forked from mrq/DL-Art-School
352 lines
19 KiB
Python
352 lines
19 KiB
Python
import functools
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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 transformers import GPT2Model, GPT2Config
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from models.arch_util import AttentionBlock
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from models.gpt_voice.gpt_asr_hf import GPT2InferenceModel
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from models.gpt_voice.gpt_asr_hf2 import ResBlock
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from models.tacotron2.text import symbols
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from trainer.networks import register_model
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from utils.util import opt_get
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class ConditioningEncoder(nn.Module):
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def __init__(self,
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spec_dim,
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embedding_dim,
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attn_blocks=6,
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num_attn_heads=4,
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do_checkpointing=False):
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super().__init__()
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attn = []
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self.init = nn.Conv1d(spec_dim, embedding_dim, kernel_size=1)
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for a in range(attn_blocks):
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attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=do_checkpointing))
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self.attn = nn.Sequential(*attn)
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self.dim = embedding_dim
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self.do_checkpointing = do_checkpointing
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def forward(self, x):
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h = self.init(x)
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h = self.attn(h)
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return h[:, :, 0]
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class MelEncoder(nn.Module):
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def __init__(self, channels, mel_channels=80, resblocks_per_reduction=2):
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super().__init__()
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self.channels = channels
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self.encoder = nn.Sequential(nn.Conv1d(mel_channels, channels//4, kernel_size=3, padding=1),
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nn.Sequential(*[ResBlock(channels//4) for _ in range(resblocks_per_reduction)]),
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nn.Conv1d(channels//4, channels//2, kernel_size=3, stride=2, padding=1),
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nn.GroupNorm(channels//16, channels//2),
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nn.ReLU(),
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nn.Sequential(*[ResBlock(channels//2) for _ in range(resblocks_per_reduction)]),
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nn.Conv1d(channels//2, channels, kernel_size=3, stride=2, padding=1),
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nn.GroupNorm(channels//8, channels),
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nn.ReLU(),
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nn.Sequential(*[ResBlock(channels) for _ in range(resblocks_per_reduction)]),
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)
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self.reduction = 4
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def forward(self, x):
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for e in self.encoder:
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x = e(x)
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return x.permute(0,2,1)
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def null_position_embeddings(range, dim):
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return torch.zeros((range.shape[0], range.shape[1], dim), device=range.device)
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class UnifiedGptVoice(nn.Module):
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"""
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Derived from GptTtsHf, but offers multiple modes of autoregressive operation:
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- Text only
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- Voice only
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- Text conditioned on voice
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- Voice conditioned on text
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"""
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def __init__(self, layers=8, model_dim=512, heads=8, max_text_tokens=120, max_mel_tokens=250, max_conditioning_inputs=1,
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max_conditioning_length=60, shuffle_conditioning=True, mel_length_compression=1024, number_text_tokens=256,
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start_text_token=255, stop_text_token=0, number_mel_codes=8194, start_mel_token=8192,
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stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True,
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checkpointing=True):
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"""
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Args:
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layers: Number of layers in transformer stack.
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model_dim: Operating dimensions of the transformer
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heads: Number of transformer heads. Must be divisible by model_dim. Recommend model_dim//64
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max_text_tokens: Maximum number of text tokens that will be encountered by model.
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max_mel_tokens: Maximum number of MEL tokens that will be encountered by model.
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max_conditioning_inputs: Maximum number of conditioning inputs provided to the model. If (1), conditioning input can be of format (b,80,s), otherwise (b,n,80,s).
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max_conditioning_length: Maximum length of conditioning input. Only needed if shuffle_conditioning=True
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shuffle_conditioning: Whether or not the conditioning inputs will be shuffled across the sequence dimension. Useful if you want to provide the same input as conditioning and mel_codes.
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mel_length_compression: The factor between <number_input_samples> and <mel_tokens>. Used to compute MEL code padding given wav input length.
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number_text_tokens:
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start_text_token:
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stop_text_token:
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number_mel_codes:
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start_mel_token:
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stop_mel_token:
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train_solo_embeddings:
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use_mel_codes_as_input:
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checkpointing:
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"""
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super().__init__()
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self.number_text_tokens = number_text_tokens
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self.start_text_token = start_text_token
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self.stop_text_token = stop_text_token
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self.number_mel_codes = number_mel_codes
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self.start_mel_token = start_mel_token
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self.stop_mel_token = stop_mel_token
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self.shuffle_conditioning = shuffle_conditioning
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self.max_mel_tokens = max_mel_tokens
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self.max_text_tokens = max_text_tokens
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self.model_dim = model_dim
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self.max_conditioning_inputs = max_conditioning_inputs
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self.mel_length_compression = mel_length_compression
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self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads)
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self.text_embedding = nn.Embedding(self.number_text_tokens, model_dim)
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self.text_pos_embedding = nn.Embedding(self.max_text_tokens + 2, model_dim)
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self.mel_pos_embedding = nn.Embedding(self.max_mel_tokens + 2, model_dim)
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seq_length = 4+max_text_tokens+self.max_mel_tokens+self.max_conditioning_inputs
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self.gpt_config = GPT2Config(vocab_size=self.number_mel_codes,
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n_positions=seq_length,
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n_ctx=seq_length,
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n_embd=model_dim,
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n_layer=layers,
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n_head=heads,
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gradient_checkpointing=checkpointing,
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use_cache=not checkpointing)
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self.gpt = GPT2Model(self.gpt_config)
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if train_solo_embeddings:
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self.mel_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * self.gpt.config.initializer_range, requires_grad=True)
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self.text_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * self.gpt.config.initializer_range, requires_grad=True)
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else:
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self.mel_solo_embedding = 0
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self.text_solo_embedding = 0
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# Override the built in positional embeddings
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del self.gpt.wpe
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self.gpt.wpe = functools.partial(null_position_embeddings, dim=model_dim)
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if not use_mel_codes_as_input:
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self.gpt.wte = MelEncoder(model_dim, resblocks_per_reduction=1)
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self.final_norm = nn.LayerNorm(model_dim)
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self.text_head = nn.Linear(model_dim, self.number_text_tokens)
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self.mel_head = nn.Linear(model_dim, self.number_mel_codes)
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self.max_conditioning_length = max_conditioning_length
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# Initialize the embeddings per the GPT-2 scheme
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for module in [self.text_embedding, self.text_pos_embedding, self.mel_pos_embedding]:
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module.weight.data.normal_(mean=0.0, std=self.gpt.config.initializer_range)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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def load_state_dict(self, state_dict: 'OrderedDict[str, Tensor]', strict: bool = True):
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# Remove the attention biases. I don't know why these are called biases because they are really just fixed attention masks forced into nn.Parameters, which are
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# easily regenerated and do not need to be saved. This is a hack to allow length modifications and should be removed in the future.
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filtered = dict(filter(lambda i: not i[0].endswith('.attn.bias'), state_dict.items()))
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assert len(filtered) == len(state_dict) - len(self.gpt.h)
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return super().load_state_dict(filtered, strict)
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def build_aligned_inputs_and_targets(self, input, start_token, stop_token):
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inp = F.pad(input, (1,0), value=start_token)
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tar = F.pad(input, (0,1), value=stop_token)
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return inp, tar
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def set_mel_padding(self, mel_input_tokens, wav_lengths):
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"""
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Given mel tokens that are derived from a padded audio clip and the actual lengths of each batch element in
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that audio clip, reformats the tokens with STOP_MEL_TOKEN in place of the zero padding. This is required
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preformatting to create a working TTS model.
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"""
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# Set padding areas within MEL (currently it is coded with the MEL code for <zero>).
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mel_lengths = wav_lengths // self.mel_length_compression
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for b in range(len(mel_lengths)):
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actual_end = mel_lengths[b] + 1 # Due to the convolutional nature of how these tokens are generated, it would be best if the model predicts a token past the actual last token.
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if actual_end < mel_input_tokens.shape[-1]:
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mel_input_tokens[b, actual_end:] = self.stop_mel_token
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return mel_input_tokens
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def randomly_permute_conditioning_input(self, speech_conditioning_input):
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"""
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Randomly permute the conditioning spectrogram, to destroy any structure present. Note that since the
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conditioning input is derived from a discrete spectrogram, it does actually retain structure, but only a little
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bit (actually: exactly how much we want; enough to discriminate different vocal qualities, but nothing about
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what is being said).
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"""
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cond_input = speech_conditioning_input[:,:,torch.randperm(speech_conditioning_input.shape[-1])]
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if cond_input.shape[-1] > self.max_conditioning_length:
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cond_input = cond_input[:,:,:self.max_conditioning_length]
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return cond_input
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def get_logits(self, speech_conditioning_input, first_inputs, first_head, second_inputs=None, second_head=None, get_attns=False):
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if second_inputs is not None:
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emb = torch.cat([speech_conditioning_input, first_inputs, second_inputs], dim=1)
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else:
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emb = torch.cat([speech_conditioning_input, first_inputs], dim=1)
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gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns)
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if get_attns:
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return gpt_out.attentions
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enc = gpt_out.last_hidden_state[:, 1:] # The first logit is tied to the speech_conditioning_input
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enc = self.final_norm(enc)
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first_logits = enc[:, :first_inputs.shape[1]]
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first_logits = first_head(first_logits)
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first_logits = first_logits.permute(0,2,1)
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if second_inputs is not None:
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second_logits = enc[:, -second_inputs.shape[1]:]
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second_logits = second_head(second_logits)
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second_logits = second_logits.permute(0,2,1)
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return first_logits, second_logits
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else:
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return first_logits
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def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, text_first=True, raw_mels=None, return_attentions=False):
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"""
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Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
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(actuated by `text_first`).
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speech_conditioning_input: MEL float tensor, (b,80,s)
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text_inputs: long tensor, (b,t)
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text_lengths: long tensor, (b,)
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mel_inputs: long tensor, (b,m)
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wav_lengths: long tensor, (b,)
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raw_mels: MEL float tensor (b,80,s)
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"""
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assert self.max_mel_tokens >= mel_codes.shape[1], f'{mel_codes.shape[1]}'
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assert self.max_text_tokens >= text_inputs.shape[1], f'{text_inputs.shape[1]}'
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# This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
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# chopping the inputs by the maximum actual length.
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max_text_len = text_lengths.max()
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text_inputs = F.pad(text_inputs[:, :max_text_len], (0,1), value=self.stop_text_token)
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max_mel_len = wav_lengths.max() // self.mel_length_compression
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mel_codes = F.pad(mel_codes[:, :max_mel_len], (0,1), value=self.stop_mel_token)
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if raw_mels is not None:
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raw_mels = raw_mels[:, :, :max_mel_len*4]
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mel_codes = self.set_mel_padding(mel_codes, wav_lengths)
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if self.shuffle_conditioning:
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speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
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speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
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text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
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text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device))
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mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token)
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if raw_mels is not None:
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mel_inp = F.pad(raw_mels, (0, 8))
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else:
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mel_inp = mel_codes
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mel_emb = self.gpt.get_input_embeddings()(mel_inp)
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mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
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if text_first:
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text_logits, mel_logits = self.get_logits(speech_conditioning_input, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions)
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else:
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mel_logits, text_logits = self.get_logits(speech_conditioning_input, mel_emb, self.mel_head, text_emb, self.text_head, get_attns=return_attentions)
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if return_attentions:
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return mel_logits
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loss_text = F.cross_entropy(text_logits, text_targets.long())
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loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
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return loss_text.mean(), loss_mel.mean(), mel_logits
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def text_forward(self, speech_conditioning_input, text_inputs, text_lengths):
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"""
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Performs autoregressive modeling on only text. Still requires a speech_conditioning_input due to the way the
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model inputs are formatted. Just provide any audio clip (arguably, zeros could be provided).
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"""
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assert self.max_text_tokens >= text_inputs.shape[1], f'{text_inputs.shape[1]}'
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# This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
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# chopping the inputs by the maximum actual length.
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max_text_len = text_lengths.max()
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text_inputs = F.pad(text_inputs[:, :max_text_len], (0,1), value=self.stop_text_token)
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if self.shuffle_conditioning:
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speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
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speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
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text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
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text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device)) + self.text_solo_embedding
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text_logits = self.get_logits(speech_conditioning_input, text_emb, self.text_head)
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loss_text = F.cross_entropy(text_logits, text_targets.long())
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return loss_text.mean()
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def speech_forward(self, speech_conditioning_input, mel_codes, wav_lengths, raw_mels=None):
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"""
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Performs autoregressive modeling on only speech data.
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"""
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assert self.max_mel_tokens >= mel_codes.shape[1], f'{mel_codes.shape[1]}'
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# This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
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# chopping the inputs by the maximum actual length.
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max_mel_len = wav_lengths.max() // self.mel_length_compression
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mel_codes = F.pad(mel_codes[:, :max_mel_len], (0,1), value=self.stop_mel_token)
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mel_codes = self.set_mel_padding(mel_codes, wav_lengths)
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if raw_mels is not None:
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raw_mels = raw_mels[:, :, :max_mel_len*4]
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if self.shuffle_conditioning:
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speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
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speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
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mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token)
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if raw_mels is not None:
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mel_inp = F.pad(raw_mels, (0, 4))
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else:
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mel_inp = mel_codes
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mel_emb = self.gpt.get_input_embeddings()(mel_inp)
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mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device)) + self.mel_solo_embedding
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mel_logits = self.get_logits(speech_conditioning_input, mel_emb, self.mel_head)
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loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
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return loss_mel.mean()
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def inference_speech(self, speech_conditioning_input, text_inputs, **hf_generate_kwargs):
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if not hasattr(self, 'inference_model'):
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self.inference_model = GPT2InferenceModel(self.gpt_config, self.gpt, self.mel_pos_embedding, self.final_norm, self.mel_head)
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text_inputs = F.pad(text_inputs, (0, 1), value=self.stop_text_token)
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text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
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text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device))
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if self.shuffle_conditioning:
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# Randomly permute the conditioning spectrogram, to destroy any structure present.
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speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
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cond = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
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emb = torch.cat([cond, text_emb], dim=1)
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self.inference_model.store_mel_emb(emb)
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fake_inputs = torch.full((emb.shape[0], emb.shape[1]+1,), fill_value=1, dtype=torch.long, device=text_inputs.device)
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fake_inputs[:,-1] = self.start_mel_token
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gen = self.inference_model.generate(fake_inputs, bos_token_id=self.start_mel_token, pad_token_id=self.stop_mel_token, eos_token_id=self.stop_mel_token,
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max_length=self.gpt_config.n_positions, **hf_generate_kwargs)
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return gen[:, fake_inputs.shape[1]:]
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@register_model
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def register_unified_gpt_voice(opt_net, opt):
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return UnifiedGptVoice(**opt_get(opt_net, ['kwargs'], {}))
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if __name__ == '__main__':
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gpt = UnifiedGptVoice(model_dim=256, heads=4, train_solo_embeddings=True, use_mel_codes_as_input=True)
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l = gpt(torch.randn(2, 80, 800),
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torch.randint(high=len(symbols), size=(2,80)),
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torch.tensor([32, 80]),
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torch.randint(high=8192, size=(2,250)),
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torch.tensor([150*256,195*256]))
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gpt.text_forward(torch.randn(2,80,800), torch.randint(high=50, size=(2,80)), torch.tensor([32, 80]))
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