some stuff

codes/models/audio/music/m2v_code_to_mel.py

@@ -0,0 +1,57 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from models.arch_util import ResBlock, AttentionBlock
+from models.audio.music.flat_diffusion import MultiGroupEmbedding
+from trainer.networks import register_model
+from utils.util import checkpoint
+
+
+class Code2Mel(nn.Module):
+    def __init__(self, out_dim=256, base_dim=1024, num_tokens=16, num_groups=4, dropout=.1):
+        super().__init__()
+        self.emb = MultiGroupEmbedding(num_tokens, num_groups, base_dim)
+        self.base_blocks = nn.Sequential(ResBlock(base_dim, dropout, dims=1),
+                                         AttentionBlock(base_dim, num_heads=base_dim//64),
+                                         ResBlock(base_dim, dropout, dims=1))
+        l2dim = base_dim-256
+        self.l2_up_block = nn.Conv1d(base_dim, l2dim, kernel_size=5, padding=2)
+        self.l2_blocks = nn.Sequential(ResBlock(l2dim, dropout, kernel_size=5, dims=1),
+                                         AttentionBlock(l2dim, num_heads=base_dim//64),
+                                         ResBlock(l2dim, dropout, kernel_size=5, dims=1),
+                                         AttentionBlock(l2dim, num_heads=base_dim//64),
+                                         ResBlock(l2dim, dropout, dims=1),
+                                         ResBlock(l2dim, dropout, dims=1))
+        l3dim = l2dim-256
+        self.l3_up_block = nn.Conv1d(l2dim, l3dim, kernel_size=5, padding=2)
+        self.l3_blocks = nn.Sequential(ResBlock(l3dim, dropout, kernel_size=5, dims=1),
+                                       AttentionBlock(l3dim, num_heads=base_dim//64),
+                                       ResBlock(l3dim, dropout, kernel_size=5, dims=1),
+                                       ResBlock(l3dim, dropout, dims=1))
+        self.final_block = nn.Conv1d(l3dim, out_dim, kernel_size=3, padding=1)
+
+    def forward(self, codes, target):
+        with torch.autocast(codes.device.type):
+            h = self.emb(codes).permute(0,2,1)
+            h = checkpoint(self.base_blocks, h)
+            h = F.interpolate(h, scale_factor=2, mode='linear')
+            h = self.l2_up_block(h)
+            h = checkpoint(self.l2_blocks, h)
+            h = F.interpolate(h, size=target.shape[-1], mode='linear')
+            h = self.l3_up_block(h)
+        h = checkpoint(self.l3_blocks, h.float())
+        pred = self.final_block(h)
+        return F.mse_loss(pred, target), pred
+
+
+@register_model
+def register_code2mel(opt_net, opt):
+    return Code2Mel(**opt_net['kwargs'])
+
+
+if __name__ == '__main__':
+    model = Code2Mel()
+    codes = torch.randint(0,16, (2,200,4))
+    target = torch.randn(2,256,804)
+    model(codes, target)

codes/scripts/audio/prep_music/demucs_notes.txt

@@ -5,4 +5,4 @@ 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
-```
+``

codes/scripts/audio/prep_music/phase_1_split_files.py

@@ -42,9 +42,9 @@ 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='Y:\\sources\\music\\bt-music4')
+    parser.add_argument('-path', type=str, help='Path to search for files', default='Y:\\sources\\silk')
-    parser.add_argument('-progress_file', type=str, help='Place to store all files that have already been processed', default='Y:\\sources\\music\\bt-music4\\already_processed.txt')
+    parser.add_argument('-progress_file', type=str, help='Place to store all files that have already been processed', default='Y:\\sources\\silk\\already_processed.txt')
-    parser.add_argument('-output_path', type=str, help='Path for output files', default='Y:\\split\\flacced\\bt-music-4')
+    parser.add_argument('-output_path', type=str, help='Path for output files', default='Y:\\split\\silk')
     parser.add_argument('-num_threads', type=int, help='Number of concurrent workers processing files.', default=6)
     parser.add_argument('-duration', type=int, help='Duration per clip in seconds', default=30)
     args = parser.parse_args()
@@ -55,8 +55,7 @@ if __name__ == '__main__':
             for line in f.readlines():
                 processed_files.add(line.strip())
 
-    files = set(find_files_of_type(None, args.path, qualifier=lambda p: p.endswith('.flac'))[0])
+    files = set(find_audio_files(args.path, include_nonwav=True))
-    #files = set(find_audio_files(args.path, include_nonwav=True))
     orig_len = len(files)
     files = files - processed_files
     print(f"Found {len(files)} files to process. Total processing is {100*(orig_len-len(files))/orig_len}% complete.")