720 lines
35 KiB
Python
720 lines
35 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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import transformers
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from transformers import GPT2Config, LogitsProcessorList, DynamicCache
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from transformers.cache_utils import Cache
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from indextts.gpt.transformers_gpt2 import GPT2PreTrainedModel, GPT2Model
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# from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList
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from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
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from transformers.utils.model_parallel_utils import (assert_device_map,
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get_device_map)
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from indextts.gpt.conformer_encoder import ConformerEncoder
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from indextts.gpt.perceiver import PerceiverResampler
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from indextts.utils.arch_util import AttentionBlock
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from indextts.utils.typical_sampling import TypicalLogitsWarper
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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 ResBlock(nn.Module):
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"""
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Basic residual convolutional block that uses GroupNorm.
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"""
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def __init__(self, chan):
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super().__init__()
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self.net = nn.Sequential(
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nn.Conv1d(chan, chan, kernel_size=3, padding=1),
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nn.GroupNorm(chan // 8, chan),
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nn.ReLU(),
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nn.Conv1d(chan, chan, kernel_size=3, padding=1),
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nn.GroupNorm(chan // 8, chan)
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)
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def forward(self, x):
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return F.relu(self.net(x) + x)
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class GPT2InferenceModel(GPT2PreTrainedModel):
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def __init__(self, config, gpt, text_pos_emb, embeddings, norm, linear, kv_cache=False):
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super().__init__(config)
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# Note: the argument named `text_pos_emb` here actually represents the mel position embedding
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self.transformer = gpt
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self.text_pos_embedding = text_pos_emb
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self.embeddings = embeddings
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self.final_norm = norm
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self.lm_head = nn.Sequential(norm, linear)
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self.kv_cache = kv_cache
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# Model parallel
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self.model_parallel = False
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self.device_map = None
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self.cached_mel_emb = None
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def parallelize(self, device_map=None):
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self.device_map = (
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get_device_map(len(self.transformer.h), range(max(1, torch.cuda.device_count())))
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if device_map is None
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else device_map
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)
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assert_device_map(self.device_map, len(self.transformer.h))
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self.transformer.parallelize(self.device_map)
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self.lm_head = self.lm_head.to(self.transformer.first_device)
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self.model_parallel = True
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def deparallelize(self):
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self.transformer.deparallelize()
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self.transformer = self.transformer.to("cpu")
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self.lm_head = self.lm_head.to("cpu")
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self.model_parallel = False
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torch.cuda.empty_cache()
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if torch.backends.mps.is_available():
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torch.mps.empty_cache()
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def get_output_embeddings(self):
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return self.lm_head
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def set_output_embeddings(self, new_embeddings):
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self.lm_head = new_embeddings
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def store_mel_emb(self, mel_emb):
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self.cached_mel_emb = mel_emb
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def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
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token_type_ids = kwargs.get("token_type_ids", None) # usually None
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if not self.kv_cache:
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past_key_values = None
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# 将旧格式的元组转换为 DynamicCache（支持 v4.53.0+）
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if past_key_values is not None and not isinstance(past_key_values, Cache):
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past_key_values = DynamicCache.from_legacy_cache(past_key_values)
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# only last token for inputs_ids if past is defined in kwargs
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if past_key_values is not None:
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input_ids = input_ids[:, -1].unsqueeze(-1)
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if token_type_ids is not None:
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token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
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attention_mask = kwargs.get("attention_mask", None)
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position_ids = kwargs.get("position_ids", None)
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if attention_mask is not None and position_ids is None:
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# create position_ids on the fly for batch generation
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position_ids = attention_mask.long().cumsum(-1) - 1
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position_ids.masked_fill_(attention_mask == 0, 0)
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if past_key_values is not None:
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position_ids = position_ids[:, -1].unsqueeze(-1)
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else:
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position_ids = None
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return {
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"input_ids": input_ids,
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"past_key_values": past_key_values,
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"use_cache": kwargs.get("use_cache"),
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"position_ids": position_ids,
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"attention_mask": attention_mask,
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"token_type_ids": token_type_ids,
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}
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def forward(
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self,
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input_ids=None,
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past_key_values=None,
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attention_mask=None,
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token_type_ids=None,
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position_ids=None,
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head_mask=None,
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inputs_embeds=None,
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encoder_hidden_states=None,
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encoder_attention_mask=None,
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labels=None,
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use_cache=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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):
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assert self.cached_mel_emb is not None
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assert inputs_embeds is None # Not supported by this inference model.
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assert labels is None # Training not supported by this inference model.
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return_dict = (
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return_dict if return_dict is not None else self.config.use_return_dict
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)
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# Create embedding
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mel_len = self.cached_mel_emb.shape[1]
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if input_ids.shape[1] != 1:
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text_inputs = input_ids[:, mel_len:]
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text_emb = self.embeddings(text_inputs)
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text_emb = text_emb + self.text_pos_embedding(text_emb)
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if self.cached_mel_emb.shape[0] != text_emb.shape[0]:
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mel_emb = self.cached_mel_emb.repeat_interleave(
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text_emb.shape[0] // self.cached_mel_emb.shape[0], 0
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)
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else: # this outcome only occurs once per loop in most cases
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mel_emb = self.cached_mel_emb
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emb = torch.cat([mel_emb, text_emb], dim=1)
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else:
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emb = self.embeddings(input_ids)
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emb = emb + self.text_pos_embedding.get_fixed_embedding(
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attention_mask.shape[1] - mel_len, attention_mask.device
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)
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transformer_outputs = self.transformer(
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inputs_embeds=emb,
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past_key_values=past_key_values,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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hidden_states = transformer_outputs[0]
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# Set device for model parallelism
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if self.model_parallel:
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if torch.backends.mps.is_available():
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self.to(self.transformer.first_device)
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else:
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torch.cuda.set_device(self.transformer.first_device)
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hidden_states = hidden_states.to(self.lm_head.weight.device)
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lm_logits = self.lm_head(hidden_states)
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if not return_dict:
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return (lm_logits,) + transformer_outputs[1:]
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return CausalLMOutputWithCrossAttentions(
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loss=None,
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logits=lm_logits,
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past_key_values=transformer_outputs.past_key_values,
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hidden_states=transformer_outputs.hidden_states,
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attentions=transformer_outputs.attentions,
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cross_attentions=transformer_outputs.cross_attentions,
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)
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@staticmethod
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def _reorder_cache(past, beam_idx):
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"""
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This function is used to re-order the :obj:`past_key_values` cache if
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:meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is
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called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
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"""
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return tuple(
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tuple(
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past_state.index_select(0, beam_idx.to(past_state.device))
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for past_state in layer_past
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)
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for layer_past in past
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)
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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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mean=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))
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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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self.mean = mean
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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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if self.mean:
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return h.mean(dim=2)
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else:
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return h
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# return h[:, :, 0]
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class LearnedPositionEmbeddings(nn.Module):
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def __init__(self, seq_len, model_dim, init=.02):
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super().__init__()
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self.emb = nn.Embedding(seq_len, model_dim)
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# Initializing this way is standard for GPT-2
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self.emb.weight.data.normal_(mean=0.0, std=init)
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def forward(self, x):
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sl = x.shape[1]
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return self.emb(torch.arange(0, sl, device=x.device))
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def get_fixed_embedding(self, ind, dev):
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return self.emb(torch.tensor([ind], device=dev)).unsqueeze(0)
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def build_hf_gpt_transformer(layers, model_dim, heads, max_mel_seq_len, max_text_seq_len, checkpointing, activation_function):
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"""
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GPT-2 implemented by the HuggingFace library.
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"""
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from transformers import GPT2Config, GPT2Model
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gpt_config = GPT2Config(vocab_size=256, # Unused.
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n_positions=max_mel_seq_len + max_text_seq_len,
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n_ctx=max_mel_seq_len + max_text_seq_len,
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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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activation_function=activation_function or "gelu_new",
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gradient_checkpointing=checkpointing,
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use_cache=not checkpointing)
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gpt = GPT2Model(gpt_config)
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# Override the built in positional embeddings
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del gpt.wpe
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gpt.wpe = functools.partial(null_position_embeddings, dim=model_dim)
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# Built-in token embeddings are unused.
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del gpt.wte
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return gpt, LearnedPositionEmbeddings(max_mel_seq_len, model_dim), LearnedPositionEmbeddings(max_text_seq_len, model_dim), \
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None, None
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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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class UnifiedVoice(nn.Module):
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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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mel_length_compression=1024, number_text_tokens=256,
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start_text_token=0, stop_text_token=1, number_mel_codes=8194, start_mel_token=8192, stop_mel_token=8193,
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train_solo_embeddings=False, use_mel_codes_as_input=True,
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checkpointing=True, types=1, activation_function=None,
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condition_num_latent=32, condition_type="perceiver", condition_module=None):
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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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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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condition_type: perceiver, gst or default encoder
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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.layers = layers
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self.heads = heads
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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.condition_type = condition_type
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self.cond_num = condition_num_latent
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self.cond_mask_pad = nn.ConstantPad1d((self.cond_num, 0), True)
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if condition_type == "perceiver":
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self.conditioning_encoder = ConditioningEncoder(100, model_dim, num_attn_heads=heads)
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self.perceiver_encoder = PerceiverResampler(model_dim, dim_context=model_dim, num_latents=self.cond_num)
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elif condition_type == "conformer_perceiver" or condition_type == "conformer_encoder":
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self.conditioning_encoder = ConformerEncoder(input_size=100,
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output_size=condition_module['output_size'],
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linear_units=condition_module['linear_units'],
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attention_heads=condition_module['attention_heads'],
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num_blocks=condition_module['num_blocks'],
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input_layer=condition_module['input_layer'])
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if condition_type == "conformer_perceiver":
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self.perceiver_encoder = PerceiverResampler(model_dim, dim_context=condition_module['output_size'],
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ff_mult=condition_module['perceiver_mult'],
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heads=condition_module['attention_heads'],
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num_latents=self.cond_num)
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else:
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self.conditioning_encoder = ConditioningEncoder(100, model_dim, num_attn_heads=heads, mean=True)
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self.text_embedding = nn.Embedding(self.number_text_tokens * types + 1, model_dim)
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if use_mel_codes_as_input:
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self.mel_embedding = nn.Embedding(self.number_mel_codes, model_dim)
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else:
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self.mel_embedding = MelEncoder(model_dim, resblocks_per_reduction=1)
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self.gpt, self.mel_pos_embedding, self.text_pos_embedding, self.mel_layer_pos_embedding, self.text_layer_pos_embedding = \
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build_hf_gpt_transformer(layers, model_dim, heads, self.max_mel_tokens + 2 + self.max_conditioning_inputs,
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self.max_text_tokens + 2, checkpointing, activation_function)
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if train_solo_embeddings:
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self.mel_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * .02, requires_grad=True)
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self.text_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * .02, 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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self.final_norm = nn.LayerNorm(model_dim)
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self.text_head = nn.Linear(model_dim, self.number_text_tokens * types + 1)
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self.mel_head = nn.Linear(model_dim, self.number_mel_codes)
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# Initialize the embeddings per the GPT-2 scheme
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embeddings = [self.text_embedding]
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if use_mel_codes_as_input:
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embeddings.append(self.mel_embedding)
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for module in embeddings:
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module.weight.data.normal_(mean=0.0, std=.02)
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def post_init_gpt2_config(self, use_deepspeed=False, kv_cache=False, half=False):
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seq_length = self.max_mel_tokens + self.max_text_tokens + 2
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gpt_config = GPT2Config(
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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=self.model_dim,
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n_layer=self.layers,
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n_head=self.heads,
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gradient_checkpointing=False,
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use_cache=True,
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)
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self.inference_model = GPT2InferenceModel(
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gpt_config,
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self.gpt,
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self.mel_pos_embedding,
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self.mel_embedding,
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self.final_norm,
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self.mel_head,
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kv_cache=kv_cache,
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)
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if use_deepspeed and half and torch.cuda.is_available():
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import deepspeed
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self.ds_engine = deepspeed.init_inference(model=self.inference_model,
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mp_size=1,
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replace_with_kernel_inject=False,
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dtype=torch.float16)
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self.inference_model = self.ds_engine.module.eval()
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elif use_deepspeed and torch.cuda.is_available():
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import deepspeed
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self.ds_engine = deepspeed.init_inference(model=self.inference_model,
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mp_size=1,
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replace_with_kernel_inject=False,
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dtype=torch.float32)
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self.inference_model = self.ds_engine.module.eval()
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else:
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self.inference_model = self.inference_model.eval()
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# self.inference_model = PrunedGPT2InferenceModel(gpt_config, self.gpt, self.mel_pos_embedding, self.mel_embedding, self.final_norm, self.mel_head)
|
||
self.gpt.wte = self.mel_embedding
|
||
|
||
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, mel_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.
|
||
"""
|
||
for b in range(len(mel_lengths)):
|
||
# 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.
|
||
actual_end = mel_lengths[b]
|
||
if actual_end < mel_input_tokens.shape[-1]:
|
||
mel_input_tokens[b, actual_end:] = self.stop_mel_token
|
||
return mel_input_tokens
|
||
|
||
def set_text_padding(self, text_input_tokens, text_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.
|
||
"""
|
||
for b in range(len(text_lengths)):
|
||
# 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.
|
||
actual_end = text_lengths[b]
|
||
if actual_end < text_input_tokens.shape[-1]:
|
||
text_input_tokens[b, actual_end:] = self.stop_text_token
|
||
return text_input_tokens
|
||
|
||
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:
|
||
emb = torch.cat([speech_conditioning_inputs, 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
|
||
|
||
offset = speech_conditioning_inputs.shape[1]
|
||
enc = gpt_out.last_hidden_state[:, offset:]
|
||
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)
|
||
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(self, speech_conditioning_input, cond_mel_lengths=None):
|
||
if self.condition_type == "perceiver":
|
||
if speech_conditioning_input.ndim == 4:
|
||
speech_conditioning_input = speech_conditioning_input.squeeze(1)
|
||
speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input) # (b, d, s)
|
||
conds = self.perceiver_encoder(speech_conditioning_input.transpose(1, 2)) # (b, 32, d)
|
||
elif self.condition_type == "conformer_perceiver":
|
||
speech_conditioning_input, mask = self.conditioning_encoder(speech_conditioning_input.transpose(1, 2),
|
||
cond_mel_lengths) # (b, s, d), (b, 1, s)
|
||
if self.condition_type == "conformer_perceiver":
|
||
# conds_mask = torch.cat([torch.ones((mask.shape[0], self.cond_num), dtype=torch.bool), mask.squeeze(1)], dim=1)
|
||
conds_mask = self.cond_mask_pad(mask.squeeze(1))
|
||
conds = self.perceiver_encoder(speech_conditioning_input, conds_mask) # (b, 32, d)
|
||
elif self.condition_type == "gst":
|
||
if speech_conditioning_input.ndim == 4:
|
||
speech_conditioning_input = speech_conditioning_input.squeeze(1)
|
||
conds = self.gst_encoder(speech_conditioning_input.transpose(1, 2)) # (b, 1, d)
|
||
else:
|
||
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)
|
||
conds = conds.unsqueeze(1)
|
||
return conds
|
||
|
||
def forward(self, speech_conditioning_latent, text_inputs, text_lengths, mel_codes, wav_lengths,
|
||
cond_mel_lengths=None, types=None, text_first=True, raw_mels=None, return_attentions=False,
|
||
return_latent=False, clip_inputs=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,1024)
|
||
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)
|
||
|
||
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.
|
||
If clip_inputs is True, the inputs will be clipped to the smallest input size across each input modality.
|
||
"""
|
||
|
||
speech_conditioning_latent = self.get_conditioning(speech_conditioning_latent, cond_mel_lengths)
|
||
# Types are expressed by expanding the text embedding space.
|
||
if types is not None:
|
||
text_inputs = text_inputs * (1 + types).unsqueeze(-1)
|
||
|
||
if clip_inputs:
|
||
# 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 = text_inputs[:, :max_text_len]
|
||
max_mel_len = wav_lengths.max() // self.mel_length_compression
|
||
mel_codes = mel_codes[:, :max_mel_len]
|
||
if raw_mels is not None:
|
||
raw_mels = raw_mels[:, :, :max_mel_len * 4]
|
||
|
||
# Set padding areas within MEL (currently it is coded with the MEL code for <zero>).
|
||
# mel_codes_lengths = torch.div(wav_lengths, self.mel_length_compression, rounding_mode='trunc')
|
||
mel_codes_lengths = torch.ceil(wav_lengths / self.mel_length_compression).long() + 1
|
||
mel_codes = self.set_mel_padding(mel_codes, mel_codes_lengths)
|
||
text_inputs = self.set_text_padding(text_inputs, text_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 = speech_conditioning_latent
|
||
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)
|
||
if raw_mels is not None:
|
||
mel_inp = F.pad(raw_mels, (0, 8))
|
||
else:
|
||
mel_inp = mel_codes
|
||
mel_emb = self.mel_embedding(mel_inp)
|
||
mel_emb = mel_emb + self.mel_pos_embedding(mel_codes)
|
||
|
||
if text_first:
|
||
# print(f"conds: {conds.shape}, text_emb: {text_emb.shape}, mel_emb: {mel_emb.shape}")
|
||
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[:, :-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, get_attns=return_attentions, 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.
|
||
|
||
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 prepare_gpt_inputs(
|
||
self,
|
||
conditional_latents: torch.Tensor,
|
||
text_inputs: torch.Tensor,
|
||
):
|
||
|
||
"""
|
||
Prepare the inputs for the GPT2InferenceModel to generate.
|
||
Args:
|
||
conds_latent: (b, 32, dim) audio conditioning embedding by `get_conditioning()`
|
||
text_inputs: (b, L)
|
||
Returns:
|
||
input_ids: (b, s+1) the input ids for the GPT2InferenceModel.generate()
|
||
inputs_embeds: (b, s+1, dim) the input embeddings for the GPT2InferenceModel.forward()
|
||
attention_mask: (b, s+1) the attention mask for the GPT2InferenceModel.generate()
|
||
"""
|
||
b, L = text_inputs.shape[:2]
|
||
device = text_inputs.device
|
||
single_cond = conditional_latents.ndim == 3 and conditional_latents.shape[0] == 1
|
||
if not single_cond:
|
||
assert conditional_latents.shape[0] == b, f"batch size mismatch: {conditional_latents.shape[0]} vs {b}"
|
||
batched_mel_emb = []
|
||
attention_masks = []
|
||
target_len = conditional_latents.shape[1] + L + 2
|
||
for i in range(b):
|
||
valid_mask = (text_inputs[i] != self.stop_text_token) & (text_inputs[i] != self.start_text_token)
|
||
text_input = text_inputs[i][valid_mask]
|
||
text_input = F.pad(text_input, (1, 0), value=self.start_text_token)
|
||
text_input = F.pad(text_input, (0, 1), value=self.stop_text_token)
|
||
text_input_pos = torch.arange(0, text_input.size(-1), device=device)
|
||
text_emb = self.text_embedding(text_input) + self.text_pos_embedding.emb(text_input_pos)
|
||
# concatenate [conditional latents][text embeddings]
|
||
conds_text_emb = [
|
||
conditional_latents.squeeze(0) if single_cond else conditional_latents[i],
|
||
text_emb,
|
||
]
|
||
# +1 for the start_mel_token
|
||
attention_mask = torch.ones(target_len+1, dtype=torch.long, device=device)
|
||
# check this text input is padded
|
||
padding: int = L + 2 - text_input.size(-1)
|
||
# pad left of [cond][text] -> [pad][cond][text]
|
||
if padding > 0:
|
||
pad = torch.zeros((padding, conditional_latents.size(-1)), dtype=text_emb.dtype, device=device) # [p, dim]
|
||
conds_text_emb.insert(0, pad)
|
||
attention_mask[:padding] = 0
|
||
mel_emb = torch.cat(conds_text_emb) #[s, dim]
|
||
assert mel_emb.shape[0] == target_len, f"mel_emb.shape: {mel_emb.shape}, target_len: {target_len}"
|
||
batched_mel_emb.append(mel_emb)
|
||
attention_masks.append(attention_mask)
|
||
# [b, s, dim]
|
||
batched_mel_emb = torch.stack(batched_mel_emb, dim=0)
|
||
# [b, s+1]
|
||
attention_mask = torch.stack(attention_masks, dim=0)
|
||
# [b, s+1]
|
||
fake_inputs = torch.ones(
|
||
(
|
||
batched_mel_emb.shape[0],
|
||
batched_mel_emb.shape[1] + 1, # +1 for the start_mel_token
|
||
),
|
||
dtype=torch.long,
|
||
device=device,
|
||
)
|
||
fake_inputs[:, -1] = self.start_mel_token
|
||
return fake_inputs, batched_mel_emb, attention_mask
|
||
def inference_speech(self, speech_conditioning_mel, text_inputs, cond_mel_lengths=None, input_tokens=None, num_return_sequences=1,
|
||
max_generate_length=None, typical_sampling=False, typical_mass=.9, **hf_generate_kwargs):
|
||
"""
|
||
Args:
|
||
speech_conditioning_mel: (b, n_mels, frames) or (n_mels, frames)
|
||
text_inputs: (b, L)
|
||
cond_mel_lengths: lengths of the conditioning mel spectrograms in shape (b,) or (1,)
|
||
input_tokens: additional tokens for generation in shape (b, s) or (s,)
|
||
max_generate_length: limit the number of generated tokens
|
||
hf_generate_kwargs: kwargs for `GPT2InferenceModel.generate(**hf_generate_kwargs)`
|
||
"""
|
||
if speech_conditioning_mel.ndim == 2:
|
||
speech_conditioning_mel = speech_conditioning_mel.unsqueeze(0)
|
||
if cond_mel_lengths is None:
|
||
cond_mel_lengths = torch.tensor([speech_conditioning_mel.shape[-1]], device=speech_conditioning_mel.device)
|
||
conds_latent = self.get_conditioning(speech_conditioning_mel, cond_mel_lengths)
|
||
input_ids, inputs_embeds, attention_mask = self.prepare_gpt_inputs(conds_latent, text_inputs)
|
||
self.inference_model.store_mel_emb(inputs_embeds)
|
||
if input_tokens is None:
|
||
inputs = input_ids
|
||
else:
|
||
if input_tokens.ndim == 1:
|
||
input_tokens = input_tokens.unsqueeze(0)
|
||
assert num_return_sequences % input_tokens.shape[0] == 0, \
|
||
"The num_return_sequences must be divisible by the batch number of input_tokens"
|
||
assert num_return_sequences % text_inputs.shape[0] == 0, \
|
||
"The num_return_sequences must be divisible by the batch number of text_inputs"
|
||
b = num_return_sequences // input_ids.shape[0]
|
||
if b > 1:
|
||
input_ids = input_ids.repeat(b, 1)
|
||
attention_mask = attention_mask.repeat(b, 1)
|
||
input_tokens = input_tokens.repeat(num_return_sequences // input_tokens.shape[0], 1)
|
||
inputs = torch.cat([input_ids, input_tokens], dim=1)
|
||
attention_mask = F.pad(attention_mask, (0, input_tokens.shape[1]), value=1)
|
||
trunc_index = inputs.shape[1]
|
||
logits_processor = LogitsProcessorList()
|
||
if typical_sampling:
|
||
# employ custom typical sampling
|
||
if not (typical_mass > 0.0 and typical_mass < 1.0):
|
||
raise ValueError(f"`typical_mass` has to be a float > 0 and < 1, but is {typical_mass}")
|
||
min_tokens_to_keep = 2 if hf_generate_kwargs.get("num_beams", 1) > 1 else 1
|
||
logits_processor.append(TypicalLogitsWarper(mass=typical_mass, min_tokens_to_keep=min_tokens_to_keep))
|
||
max_length = (trunc_index + self.max_mel_tokens - 1) if max_generate_length is None else trunc_index + max_generate_length
|
||
output = self.inference_model.generate(inputs,
|
||
bos_token_id=self.start_mel_token, pad_token_id=self.stop_mel_token,
|
||
eos_token_id=self.stop_mel_token, attention_mask=attention_mask,
|
||
max_length=max_length, logits_processor=logits_processor,
|
||
num_return_sequences=num_return_sequences,
|
||
**hf_generate_kwargs)
|
||
if isinstance(output, torch.Tensor):
|
||
return output[:, trunc_index:]
|
||
# GenerateOutput
|
||
output.sequences = output.sequences[:, trunc_index:]
|
||
return output
|