diff --git a/codes/models/audio/music/transformer_diffusion.py b/codes/models/audio/music/transformer_diffusion.py index ae995060..b012c738 100644 --- a/codes/models/audio/music/transformer_diffusion.py +++ b/codes/models/audio/music/transformer_diffusion.py @@ -243,15 +243,6 @@ class TransformerDiffusion(nn.Module): return out, mel_pred return out - def get_conditioning_latent(self, conditioning_input): - speech_conditioning_input = conditioning_input.unsqueeze(1) if len( - conditioning_input.shape) == 3 else conditioning_input - conds = [] - for j in range(speech_conditioning_input.shape[1]): - conds.append(self.conditioning_embedder(speech_conditioning_input[:, j])) - conds = torch.cat(conds, dim=-1) - return conds.mean(dim=-1) - @register_model def register_transformer_diffusion(opt_net, opt): diff --git a/codes/models/audio/tts/unet_diffusion_tts10.py b/codes/models/audio/tts/unet_diffusion_tts10.py new file mode 100644 index 00000000..f417905a --- /dev/null +++ b/codes/models/audio/tts/unet_diffusion_tts10.py @@ -0,0 +1,329 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import autocast + +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.lucidrains.x_transformers import Encoder +from scripts.audio.gen.use_diffuse_tts import ceil_multiple +from trainer.networks import register_model +from utils.util import checkpoint + + +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 if kernel_size == 3 else 2 + + 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): + def __init__( + self, + model_channels, + in_channels=100, + num_tokens=256, + out_channels=200, # mean and variance + dropout=0, + # m 1, 2, 4, 8 + block_channels= (512,640, 768,1024), + num_res_blocks= (3, 3, 3, 3), + token_conditioning_resolutions=(2,4,8), + attention_resolutions=(2,4,8), + conv_resample=True, + dims=1, + use_fp16=False, + kernel_size=3, + scale_factor=2, + time_embed_dim_multiplier=4, + nil_guidance_fwd_proportion=.15, + ): + super().__init__() + + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.conv_resample = conv_resample + self.dtype = torch.float16 if use_fp16 else torch.float32 + self.dims = dims + self.nil_guidance_fwd_proportion = nil_guidance_fwd_proportion + self.mask_token_id = num_tokens + num_heads = model_channels // 64 + + 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), + ) + + self.code_embedding = nn.Embedding(num_tokens+1, model_channels) + self.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, model_channels // 2, 3, padding=1, stride=2), + nn.Conv1d(model_channels//2, model_channels,3,padding=1,stride=2)) + self.conditioning_encoder = Encoder( + dim=model_channels, + depth=4, + heads=num_heads, + ff_dropout=dropout, + attn_dropout=dropout, + use_rmsnorm=True, + ff_glu=True, + rotary_pos_emb=True, + ) + + self.codes_encoder = Encoder( + dim=model_channels, + depth=8, + heads=num_heads, + ff_dropout=dropout, + attn_dropout=dropout, + use_rms_scaleshift_norm=True, + ff_glu=True, + rotary_pos_emb=True, + zero_init_branch_output=True, + ) + + 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, (blk_chan, num_blocks) in enumerate(zip(block_channels, num_res_blocks)): + if ds in token_conditioning_resolutions: + token_conditioning_block = nn.Conv1d(model_channels, 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=blk_chan, + dims=dims, + kernel_size=kernel_size, + ) + ] + ch = blk_chan + if ds in attention_resolutions: + layers.append( + AttentionBlock( + ch, + num_heads=num_heads, + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(block_channels) - 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 + + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + ), + AttentionBlock( + ch, + num_heads=num_heads, + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + ), + ) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, (blk_chan, num_blocks) in list(enumerate(zip(block_channels, 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=blk_chan, + dims=dims, + kernel_size=kernel_size, + ) + ] + ch = blk_chan + if ds in attention_resolutions: + layers.append( + AttentionBlock( + ch, + ) + ) + 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 forward(self, x, timesteps, tokens, conditioning_input=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 tokens: an aligned text input. + :return: an [N x C x ...] Tensor of outputs. + """ + with autocast(x.device.type): + orig_x_shape = x.shape[-1] + cm = ceil_multiple(x.shape[-1], 16) + if cm != 0: + pc = (cm-x.shape[-1])/x.shape[-1] + x = F.pad(x, (0,cm-x.shape[-1])) + tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1]))) + + hs = [] + time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + + # Mask out guidance tokens for un-guided diffusion. + if self.training and self.nil_guidance_fwd_proportion > 0: + token_mask = torch.rand(tokens.shape, device=tokens.device) < self.nil_guidance_fwd_proportion + tokens = torch.where(token_mask, self.mask_token_id, tokens) + code_emb = self.code_embedding(tokens).permute(0,2,1) + cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1) + cond_emb = self.conditioning_encoder(cond_emb)[:, 0] + code_emb = self.codes_encoder(code_emb.permute(0,2,1), norm_scale_shift_inp=cond_emb).permute(0,2,1) + + 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=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_tts10(opt_net, opt): + return DiffusionTts(**opt_net['kwargs']) + + +if __name__ == '__main__': + clip = torch.randn(2, 100, 500).cuda() + tok = torch.randint(0,256, (2,230)).cuda() + cond = torch.randn(2, 100, 300).cuda() + ts = torch.LongTensor([600, 600]).cuda() + model = DiffusionTts(512).cuda() + print(sum(p.numel() for p in model.parameters()) / 1000000) + model(clip, ts, tok, cond) + diff --git a/codes/scripts/audio/gen/speech_synthesis_utils.py b/codes/scripts/audio/gen/speech_synthesis_utils.py index 4902db19..b72bfd2f 100644 --- a/codes/scripts/audio/gen/speech_synthesis_utils.py +++ b/codes/scripts/audio/gen/speech_synthesis_utils.py @@ -42,7 +42,7 @@ def wav_to_univnet_mel(wav, do_normalization=False): """ return MelSpectrogramInjector({'in': 'wav', 'out': 'mel', 'sampling_rate': 24000, 'n_mel_channels': 100, 'mel_fmax': 12000, - 'do_normalizattion': do_normalization},{})({'wav': wav})['mel'] + 'do_normalization': do_normalization},{})({'wav': wav})['mel'] def convert_mel_to_codes(dvae_model, mel):