Update use_gpt_tts to be usable with unified_voice2
This commit is contained in:
James Betker
parent
7b4544b83a
commit
dc9cd8c206
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@ -42,6 +42,9 @@ class LearnedPositionEmbeddings(nn.Module):
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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):
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"""
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@ -3,10 +3,11 @@ 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 transformers import GPT2Model, GPT2Config, GPT2PreTrainedModel
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from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
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from transformers.utils.model_parallel_utils import get_device_map, assert_device_map
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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.gpt_voice.transformer_builders import build_hf_gpt_transformer
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from models.tacotron2.text import symbols
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@ -14,6 +15,160 @@ from trainer.networks import register_model
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from utils.util import opt_get
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class GPT2InferenceModel(GPT2PreTrainedModel):
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def __init__(self, config, gpt, text_pos_emb, embeddings, norm, linear):
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super().__init__(config)
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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.lm_head = nn.Sequential(norm, linear)
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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(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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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=None, **kwargs):
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token_type_ids = kwargs.get("token_type_ids", None)
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# only last token for inputs_ids if past is defined in kwargs
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if past:
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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, 1)
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if past:
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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,
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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 = return_dict if return_dict is not None else self.config.use_return_dict
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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(text_emb.shape[0]//self.cached_mel_emb.shape[0], 0)
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else:
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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(attention_mask.shape[1]-mel_len, attention_mask.device)
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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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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(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
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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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@ -275,9 +430,9 @@ class UnifiedVoice(nn.Module):
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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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seq_length = self.max_mel_tokens + self.max_text_tokens + 2
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if not hasattr(self, 'inference_model'):
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# TODO: Decouple gpt_config from this inference model.
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seq_length = self.max_mel_tokens + self.max_text_tokens + 5
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gpt_config = GPT2Config(vocab_size=self.max_mel_tokens,
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n_positions=seq_length,
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n_ctx=seq_length,
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@ -286,7 +441,8 @@ class UnifiedVoice(nn.Module):
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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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self.inference_model = GPT2InferenceModel(gpt_config, self.gpt, self.mel_pos_embedding, self.final_norm, self.mel_head)
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self.inference_model = GPT2InferenceModel(gpt_config, self.gpt, self.mel_pos_embedding, self.mel_embedding, self.final_norm, self.mel_head)
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self.gpt.wte = self.mel_embedding
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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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@ -301,11 +457,11 @@ class UnifiedVoice(nn.Module):
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emb = torch.cat([conds, 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 = torch.full((emb.shape[0], conds.shape[1]+emb.shape[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.seq_length, **hf_generate_kwargs)
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max_length=seq_length, **hf_generate_kwargs)
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return gen[:, fake_inputs.shape[1]:]
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@ -80,13 +80,13 @@ def fix_autoregressive_output(codes, stop_token):
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if __name__ == '__main__':
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preselected_cond_voices = {
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'trump': 'D:\\data\\audio\\sample_voices\\trump.wav',
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'ryan_reynolds': 'D:\\data\\audio\\sample_voices\\ryan_reynolds.wav',
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'ed_sheeran': 'D:\\data\\audio\\sample_voices\\ed_sheeran.wav',
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'simmons': 'Y:\\clips\\books1\\754_Dan Simmons - The Rise Of Endymion 356 of 450\\00026.wav',
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'news_girl': 'Y:\\clips\\podcasts-0\\8288_20210113-Is More Violence Coming_\\00022.wav',
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'dan_carlin': 'Y:\\clips\\books1\\5_dchha06 Shield of the West\\00476.wav',
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'libri_test': 'Y:\\libritts\\test-clean\\672\\122797\\672_122797_000057_000002.wav'
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'trump': ['D:\\data\\audio\\sample_voices\\trump.wav'],
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'ryan_reynolds': ['D:\\data\\audio\\sample_voices\\ryan_reynolds.wav'],
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'ed_sheeran': ['D:\\data\\audio\\sample_voices\\ed_sheeran.wav'],
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'simmons': ['Y:\\clips\\books1\\754_Dan Simmons - The Rise Of Endymion 356 of 450\\00026.wav'],
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'news_girl': ['Y:\\clips\\podcasts-0\\8288_20210113-Is More Violence Coming_\\00022.wav', 'Y:\\clips\\podcasts-0\\8288_20210113-Is More Violence Coming_\\00016.wav'],
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'dan_carlin': ['Y:\\clips\\books1\\5_dchha06 Shield of the West\\00476.wav'],
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'libri_test': ['Y:\\libritts\\test-clean\\672\\122797\\672_122797_000057_000002.wav']
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}
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parser = argparse.ArgumentParser()
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@ -94,17 +94,16 @@ if __name__ == '__main__':
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parser.add_argument('-diffusion_model_name', type=str, help='Name of the diffusion model in opt.', default='generator')
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parser.add_argument('-diffusion_model_path', type=str, help='Diffusion model checkpoint to load.', default='X:\\dlas\\experiments\\train_diffusion_vocoder_with_cond_new_dvae_full\\models\\6100_generator_ema.pth')
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parser.add_argument('-dvae_model_name', type=str, help='Name of the DVAE model in opt.', default='dvae')
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parser.add_argument('-opt_gpt_tts', type=str, help='Path to options YAML file used to train the GPT-TTS model', default='X:\\dlas\\experiments\\train_gpt_tts_unified\\train_gpt_tts_unified.yml')
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parser.add_argument('-opt_gpt_tts', type=str, help='Path to options YAML file used to train the GPT-TTS model', default='X:\\dlas\\experiments\\train_gpt_tts_unified.yml')
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parser.add_argument('-gpt_tts_model_name', type=str, help='Name of the GPT TTS model in opt.', default='gpt')
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parser.add_argument('-gpt_tts_model_path', type=str, help='GPT TTS model checkpoint to load.', default='X:\\dlas\\experiments\\train_gpt_tts_unified\\models\\60000_gpt_ema.pth')
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parser.add_argument('-gpt_tts_model_path', type=str, help='GPT TTS model checkpoint to load.', default='X:\\dlas\\experiments\\train_gpt_tts_unified_large\\models\\40000_gpt_ema.pth')
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parser.add_argument('-opt_clip', type=str, help='Path to options YAML file used to train the CLIP model', default='X:\\dlas\\experiments\\train_clip_text_to_voice.yml')
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parser.add_argument('-clip_model_name', type=str, help='Name of the CLIP model in opt.', default='clip')
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parser.add_argument('-clip_model_path', type=str, help='CLIP model checkpoint to load.', default='X:\\dlas\\experiments\\train_clip_text_to_voice_masking_bigger_batch\\models\\23500_clip_ema.pth')
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parser.add_argument('-text', type=str, help='Text to speak.', default="I am a language model that has learned to speak.")
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parser.add_argument('-cond_path', type=str, help='Path to condioning sample.', default='')
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parser.add_argument('-cond_preset', type=str, help='Use a preset conditioning voice (defined above). Overrides cond_path.', default='libri_test')
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parser.add_argument('-num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=128)
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parser.add_argument('-num_batches', type=int, help='How many batches those samples should be produced over.', default=2)
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parser.add_argument('-num_batches', type=int, help='How many batches those samples should be produced over.', default=8)
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parser.add_argument('-num_outputs', type=int, help='Number of outputs to produce.', default=2)
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parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='../results/use_gpt_tts')
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args = parser.parse_args()
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@ -115,7 +114,7 @@ if __name__ == '__main__':
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with open(args.opt_gpt_tts, mode='r') as f:
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gpt_opt = yaml.load(f, Loader=Loader)
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gpt_opt['networks'][args.gpt_tts_model_name]['kwargs']['checkpointing'] = False # Required for beam search
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gpt = load_model_from_config(preloaded_options=gpt_opt, model_name=args.gpt_tts_model_name, also_load_savepoint=False, load_path=args.gpt_tts_model_path, strict_load=False).eval()
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gpt = load_model_from_config(preloaded_options=gpt_opt, model_name=args.gpt_tts_model_name, also_load_savepoint=False, load_path=args.gpt_tts_model_path).eval()
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stop_mel_token = gpt.stop_mel_token
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print("Loading data..")
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@ -123,8 +122,12 @@ if __name__ == '__main__':
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text = torch.IntTensor(tokenizer.encode(args.text)).unsqueeze(0).cuda()
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text = F.pad(text, (0,1)) # This may not be necessary.
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cond_path = args.cond_path if args.cond_preset is None else preselected_cond_voices[args.cond_preset]
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conds, cond_wav = load_conditioning(cond_path, cond_length=88000)
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cond_paths = preselected_cond_voices[args.cond_preset]
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conds = []
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for cond_path in cond_paths:
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c, cond_wav = load_conditioning(cond_path, cond_length=132300)
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conds.append(c)
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conds = torch.stack(conds, dim=1) # And just use the last cond_wav for the diffusion model.
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with torch.no_grad():
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print("Performing GPT inference..")
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