new gap_filler

codes/models/audio/music/music_gen_fill_gaps.py

@@ -185,14 +185,14 @@ class MusicGenerator(nn.Module):
         return truth * mask
 
 
-    def timestep_independent(self, truth, expected_seq_len, return_code_pred):
+    def timestep_independent(self, truth):
         truth_emb = self.conditioner(truth)
         # Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
         if self.training and self.unconditioned_percentage > 0:
             unconditioned_batches = torch.rand((truth_emb.shape[0], 1, 1),
                                                device=truth_emb.device) < self.unconditioned_percentage
             truth_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(truth.shape[0], 1, 1),
-                                   truth_emb)
+                                    truth_emb)
         return truth_emb
 
 
@@ -219,7 +219,7 @@ class MusicGenerator(nn.Module):
             else:
                 if self.training:
                     truth = self.do_masking(truth)
-                truth_emb = self.timestep_independent(truth, x.shape[-1], True)
+                truth_emb = self.timestep_independent(truth)
             unused_params.append(self.unconditioned_embedding)
 
         time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))

codes/models/audio/music/music_gen_fill_gaps_v2.py

@@ -0,0 +1,266 @@
+import random
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import autocast
+from torchaudio.transforms import TimeMasking, FrequencyMasking
+
+from models.audio.tts.unified_voice2 import ConditioningEncoder
+from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
+from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, TimestepBlock
+from models.lucidrains.x_transformers import Encoder
+from trainer.networks import register_model
+from utils.util import checkpoint
+
+def is_sequence(t):
+    return t.dtype == torch.long
+
+
+class ResBlock(TimestepBlock):
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        dims=2,
+        kernel_size=3,
+        efficient_config=True,
+        use_scale_shift_norm=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_scale_shift_norm = use_scale_shift_norm
+        padding = {1: 0, 3: 1, 5: 2}[kernel_size]
+        eff_kernel = 1 if efficient_config else 3
+        eff_padding = 0 if efficient_config else 1
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding),
+        )
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            linear(
+                emb_channels,
+                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+            ),
+        )
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels),
+            nn.SiLU(),
+            nn.Dropout(p=dropout),
+            zero_module(
+                conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding)
+            ),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        else:
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding)
+
+    def forward(self, x, emb):
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+
+        :param x: an [N x C x ...] Tensor of features.
+        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        return checkpoint(
+            self._forward, x, emb
+        )
+
+    def _forward(self, x, emb):
+        h = self.in_layers(x)
+        emb_out = self.emb_layers(emb).type(h.dtype)
+        while len(emb_out.shape) < len(h.shape):
+            emb_out = emb_out[..., None]
+        if self.use_scale_shift_norm:
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            scale, shift = torch.chunk(emb_out, 2, dim=1)
+            h = out_norm(h) * (1 + scale) + shift
+            h = out_rest(h)
+        else:
+            h = h + emb_out
+            h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class DiffusionLayer(TimestepBlock):
+    def __init__(self, model_channels, dropout, num_heads):
+        super().__init__()
+        self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True)
+        self.attn = AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True)
+
+    def forward(self, x, time_emb):
+        y = self.resblk(x, time_emb)
+        return self.attn(y)
+
+
+class ConditioningEncoder(nn.Module):
+    def __init__(self,
+                 spec_dim,
+                 embedding_dim,
+                 attn_blocks=6):
+        super().__init__()
+        attn = []
+        self.init = nn.Sequential(nn.Conv1d(spec_dim, embedding_dim//2, kernel_size=3, padding=1, stride=2),
+                                  nn.Conv1d(embedding_dim//2, embedding_dim, kernel_size=3, padding=1, stride=2))
+        self.attn = Encoder(dim=embedding_dim, depth=attn_blocks, use_scalenorm=True, rotary_pos_emb=True,
+                            heads=embedding_dim//64, ff_mult=1)
+        self.dim = embedding_dim
+
+    def forward(self, x):
+        h = self.init(x)
+        h = self.attn(h.permute(0,2,1))
+        return h.mean(dim=1)
+
+
+class MusicGenerator(nn.Module):
+    def __init__(
+            self,
+            model_channels=512,
+            num_layers=8,
+            in_channels=100,
+            out_channels=200,  # mean and variance
+            dropout=0,
+            use_fp16=False,
+            num_heads=16,
+            # Parameters for regularization.
+            layer_drop=.1,
+            unconditioned_percentage=.1,  # This implements a mechanism similar to what is used in classifier-free training.
+            # Masking parameters.
+            frequency_mask_percent_max=0.2,
+            time_mask_percent_max=0.2,
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.dropout = dropout
+        self.num_heads = num_heads
+        self.unconditioned_percentage = unconditioned_percentage
+        self.enable_fp16 = use_fp16
+        self.layer_drop = layer_drop
+        self.time_mask_percent_max = time_mask_percent_max
+        self.frequency_mask_percent_mask = frequency_mask_percent_max
+
+        self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
+        self.time_embed = nn.Sequential(
+            linear(model_channels, model_channels),
+            nn.SiLU(),
+            linear(model_channels, model_channels),
+        )
+
+        self.conditioner = ConditioningEncoder(in_channels, model_channels)
+        self.unconditioned_embedding = nn.Parameter(torch.randn(1, model_channels))
+        self.layers = nn.ModuleList([DiffusionLayer(model_channels, dropout, num_heads) for _ in range(num_layers)] +
+                                    [ResBlock(model_channels, model_channels, dropout, dims=1, use_scale_shift_norm=True) for _ in range(3)])
+
+        self.out = nn.Sequential(
+            normalization(model_channels),
+            nn.SiLU(),
+            zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
+        )
+
+    def get_grad_norm_parameter_groups(self):
+        groups = {
+            'layers': list(self.layers.parameters()),
+            'conditioner': list(self.conditioner.parameters()),
+            'time_embed': list(self.time_embed.parameters()),
+        }
+        return groups
+
+    def do_masking(self, truth):
+        b, c, s = truth.shape
+
+        # Frequency mask
+        mask_freq = torch.ones_like(truth)
+        cs = random.randint(0, c-10)
+        ce = min(c-1, cs+random.randint(1, int(self.frequency_mask_percent_mask*c)))
+        mask_freq[:, cs:ce] = 0
+
+        # Time mask
+        mask_time = torch.ones_like(truth)
+        cs = random.randint(0, s-5)
+        ce = min(s-1, cs+random.randint(1, int(self.frequency_mask_percent_mask*s)))
+        mask_time[:, :, cs:ce] = 0
+
+        return truth * mask_time * mask_freq
+
+
+    def timestep_independent(self, truth):
+        if self.training:
+            truth = self.do_masking(truth)
+        truth_emb = self.conditioner(truth)
+        return truth_emb
+
+
+    def forward(self, x, timesteps, truth=None, precomputed_aligned_embeddings=None, conditioning_free=False):
+        """
+        Apply the model to an input batch.
+
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :param truth: Input value is either pre-masked (in inference), or unmasked (during training)
+        :param precomputed_aligned_embeddings: Embeddings returned from self.timestep_independent()
+        :param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        assert precomputed_aligned_embeddings is not None or truth is not None
+
+        unused_params = []
+        if conditioning_free:
+            truth_emb = self.unconditioned_embedding
+            unused_params.extend(list(self.conditioner.parameters()))
+        else:
+            if precomputed_aligned_embeddings is not None:
+                truth_emb = precomputed_aligned_embeddings
+            else:
+                truth_emb = self.timestep_independent(truth)
+            unused_params.append(self.unconditioned_embedding)
+        time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + truth_emb
+
+        x = self.inp_block(x)
+        for i, lyr in enumerate(self.layers):
+            # Do layer drop where applicable. Do not drop first and last layers.
+            if self.training and self.layer_drop > 0 and i != 0 and i != (len(self.layers)-1) and random.random() < self.layer_drop:
+                unused_params.extend(list(lyr.parameters()))
+            else:
+                # First and last blocks will have autocast disabled for improved precision.
+                with autocast(x.device.type, enabled=self.enable_fp16 and i != 0):
+                    x = lyr(x, time_emb)
+
+        x = x.float()
+        out = self.out(x)
+
+        # Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
+        extraneous_addition = 0
+        for p in unused_params:
+            extraneous_addition = extraneous_addition + p.mean()
+        out = out + extraneous_addition * 0
+
+        return out
+
+
+@register_model
+def register_music_gap_gen2(opt_net, opt):
+    return MusicGenerator(**opt_net['kwargs'])
+
+
+if __name__ == '__main__':
+    clip = torch.randn(2, 100, 400)
+    aligned_latent = torch.randn(2,100,388)
+    ts = torch.LongTensor([600, 600])
+    model = MusicGenerator(512, layer_drop=.3, unconditioned_percentage=.5)
+    o = model(clip, ts, aligned_latent)
+

codes/train.py

@@ -327,7 +327,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_contrastive_audio.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../experiments/train_music_gap_filler.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     args = parser.parse_args()
     opt = option.parse(args.opt, is_train=True)