forked from mrq/tortoise-tts
01b783fc02
- Adds a new script and API endpoints for doing this - Reworks autoregressive and diffusion models so that the conditioning is computed separately (which will actually provide a mild performance boost) - Updates README This is untested. Need to do the following manual tests (and someday write unit tests for this behemoth before it becomes a problem..) 1) Does get_conditioning_latents.py work? 2) Can I feed those latents back into the model by creating a new voice? 3) Can I still mix and match voices (both with conditioning latents and normal voices) with read.py?
575 lines
27 KiB
Python
575 lines
27 KiB
Python
import functools
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList
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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 tortoise.models.arch_util import AttentionBlock
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from tortoise.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):
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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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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[:, :, 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):
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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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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=None, number_mel_codes=8194, start_mel_token=8192,
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stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True,
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checkpointing=True, average_conditioning_embeddings=False,
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types=1):
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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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average_conditioning_embeddings: Whether or not conditioning embeddings should be averaged, instead of fed piecewise into the model.
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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 = number_text_tokens * types if start_text_token is None else start_text_token
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self.stop_text_token = 0
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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.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads)
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self.average_conditioning_embeddings = average_conditioning_embeddings
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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, self.max_text_tokens+2, checkpointing)
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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 build_aligned_inputs_and_targets(self, input, start_token, stop_token):
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inp = F.pad(input, (1,0), value=start_token)
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tar = F.pad(input, (0,1), value=stop_token)
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return inp, tar
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def set_mel_padding(self, mel_input_tokens, wav_lengths):
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"""
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Given mel tokens that are derived from a padded audio clip and the actual lengths of each batch element in
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that audio clip, reformats the tokens with STOP_MEL_TOKEN in place of the zero padding. This is required
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preformatting to create a working TTS model.
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"""
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# Set padding areas within MEL (currently it is coded with the MEL code for <zero>).
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mel_lengths = torch.div(wav_lengths, self.mel_length_compression, rounding_mode='trunc')
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for b in range(len(mel_lengths)):
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actual_end = mel_lengths[b] + 1 # Due to the convolutional nature of how these tokens are generated, it would be best if the model predicts a token past the actual last token.
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if actual_end < mel_input_tokens.shape[-1]:
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mel_input_tokens[b, actual_end:] = self.stop_mel_token
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return mel_input_tokens
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def get_logits(self, speech_conditioning_inputs, first_inputs, first_head, second_inputs=None, second_head=None, get_attns=False, return_latent=False):
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if second_inputs is not None:
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emb = torch.cat([speech_conditioning_inputs, first_inputs, second_inputs], dim=1)
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else:
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emb = torch.cat([speech_conditioning_inputs, first_inputs], dim=1)
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gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns)
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if get_attns:
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return gpt_out.attentions
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enc = gpt_out.last_hidden_state[:, 1:] # The first logit is tied to the speech_conditioning_input
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enc = self.final_norm(enc)
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if return_latent:
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return enc[:, speech_conditioning_inputs.shape[1]:speech_conditioning_inputs.shape[1]+first_inputs.shape[1]], enc[:, -second_inputs.shape[1]:]
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first_logits = enc[:, :first_inputs.shape[1]]
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first_logits = first_head(first_logits)
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first_logits = first_logits.permute(0,2,1)
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if second_inputs is not None:
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second_logits = enc[:, -second_inputs.shape[1]:]
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second_logits = second_head(second_logits)
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second_logits = second_logits.permute(0,2,1)
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return first_logits, second_logits
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else:
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return first_logits
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def get_conditioning(self, speech_conditioning_input):
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speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(
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speech_conditioning_input.shape) == 3 else speech_conditioning_input
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conds = []
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for j in range(speech_conditioning_input.shape[1]):
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conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
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conds = torch.stack(conds, dim=1)
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if self.average_conditioning_embeddings:
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conds = conds.mean(dim=1).unsqueeze(1)
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return conds
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def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, types=None, text_first=True, raw_mels=None, return_attentions=False,
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return_latent=False, clip_inputs=True):
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"""
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Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
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(actuated by `text_first`).
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speech_conditioning_input: MEL float tensor, (b,80,s)
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text_inputs: long tensor, (b,t)
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text_lengths: long tensor, (b,)
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mel_inputs: long tensor, (b,m)
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wav_lengths: long tensor, (b,)
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raw_mels: MEL float tensor (b,80,s)
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If return_attentions is specified, only logits are returned.
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If return_latent is specified, loss & logits are not computed or returned. Only the predicted latents are returned.
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If clip_inputs is True, the inputs will be clipped to the smallest input size across each input modality.
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"""
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# Types are expressed by expanding the text embedding space.
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if types is not None:
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text_inputs = text_inputs * (1+types).unsqueeze(-1)
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|
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if clip_inputs:
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# This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
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# chopping the inputs by the maximum actual length.
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max_text_len = text_lengths.max()
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text_inputs = text_inputs[:, :max_text_len]
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max_mel_len = wav_lengths.max() // self.mel_length_compression
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mel_codes = mel_codes[:, :max_mel_len]
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if raw_mels is not None:
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raw_mels = raw_mels[:, :, :max_mel_len*4]
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mel_codes = self.set_mel_padding(mel_codes, wav_lengths)
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text_inputs = F.pad(text_inputs, (0,1), value=self.stop_text_token)
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mel_codes = F.pad(mel_codes, (0,1), value=self.stop_mel_token)
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|
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conds = self.get_conditioning(speech_conditioning_input)
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text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
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text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs)
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mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token)
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if raw_mels is not None:
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mel_inp = F.pad(raw_mels, (0, 8))
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else:
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mel_inp = mel_codes
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mel_emb = self.mel_embedding(mel_inp)
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mel_emb = mel_emb + self.mel_pos_embedding(mel_codes)
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|
|
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if text_first:
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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)
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if return_latent:
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return mel_logits[:, :-2] # Despite the name, these are not logits. Strip off the two tokens added by this forward pass.
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else:
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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)
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if return_latent:
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|
return text_logits[:, :-2] # Despite the name, these are not logits. Strip off the two tokens added by this forward pass.
|
|
|
|
if return_attentions:
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return mel_logits
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|
loss_text = F.cross_entropy(text_logits, text_targets.long())
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loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
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return loss_text.mean(), loss_mel.mean(), mel_logits
|
|
|
|
def text_forward(self, speech_conditioning_input, text_inputs, text_lengths):
|
|
"""
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|
Performs autoregressive modeling on only text. Still requires a speech_conditioning_input due to the way the
|
|
model inputs are formatted. Just provide any audio clip (arguably, zeros could be provided).
|
|
"""
|
|
assert self.max_text_tokens >= text_inputs.shape[1], f'{text_inputs.shape[1]}'
|
|
|
|
# This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
|
|
# chopping the inputs by the maximum actual length.
|
|
max_text_len = text_lengths.max()
|
|
text_inputs = F.pad(text_inputs[:, :max_text_len], (0,1), value=self.stop_text_token)
|
|
|
|
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)
|
|
if self.average_conditioning_embeddings:
|
|
conds = conds.mean(dim=1).unsqueeze(1)
|
|
|
|
text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
|
|
text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs) + self.text_solo_embedding
|
|
text_logits = self.get_logits(conds, text_emb, self.text_head)
|
|
loss_text = F.cross_entropy(text_logits, text_targets.long())
|
|
return loss_text.mean()
|
|
|
|
def speech_forward(self, speech_conditioning_input, mel_codes, wav_lengths, raw_mels=None):
|
|
"""
|
|
Performs autoregressive modeling on only speech data.
|
|
"""
|
|
assert self.max_mel_tokens >= mel_codes.shape[1], f'{mel_codes.shape[1]}'
|
|
|
|
# This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
|
|
# chopping the inputs by the maximum actual length.
|
|
max_mel_len = wav_lengths.max() // self.mel_length_compression
|
|
mel_codes = F.pad(mel_codes[:, :max_mel_len], (0,1), value=self.stop_mel_token)
|
|
mel_codes = self.set_mel_padding(mel_codes, wav_lengths)
|
|
if raw_mels is not None:
|
|
raw_mels = raw_mels[:, :, :max_mel_len*4]
|
|
|
|
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)
|
|
if self.average_conditioning_embeddings:
|
|
conds = conds.mean(dim=1).unsqueeze(1)
|
|
|
|
mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token)
|
|
if raw_mels is not None:
|
|
mel_inp = F.pad(raw_mels, (0, 4))
|
|
else:
|
|
mel_inp = mel_codes
|
|
mel_emb = self.mel_embedding(mel_inp)
|
|
mel_emb = mel_emb + self.mel_pos_embedding(mel_codes) + self.mel_solo_embedding
|
|
mel_logits = self.get_logits(conds, mel_emb, self.mel_head)
|
|
loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
|
|
return loss_mel.mean()
|
|
|
|
def inference_speech(self, speech_conditioning_latent, text_inputs, input_tokens=None, num_return_sequences=1,
|
|
max_generate_length=None, typical_sampling=False, typical_mass=.9, **hf_generate_kwargs):
|
|
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)
|
|
|
|
conds = speech_conditioning_latent
|
|
emb = torch.cat([conds, text_emb], dim=1)
|
|
self.inference_model.store_mel_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
|
|
trunc_index = fake_inputs.shape[1]
|
|
if input_tokens is None:
|
|
inputs = fake_inputs
|
|
else:
|
|
assert num_return_sequences % input_tokens.shape[0] == 0, "The number of return sequences must be divisible by the number of input sequences"
|
|
fake_inputs = fake_inputs.repeat(num_return_sequences, 1)
|
|
input_tokens = input_tokens.repeat(num_return_sequences // input_tokens.shape[0], 1)
|
|
inputs = torch.cat([fake_inputs, input_tokens], dim=1)
|
|
|
|
logits_processor = LogitsProcessorList([TypicalLogitsWarper(mass=typical_mass)]) if typical_sampling else LogitsProcessorList()
|
|
max_length = trunc_index + self.max_mel_tokens - 1 if max_generate_length is None else trunc_index + max_generate_length
|
|
gen = 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,
|
|
max_length=max_length, logits_processor=logits_processor,
|
|
num_return_sequences=num_return_sequences, **hf_generate_kwargs)
|
|
return gen[:, trunc_index:]
|
|
|
|
|
|
if __name__ == '__main__':
|
|
gpt = UnifiedVoice(model_dim=256, heads=4, train_solo_embeddings=True, use_mel_codes_as_input=True, max_conditioning_inputs=4)
|
|
l = gpt(torch.randn(2, 3, 80, 800),
|
|
torch.randint(high=120, size=(2,120)),
|
|
torch.tensor([32, 120]),
|
|
torch.randint(high=8192, size=(2,250)),
|
|
torch.tensor([250*256,195*256]))
|
|
gpt.text_forward(torch.randn(2,80,800), torch.randint(high=50, size=(2,80)), torch.tensor([32, 80]))
|