Spaces:
Running
Running
| import random | |
| import numpy as np | |
| import torch | |
| def linear2db(x, eps=1e-5, scale=20): | |
| return scale * np.log10(x + eps) | |
| def db2linear(x, eps=1e-5, scale=20): | |
| return 10 ** (x / scale) - eps | |
| def normalize_mag_spec(S, min_level_db=-100.0): | |
| return torch.clamp((S - min_level_db) / -min_level_db, min=0.0, max=1.0) | |
| def denormalize_mag_spec(S, min_level_db=-100.0): | |
| return torch.clamp(S, min=0.0, max=1.0) * -min_level_db + min_level_db | |
| def loudness_match_and_norm(audio1, audio2, meter): | |
| lufs_1 = meter.integrated_loudness(audio1) | |
| lufs_2 = meter.integrated_loudness(audio2) | |
| if np.isinf(lufs_1) or np.isinf(lufs_2): | |
| return audio1, audio2 | |
| else: | |
| audio2 = audio2 * db2linear(lufs_1 - lufs_2) | |
| return audio1, audio2 | |
| def loudness_normal_match_and_norm(audio1, audio2, meter): | |
| lufs_1 = meter.integrated_loudness(audio1) | |
| lufs_2 = meter.integrated_loudness(audio2) | |
| if np.isinf(lufs_1) or np.isinf(lufs_2): | |
| return audio1, audio2 | |
| else: | |
| target_lufs = random.normalvariate(lufs_1, 6.0) | |
| audio2 = audio2 * db2linear(target_lufs - lufs_2) | |
| return audio1, audio2 | |
| def loudness_normal_match_and_norm_output_louder_first(audio1, audio2, meter): | |
| lufs_1 = meter.integrated_loudness(audio1) | |
| lufs_2 = meter.integrated_loudness(audio2) | |
| if np.isinf(lufs_1) or np.isinf(lufs_2): | |
| return audio1, audio2 | |
| else: | |
| target_lufs = random.normalvariate( | |
| lufs_1 - 2.0, 2.0 | |
| ) # we want audio1 to be louder than audio2 about target_lufs_diff | |
| audio2 = audio2 * db2linear(target_lufs - lufs_2) | |
| return audio1, audio2 | |
| def loudnorm(audio, target_lufs, meter, eps=1e-5): | |
| lufs = meter.integrated_loudness(audio) | |
| if np.isinf(lufs): | |
| return audio, 0.0 | |
| else: | |
| adjusted_gain = target_lufs - lufs | |
| audio = audio * db2linear(adjusted_gain, eps) | |
| return audio, adjusted_gain | |