misc

codes/scripts/audio/play_with_spectral_representations.py

@@ -0,0 +1,11 @@
+import torchvision.utils
+
+from utils.music_utils import music2mel, music2cqt
+from utils.util import load_audio
+
+if __name__ == '__main__':
+    clip = load_audio('Y:\\split\\yt-music-eval\\00001.wav', 22050)
+    mel = music2mel(clip)
+    cqt = music2cqt(clip)
+    torchvision.utils.save_image((mel.unsqueeze(1) + 1) / 2, 'mel.png')
+    torchvision.utils.save_image((cqt.unsqueeze(1) + 1) / 2, 'cqt.png')

codes/scripts/audio/prep_music/demucs_notes.txt

@@ -5,4 +5,14 @@ https://github.com/neonbjb/demucs
 conda activate demucs
 python setup.py install
 CUDA_VISIBLE_DEVICES=0 python -m demucs /y/split/bt-music-5 --out=/y/separated/bt-music-5 --num_workers=2 --device cuda --two-stems=vocals
-``
+```
+
+Example usage of generate_long_cheaters and generate_long_mels, post demucs:
+
+```
+CUDA_VISIBLE_DEVICES=0 python generate_long_mels.py --path=/y/separated/mpm/1 --progress_file=/y/separated/large_mels/mpm/already_processed.txt \
+--output_path=/y/separated/large_mels/mpm/1 --num_threads=2
+
+CUDA_VISIBLE_DEVICES=2 python generate_long_cheaters.py --path=/y/separated/large_mels/mpm/3 --progress_file=/y/separated/large_mel_cheaters/mpm/already_processed.txt \
+--output_path=/y/separated/large_mel_cheaters/mpm/3 --num_threads=1
+```

codes/scripts/audio/prep_music/phase_1_split_files.py

@@ -47,9 +47,9 @@ def process_file(file, base_path, output_path, progress_file, duration_per_clip,
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-path', type=str, help='Path to search for files', default='C:\\Users\\James\\Downloads\\soundcloud-dl\\sc2')
-    parser.add_argument('-progress_file', type=str, help='Place to store all files that have already been processed', default='C:\\Users\\James\\Downloads\\soundcloud-dl\\sc2\\already_processed.txt')
-    parser.add_argument('-output_path', type=str, help='Path for output files', default='Y:\\split\\soundcloud_mixes\\bigmix1')
+    parser.add_argument('-path', type=str, help='Path to search for files', default='Y:\\sources\\soundcloud-mixes\\mixes2')
+    parser.add_argument('-progress_file', type=str, help='Place to store all files that have already been processed', default='Y:\\sources\\soundcloud-mixes\\mixes2\\already_processed.txt')
+    parser.add_argument('-output_path', type=str, help='Path for output files', default='Y:\\split\\soundcloud-mixes2')
     parser.add_argument('-num_threads', type=int, help='Number of concurrent workers processing files.', default=4)
     parser.add_argument('-duration', type=int, help='Duration per clip in seconds', default=30)
     args = parser.parse_args()

codes/utils/music_utils.py

@@ -1,6 +1,31 @@
 import torch
 
 
+def music2mel(clip):
+    if len(clip.shape) == 1:
+        clip = clip.unsqueeze(0)
+
+    from trainer.injectors.audio_injectors import TorchMelSpectrogramInjector
+    inj = TorchMelSpectrogramInjector({'n_mel_channels': 256, 'mel_fmax': 11000, 'filter_length': 16000,
+                                       'normalize': True, 'true_normalization': True, 'in': 'in', 'out': 'out'}, {})
+    return inj({'in': clip})['out']
+
+
+def music2cqt(clip):
+    def normalize_cqt(cqt):
+        # CQT_MIN = 0
+        CQT_MAX = 18
+        return 2 * cqt / CQT_MAX - 1
+
+    if len(clip.shape) == 1:
+        clip = clip.unsqueeze(0)
+    from nnAudio.features.cqt import CQT
+    # Visually, filter_scale=.25 seems to be the most descriptive representation, but loses frequency fidelity.
+    # It may be desirable to mix filter_scale=.25 with filter_scale=1.
+    cqt = CQT(sr=22050, hop_length=256, n_bins=256, bins_per_octave=32, filter_scale=.25, norm=1, verbose=False)
+    return normalize_cqt(cqt(clip))
+
+
 def get_mel2wav_model():
     from models.audio.music.unet_diffusion_waveform_gen_simple import DiffusionWaveformGen
     model = DiffusionWaveformGen(model_channels=256, in_channels=16, in_mel_channels=256, out_channels=32, channel_mult=[1,2,3,4,4],