238KB.py +++ /dev/null @@ -1,113 +0,0 @@ -import torch -import numpy as np -from torch import nn -import torch.nn.functional as F - -from lib_v5 import layers_537238KB as layers - - -class BaseASPPNet(nn.Module): - - def __init__(self, nin, ch, dilations=(4, 8, 16)): - super(BaseASPPNet, self).__init__() - self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) - self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) - self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) - self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) - - self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) - - self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) - self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) - self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) - self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) - - def __call__(self, x): - h, e1 = self.enc1(x) - h, e2 = self.enc2(h) - h, e3 = self.enc3(h) - h, e4 = self.enc4(h) - - h = self.aspp(h) - - h = self.dec4(h, e4) - h = self.dec3(h, e3) - h = self.dec2(h, e2) - h = self.dec1(h, e1) - - return h - - -class CascadedASPPNet(nn.Module): - - def __init__(self, n_fft): - super(CascadedASPPNet, self).__init__() - self.stg1_low_band_net = BaseASPPNet(2, 64) - self.stg1_high_band_net = BaseASPPNet(2, 64) - - self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0) - self.stg2_full_band_net = BaseASPPNet(32, 64) - - self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0) - self.stg3_full_band_net = BaseASPPNet(64, 128) - - self.out = nn.Conv2d(128, 2, 1, bias=False) - self.aux1_out = nn.Conv2d(64, 2, 1, bias=False) - self.aux2_out = nn.Conv2d(64, 2, 1, bias=False) - - self.max_bin = n_fft // 2 - self.output_bin = n_fft // 2 + 1 - - self.offset = 128 - - def forward(self, x, aggressiveness=None): - mix = x.detach() - x = x.clone() - - x = x[:, :, :self.max_bin] - - bandw = x.size()[2] // 2 - aux1 = torch.cat([ - self.stg1_low_band_net(x[:, :, :bandw]), - self.stg1_high_band_net(x[:, :, bandw:]) - ], dim=2) - - h = torch.cat([x, aux1], dim=1) - aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) - - h = torch.cat([x, aux1, aux2], dim=1) - h = self.stg3_full_band_net(self.stg3_bridge(h)) - - mask = torch.sigmoid(self.out(h)) - mask = F.pad( - input=mask, - pad=(0, 0, 0, self.output_bin - mask.size()[2]), - mode='replicate') - - if self.training: - aux1 = torch.sigmoid(self.aux1_out(aux1)) - aux1 = F.pad( - input=aux1, - pad=(0, 0, 0, self.output_bin - aux1.size()[2]), - mode='replicate') - aux2 = torch.sigmoid(self.aux2_out(aux2)) - aux2 = F.pad( - input=aux2, - pad=(0, 0, 0, self.output_bin - aux2.size()[2]), - mode='replicate') - return mask * mix, aux1 * mix, aux2 * mix - else: - if aggressiveness: - mask[:, :, :aggressiveness['split_bin']] = torch.pow(mask[:, :, :aggressiveness['split_bin']], 1 + aggressiveness['value'] / 3) - mask[:, :, aggressiveness['split_bin']:] = torch.pow(mask[:, :, aggressiveness['split_bin']:], 1 + aggressiveness['value']) - - return mask * mix - - def predict(self, x_mag, aggressiveness=None): - h = self.forward(x_mag, aggressiveness) - - if self.offset > 0: - h = h[:, :, :, self.offset:-self.offset] - assert h.size()[3] > 0 - - return h diff --git a/lib_v5/sox/MDX-NET_Noise_Profile_14_kHz.prof b/lib_v5/sox/MDX-NET_Noise_Profile_14_kHz.prof deleted file mode 100644 index e84270d..0000000 --- a/lib_v5/sox/MDX-NET_Noise_Profile_14_kHz.prof +++ /dev/null @@ -1,2 +0,0 @@ -Channel 0: -7.009383, -9.291822, -8.961462, -8.988426, -8.133916, -7.550877, 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-11.123456, -11.159070, -11.205329, -11.170352, -11.195209, -11.192614, -11.211015, -11.148291, -11.120795, -11.191674, -11.138820, -11.281963, -11.270242, -11.489305, -12.294074, -12.989191 diff --git a/lib_v5/spec_utils.py b/lib_v5/spec_utils.py deleted file mode 100644 index f57853d..0000000 --- a/lib_v5/spec_utils.py +++ /dev/null @@ -1,549 +0,0 @@ -import os - -import librosa -import numpy as np -import soundfile as sf -import math -import json -import hashlib -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] * .5)) - wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5)) - else: - wave_left = np.asfortranarray(wave[0]) - wave_right = np.asfortranarray(wave[1]) - - spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length) - spec_right = librosa.stft(wave_right, 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] * .5)) - wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .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, hop_length=hop_length) - thread.join() - - spec = np.asfortranarray([spec_left, spec_right]) - - return spec - -def normalize(wave_res): - """Save output music files""" - maxv = np.abs(wave_res).max() - if maxv > 1.0: - print(f"\nNormalization Set On: Input above threshold for clipping. The result was normalized. Max:{maxv}\n") - wave_res /= maxv - else: - print(f"\nNormalization Set On: Input not above threshold for clipping. Max:{maxv}\n") - - return wave_res - -def nonormalize(wave_res): - """Save output music files""" - maxv = np.abs(wave_res).max() - if maxv > 1.0: - print(f"\nNormalization Set Off: Input above threshold for clipping. The result was not normalized. Max:{maxv}\n") - else: - print(f"\nNormalization Set Off: Input not above threshold for clipping. Max:{maxv}\n") - - return wave_res - -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.j * 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, bp['sr'], False, dtype=np.float32, res_type=bp['res_type']) - y_wave[d], _ = librosa.load( - inst_path, bp['sr'], False, dtype=np.float32, res_type=bp['res_type']) - else: # lower bands - X_wave[d] = librosa.resample(X_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type']) - y_wave[d] = librosa.resample(y_wave[d+1], mp.param['band'][d+1]['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, clamp=False): - 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, .4 * wave_left), np.subtract(wave_left / 1.25, .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, .4 * wave_left), np.subtract(wave_left / 1.25, .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']), bp['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.resample(wave2, bp['sr'], sr, res_type="sinc_fastest") - - return wave.T - -def cmb_spectrogram_to_wave_d(spec_m, mp, extra_bins_h=None, extra_bins=None, demucs=True): - 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']), bp['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.resample(wave2, bp['sr'], sr, res_type="sinc_fastest") - - #print(demucs) - - if demucs == True: - wave = librosa.resample(wave, bp['sr'], 44100, res_type="sinc_fastest") - return wave - else: - return wave - -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.j * 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, 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.j * 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)) diff --git a/lib_v5/sv_ttk/__init__.py b/lib_v5/sv_ttk/__init__.py deleted file mode 100644 index b265472..0000000 --- a/lib_v5/sv_ttk/__init__.py +++ /dev/null @@ -1,61 +0,0 @@ -from pathlib import Path - -inited = False -root = None - - -def init(func): - def wrapper(*args, **kwargs): - global inited - global root - - if not inited: - from tkinter import _default_root - - path = (Path(__file__).parent / "sun-valley.tcl").resolve() - - try: - _default_root.tk.call("source", str(path)) - except AttributeError: - raise RuntimeError( - "can't set theme. " - "Tk is not initialized. " - "Please first create a tkinter.Tk instance, then set the theme." - ) from None - else: - inited = True - root = _default_root - - return func(*args, **kwargs) - - return wrapper - - -@init -def set_theme(theme): - if theme not in {"dark", "light"}: - raise RuntimeError(f"not a valid theme name: {theme}") - - root.tk.call("set_theme", theme) - - -@init -def get_theme(): - theme = root.tk.call("ttk::style", "theme", "use") - - try: - return {"sun-valley-dark": "dark", "sun-valley-light": "light"}[theme] - except KeyError: - return theme - - -@init -def toggle_theme(): - if get_theme() == "dark": - use_light_theme() - else: - use_dark_theme() - - -use_dark_theme = lambda: set_theme("dark") -use_light_theme = lambda: set_theme("light") diff --git a/lib_v5/sv_ttk/__pycache__/__init__.cpython-38.pyc b/lib_v5/sv_ttk/__pycache__/__init__.cpython-38.pyc deleted file mode 100644 index 471fa8995d8ead255caab6b510d70eb6be0ecc5a..0000000000000000000000000000000000000000 GIT binary patch literal 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$colors(-selectbg) \ - -selectforeground $colors(-selectfg) \ - -insertwidth 0 \ - -insertcolor $colors(-fg) \ - -fieldbackground $colors(-selectbg) \ - -font {"Century Gothic" 10} \ - -borderwidth 0 \ - -relief flat - - tk_setPalette \ - background [ttk::style lookup . -background] \ - foreground [ttk::style lookup . -foreground] \ - highlightColor [ttk::style lookup . -focuscolor] \ - selectBackground [ttk::style lookup . -selectbackground] \ - selectForeground [ttk::style lookup . -selectforeground] \ - activeBackground [ttk::style lookup . -selectbackground] \ - activeForeground [ttk::style lookup . -selectforeground] - - ttk::style map . -foreground [list disabled $colors(-disabledfg)] - - option add *font [ttk::style lookup . -font] - option add *Menu.selectcolor $colors(-fg) - option add *Menu.background #0e0e0f - } -} diff --git a/lib_v5/sv_ttk/theme/dark.tcl b/lib_v5/sv_ttk/theme/dark.tcl deleted file mode 100644 index 33a815d..0000000 --- a/lib_v5/sv_ttk/theme/dark.tcl +++ /dev/null @@ -1,534 +0,0 @@ -# Copyright © 2021 rdbende - -# A stunning dark theme for ttk based on Microsoft's Sun Valley visual style - -package require Tk 8.6 - -namespace eval ttk::theme::sun-valley-dark { - variable version 1.0 - package provide ttk::theme::sun-valley-dark $version - - ttk::style theme create sun-valley-dark -parent clam -settings { - proc load_images {imgdir} { - variable images - foreach file [glob -directory $imgdir *.png] { - set images([file tail [file rootname $file]]) \ - [image create photo -file $file -format png] - } - } - - load_images [file join [file dirname [info script]] dark] - - array set colors { - -fg "#F6F6F7" - -bg "#0e0e0f" - -disabledfg "#F6F6F7" - -selectfg "#ffffff" - -selectbg "#2f60d8" - } - - ttk::style layout TButton { - Button.button -children { - Button.padding -children { - Button.label -side left -expand 1 - } - } - } - - ttk::style layout Toolbutton { - Toolbutton.button -children { - Toolbutton.padding -children { - Toolbutton.label -side left -expand 1 - } - } - } - - ttk::style layout TMenubutton { - Menubutton.button -children { - Menubutton.padding -children { - Menubutton.label -side left -expand 1 - Menubutton.indicator -side right -sticky nsew - } - } - } - - ttk::style layout TOptionMenu { - OptionMenu.button -children { - OptionMenu.padding -children { - OptionMenu.label -side left -expand 0 - OptionMenu.indicator -side right -sticky nsew - } - } - } - - ttk::style layout Accent.TButton { - AccentButton.button -children { - AccentButton.padding -children { - AccentButton.label -side left -expand 1 - } - } - } - - ttk::style layout Titlebar.TButton { - TitlebarButton.button -children { - TitlebarButton.padding -children { - TitlebarButton.label -side left -expand 1 - } - } - } - - ttk::style layout Close.Titlebar.TButton { - CloseButton.button -children { - CloseButton.padding -children { - CloseButton.label -side left -expand 1 - } - } - } - - ttk::style layout TCheckbutton { - Checkbutton.button -children { - Checkbutton.padding -children { - Checkbutton.indicator -side left - Checkbutton.label -side right -expand 1 - } - } - } - - ttk::style layout Switch.TCheckbutton { - Switch.button -children { - Switch.padding -children { - Switch.indicator -side left - Switch.label -side right -expand 1 - } - } - } - - ttk::style layout Toggle.TButton { - ToggleButton.button -children { - ToggleButton.padding -children { - ToggleButton.label -side left -expand 1 - } - } - } - - ttk::style layout TRadiobutton { - Radiobutton.button -children { - Radiobutton.padding -children { - Radiobutton.indicator -side left - Radiobutton.label -side right -expand 1 - } - } - } - - ttk::style layout Vertical.TScrollbar { - Vertical.Scrollbar.trough -sticky ns -children { - Vertical.Scrollbar.uparrow -side top - Vertical.Scrollbar.downarrow -side bottom - Vertical.Scrollbar.thumb -expand 1 - } - } - - ttk::style layout Horizontal.TScrollbar { - Horizontal.Scrollbar.trough -sticky ew -children { - Horizontal.Scrollbar.leftarrow -side left - Horizontal.Scrollbar.rightarrow -side right - Horizontal.Scrollbar.thumb -expand 1 - } - } - - ttk::style layout TSeparator { - TSeparator.separator -sticky nsew - } - - ttk::style layout TCombobox { - Combobox.field -sticky nsew -children { - Combobox.padding -expand 1 -sticky nsew -children { - Combobox.textarea -sticky nsew - } - } - null -side right -sticky ns -children { - Combobox.arrow -sticky nsew - } - } - - ttk::style layout TSpinbox { - Spinbox.field -sticky nsew -children { - Spinbox.padding -expand 1 -sticky nsew -children { - Spinbox.textarea -sticky nsew - } - - } - null -side right -sticky nsew -children { - Spinbox.uparrow -side left -sticky nsew - Spinbox.downarrow -side right -sticky nsew - } - } - - ttk::style layout Card.TFrame { - Card.field { - Card.padding -expand 1 - } - } - - ttk::style layout TLabelframe { - Labelframe.border { - Labelframe.padding -expand 1 -children { - Labelframe.label -side left - } - } - } - - ttk::style layout TNotebook { - Notebook.border -children { - TNotebook.Tab -expand 1 - Notebook.client -sticky nsew - } - } - - ttk::style layout Treeview.Item { - Treeitem.padding -sticky nsew -children { - Treeitem.image -side left -sticky {} - Treeitem.indicator -side left -sticky {} - Treeitem.text -side left -sticky {} - } - } - - # Button - ttk::style configure TButton -padding {8 4} -anchor center -foreground $colors(-fg) - - ttk::style map TButton -foreground \ - [list disabled #7a7a7a \ - pressed #d0d0d0] - - ttk::style element create Button.button image \ - [list $images(button-rest) \ - {selected disabled} $images(button-disabled) \ - disabled $images(button-disabled) \ - selected $images(button-rest) \ - pressed $images(button-pressed) \ - active $images(button-hover) \ - ] -border 4 -sticky nsew - - # Toolbutton - ttk::style configure Toolbutton -padding {8 4} -anchor center - - ttk::style element create Toolbutton.button image \ - [list $images(empty) \ - {selected disabled} $images(button-disabled) \ - selected $images(button-rest) \ - pressed $images(button-pressed) \ - active $images(button-hover) \ - ] -border 4 -sticky nsew - - # Menubutton - ttk::style configure TMenubutton -padding {8 4 0 4} - - ttk::style element create Menubutton.button \ - image [list $images(button-rest) \ - disabled $images(button-disabled) \ - pressed $images(button-pressed) \ - active $images(button-hover) \ - ] -border 4 -sticky nsew - - ttk::style element create Menubutton.indicator image $images(arrow-down) -width 28 -sticky {} - - # OptionMenu - ttk::style configure TOptionMenu -padding {8 4 0 4} - - ttk::style element create OptionMenu.button \ - image [list $images(button-rest) \ - disabled $images(button-disabled) \ - pressed $images(button-pressed) \ - active $images(button-hover) \ - ] -border 0 -sticky nsew - - ttk::style element create OptionMenu.indicator image $images(arrow-down) -width 28 -sticky {} - - # Accent.TButton - ttk::style configure Accent.TButton -padding {8 4} -anchor center -foreground #ffffff - - ttk::style map Accent.TButton -foreground \ - [list pressed #25536a \ - disabled #a5a5a5] - - ttk::style element create AccentButton.button image \ - [list $images(button-accent-rest) \ - {selected disabled} $images(button-accent-disabled) \ - disabled $images(button-accent-disabled) \ - selected $images(button-accent-rest) \ - pressed $images(button-accent-pressed) \ - active $images(button-accent-hover) \ - ] -border 4 -sticky nsew - - # Titlebar.TButton - ttk::style configure Titlebar.TButton -padding {8 4} -anchor center -foreground #ffffff - - ttk::style map Titlebar.TButton -foreground \ - [list disabled #6f6f6f \ - pressed #d1d1d1 \ - active #ffffff] - - ttk::style element create TitlebarButton.button image \ - [list $images(empty) \ - disabled $images(empty) \ - pressed $images(button-titlebar-pressed) \ - active $images(button-titlebar-hover) \ - ] -border 4 -sticky nsew - - # Close.Titlebar.TButton - ttk::style configure Close.Titlebar.TButton -padding {8 4} -anchor center -foreground #ffffff - - ttk::style map Close.Titlebar.TButton -foreground \ - [list disabled #6f6f6f \ - pressed #e8bfbb \ - active #ffffff] - - ttk::style element create CloseButton.button image \ - [list $images(empty) \ - disabled $images(empty) \ - pressed $images(button-close-pressed) \ - active $images(button-close-hover) \ - ] -border 4 -sticky nsew - - # Checkbutton - ttk::style configure TCheckbutton -padding 4 - - ttk::style element create Checkbutton.indicator image \ - [list $images(check-unsel-rest) \ - {alternate disabled} $images(check-tri-disabled) \ - {selected disabled} $images(check-disabled) \ - disabled $images(check-unsel-disabled) \ - {pressed alternate} $images(check-tri-hover) \ - {active alternate} $images(check-tri-hover) \ - alternate $images(check-tri-rest) \ - {pressed selected} $images(check-hover) \ - {active selected} $images(check-hover) \ - selected $images(check-rest) \ - {pressed !selected} $images(check-unsel-pressed) \ - active $images(check-unsel-hover) \ - ] -width 26 -sticky w - - # Switch.TCheckbutton - ttk::style element create Switch.indicator image \ - [list $images(switch-off-rest) \ - {selected disabled} $images(switch-on-disabled) \ - disabled $images(switch-off-disabled) \ - {pressed selected} $images(switch-on-pressed) \ - {active selected} $images(switch-on-hover) \ - selected $images(switch-on-rest) \ - {pressed !selected} $images(switch-off-pressed) \ - active $images(switch-off-hover) \ - ] -width 46 -sticky w - - # Toggle.TButton - ttk::style configure Toggle.TButton -padding {8 4 8 4} -anchor center -foreground $colors(-fg) - - ttk::style map Toggle.TButton -foreground \ - [list {selected disabled} #a5a5a5 \ - {selected pressed} #d0d0d0 \ - selected #ffffff \ - pressed #25536a \ - disabled #7a7a7a - ] - - ttk::style element create ToggleButton.button image \ - [list $images(button-rest) \ - {selected disabled} $images(button-accent-disabled) \ - disabled $images(button-disabled) \ - {pressed selected} $images(button-rest) \ - {active selected} $images(button-accent-hover) \ - selected $images(button-accent-rest) \ - {pressed !selected} $images(button-accent-rest) \ - active $images(button-hover) \ - ] -border 4 -sticky nsew - - # Radiobutton - ttk::style configure TRadiobutton -padding 0 - - ttk::style element create Radiobutton.indicator image \ - [list $images(radio-unsel-rest) \ - {selected disabled} $images(radio-disabled) \ - disabled $images(radio-unsel-disabled) \ - {pressed selected} $images(radio-pressed) \ - {active selected} $images(radio-hover) \ - selected $images(radio-rest) \ - {pressed !selected} $images(radio-unsel-pressed) \ - active $images(radio-unsel-hover) \ - ] -width 20 -sticky w - - ttk::style configure Menu.TRadiobutton -padding 0 - - ttk::style element create Menu.Radiobutton.indicator image \ - [list $images(radio-unsel-rest) \ - {selected disabled} $images(radio-disabled) \ - disabled $images(radio-unsel-disabled) \ - {pressed selected} $images(radio-pressed) \ - {active selected} $images(radio-hover) \ - selected $images(radio-rest) \ - {pressed !selected} $images(radio-unsel-pressed) \ - active $images(radio-unsel-hover) \ - ] -width 20 -sticky w - - # Scrollbar - ttk::style element create Horizontal.Scrollbar.trough image $images(scroll-hor-trough) -sticky ew -border 6 - ttk::style element create Horizontal.Scrollbar.thumb image $images(scroll-hor-thumb) -sticky ew -border 3 - - ttk::style element create Horizontal.Scrollbar.rightarrow image $images(scroll-right) -sticky {} -width 12 - ttk::style element create Horizontal.Scrollbar.leftarrow image $images(scroll-left) -sticky {} -width 12 - - ttk::style element create Vertical.Scrollbar.trough image $images(scroll-vert-trough) -sticky ns -border 6 - ttk::style element create Vertical.Scrollbar.thumb image $images(scroll-vert-thumb) -sticky ns -border 3 - - ttk::style element create Vertical.Scrollbar.uparrow image $images(scroll-up) -sticky {} -height 12 - ttk::style element create Vertical.Scrollbar.downarrow image $images(scroll-down) -sticky {} -height 12 - - # Scale - ttk::style element create Horizontal.Scale.trough image $images(scale-trough-hor) \ - -border 5 -padding 0 - - ttk::style element create Vertical.Scale.trough image $images(scale-trough-vert) \ - -border 5 -padding 0 - - ttk::style element create Scale.slider \ - image [list $images(scale-thumb-rest) \ - disabled $images(scale-thumb-disabled) \ - pressed $images(scale-thumb-pressed) \ - active $images(scale-thumb-hover) \ - ] -sticky {} - - # Progressbar - ttk::style element create Horizontal.Progressbar.trough image $images(progress-trough-hor) \ - -border 1 -sticky ew - - ttk::style element create Horizontal.Progressbar.pbar image $images(progress-pbar-hor) \ - -border 2 -sticky ew - - ttk::style element create Vertical.Progressbar.trough image $images(progress-trough-vert) \ - -border 1 -sticky ns - - ttk::style element create Vertical.Progressbar.pbar image $images(progress-pbar-vert) \ - -border 2 -sticky ns - - # Entry - ttk::style configure TEntry -foreground $colors(-fg) - - ttk::style map TEntry -foreground \ - [list disabled #757575 \ - pressed #cfcfcf - ] - - ttk::style element create Entry.field \ - image [list $images(entry-rest) \ - {focus hover !invalid} $images(entry-focus) \ - invalid $images(entry-invalid) \ - disabled $images(entry-disabled) \ - {focus !invalid} $images(entry-focus) \ - hover $images(entry-hover) \ - ] -border 5 -padding 8 -sticky nsew - - # Combobox - ttk::style configure TCombobox -foreground $colors(-fg) - - ttk::style map TCombobox -foreground \ - [list disabled #757575 \ - pressed #cfcfcf - ] - - ttk::style configure ComboboxPopdownFrame -borderwidth 0 -flat solid - - ttk::style map TCombobox -selectbackground [list \ - {readonly hover} $colors(-selectbg) \ - {readonly focus} $colors(-selectbg) \ - ] -selectforeground [list \ - {readonly hover} $colors(-selectfg) \ - {readonly focus} $colors(-selectfg) \ - ] - - ttk::style element create Combobox.field \ - image [list $images(entry-rest) \ - {readonly disabled} $images(button-disabled) \ - {readonly pressed} $images(button-pressed) \ - {readonly hover} $images(button-hover) \ - readonly $images(button-rest) \ - invalid $images(entry-invalid) \ - disabled $images(entry-disabled) \ - focus $images(entry-focus) \ - hover $images(entry-hover) \ - ] -border 0 -padding {8 8 28 8} - - ttk::style element create Combobox.arrow image $images(arrow-down) -width 35 -sticky {} - - # Spinbox - ttk::style configure TSpinbox -foreground $colors(-fg) - - ttk::style map TSpinbox -foreground \ - [list disabled #757575 \ - pressed #cfcfcf - ] - - ttk::style element create Spinbox.field \ - image [list $images(entry-rest) \ - invalid $images(entry-invalid) \ - disabled $images(entry-disabled) \ - focus $images(entry-focus) \ - hover $images(entry-hover) \ - ] -border 5 -padding {8 8 54 8} -sticky nsew - - ttk::style element create Spinbox.uparrow image $images(arrow-up) -width 35 -sticky {} - ttk::style element create Spinbox.downarrow image $images(arrow-down) -width 35 -sticky {} - - # Sizegrip - ttk::style element create Sizegrip.sizegrip image $images(sizegrip) \ - -sticky nsew - - # Separator - ttk::style element create TSeparator.separator image $images(separator) - - # Card - ttk::style element create Card.field image $images(card) \ - -border 10 -padding 4 -sticky nsew - - # Labelframe - ttk::style element create Labelframe.border image $images(card) \ - -border 5 -padding 4 -sticky nsew - - # Notebook - ttk::style configure TNotebook -padding 1 - - ttk::style element create Notebook.border \ - image $images(notebook-border) -border 5 -padding 5 - - ttk::style element create Notebook.client image $images(notebook) - - ttk::style element create Notebook.tab \ - image [list $images(tab-rest) \ - selected $images(tab-selected) \ - active $images(tab-hover) \ - ] -border 13 -padding {16 14 16 6} -height 32 - - # Treeview - ttk::style element create Treeview.field image $images(card) \ - -border 5 - - ttk::style element create Treeheading.cell \ - image [list $images(treeheading-rest) \ - pressed $images(treeheading-pressed) \ - active $images(treeheading-hover) - ] -border 5 -padding 15 -sticky nsew - - ttk::style element create Treeitem.indicator \ - image [list $images(arrow-right) \ - user2 $images(empty) \ - user1 $images(arrow-down) \ - ] -width 26 -sticky {} - - ttk::style configure Treeview -background $colors(-bg) -rowheight [expr {[font metrics font -linespace] + 2}] - ttk::style map Treeview \ - -background [list selected #292929] \ - -foreground [list selected $colors(-selectfg)] - - # Panedwindow - # Insane hack to remove clam's ugly sash - ttk::style configure Sash -gripcount 0 - } -} \ No newline at end of file diff --git a/lib_v5/sv_ttk/theme/dark/arrow-down.png b/lib_v5/sv_ttk/theme/dark/arrow-down.png 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