train quantizer with diffusion
This commit is contained in:
James Betker
parent
136021bf8d
commit
f7d237a50a
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@ -542,7 +542,7 @@ class Wav2Vec2GumbelVectorQuantizer(nn.Module):
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idxs = codevector_idx.view(batch_size, sequence_length, self.num_groups)
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return idxs
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def forward(self, hidden_states, mask_time_indices=None):
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def forward(self, hidden_states, mask_time_indices=None, return_probs=False):
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batch_size, sequence_length, hidden_size = hidden_states.shape
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# project to codevector dim
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@ -580,6 +580,8 @@ class Wav2Vec2GumbelVectorQuantizer(nn.Module):
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.view(batch_size, sequence_length, -1)
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)
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if return_probs:
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return codevectors, perplexity, codevector_probs.view(batch_size, sequence_length, self.num_groups, self.num_vars)
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return codevectors, perplexity
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@ -1,257 +0,0 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
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from models.diffusion.unet_diffusion import TimestepBlock
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from models.lucidrains.x_transformers import Encoder, Attention, FeedForward, RMSScaleShiftNorm, RotaryEmbedding
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from trainer.networks import register_model
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from utils.util import checkpoint, print_network
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def is_latent(t):
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return t.dtype == torch.float
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def is_sequence(t):
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return t.dtype == torch.long
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class MultiGroupEmbedding(nn.Module):
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def __init__(self, tokens, groups, dim):
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super().__init__()
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self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
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def forward(self, x):
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h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
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return torch.cat(h, dim=-1)
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class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
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def forward(self, x, emb, rotary_emb):
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for layer in self:
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if isinstance(layer, TimestepBlock):
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x = layer(x, emb, rotary_emb)
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else:
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x = layer(x, rotary_emb)
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return x
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class AttentionBlock(TimestepBlock):
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def __init__(self, dim, heads, dropout):
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super().__init__()
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self.attn = Attention(dim, heads=heads, causal=False, dropout=dropout, zero_init_output=False)
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self.ff = FeedForward(dim, mult=1, dropout=dropout, zero_init_output=True)
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self.rms_scale_norm = RMSScaleShiftNorm(dim)
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def forward(self, x, timestep_emb, rotary_emb):
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h = self.rms_scale_norm(x, norm_scale_shift_inp=timestep_emb)
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h, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb)
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h = checkpoint(self.ff, h)
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return h + x
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class TransformerDiffusion(nn.Module):
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"""
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A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
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"""
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def __init__(
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self,
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model_channels=512,
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num_layers=8,
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in_channels=256,
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in_latent_channels=512,
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rotary_emb_dim=32,
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token_count=8,
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in_groups=None,
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out_channels=512, # mean and variance
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dropout=0,
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use_fp16=False,
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# Parameters for regularization.
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unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
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):
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super().__init__()
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.out_channels = out_channels
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self.dropout = dropout
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self.unconditioned_percentage = unconditioned_percentage
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self.enable_fp16 = use_fp16
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heads = model_channels//64
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self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
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self.time_embed = nn.Sequential(
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linear(model_channels, model_channels),
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nn.SiLU(),
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linear(model_channels, model_channels),
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)
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self.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, model_channels // 2, 3, padding=1, stride=2),
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nn.Conv1d(model_channels//2, model_channels,3,padding=1,stride=2))
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self.conditioning_encoder = Encoder(
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dim=model_channels,
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depth=4,
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heads=heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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zero_init_branch_output=True,
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ff_mult=1,
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)
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# Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
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# This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
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# complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
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# transformer network.
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if in_groups is None:
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self.embeddings = nn.Embedding(token_count, model_channels)
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else:
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self.embeddings = MultiGroupEmbedding(token_count, in_groups, model_channels)
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self.latent_conditioner = nn.Sequential(
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nn.Conv1d(in_latent_channels, model_channels, 3, padding=1),
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Encoder(
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dim=model_channels,
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depth=2,
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heads=heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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zero_init_branch_output=True,
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ff_mult=1,
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)
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)
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self.latent_fade = nn.Parameter(torch.zeros(1,1,model_channels))
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self.code_converter = Encoder(
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dim=model_channels,
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depth=3,
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heads=heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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zero_init_branch_output=True,
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ff_mult=1,
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)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
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self.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=1)
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self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
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self.intg = nn.Linear(model_channels*2, model_channels)
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self.layers = TimestepRotaryEmbedSequential(*[AttentionBlock(model_channels, model_channels//64, dropout) for _ in range(num_layers)])
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self.out = nn.Sequential(
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normalization(model_channels),
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nn.SiLU(),
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zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
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)
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self.debug_codes = {}
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def get_grad_norm_parameter_groups(self):
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groups = {
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'contextual_embedder': list(self.conditioning_embedder.parameters()),
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'layers': list(self.layers.parameters()) + list(self.inp_block.parameters()),
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'code_converters': list(self.embeddings.parameters()) + list(self.code_converter.parameters()) + list(self.latent_conditioner.parameters()),
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'time_embed': list(self.time_embed.parameters()),
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}
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return groups
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def timestep_independent(self, codes, conditioning_input, expected_seq_len, prenet_latent=None, return_code_pred=False):
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cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
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cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
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code_emb = self.embeddings(codes)
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if prenet_latent is not None:
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latent_conditioning = self.latent_conditioner(prenet_latent)
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code_emb = code_emb + latent_conditioning * self.latent_fade
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unconditioned_batches = torch.zeros((code_emb.shape[0], 1, 1), device=code_emb.device)
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# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
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if self.training and self.unconditioned_percentage > 0:
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unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
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device=code_emb.device) < self.unconditioned_percentage
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code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(codes.shape[0], 1, 1),
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code_emb)
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code_emb = self.code_converter(code_emb)
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expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
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if not return_code_pred:
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return expanded_code_emb, cond_emb
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else:
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# Perform the mel_head computation on the pre-exanded code embeddings, then interpolate it separately.
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mel_pred = self.mel_head(code_emb.permute(0,2,1))
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mel_pred = F.interpolate(mel_pred, size=expected_seq_len, mode='nearest')
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# Multiply mel_pred by !unconditioned_branches, which drops the gradient on unconditioned branches.
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# This is because we don't want that gradient being used to train parameters through the codes_embedder as
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# it unbalances contributions to that network from the MSE loss.
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mel_pred = mel_pred * unconditioned_batches.logical_not()
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return expanded_code_emb, cond_emb, mel_pred
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def forward(self, x, timesteps, codes=None, conditioning_input=None, prenet_latent=None, precomputed_code_embeddings=None,
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precomputed_cond_embeddings=None, conditioning_free=False, return_code_pred=False):
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if precomputed_code_embeddings is not None:
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assert precomputed_cond_embeddings is not None, "Must specify both precomputed embeddings if one is specified"
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assert codes is None and conditioning_input is None and prenet_latent is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
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assert not (return_code_pred and precomputed_code_embeddings is not None), "I cannot compute a code_pred output for you."
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unused_params = []
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if not return_code_pred:
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unused_params.extend(list(self.mel_head.parameters()))
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if conditioning_free:
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code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
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unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
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unused_params.extend(list(self.latent_conditioner.parameters()))
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else:
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if precomputed_code_embeddings is not None:
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code_emb = precomputed_code_embeddings
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cond_emb = precomputed_cond_embeddings
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else:
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code_emb, cond_emb, mel_pred = self.timestep_independent(codes, conditioning_input, x.shape[-1], prenet_latent, True)
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if prenet_latent is None:
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unused_params.extend(list(self.latent_conditioner.parameters()) + [self.latent_fade])
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unused_params.append(self.unconditioned_embedding)
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blk_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + cond_emb
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x = self.inp_block(x).permute(0,2,1)
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rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device)
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x = self.intg(torch.cat([x, code_emb], dim=-1))
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x = self.layers(x, blk_emb, rotary_pos_emb)
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x = x.float().permute(0,2,1)
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out = self.out(x)
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# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
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extraneous_addition = 0
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for p in unused_params:
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extraneous_addition = extraneous_addition + p.mean()
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out = out + extraneous_addition * 0
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if return_code_pred:
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return out, mel_pred
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return out
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@register_model
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def register_transformer_diffusion3(opt_net, opt):
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return TransformerDiffusion(**opt_net['kwargs'])
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if __name__ == '__main__':
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clip = torch.randn(2, 256, 400)
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aligned_latent = torch.randn(2,100,512)
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aligned_sequence = torch.randint(0,8,(2,100,8))
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cond = torch.randn(2, 256, 400)
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ts = torch.LongTensor([600, 600])
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model = TransformerDiffusion(model_channels=2048, num_layers=8)
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print_network(model)
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#torchsummary.torchsummary.summary(model, clip, ts, aligned_sequence, cond, return_code_pred=True)
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#o = model(clip, ts, aligned_sequence, cond, aligned_latent)
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@ -1,221 +0,0 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
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from models.diffusion.unet_diffusion import TimestepBlock
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from models.lucidrains.x_transformers import Encoder, Attention, FeedForward, RMSScaleShiftNorm, RotaryEmbedding
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from trainer.networks import register_model
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from utils.util import checkpoint, print_network
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def is_latent(t):
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return t.dtype == torch.float
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def is_sequence(t):
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return t.dtype == torch.long
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class MultiGroupEmbedding(nn.Module):
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def __init__(self, tokens, groups, dim):
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super().__init__()
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self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
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def forward(self, x):
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h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
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return torch.cat(h, dim=-1)
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class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
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def forward(self, x, emb, rotary_emb):
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for layer in self:
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if isinstance(layer, TimestepBlock):
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x = layer(x, emb, rotary_emb)
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else:
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x = layer(x, rotary_emb)
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return x
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class DietAttentionBlock(TimestepBlock):
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def __init__(self, in_dim, dim, heads, dropout):
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super().__init__()
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self.rms_scale_norm = RMSScaleShiftNorm(in_dim)
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self.proj = nn.Linear(in_dim, dim)
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self.attn = Attention(dim, heads=heads, causal=False, dropout=dropout)
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self.ff = FeedForward(dim, in_dim, mult=1, dropout=dropout, zero_init_output=True)
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def forward(self, x, timestep_emb, rotary_emb):
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h = self.rms_scale_norm(x, norm_scale_shift_inp=timestep_emb)
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h = self.proj(h)
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h, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb)
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h = checkpoint(self.ff, h)
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return h + x
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class TransformerDiffusion(nn.Module):
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"""
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A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
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"""
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def __init__(
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self,
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prenet_channels=256,
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model_channels=512,
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block_channels=256,
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num_layers=8,
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in_channels=256,
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rotary_emb_dim=32,
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token_count=8,
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in_groups=None,
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out_channels=512, # mean and variance
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dropout=0,
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use_fp16=False,
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# Parameters for regularization.
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unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
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):
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super().__init__()
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.prenet_channels = prenet_channels
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self.out_channels = out_channels
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self.dropout = dropout
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self.unconditioned_percentage = unconditioned_percentage
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self.enable_fp16 = use_fp16
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self.inp_block = conv_nd(1, in_channels, prenet_channels, 3, 1, 1)
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self.time_embed = nn.Sequential(
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linear(prenet_channels, prenet_channels),
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nn.SiLU(),
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linear(prenet_channels, prenet_channels),
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)
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prenet_heads = prenet_channels//64
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self.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, prenet_channels // 2, 3, padding=1, stride=2),
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nn.Conv1d(prenet_channels//2, prenet_channels,3,padding=1,stride=2))
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self.conditioning_encoder = Encoder(
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dim=prenet_channels,
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depth=4,
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heads=prenet_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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zero_init_branch_output=True,
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ff_mult=1,
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)
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# Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
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# This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
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# complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
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# transformer network.
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if in_groups is None:
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self.embeddings = nn.Embedding(token_count, prenet_channels)
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else:
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self.embeddings = MultiGroupEmbedding(token_count, in_groups, prenet_channels)
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self.code_converter = Encoder(
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dim=prenet_channels,
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depth=3,
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heads=prenet_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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zero_init_branch_output=True,
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ff_mult=1,
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)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,prenet_channels))
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self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
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self.cond_intg = nn.Linear(prenet_channels*2, model_channels)
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self.intg = nn.Linear(prenet_channels*2, model_channels)
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self.layers = TimestepRotaryEmbedSequential(*[DietAttentionBlock(model_channels, block_channels, block_channels // 64, dropout) for _ in range(num_layers)])
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self.out = nn.Sequential(
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normalization(model_channels),
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nn.SiLU(),
|
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zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
|
||||
)
|
||||
|
||||
self.debug_codes = {}
|
||||
|
||||
def get_grad_norm_parameter_groups(self):
|
||||
groups = {
|
||||
'contextual_embedder': list(self.conditioning_embedder.parameters()),
|
||||
'layers': list(self.layers.parameters()) + list(self.inp_block.parameters()),
|
||||
'code_converters': list(self.embeddings.parameters()) + list(self.code_converter.parameters()),
|
||||
'time_embed': list(self.time_embed.parameters()),
|
||||
}
|
||||
return groups
|
||||
|
||||
def timestep_independent(self, codes, conditioning_input, expected_seq_len):
|
||||
cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
|
||||
cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
|
||||
|
||||
code_emb = self.embeddings(codes)
|
||||
|
||||
# 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(codes.shape[0], 1, 1),
|
||||
code_emb)
|
||||
code_emb = self.code_converter(code_emb)
|
||||
|
||||
expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
|
||||
return expanded_code_emb, cond_emb
|
||||
|
||||
def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None,
|
||||
precomputed_cond_embeddings=None, conditioning_free=False):
|
||||
if precomputed_code_embeddings is not None:
|
||||
assert codes is None and conditioning_input is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
|
||||
|
||||
unused_params = []
|
||||
if conditioning_free:
|
||||
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
|
||||
unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
|
||||
else:
|
||||
if precomputed_code_embeddings is not None:
|
||||
code_emb = precomputed_code_embeddings
|
||||
cond_emb = precomputed_cond_embeddings
|
||||
else:
|
||||
code_emb, cond_emb = self.timestep_independent(codes, conditioning_input, x.shape[-1])
|
||||
unused_params.append(self.unconditioned_embedding)
|
||||
|
||||
blk_emb = torch.cat([self.time_embed(timestep_embedding(timesteps, self.prenet_channels)), cond_emb], dim=-1)
|
||||
blk_emb = self.cond_intg(blk_emb)
|
||||
x = self.inp_block(x).permute(0,2,1)
|
||||
|
||||
rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device)
|
||||
x = self.intg(torch.cat([x, code_emb], dim=-1))
|
||||
for layer in self.layers:
|
||||
x = checkpoint(layer, x, blk_emb, rotary_pos_emb)
|
||||
|
||||
x = x.float().permute(0,2,1)
|
||||
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_transformer_diffusion4(opt_net, opt):
|
||||
return TransformerDiffusion(**opt_net['kwargs'])
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
clip = torch.randn(2, 256, 400)
|
||||
aligned_sequence = torch.randint(0,8,(2,100,8))
|
||||
cond = torch.randn(2, 256, 400)
|
||||
ts = torch.LongTensor([600, 600])
|
||||
model = TransformerDiffusion(model_channels=3072, block_channels=1536, prenet_channels=1536, num_layers=16, in_groups=8)
|
||||
torch.save(model, 'sample.pth')
|
||||
print_network(model)
|
||||
o = model(clip, ts, aligned_sequence, cond)
|
||||
|
||||
|
|
@ -164,8 +164,10 @@ class TransformerDiffusion(nn.Module):
|
|||
|
||||
unused_params = []
|
||||
if conditioning_free:
|
||||
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
|
||||
unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
|
||||
code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
|
||||
cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
|
||||
cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
|
||||
unused_params.extend(list(self.code_converter.parameters()))
|
||||
else:
|
||||
if precomputed_code_embeddings is not None:
|
||||
code_emb = precomputed_code_embeddings
|
||||
|
|
@ -195,11 +197,70 @@ class TransformerDiffusion(nn.Module):
|
|||
return out
|
||||
|
||||
|
||||
class TransformerDiffusionWithQuantizer(nn.Module):
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__()
|
||||
|
||||
self.diff = TransformerDiffusion(**kwargs)
|
||||
from models.audio.mel2vec import ContrastiveTrainingWrapper
|
||||
self.m2v = ContrastiveTrainingWrapper(mel_input_channels=256, inner_dim=1024, layers=24, dropout=0.1,
|
||||
mask_time_prob=0, mask_time_length=6, num_negatives=100, codebook_size=16, codebook_groups=4,
|
||||
disable_custom_linear_init=True, do_reconstruction_loss=True)
|
||||
self.m2v.quantizer.temperature = self.m2v.min_gumbel_temperature
|
||||
|
||||
self.codes = torch.zeros((3000000,), dtype=torch.long)
|
||||
self.internal_step = 0
|
||||
self.code_ind = 0
|
||||
self.total_codes = 0
|
||||
|
||||
del self.m2v.m2v.encoder
|
||||
|
||||
def update_for_step(self, step, *args):
|
||||
self.internal_step = step
|
||||
self.m2v.quantizer.temperature = max(
|
||||
self.m2v.max_gumbel_temperature * self.m2v.gumbel_temperature_decay**step,
|
||||
self.m2v.min_gumbel_temperature,
|
||||
)
|
||||
|
||||
def forward(self, x, timesteps, truth_mel, conditioning_input, conditioning_free=False):
|
||||
proj = self.m2v.m2v.input_blocks(truth_mel).permute(0,2,1)
|
||||
_, proj = self.m2v.m2v.projector(proj)
|
||||
vectors, _, probs = self.m2v.quantizer(proj, return_probs=True)
|
||||
self.log_codes(probs)
|
||||
return self.diff(x, timesteps, codes=vectors, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
|
||||
|
||||
def log_codes(self, codes):
|
||||
if self.internal_step % 5 == 0:
|
||||
codes = torch.argmax(codes, dim=-1)
|
||||
codes = codes[:,:,0] + codes[:,:,1] * 16 + codes[:,:,2] * 16 ** 2 + codes[:,:,3] * 16 ** 3
|
||||
codes = codes.flatten()
|
||||
l = codes.shape[0]
|
||||
i = self.code_ind if (self.codes.shape[0] - self.code_ind) > l else self.codes.shape[0] - l
|
||||
self.codes[i:i+l] = codes.cpu()
|
||||
self.code_ind = self.code_ind + l
|
||||
if self.code_ind >= self.codes.shape[0]:
|
||||
self.code_ind = 0
|
||||
self.total_codes += 1
|
||||
|
||||
def get_debug_values(self, step, __):
|
||||
if self.total_codes > 0:
|
||||
return {'histogram_codes': self.codes[:self.total_codes]}
|
||||
else:
|
||||
return {}
|
||||
|
||||
|
||||
@register_model
|
||||
def register_transformer_diffusion5(opt_net, opt):
|
||||
return TransformerDiffusion(**opt_net['kwargs'])
|
||||
|
||||
|
||||
@register_model
|
||||
def register_transformer_diffusion5_with_quantizer(opt_net, opt):
|
||||
return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
|
||||
|
||||
|
||||
"""
|
||||
# For TFD5
|
||||
if __name__ == '__main__':
|
||||
clip = torch.randn(2, 256, 400)
|
||||
aligned_sequence = torch.randn(2,100,512)
|
||||
|
|
@ -209,4 +270,20 @@ if __name__ == '__main__':
|
|||
torch.save(model, 'sample.pth')
|
||||
print_network(model)
|
||||
o = model(clip, ts, aligned_sequence, cond)
|
||||
"""
|
||||
|
||||
if __name__ == '__main__':
|
||||
clip = torch.randn(2, 256, 400)
|
||||
cond = torch.randn(2, 256, 400)
|
||||
ts = torch.LongTensor([600, 600])
|
||||
model = TransformerDiffusionWithQuantizer(model_channels=2048, block_channels=1024, prenet_channels=1024, num_layers=16)
|
||||
|
||||
quant_weights = torch.load('../experiments/m2v_music.pth')
|
||||
diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd5\\models\\48000_generator_ema.pth')
|
||||
model.m2v.load_state_dict(quant_weights, strict=False)
|
||||
model.diff.load_state_dict(diff_weights)
|
||||
|
||||
torch.save(model.state_dict(), 'sample.pth')
|
||||
print_network(model)
|
||||
o = model(clip, ts, clip, cond)
|
||||
|
||||
|
|
|
|||
|
|
@ -332,7 +332,7 @@ class Trainer:
|
|||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_encoder_build_ctc_alignments.yml')
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_music_diffusion_tfd.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)
|
||||
|
|
|
|||
|
|
@ -60,6 +60,8 @@ class MusicDiffusionFid(evaluator.Evaluator):
|
|||
elif 'from_codes' == mode:
|
||||
self.diffusion_fn = self.perform_diffusion_from_codes
|
||||
self.local_modules['codegen'] = get_music_codegen()
|
||||
elif 'from_codes_quant' == mode:
|
||||
self.diffusion_fn = self.perform_diffusion_from_codes_quant
|
||||
self.spec_fn = TorchMelSpectrogramInjector({'n_mel_channels': 256, 'mel_fmax': 11000, 'filter_length': 16000,
|
||||
'normalize': True, 'in': 'in', 'out': 'out'}, {})
|
||||
|
||||
|
|
@ -92,12 +94,35 @@ class MusicDiffusionFid(evaluator.Evaluator):
|
|||
codes = codegen.get_codes(mel, project=True)
|
||||
mel_norm = normalize_mel(mel)
|
||||
gen_mel = self.diffuser.p_sample_loop(self.model, mel_norm.shape,
|
||||
model_kwargs={'codes': codes, 'conditioning_input': mel_norm[:,:,:140]})
|
||||
model_kwargs={'codes': codes, 'conditioning_input': torch.zeros_like(mel_norm[:,:,:390])})
|
||||
|
||||
gen_mel_denorm = denormalize_mel(gen_mel)
|
||||
output_shape = (1,16,audio.shape[-1]//16)
|
||||
self.spec_decoder = self.spec_decoder.to(audio.device)
|
||||
gen_wav = self.diffuser.p_sample_loop(self.spec_decoder, output_shape, model_kwargs={'aligned_conditioning': gen_mel_denorm})
|
||||
gen_wav = self.diffuser.p_sample_loop(self.spec_decoder, output_shape,
|
||||
model_kwargs={'aligned_conditioning': gen_mel_denorm})
|
||||
gen_wav = pixel_shuffle_1d(gen_wav, 16)
|
||||
|
||||
return gen_wav, real_resampled, gen_mel, mel_norm, sample_rate
|
||||
|
||||
def perform_diffusion_from_codes_quant(self, audio, sample_rate=22050):
|
||||
if sample_rate != sample_rate:
|
||||
real_resampled = torchaudio.functional.resample(audio, 22050, sample_rate).unsqueeze(0)
|
||||
else:
|
||||
real_resampled = audio
|
||||
audio = audio.unsqueeze(0)
|
||||
|
||||
mel = self.spec_fn({'in': audio})['out']
|
||||
mel_norm = normalize_mel(mel)
|
||||
gen_mel = self.diffuser.p_sample_loop(self.model, mel_norm.shape,
|
||||
model_kwargs={'truth_mel': mel,
|
||||
'conditioning_input': torch.zeros_like(mel_norm[:,:,:390])})
|
||||
|
||||
gen_mel_denorm = denormalize_mel(gen_mel)
|
||||
output_shape = (1,16,audio.shape[-1]//16)
|
||||
self.spec_decoder = self.spec_decoder.to(audio.device)
|
||||
gen_wav = self.diffuser.p_sample_loop(self.spec_decoder, output_shape,
|
||||
model_kwargs={'aligned_conditioning': gen_mel_denorm})
|
||||
gen_wav = pixel_shuffle_1d(gen_wav, 16)
|
||||
|
||||
return gen_wav, real_resampled, gen_mel, mel_norm, sample_rate
|
||||
|
|
@ -164,18 +189,19 @@ class MusicDiffusionFid(evaluator.Evaluator):
|
|||
# Put modules used for evaluation back into CPU memory.
|
||||
for k, mod in self.local_modules.items():
|
||||
self.local_modules[k] = mod.cpu()
|
||||
self.spec_decoder = self.spec_decoder.cpu()
|
||||
|
||||
return {"frechet_distance": frechet_distance}
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_diffusion_tfd.yml', 'generator',
|
||||
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_diffusion_tfd_quant.yml', 'generator',
|
||||
also_load_savepoint=False,
|
||||
load_path='X:\\dlas\\experiments\\train_music_diffusion_tfd\\models\\3000_generator_ema.pth'
|
||||
load_path='X:\\dlas\\experiments\\music_tfd5_with_quantizer_basis.pth'
|
||||
).cuda()
|
||||
opt_eval = {'path': 'Y:\\split\\yt-music-eval', 'diffusion_steps': 500,
|
||||
'conditioning_free': True, 'conditioning_free_k': 1,
|
||||
'diffusion_schedule': 'linear', 'diffusion_type': 'from_codes'}
|
||||
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 26, 'device': 'cuda', 'opt': {}}
|
||||
opt_eval = {'path': 'Y:\\split\\yt-music-eval', 'diffusion_steps': 100,
|
||||
'conditioning_free': False, 'conditioning_free_k': 2,
|
||||
'diffusion_schedule': 'linear', 'diffusion_type': 'from_codes_quant'}
|
||||
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 558, 'device': 'cuda', 'opt': {}}
|
||||
eval = MusicDiffusionFid(diffusion, opt_eval, env)
|
||||
print(eval.perform_eval())
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user