Mods to support unet diffusion vocoder with conditioning

codes/models/diffusion/gaussian_diffusion.py

@@ -770,9 +770,11 @@ class GaussianDiffusion:
                 terms["loss"] *= self.num_timesteps
         elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
             model_outputs = model(x_t, self._scale_timesteps(t), **model_kwargs)
-            model_output = model_outputs[0]
+            if isinstance(model_outputs, tuple):
-            if len(model_outputs) > 1:
+                model_output = model_outputs[0]
                 terms['extra_outputs'] = model_outputs[1:]
+            else:
+                model_output = model_outputs
 
             if self.model_var_type in [
                 ModelVarType.LEARNED,

codes/models/gpt_voice/mini_encoder.py

@@ -3,8 +3,7 @@ import torch.nn as nn
 
 
 from models.diffusion.nn import normalization, conv_nd, zero_module
-from models.diffusion.unet_diffusion import Downsample, AttentionBlock, QKVAttention, QKVAttentionLegacy
+from models.diffusion.unet_diffusion import Downsample, AttentionBlock, QKVAttention, QKVAttentionLegacy, Upsample
-
 
 # Combined resnet & full-attention encoder for converting an audio clip into an embedding.
 from utils.util import checkpoint
@@ -90,17 +89,17 @@ class ResBlock(nn.Module):
 
 
 class AudioMiniEncoder(nn.Module):
-    def __init__(self, spec_dim, embedding_dim, resnet_blocks=2, attn_blocks=4, num_attn_heads=4, dropout=0):
+    def __init__(self, spec_dim, embedding_dim, base_channels=128, depth=2, resnet_blocks=2, attn_blocks=4, num_attn_heads=4, dropout=0, downsample_factor=2, kernel_size=3):
         super().__init__()
         self.init = nn.Sequential(
-            conv_nd(1, spec_dim, 128, 3, padding=1)
+            conv_nd(1, spec_dim, base_channels, 3, padding=1)
         )
-        ch = 128
+        ch = base_channels
         res = []
-        for l in range(2):
+        for l in range(depth):
             for r in range(resnet_blocks):
-                res.append(ResBlock(ch, dropout, dims=1, do_checkpoint=False))
+                res.append(ResBlock(ch, dropout, dims=1, do_checkpoint=False, kernel_size=kernel_size))
-            res.append(Downsample(ch, use_conv=True, dims=1, out_channels=ch*2, factor=2))
+            res.append(Downsample(ch, use_conv=True, dims=1, out_channels=ch*2, factor=downsample_factor))
             ch *= 2
         self.res = nn.Sequential(*res)
         self.final = nn.Sequential(

codes/models/gpt_voice/unet_diffusion_vocoder_with_ref.py

@@ -0,0 +1,335 @@
+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 models.gpt_voice.mini_encoder import AudioMiniEncoder, EmbeddingCombiner
+from trainer.networks import register_model
+from utils.util import get_mask_from_lengths
+
+
+class DiscreteSpectrogramConditioningBlock(nn.Module):
+    def __init__(self, discrete_codes, channels):
+        super().__init__()
+        self.emb = nn.Embedding(discrete_codes, channels)
+
+    """
+    Embeds the given codes and concatenates them onto x. Return shape: bx2cxS
+    
+    :param x: bxcxS waveform latent
+    :param codes: bxN discrete codes, N <= S
+    """
+    def forward(self, x, codes):
+        _, c, S = x.shape
+        b, N = codes.shape
+        assert N <= S
+        emb = self.emb(codes).permute(0,2,1)
+        emb = nn.functional.interpolate(emb, size=(S,), mode='nearest')
+        return torch.cat([x, emb], dim=1)
+
+
+class DiffusionVocoderWithRef(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
+            discrete_codes=8192,
+            dropout=0,
+            # 38400 -> 19200 -> 9600 -> 4800 -> 2400 -> 1200 -> 600 -> 300 -> 150 for ~2secs@22050Hz
+            channel_mult=(1, 1, 2, 2, 4, 8, 16, 32, 64),
+            spectrogram_conditioning_resolutions=(4,8,16,32),
+            attention_resolutions=(64,128,256),
+            conv_resample=True,
+            dims=1,
+            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,
+            kernel_size=3,
+            scale_factor=2,
+            conditioning_inputs_provided=True,
+            conditioning_input_dim=80,
+    ):
+        super().__init__()
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        self.in_channels = in_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
+
+        padding = 1 if kernel_size == 3 else 2
+
+        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.conditioning_enabled = conditioning_inputs_provided
+        if conditioning_inputs_provided:
+            self.contextual_embedder = AudioMiniEncoder(conditioning_input_dim, time_embed_dim)
+            self.query_gen = AudioMiniEncoder(in_channels, time_embed_dim, base_channels=32, depth=6, resnet_blocks=1,
+                                              attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
+            self.embedding_combiner = EmbeddingCombiner(time_embed_dim)
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding)
+                )
+            ]
+        )
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+
+        for level, mult in enumerate(channel_mult):
+            if ds in spectrogram_conditioning_resolutions:
+                self.input_blocks.append(DiscreteSpectrogramConditioningBlock(discrete_codes, ch))
+                ch *= 2
+
+            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,
+                        kernel_size=kernel_size,
+                    )
+                ]
+                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,
+                            kernel_size=kernel_size,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_scale_shift_norm=use_scale_shift_norm,
+                kernel_size=kernel_size,
+            ),
+            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,
+                kernel_size=kernel_size,
+            ),
+        )
+        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,
+                        kernel_size=kernel_size,
+                    )
+                ]
+                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,
+                            kernel_size=kernel_size,
+                        )
+                        if resblock_updown
+                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
+                    )
+                    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, kernel_size, padding=padding)),
+        )
+
+    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, discrete_spectrogram, conditioning_inputs=None, num_conditioning_signals=None):
+        """
+        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.
+        if self.conditioning_enabled:
+            assert conditioning_inputs is not None
+            assert num_conditioning_signals is not None
+
+        hs = []
+        emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+        if self.conditioning_enabled:
+            emb2 = torch.stack([self.contextual_embedder(ci.squeeze(1)) for ci in list(torch.chunk(conditioning_inputs, conditioning_inputs.shape[1], dim=1))], dim=1)
+            emb = torch.cat([emb1.unsqueeze(1), emb2], dim=1)
+            emb = self.embedding_combiner(emb, None, self.query_gen(x))
+        else:
+            emb = emb1
+
+        h = x.type(self.dtype)
+        for k, module in enumerate(self.input_blocks):
+            if isinstance(module, DiscreteSpectrogramConditioningBlock):
+                h = module(h, discrete_spectrogram)
+            else:
+                h = module(h, emb)
+                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_with_ref(opt_net, opt):
+    return DiffusionVocoderWithRef(**opt_net['kwargs'])
+
+
+# Test for ~4 second audio clip at 22050Hz
+if __name__ == '__main__':
+    clip = torch.randn(2, 1, 81920)
+    spec = torch.randint(8192, (2, 500,))
+    cond = torch.randn(2, 4, 80, 600)
+    ts = torch.LongTensor([555, 556])
+    model = DiffusionVocoderWithRef(32, 2)
+    print(model(clip, ts, spec, cond, 4).shape)

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_dvae_clips_with_discretization_loss.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_diffusion_vocoder_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()