From 96a5cc66ee0d089730f9409d2585489c85973006 Mon Sep 17 00:00:00 2001
From: James Betker <jbetker@gmail.com>
Date: Mon, 9 May 2022 15:35:51 -0600
Subject: [PATCH] uv3
---
codes/models/audio/tts/unified_voice3.py | 444 +++++++++++++++++++++++
1 file changed, 444 insertions(+)
create mode 100644 codes/models/audio/tts/unified_voice3.py
diff --git a/codes/models/audio/tts/unified_voice3.py b/codes/models/audio/tts/unified_voice3.py
new file mode 100644
index 00000000..134147cd
--- /dev/null
+++ b/codes/models/audio/tts/unified_voice3.py
@@ -0,0 +1,444 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import GPT2Config, GPT2PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
+from transformers.models.gpt2.modeling_gpt2 import GPT2Attention
+from transformers.utils.model_parallel_utils import get_device_map, assert_device_map
+
+from models.arch_util import AttentionBlock
+from models.audio.tts.transformer_builders import build_hf_gpt_transformer
+from models.lucidrains.x_transformers import RotaryEmbedding, apply_rotary_pos_emb
+from trainer.networks import register_model
+from utils.util import opt_get
+
+
+class ResBlock(nn.Module):
+ """
+ Basic residual convolutional block that uses GroupNorm.
+ """
+ def __init__(self, chan):
+ super().__init__()
+ self.net = nn.Sequential(
+ nn.Conv1d(chan, chan, kernel_size=3, padding=1),
+ nn.GroupNorm(chan//8, chan),
+ nn.ReLU(),
+ nn.Conv1d(chan, chan, kernel_size=3, padding=1),
+ nn.GroupNorm(chan//8, chan)
+ )
+
+ def forward(self, x):
+ return F.relu(self.net(x) + x)
+
+
+class GPT2InferenceModel(GPT2PreTrainedModel):
+ def __init__(self, config, gpt, posterior_pos_emb, embeddings, norm, linear):
+ super().__init__(config)
+ self.transformer = gpt
+ self.posterior_pos_embedding = posterior_pos_emb
+ self.embeddings = embeddings
+ self.head = nn.Sequential(norm, linear)
+
+ # Model parallel
+ self.model_parallel = False
+ self.device_map = None
+ self.cached_prior_emb = None
+
+ def parallelize(self, device_map=None):
+ self.device_map = (
+ get_device_map(len(self.transformer.h), range(torch.cuda.device_count()))
+ if device_map is None
+ else device_map
+ )
+ assert_device_map(self.device_map, len(self.transformer.h))
+ self.transformer.parallelize(self.device_map)
+ self.head = self.head.to(self.transformer.first_device)
+ self.model_parallel = True
+
+ def deparallelize(self):
+ self.transformer.deparallelize()
+ self.transformer = self.transformer.to("cpu")
+ self.head = self.head.to("cpu")
+ self.model_parallel = False
+ torch.cuda.empty_cache()
+
+ def get_output_embeddings(self):
+ return self.head
+
+ def set_output_embeddings(self, new_embeddings):
+ self.head = new_embeddings
+
+ def store_prior_emb(self, mel_emb):
+ self.cached_prior_emb = mel_emb
+
+ def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
+ token_type_ids = kwargs.get("token_type_ids", None)
+ # only last token for inputs_ids if past is defined in kwargs
+ if past:
+ input_ids = input_ids[:, -1].unsqueeze(-1)
+ if token_type_ids is not None:
+ token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
+
+ attention_mask = kwargs.get("attention_mask", None)
+ position_ids = kwargs.get("position_ids", None)
+
+ if attention_mask is not None and position_ids is None:
+ # create position_ids on the fly for batch generation
+ position_ids = attention_mask.long().cumsum(-1) - 1
+ position_ids.masked_fill_(attention_mask == 0, 1)
+ if past:
+ position_ids = position_ids[:, -1].unsqueeze(-1)
+ else:
+ position_ids = None
+ return {
+ "input_ids": input_ids,
+ "past_key_values": past,
+ "use_cache": kwargs.get("use_cache"),
+ "position_ids": position_ids,
+ "attention_mask": attention_mask,
+ "token_type_ids": token_type_ids,
+ }
+
+ def forward(
+ self,
+ input_ids=None,
+ past_key_values=None,
+ attention_mask=None,
+ token_type_ids=None,
+ position_ids=None,
+ head_mask=None,
+ inputs_embeds=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ labels=None,
+ use_cache=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ ):
+ assert self.cached_prior_emb is not None
+ assert inputs_embeds is None # Not supported by this inference model.
+ assert labels is None # Training not supported by this inference model.
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # Create embedding
+ prior_len = self.cached_prior_emb.shape[1]
+ if input_ids.shape[1] != 1:
+ posterior_inputs = input_ids[:, prior_len:]
+ posterior_emb = self.embeddings(posterior_inputs)
+ posterior_emb = posterior_emb + self.posterior_pos_embedding(posterior_emb)
+ if self.cached_prior_emb.shape[0] != posterior_emb.shape[0]:
+ prior_emb = self.cached_prior_emb.repeat_interleave(posterior_emb.shape[0] // self.cached_prior_emb.shape[0], 0)
+ else:
+ prior_emb = self.cached_prior_emb
+ emb = torch.cat([prior_emb, posterior_emb], dim=1)
+ else:
+ emb = self.embeddings(input_ids)
+ emb = emb + self.posterior_pos_embedding.get_fixed_embedding(attention_mask.shape[1] - prior_len, attention_mask.device)
+
+ transformer_outputs = self.transformer(
+ inputs_embeds=emb,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = transformer_outputs[0]
+
+ # Set device for model parallelism
+ if self.model_parallel:
+ torch.cuda.set_device(self.transformer.first_device)
+ hidden_states = hidden_states.to(self.head.weight.device)
+
+ logits = self.head(hidden_states)
+
+ if not return_dict:
+ return (logits,) + transformer_outputs[1:]
+
+ return CausalLMOutputWithCrossAttentions(
+ loss=None,
+ logits=logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ cross_attentions=transformer_outputs.cross_attentions,
+ )
+
+ @staticmethod
+ def _reorder_cache(past, beam_idx):
+ """
+ This function is used to re-order the :obj:`past_key_values` cache if
+ :meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is
+ called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
+ """
+ return tuple(
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
+ for layer_past in past
+ )
+
+
+class ConditioningEncoder(nn.Module):
+ def __init__(self,
+ spec_dim,
+ embedding_dim,
+ attn_blocks=6,
+ num_attn_heads=4,
+ do_checkpointing=False,
+ mean=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
+ self.mean = mean
+
+ def forward(self, x):
+ h = self.init(x)
+ h = self.attn(h)
+ if self.mean:
+ return h.mean(dim=2)
+ else:
+ 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)
+
+
+class UnifiedVoice(nn.Module):
+ def __init__(self, layers=8, model_dim=512, heads=8, max_text_tokens=120, max_mel_tokens=250, max_conditioning_inputs=1,
+ mel_length_compression=1024, number_text_tokens=256, number_mel_codes=8194, start_mel_token=8192,
+ stop_mel_token=8193, start_text_token=None, checkpointing=True, types=1):
+ """
+ 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).
+ mel_length_compression: The factor between <number_input_samples> and <mel_tokens>. Used to compute MEL code padding given wav input length.
+ number_text_tokens:
+ number_mel_codes:
+ start_mel_token:
+ stop_mel_token:
+ checkpointing:
+ """
+ super().__init__()
+
+ self.number_text_tokens = number_text_tokens
+ self.start_text_token = number_text_tokens * types if start_text_token is None else start_text_token
+ self.stop_text_token = 0
+ self.number_mel_codes = number_mel_codes
+ self.start_mel_token = start_mel_token
+ self.stop_mel_token = stop_mel_token
+ self.layers = layers
+ self.heads = heads
+ self.max_conditioning_inputs = max_conditioning_inputs
+ self.max_mel_tokens = -1 if max_mel_tokens == -1 else max_mel_tokens+2+self.max_conditioning_inputs
+ self.max_text_tokens = -1 if max_text_tokens == -1 else max_text_tokens+2
+ self.model_dim = model_dim
+ 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*types+1, model_dim)
+ self.mel_embedding = nn.Embedding(self.number_mel_codes, model_dim)
+ self.gpt, self.mel_pos_embedding, self.text_pos_embedding, self.mel_layer_pos_embedding, self.text_layer_pos_embedding = \
+ build_hf_gpt_transformer(layers, model_dim, heads, self.max_mel_tokens, self.max_text_tokens, checkpointing)
+
+ self.final_norm = nn.LayerNorm(model_dim)
+ self.text_head = nn.Linear(model_dim, self.number_text_tokens*types+1)
+ self.mel_head = nn.Linear(model_dim, self.number_mel_codes)
+
+ # Initialize the embeddings per the GPT-2 scheme
+ embeddings = [self.text_embedding, self.mel_embedding]
+ 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)
+ 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 <zero>).
+ 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 get_logits(self, speech_conditioning_inputs, first_inputs, first_head, second_inputs=None, second_head=None, return_latent=False):
+ if second_inputs is not None:
+ emb = torch.cat([speech_conditioning_inputs, first_inputs, second_inputs], dim=1)
+ else:
+ emb = torch.cat([speech_conditioning_inputs, first_inputs], dim=1)
+
+ gpt_out = self.gpt(inputs_embeds=emb, return_dict=True)
+
+ 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[:, speech_conditioning_inputs.shape[1]:speech_conditioning_inputs.shape[1]+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)
+ 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 get_conditioning_latent(self, speech_conditioning_input):
+ speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(speech_conditioning_input.shape) == 3 else speech_conditioning_input
+ conds = []
+ for j in range(speech_conditioning_input.shape[1]):
+ conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
+ conds = torch.stack(conds, dim=1)
+ conds = conds.mean(dim=1).unsqueeze(1)
+ return conds
+
+
+ def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, types=None, text_first=True, return_latent=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,)
+
+ If return_latent is specified, loss & logits are not computed or returned. Only the predicted latents are returned.
+ """
+ # Types are expressed by expanding the text embedding space.
+ if types is not None:
+ text_inputs = text_inputs * (1+types).unsqueeze(-1)
+
+ mel_codes = self.set_mel_padding(mel_codes, wav_lengths)
+ text_inputs = F.pad(text_inputs, (0,1), value=self.stop_text_token)
+ mel_codes = F.pad(mel_codes, (0,1), value=self.stop_mel_token)
+
+ conds = self.get_conditioning_latent(speech_conditioning_input)
+
+ 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(text_inputs)
+ mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token)
+ 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, return_latent=return_latent)
+ if return_latent:
+ return mel_logits[:, :-2] # Despite the name, these are not logits. Strip off the two tokens added by this forward pass.
+ else:
+ mel_logits, text_logits = self.get_logits(conds, mel_emb, self.mel_head, text_emb, self.text_head, return_latent=return_latent)
+ if return_latent:
+ return text_logits[:, :-2] # Despite the name, these are not logits. Strip off the two tokens added by this forward pass.
+
+ 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 inference_speech(self, speech_conditioning_input, text_inputs, **hf_generate_kwargs):
+ if self.max_mel_tokens == -1: # Assume if this is the case, max_mel_tokens=-1 also
+ seq_length = 2002 # Arbitrary default.
+ else:
+ seq_length = self.max_mel_tokens + self.max_text_tokens + 2
+ if not hasattr(self, 'inference_model'):
+ # TODO: Decouple gpt_config from this inference model.
+ gpt_config = GPT2Config(vocab_size=self.max_mel_tokens,
+ n_positions=seq_length,
+ n_ctx=seq_length,
+ n_embd=self.model_dim,
+ n_layer=self.layers,
+ n_head=self.heads,
+ gradient_checkpointing=False,
+ use_cache=True)
+ self.inference_model = GPT2InferenceModel(gpt_config, self.gpt, self.mel_pos_embedding, self.mel_embedding, self.final_norm, self.mel_head)
+ self.gpt.wte = self.mel_embedding
+
+ 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(text_inputs)
+
+ speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(speech_conditioning_input.shape) == 3 else speech_conditioning_input
+ conds = []
+ for j in range(speech_conditioning_input.shape[1]):
+ conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
+ conds = torch.stack(conds, dim=1)
+ conds = conds.mean(dim=1).unsqueeze(1)
+
+ emb = torch.cat([conds, text_emb], dim=1)
+ self.inference_model.store_prior_emb(emb)
+
+ fake_inputs = torch.full((emb.shape[0], conds.shape[1]+emb.shape[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=seq_length, return_dict_in_generate=True, **hf_generate_kwargs)
+ return gen.sequences[:, fake_inputs.shape[1]:]
+
+
+@register_model
+def register_unified_voice2(opt_net, opt):
+ return UnifiedVoice(**opt_get(opt_net, ['kwargs'], {}))
+
+
+if __name__ == '__main__':
+ gpt = UnifiedVoice(model_dim=256, heads=4, max_conditioning_inputs=4, types=2)
+ l = gpt(torch.randn(2, 3, 80, 800),
+ torch.randint(high=256, size=(2,120)),
+ torch.tensor([32, 120]),
+ torch.randint(high=8192, size=(2,250)),
+ torch.tensor([250*256,195*256]),
+ types=torch.tensor([0, 1]))