diff --git a/codes/models/gpt_voice/unet_diffusion_tts9.py b/codes/models/gpt_voice/unet_diffusion_tts9.py new file mode 100644 index 00000000..94a3d09e --- /dev/null +++ b/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) + diff --git a/codes/models/gpt_voice/unified_voice2.py b/codes/models/gpt_voice/unified_voice2.py index d3ee7648..7c16c22b 100644 --- a/codes/models/gpt_voice/unified_voice2.py +++ b/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 diff --git a/codes/train.py b/codes/train.py index 17de6580..ea187213 100644 --- a/codes/train.py +++ b/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) diff --git a/codes/trainer/injectors/audio_injectors.py b/codes/trainer/injectors/audio_injectors.py index 8ce5209c..ffb1b43a 100644 --- a/codes/trainer/injectors/audio_injectors.py +++ b/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}