Add unet_diffusion_vocoder

codes/data/audio/wavfile_dataset.py

@@ -33,6 +33,7 @@ class WavfileDataset(torch.utils.data.Dataset):
         self.pad_to = opt_get(opt, ['pad_to_seconds'], None)
         if self.pad_to is not None:
             self.pad_to *= self.sampling_rate
+        self.min_sz = opt_get(opt, ['minimum_samples'], 0)
 
         self.augment = opt_get(opt, ['do_augmentation'], False)
         if self.augment:
@@ -90,6 +91,10 @@ class WavfileDataset(torch.utils.data.Dataset):
                 #print(f"Warning! Truncating clip {filename} from {audio_norm.shape[-1]} to {self.pad_to}")
                 audio_norm = audio_norm[:, :self.pad_to]
 
+        # Bail and try the next clip if there is not enough data.
+        if audio_norm.shape[-1] < self.min_sz:
+            return self[(index + 1) % len(self)]
+
         output = {
             'clip': audio_norm,
             'path': filename,

codes/models/diffusion/nn.py

@@ -97,7 +97,12 @@ def normalization(channels):
     :param channels: number of input channels.
     :return: an nn.Module for normalization.
     """
-    return GroupNorm32(32, channels)
+    if channels <= 16:
+        return GroupNorm32(8, channels)
+    elif channels <= 64:
+        return GroupNorm32(16, channels)
+    else:
+        return GroupNorm32(32, channels)
 
 
 def timestep_embedding(timesteps, dim, max_period=10000):

codes/models/diffusion/unet_diffusion.py

@@ -186,6 +186,8 @@ class ResBlock(TimestepBlock):
         dims=2,
         up=False,
         down=False,
+        kernel_size=3,
+        padding=1,
     ):
         super().__init__()
         self.channels = channels
@@ -198,7 +200,7 @@ class ResBlock(TimestepBlock):
         self.in_layers = nn.Sequential(
             normalization(channels),
             nn.SiLU(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1),
+            conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding),
         )
 
         self.updown = up or down
@@ -224,7 +226,7 @@ class ResBlock(TimestepBlock):
             nn.SiLU(),
             nn.Dropout(p=dropout),
             zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
+                conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding)
             ),
         )
 
@@ -232,7 +234,7 @@ class ResBlock(TimestepBlock):
             self.skip_connection = nn.Identity()
         elif use_conv:
             self.skip_connection = conv_nd(
-                dims, channels, self.out_channels, 3, padding=1
+                dims, channels, self.out_channels, kernel_size, padding=padding
             )
         else:
             self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)

codes/models/diffusion/unet_diffusion_vocoder.py

@@ -0,0 +1,302 @@
+from models.diffusion.fp16_util import convert_module_to_f32, convert_module_to_f16
+from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
+from models.diffusion.unet_diffusion import AttentionPool2d, AttentionBlock, ResBlock, TimestepEmbedSequential, \
+    Downsample, Upsample
+import torch
+import torch.nn as nn
+
+from trainer.networks import register_model
+
+
+class DiffusionVocoder(nn.Module):
+    """
+    The full UNet model with attention and timestep embedding.
+
+    Customized to be conditioned on a spectrogram prior.
+
+    :param in_channels: channels in the input Tensor.
+    :param spectrogram_channels: channels in the conditioning spectrogram.
+    :param model_channels: base channel count for the model.
+    :param out_channels: channels in the output Tensor.
+    :param num_res_blocks: number of residual blocks per downsample.
+    :param attention_resolutions: a collection of downsample rates at which
+        attention will take place. May be a set, list, or tuple.
+        For example, if this contains 4, then at 4x downsampling, attention
+        will be used.
+    :param dropout: the dropout probability.
+    :param channel_mult: channel multiplier for each level of the UNet.
+    :param conv_resample: if True, use learned convolutions for upsampling and
+        downsampling.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param num_heads: the number of attention heads in each attention layer.
+    :param num_heads_channels: if specified, ignore num_heads and instead use
+                               a fixed channel width per attention head.
+    :param num_heads_upsample: works with num_heads to set a different number
+                               of heads for upsampling. Deprecated.
+    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+    :param resblock_updown: use residual blocks for up/downsampling.
+    :param use_new_attention_order: use a different attention pattern for potentially
+                                    increased efficiency.
+    """
+
+    def __init__(
+            self,
+            model_channels,
+            num_res_blocks,
+            in_channels=1,
+            out_channels=2,  # mean and variance
+            spectrogram_channels=80,
+            spectrogram_conditioning_level=3,  # Level at which spectrogram conditioning is applied to the waveform.
+            dropout=0,
+            # 106496 -> 26624 -> 6656 -> 16664 -> 416 -> 104 -> 26  for ~5secs@22050Hz
+            channel_mult=(1, 2, 4, 8, 16, 32, 64),
+            attention_resolutions=(16,32,64),
+            conv_resample=True,
+            dims=1,
+            num_classes=None,
+            use_fp16=False,
+            num_heads=1,
+            num_head_channels=-1,
+            num_heads_upsample=-1,
+            use_scale_shift_norm=False,
+            resblock_updown=False,
+            use_new_attention_order=False,
+    ):
+        super().__init__()
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        self.in_channels = in_channels
+        self.spectrogram_channels = spectrogram_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.num_res_blocks = num_res_blocks
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.dtype = torch.float16 if use_fp16 else torch.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+        self.dims = dims
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                )
+            ]
+        )
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+
+        spec_chs = channel_mult[spectrogram_conditioning_level] * model_channels
+        self.spectrogram_conditioner = nn.Sequential(
+            conv_nd(dims, self.spectrogram_channels, spec_chs, 1),
+            normalization(spec_chs),
+            nn.SiLU(),
+            conv_nd(dims, spec_chs, spec_chs, 1)
+        )
+        self.convergence_conv = nn.Sequential(
+            normalization(spec_chs*2),
+            nn.SiLU(),
+            conv_nd(dims, spec_chs*2, spec_chs*2, 1)
+        )
+
+        for level, mult in enumerate(channel_mult):
+            if level == spectrogram_conditioning_level+1:
+                ch *= 2  # At this level, the spectrogram is concatenated onto the input.
+
+            for _ in range(num_res_blocks):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=mult * model_channels,
+                        dims=dims,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            num_heads=num_heads,
+                            num_head_channels=num_head_channels,
+                            use_new_attention_order=use_new_attention_order,
+                        )
+                    )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+            if level == spectrogram_conditioning_level:
+                self.input_block_injection_point = len(self.input_blocks)-1
+                input_block_chans[-1] *= 2
+
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                num_heads=num_heads,
+                num_head_channels=num_head_channels,
+                use_new_attention_order=use_new_attention_order,
+            ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(channel_mult))[::-1]:
+            for i in range(num_res_blocks + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    ResBlock(
+                        ch + ich,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=model_channels * mult,
+                        dims=dims,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = model_channels * mult
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            num_heads=num_heads_upsample,
+                            num_head_channels=num_head_channels,
+                            use_new_attention_order=use_new_attention_order,
+                        )
+                    )
+                if level and i == num_res_blocks:
+                    out_ch = ch
+                    layers.append(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            up=True,
+                        )
+                        if resblock_updown
+                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+                    )
+                    ds //= 2
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+
+        self.out = nn.Sequential(
+            normalization(ch),
+            nn.SiLU(),
+            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+        )
+
+    def convert_to_fp16(self):
+        """
+        Convert the torso of the model to float16.
+        """
+        self.input_blocks.apply(convert_module_to_f16)
+        self.middle_block.apply(convert_module_to_f16)
+        self.output_blocks.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self):
+        """
+        Convert the torso of the model to float32.
+        """
+        self.input_blocks.apply(convert_module_to_f32)
+        self.middle_block.apply(convert_module_to_f32)
+        self.output_blocks.apply(convert_module_to_f32)
+
+    def forward(self, x, timesteps, spectrogram):
+        """
+        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 y: an [N] Tensor of labels, if class-conditional.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        assert x.shape[-1] % 4096 == 0  # This model operates at base//4096 at it's bottom levels, thus this requirement.
+        hs = []
+        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+        conditioning = self.spectrogram_conditioner(spectrogram)
+
+        h = x.type(self.dtype)
+        for k, module in enumerate(self.input_blocks):
+            h = module(h, emb)
+            if k == self.input_block_injection_point:
+                cond = nn.functional.interpolate(conditioning, size=h.shape[-self.dims:], mode='nearest')
+                h = torch.cat([h, cond], dim=1)
+                h = self.convergence_conv(h)
+            hs.append(h)
+        h = self.middle_block(h, emb)
+        for module in self.output_blocks:
+            h = torch.cat([h, hs.pop()], dim=1)
+            h = module(h, emb)
+        h = h.type(x.dtype)
+        return self.out(h)
+
+
+@register_model
+def register_unet_diffusion_vocoder(opt_net, opt):
+    return DiffusionVocoder(**opt_net['kwargs'])
+
+
+# Test for ~4 second audio clip at 22050Hz
+if __name__ == '__main__':
+    clip = torch.randn(1, 1, 81920)
+    spec = torch.randn(1, 80, 416)
+    ts = torch.LongTensor([555])
+    model = DiffusionVocoder(16, 2)
+    print(model(clip, ts, spec).shape)

codes/models/gpt_voice/lucidrains_dvae.py

@@ -190,6 +190,12 @@ class DiscreteVAE(nn.Module):
         images = self.decoder(image_embeds)
         return images
 
+    def infer(self, img):
+        img = self.norm(img)
+        logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1))
+        sampled, commitment_loss, codes = self.codebook(logits)
+        return self.decode(codes)
+
     # Note: This module is not meant to be run in forward() except while training. It has special logic which performs
     # evaluation using quantized values when it detects that it is being run in eval() mode, which will be substantially
     # more lossy (but useful for determining network performance).

codes/train.py

@@ -284,7 +284,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_lrdvae_audio_clips.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_diffusion_from_dvae_clips.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()

codes/trainer/injectors/base_injectors.py

@@ -535,6 +535,18 @@ class MelSpectrogramInjector(Injector):
         return {self.output: self.stft.mel_spectrogram(inp)}
 
 
+class RandomAudioCropInjector(Injector):
+    def __init__(self, opt, env):
+        super().__init__(opt, env)
+        self.crop_sz = opt['crop_size']
+
+    def forward(self, state):
+        inp = state[self.input]
+        len = inp.shape[-1]
+        margin = len - self.crop_sz
+        start = random.randint(0, margin)
+        return {self.output: inp[:, :, start:start+self.crop_sz]}
+
 
 if __name__ == '__main__':
     inj = DecomposeDimensionInjector({'dim':2, 'in': 'x', 'out': 'y'}, None)