Support tts9

codes/models/gpt_voice/unet_diffusion_tts9.py

@@ -0,0 +1,498 @@
+import functools
+import random
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import autocast
+from x_transformers.x_transformers import AbsolutePositionalEmbedding, AttentionLayers, CrossAttender
+
+from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
+from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, \
+    Downsample, Upsample, TimestepBlock
+from models.gpt_voice.mini_encoder import AudioMiniEncoder
+from scripts.audio.gen.use_diffuse_tts import ceil_multiple
+from trainer.networks import register_model
+from utils.util import checkpoint
+from x_transformers import Encoder, ContinuousTransformerWrapper
+
+
+def clustered_mask(probability, shape, dev, lateral_expansion_radius_max=3, inverted=False):
+    """
+    Produces a masking vector of the specified shape where each element has probability to be zero.
+    lateral_expansion_radius_max neighbors of any element that is zero also have a 50% chance to be zero.
+    Effectively, this produces clusters of masks tending to be lateral_expansion_radius_max wide.
+    """
+    # Each masked token spreads out to 1+lateral_expansion_radius_max on average, therefore reduce the probability in
+    # kind
+    probability = probability / (1+lateral_expansion_radius_max)
+
+    mask = torch.rand(shape, device=dev)
+    mask = (mask < probability).float()
+    kernel = torch.tensor([.5 for _ in range(lateral_expansion_radius_max)] + [1] + [.5 for _ in range(lateral_expansion_radius_max)], device=dev)
+    mask = F.conv1d(mask.unsqueeze(1), kernel.view(1,1,2*lateral_expansion_radius_max+1), padding=lateral_expansion_radius_max).squeeze(1)
+    if inverted:
+        return torch.bernoulli(torch.clamp(mask, 0, 1)) != 0
+    else:
+        return torch.bernoulli(torch.clamp(mask, 0, 1)) == 0
+
+
+class CheckpointedLayer(nn.Module):
+    """
+    Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
+    checkpoint for all other args.
+    """
+    def __init__(self, wrap):
+        super().__init__()
+        self.wrap = wrap
+
+    def forward(self, x, *args, **kwargs):
+        for k, v in kwargs.items():
+            assert not (isinstance(v, torch.Tensor) and v.requires_grad)  # This would screw up checkpointing.
+        partial = functools.partial(self.wrap, **kwargs)
+        return torch.utils.checkpoint.checkpoint(partial, x, *args)
+
+
+class CheckpointedXTransformerEncoder(nn.Module):
+    """
+    Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid
+    to channels-last that XTransformer expects.
+    """
+    def __init__(self, **xtransformer_kwargs):
+        super().__init__()
+        self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs)
+
+        for i in range(len(self.transformer.attn_layers.layers)):
+            n, b, r = self.transformer.attn_layers.layers[i]
+            self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
+
+    def forward(self, x, **kwargs):
+        x = x.permute(0,2,1)
+        h = self.transformer(x, **kwargs)
+        return h.permute(0,2,1)
+
+
+class ResBlock(TimestepBlock):
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        dims=2,
+        kernel_size=3,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        padding = {1: 0, 3: 1, 5: 2}[kernel_size]
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            conv_nd(dims, channels, self.out_channels, 1, padding=0),
+        )
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            linear(
+                emb_channels,
+                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, 1)
+
+    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]
+        h = h + emb_out
+        h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+class DiffusionTts(nn.Module):
+    """
+    The full UNet model with attention and timestep embedding.
+
+    Customized to be conditioned on an aligned prior derived from a autoregressive
+    GPT-style model.
+
+    :param in_channels: channels in the input Tensor.
+    :param in_latent_channels: channels from the input latent.
+    :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,
+            in_channels=1,
+            in_latent_channels=1024,
+            out_channels=2,  # mean and variance
+            dropout=0,
+            # res           1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K
+            channel_mult=  (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48),
+            num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2,   2,  2,  2,  2),
+            # spec_cond:    1, 0, 0, 1, 0, 0, 1, 0,   0,  1,  0,  0)
+            # attn:         0, 0, 0, 0, 0, 0, 0, 0,   0,  1,  1,  1
+            token_conditioning_resolutions=(1,16,),
+            attention_resolutions=(512,1024,2048),
+            conv_resample=True,
+            dims=1,
+            use_fp16=False,
+            num_heads=1,
+            num_head_channels=-1,
+            num_heads_upsample=-1,
+            kernel_size=3,
+            scale_factor=2,
+            time_embed_dim_multiplier=4,
+            cond_transformer_depth=8,
+            mid_transformer_depth=8,
+            # Parameters for regularization.
+            unconditioned_percentage=.1,  # This implements a mechanism similar to what is used in classifier-free training.
+            # Parameters for super-sampling.
+            super_sampling=False,
+            super_sampling_max_noising_factor=.1,
+    ):
+        super().__init__()
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        if super_sampling:
+            in_channels *= 2  # In super-sampling mode, the LR input is concatenated directly onto the input.
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+        self.dims = dims
+        self.super_sampling_enabled = super_sampling
+        self.super_sampling_max_noising_factor = super_sampling_max_noising_factor
+        self.unconditioned_percentage = unconditioned_percentage
+        self.enable_fp16 = use_fp16
+        padding = 1 if kernel_size == 3 else 2
+
+        time_embed_dim = model_channels * time_embed_dim_multiplier
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        conditioning_dim = model_channels * 8
+        self.latent_converter = nn.Conv1d(in_latent_channels, conditioning_dim, 1)
+        self.aligned_latent_padding_embedding = nn.Parameter(torch.randn(1,conditioning_dim,1))
+        self.contextual_embedder = AudioMiniEncoder(1, conditioning_dim, base_channels=32, depth=6, resnet_blocks=1,
+                         attn_blocks=4, num_attn_heads=8, dropout=dropout, downsample_factor=4, kernel_size=5)
+        self.conditioning_conv = nn.Conv1d(conditioning_dim*2, conditioning_dim, 1)
+        self.conditioning_encoder = CheckpointedXTransformerEncoder(
+                max_seq_len=-1,  # Should be unused
+                use_pos_emb=False,
+                attn_layers=Encoder(
+                    dim=conditioning_dim,
+                    depth=cond_transformer_depth,
+                    heads=num_heads,
+                    ff_dropout=dropout,
+                    attn_dropout=dropout,
+                    ff_glu=True,
+                    rotary_pos_emb=True
+                )
+        )
+        self.unconditioned_embedding = nn.Parameter(torch.randn(1,conditioning_dim,1))
+        self.conditioning_timestep_integrator = TimestepEmbedSequential(
+                    ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1),
+                    ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1),
+        )
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding)
+                )
+            ]
+        )
+        token_conditioning_blocks = []
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+
+        for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)):
+            if ds in token_conditioning_resolutions:
+                token_conditioning_block = nn.Conv1d(conditioning_dim, ch, 1)
+                token_conditioning_block.weight.data *= .02
+                self.input_blocks.append(token_conditioning_block)
+                token_conditioning_blocks.append(token_conditioning_block)
+
+            for _ in range(num_blocks):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=int(mult * model_channels),
+                        dims=dims,
+                        kernel_size=kernel_size,
+                    )
+                ]
+                ch = int(mult * model_channels)
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            num_heads=num_heads,
+                            num_head_channels=num_head_channels,
+                        )
+                    )
+                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(
+                        Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=1, pad=0
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        mid_transformer = CheckpointedXTransformerEncoder(
+                max_seq_len=-1,  # Should be unused
+                use_pos_emb=False,
+                attn_layers=Encoder(
+                    dim=ch,
+                    depth=mid_transformer_depth,
+                    heads=num_heads,
+                    ff_dropout=dropout,
+                    attn_dropout=dropout,
+                    use_rmsnorm=True,
+                    ff_glu=True,
+                    rotary_pos_emb=True,
+                )
+            )
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                kernel_size=kernel_size,
+            ),
+            mid_transformer,
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                kernel_size=kernel_size,
+            ),
+        )
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]:
+            for i in range(num_blocks + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    ResBlock(
+                        ch + ich,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=int(model_channels * mult),
+                        dims=dims,
+                        kernel_size=kernel_size,
+                    )
+                ]
+                ch = int(model_channels * mult)
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            num_heads=num_heads_upsample,
+                            num_head_channels=num_head_channels,
+                        )
+                    )
+                if level and i == num_blocks:
+                    out_ch = ch
+                    layers.append(
+                        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 get_grad_norm_parameter_groups(self):
+        groups = {
+            'minicoder': list(self.contextual_embedder.parameters()),
+            'input_blocks': list(self.input_blocks.parameters()),
+            'output_blocks': list(self.output_blocks.parameters()),
+            'middle_transformer': list(self.middle_block.parameters()),
+            'conditioning_encoder': list(self.conditioning_encoder.parameters())
+        }
+        return groups
+
+    def forward(self, x, timesteps, aligned_latent, conditioning_input, lr_input=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 aligned_latent: an aligned latent providing useful data about the sample to be produced.
+        :param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded.
+        :param lr_input: for super-sampling models, a guidance audio clip at a lower sampling rate.
+        :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 conditioning_input is not None
+        if self.super_sampling_enabled:
+            assert lr_input is not None
+            if self.training and self.super_sampling_max_noising_factor > 0:
+                noising_factor = random.uniform(0,self.super_sampling_max_noising_factor)
+                lr_input = torch.randn_like(lr_input) * noising_factor + lr_input
+            lr_input = F.interpolate(lr_input, size=(x.shape[-1],), mode='nearest')
+            x = torch.cat([x, lr_input], dim=1)
+
+        with autocast(x.device.type, enabled=self.enable_fp16):
+            # Shuffle aligned_latent to BxCxS format
+            aligned_latent = aligned_latent.permute(0,2,1)
+
+            # Fix input size to the proper multiple of 2 so we don't get alignment errors going down and back up the U-net.
+            orig_x_shape = x.shape[-1]
+            cm = ceil_multiple(x.shape[-1], 2048)
+            if cm != 0:
+                pc = (cm-x.shape[-1])/x.shape[-1]
+                x = F.pad(x, (0,cm-x.shape[-1]))
+                # Also fix aligned_latent, which is aligned to x.
+                aligned_latent = torch.cat([aligned_latent,
+                                            self.aligned_latent_padding_embedding.repeat(x.shape[0],1,int(pc*aligned_latent.shape[-1]))], dim=-1)
+
+            hs = []
+            time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+
+            # Note: this block does not need to repeated on inference, since it is not timestep-dependent.
+            if conditioning_free:
+                code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
+            else:
+                cond_emb = self.contextual_embedder(conditioning_input)
+                code_emb = self.latent_converter(aligned_latent)
+                cond_emb = cond_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
+                code_emb = self.conditioning_conv(torch.cat([cond_emb, code_emb], dim=1))
+                code_emb = self.conditioning_encoder(code_emb)
+            # 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((code_emb.shape[0],1,1), device=code_emb.device) < self.unconditioned_percentage
+                code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(x.shape[0], 1, 1), code_emb)
+
+            # Everything after this comment is timestep dependent.
+            code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
+
+            first = True
+            time_emb = time_emb.float()
+            h = x
+            for k, module in enumerate(self.input_blocks):
+                if isinstance(module, nn.Conv1d):
+                    h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
+                    h = h + h_tok
+                else:
+                    with autocast(x.device.type, enabled=self.enable_fp16 and not first):
+                        # First block has autocast disabled to allow a high precision signal to be properly vectorized.
+                        h = module(h, time_emb)
+                    hs.append(h)
+                first = False
+            h = self.middle_block(h, time_emb)
+            for module in self.output_blocks:
+                h = torch.cat([h, hs.pop()], dim=1)
+                h = module(h, time_emb)
+
+        # Last block also has autocast disabled for high-precision outputs.
+        h = h.float()
+        out = self.out(h)
+        return out[:, :, :orig_x_shape]
+
+
+@register_model
+def register_diffusion_tts9(opt_net, opt):
+    return DiffusionTts(**opt_net['kwargs'])
+
+
+if __name__ == '__main__':
+    clip = torch.randn(2, 1, 32868)
+    aligned_latent = torch.randn(2,388,1024)
+    cond = torch.randn(2, 1, 44000)
+    ts = torch.LongTensor([600, 600])
+    model = DiffusionTts(128,
+                         channel_mult=[1,1.5,2, 3, 4, 6, 8],
+                         num_res_blocks=[2, 2, 2, 2, 2, 2, 1],
+                         token_conditioning_resolutions=[1,4,16,64],
+                         attention_resolutions=[],
+                         num_heads=8,
+                         kernel_size=3,
+                         scale_factor=2,
+                         time_embed_dim_multiplier=4,
+                         super_sampling=False)
+    o = model(clip, ts, aligned_latent, cond)
+

codes/models/gpt_voice/unified_voice2.py

@@ -303,6 +303,13 @@ class UnifiedVoice(nn.Module):
         for module in embeddings:
             module.weight.data.normal_(mean=0.0, std=.02)
 
+    def get_grad_norm_parameter_groups(self):
+        return {
+            'conditioning_encoder': list(self.conditioning_encoder.parameters()),
+            'gpt': list(self.gpt.parameters()),
+            'heads': list(self.text_head.parameters()) + list(self.mel_head.parameters()),
+        }
+
     def build_aligned_inputs_and_targets(self, input, start_token, stop_token):
         inp = F.pad(input, (1,0), value=start_token)
         tar = F.pad(input, (0,1), value=stop_token)
@@ -322,7 +329,7 @@ class UnifiedVoice(nn.Module):
                 mel_input_tokens[b, actual_end:] = self.stop_mel_token
         return mel_input_tokens
 
-    def get_logits(self, speech_conditioning_inputs, first_inputs, first_head, second_inputs=None, second_head=None, get_attns=False):
+    def get_logits(self, speech_conditioning_inputs, first_inputs, first_head, second_inputs=None, second_head=None, get_attns=False, return_latent=False):
         if second_inputs is not None:
             emb = torch.cat([speech_conditioning_inputs, first_inputs, second_inputs], dim=1)
         else:
@@ -334,6 +341,10 @@ class UnifiedVoice(nn.Module):
 
         enc = gpt_out.last_hidden_state[:, 1:]  # The first logit is tied to the speech_conditioning_input
         enc = self.final_norm(enc)
+
+        if return_latent:
+            return enc[:, :first_inputs.shape[1]], enc[:, -second_inputs.shape[1]:]
+
         first_logits = enc[:, :first_inputs.shape[1]]
         first_logits = first_head(first_logits)
         first_logits = first_logits.permute(0,2,1)
@@ -345,7 +356,8 @@ class UnifiedVoice(nn.Module):
         else:
             return first_logits
 
-    def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, text_first=True, raw_mels=None, return_attentions=False):
+    def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, text_first=True, raw_mels=None, return_attentions=False,
+                return_latent=False):
         """
         Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
         (actuated by `text_first`).
@@ -356,6 +368,9 @@ class UnifiedVoice(nn.Module):
         mel_inputs:  long tensor, (b,m)
         wav_lengths: long tensor, (b,)
         raw_mels: MEL float tensor (b,80,s)
+
+        If return_attentions is specified, only logits are returned.
+        If return_latent is specified, loss & logits are not computed or returned. Only the predicted latents are returned.
         """
         assert self.max_mel_tokens >= mel_codes.shape[1], f'{mel_codes.shape[1]}'
         assert self.max_text_tokens >= text_inputs.shape[1], f'{text_inputs.shape[1]}'
@@ -385,10 +400,15 @@ class UnifiedVoice(nn.Module):
             mel_inp = mel_codes
         mel_emb = self.mel_embedding(mel_inp)
         mel_emb = mel_emb + self.mel_pos_embedding(mel_codes)
+
         if text_first:
-            text_logits, mel_logits = self.get_logits(conds, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions)
+            text_logits, mel_logits = self.get_logits(conds, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions, return_latent=return_latent)
+            if return_latent:
+                return mel_logits[:, :-1]  # Despite the name, these are not logits.
         else:
-            mel_logits, text_logits = self.get_logits(conds, mel_emb, self.mel_head, text_emb, self.text_head, get_attns=return_attentions)
+            mel_logits, text_logits = self.get_logits(conds, mel_emb, self.mel_head, text_emb, self.text_head, get_attns=return_attentions, return_latent=return_latent)
+            if return_latent:
+                return text_logits[:, :-1]  # Despite the name, these are not logits
 
         if return_attentions:
             return mel_logits

codes/train.py

@@ -318,7 +318,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_wav2vec_matcher.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_diffusion_tts9.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)

codes/trainer/injectors/audio_injectors.py

@@ -6,7 +6,7 @@ import torch.nn.functional as F
 import torchaudio
 
 from trainer.inject import Injector
-from utils.util import opt_get
+from utils.util import opt_get, load_model_from_config
 
 
 class MelSpectrogramInjector(Injector):
@@ -110,3 +110,65 @@ class AudioResampleInjector(Injector):
     def forward(self, state):
         inp = state[self.input]
         return {self.output: torchaudio.functional.resample(inp, self.input_sr, self.output_sr)}
+
+
+class DiscreteTokenInjector(Injector):
+    def __init__(self, opt, env):
+        super().__init__(opt, env)
+        cfg = opt_get(opt, ['dvae_config'], "../experiments/train_diffusion_vocoder_22k_level.yml")
+        dvae_name = opt_get(opt, ['dvae_name'], 'dvae')
+        self.dvae = load_model_from_config(cfg, dvae_name).cuda().eval()
+
+    def forward(self, state):
+        inp = state[self.input]
+        with torch.no_grad():
+            self.dvae = self.dvae.to(inp.device)
+            codes = self.dvae.get_codebook_indices(inp)
+            return {self.output: codes}
+
+
+class GptVoiceLatentInjector(Injector):
+    """
+    This injector does all the legwork to generate latents out of a UnifiedVoice model, including encoding all audio
+    inputs into a MEL spectrogram and discretizing the inputs.
+    """
+    def __init__(self, opt, env):
+        super().__init__(opt, env)
+        # For discrete tokenization.
+        cfg = opt_get(opt, ['dvae_config'], "../experiments/train_diffusion_vocoder_22k_level.yml")
+        dvae_name = opt_get(opt, ['dvae_name'], 'dvae')
+        self.dvae = load_model_from_config(cfg, dvae_name).cuda().eval()
+        # The unified_voice model.
+        cfg = opt_get(opt, ['gpt_config'], "../experiments/train_gpt_tts_unified.yml")
+        model_name = opt_get(opt, ['gpt_name'], 'gpt')
+        pretrained_path = opt['gpt_path']
+        self.gpt = load_model_from_config(cfg, model_name=model_name,
+                                          also_load_savepoint=False, load_path=pretrained_path).cuda().eval()
+        # Mel converter
+        self.mel_inj = TorchMelSpectrogramInjector({'in': 'wav', 'out': 'mel', 'mel_norm_file': '../experiments/clips_mel_norms.pth'},{})
+        # Aux input keys.
+        self.conditioning_key = opt['conditioning_clip']
+        self.text_input_key = opt['text']
+        self.text_lengths_key = opt['text_lengths']
+        self.input_lengths_key = opt['input_lengths']
+
+    def to_mel(self, t):
+        return self.mel_inj({'wav': t})['mel']
+
+    def forward(self, state):
+        with torch.no_grad():
+            mel_inputs = self.to_mel(state[self.input])
+            mel_cond = self.to_mel(state[self.conditioning_key])
+
+            # Use the input as a conditioning input as well. This is fine because we are not actually training the GPT network so it can't learn to cheat.
+            max_mel_len = max(mel_inputs.shape[-1], mel_cond.shape[-1])
+            mel_cond = F.pad(mel_cond, (0, max_mel_len-mel_cond.shape[-1]))
+            mel_cond2 = F.pad(mel_inputs, (0, max_mel_len-mel_inputs.shape[-1]))
+            mel_cond = torch.cat([mel_cond.unsqueeze(1), mel_cond2.unsqueeze(1)], dim=1)
+            self.dvae = self.dvae.to(mel_inputs.device)
+            codes = self.dvae.get_codebook_indices(mel_inputs)
+            self.gpt = self.gpt.to(codes.device)
+            latents = self.gpt.forward(mel_cond, state[self.text_input_key],
+                                       state[self.text_lengths_key], codes, state[self.input_lengths_key],
+                                       text_first=True, raw_mels=None, return_attentions=False, return_latent=True)
+            return {self.output: latents}