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