593 lines
28 KiB
Python
593 lines
28 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from transformers import GPT2Config, GPT2PreTrainedModel
|
|
from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
|
|
from transformers.models.gpt2.modeling_gpt2 import GPT2Attention
|
|
from transformers.utils.model_parallel_utils import get_device_map, assert_device_map
|
|
|
|
from models.arch_util import AttentionBlock
|
|
from models.audio.tts.transformer_builders import build_hf_gpt_transformer
|
|
from models.lucidrains.x_transformers import RotaryEmbedding, apply_rotary_pos_emb
|
|
from trainer.networks import register_model
|
|
from utils.util import opt_get
|
|
|
|
|
|
class ResBlock(nn.Module):
|
|
"""
|
|
Basic residual convolutional block that uses GroupNorm.
|
|
"""
|
|
def __init__(self, chan):
|
|
super().__init__()
|
|
self.net = nn.Sequential(
|
|
nn.Conv1d(chan, chan, kernel_size=3, padding=1),
|
|
nn.GroupNorm(chan//8, chan),
|
|
nn.ReLU(),
|
|
nn.Conv1d(chan, chan, kernel_size=3, padding=1),
|
|
nn.GroupNorm(chan//8, chan)
|
|
)
|
|
|
|
def forward(self, x):
|
|
return F.relu(self.net(x) + x)
|
|
|
|
|
|
class GPT2InferenceModel(GPT2PreTrainedModel):
|
|
def __init__(self, config, gpt, text_pos_emb, embeddings, norm, linear):
|
|
super().__init__(config)
|
|
self.transformer = gpt
|
|
self.text_pos_embedding = text_pos_emb
|
|
self.embeddings = embeddings
|
|
self.lm_head = nn.Sequential(norm, linear)
|
|
|
|
# Model parallel
|
|
self.model_parallel = False
|
|
self.device_map = None
|
|
self.cached_mel_emb = None
|
|
|
|
def parallelize(self, device_map=None):
|
|
self.device_map = (
|
|
get_device_map(len(self.transformer.h), range(torch.cuda.device_count()))
|
|
if device_map is None
|
|
else device_map
|
|
)
|
|
assert_device_map(self.device_map, len(self.transformer.h))
|
|
self.transformer.parallelize(self.device_map)
|
|
self.lm_head = self.lm_head.to(self.transformer.first_device)
|
|
self.model_parallel = True
|
|
|
|
def deparallelize(self):
|
|
self.transformer.deparallelize()
|
|
self.transformer = self.transformer.to("cpu")
|
|
self.lm_head = self.lm_head.to("cpu")
|
|
self.model_parallel = False
|
|
torch.cuda.empty_cache()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head = new_embeddings
|
|
|
|
def store_mel_emb(self, mel_emb):
|
|
self.cached_mel_emb = mel_emb
|
|
|
|
def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
|
|
|
|
token_type_ids = kwargs.get("token_type_ids", None)
|
|
# only last token for inputs_ids if past is defined in kwargs
|
|
if past:
|
|
input_ids = input_ids[:, -1].unsqueeze(-1)
|
|
if token_type_ids is not None:
|
|
token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
|
|
|
|
attention_mask = kwargs.get("attention_mask", None)
|
|
position_ids = kwargs.get("position_ids", None)
|
|
|
|
if attention_mask is not None and position_ids is None:
|
|
# create position_ids on the fly for batch generation
|
|
position_ids = attention_mask.long().cumsum(-1) - 1
|
|
position_ids.masked_fill_(attention_mask == 0, 1)
|
|
if past:
|
|
position_ids = position_ids[:, -1].unsqueeze(-1)
|
|
else:
|
|
position_ids = None
|
|
return {
|
|
"input_ids": input_ids,
|
|
"past_key_values": past,
|
|
"use_cache": kwargs.get("use_cache"),
|
|
"position_ids": position_ids,
|
|
"attention_mask": attention_mask,
|
|
"token_type_ids": token_type_ids,
|
|
}
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
past_key_values=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
labels=None,
|
|
use_cache=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
assert self.cached_mel_emb is not None
|
|
assert inputs_embeds is None # Not supported by this inference model.
|
|
assert labels is None # Training not supported by this inference model.
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# Create embedding
|
|
mel_len = self.cached_mel_emb.shape[1]
|
|
if input_ids.shape[1] != 1:
|
|
text_inputs = input_ids[:, mel_len:]
|
|
text_emb = self.embeddings(text_inputs)
|
|
text_emb = text_emb + self.text_pos_embedding(text_emb)
|
|
if self.cached_mel_emb.shape[0] != text_emb.shape[0]:
|
|
mel_emb = self.cached_mel_emb.repeat_interleave(text_emb.shape[0]//self.cached_mel_emb.shape[0], 0)
|
|
else:
|
|
mel_emb = self.cached_mel_emb
|
|
emb = torch.cat([mel_emb, text_emb], dim=1)
|
|
else:
|
|
emb = self.embeddings(input_ids)
|
|
emb = emb + self.text_pos_embedding.get_fixed_embedding(attention_mask.shape[1]-mel_len, attention_mask.device)
|
|
|
|
transformer_outputs = self.transformer(
|
|
inputs_embeds=emb,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
hidden_states = transformer_outputs[0]
|
|
|
|
# Set device for model parallelism
|
|
if self.model_parallel:
|
|
torch.cuda.set_device(self.transformer.first_device)
|
|
hidden_states = hidden_states.to(self.lm_head.weight.device)
|
|
|
|
lm_logits = self.lm_head(hidden_states)
|
|
|
|
if not return_dict:
|
|
return (lm_logits,) + transformer_outputs[1:]
|
|
|
|
return CausalLMOutputWithCrossAttentions(
|
|
loss=None,
|
|
logits=lm_logits,
|
|
past_key_values=transformer_outputs.past_key_values,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
cross_attentions=transformer_outputs.cross_attentions,
|
|
)
|
|
|
|
@staticmethod
|
|
def _reorder_cache(past, beam_idx):
|
|
"""
|
|
This function is used to re-order the :obj:`past_key_values` cache if
|
|
:meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is
|
|
called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
|
|
"""
|
|
return tuple(
|
|
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
|
|
for layer_past in past
|
|
)
|
|
|
|
|
|
class ConditioningEncoder(nn.Module):
|
|
def __init__(self,
|
|
spec_dim,
|
|
embedding_dim,
|
|
attn_blocks=6,
|
|
num_attn_heads=4,
|
|
do_checkpointing=False,
|
|
mean=False):
|
|
super().__init__()
|
|
attn = []
|
|
self.init = nn.Conv1d(spec_dim, embedding_dim, kernel_size=1)
|
|
for a in range(attn_blocks):
|
|
attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=do_checkpointing))
|
|
self.attn = nn.Sequential(*attn)
|
|
self.dim = embedding_dim
|
|
self.do_checkpointing = do_checkpointing
|
|
self.mean = mean
|
|
|
|
def forward(self, x):
|
|
h = self.init(x)
|
|
h = self.attn(h)
|
|
if self.mean:
|
|
return h.mean(dim=2)
|
|
else:
|
|
return h[:, :, 0]
|
|
|
|
|
|
class MelEncoder(nn.Module):
|
|
def __init__(self, channels, mel_channels=80, resblocks_per_reduction=2):
|
|
super().__init__()
|
|
self.channels = channels
|
|
self.encoder = nn.Sequential(nn.Conv1d(mel_channels, channels//4, kernel_size=3, padding=1),
|
|
nn.Sequential(*[ResBlock(channels//4) for _ in range(resblocks_per_reduction)]),
|
|
nn.Conv1d(channels//4, channels//2, kernel_size=3, stride=2, padding=1),
|
|
nn.GroupNorm(channels//16, channels//2),
|
|
nn.ReLU(),
|
|
nn.Sequential(*[ResBlock(channels//2) for _ in range(resblocks_per_reduction)]),
|
|
nn.Conv1d(channels//2, channels, kernel_size=3, stride=2, padding=1),
|
|
nn.GroupNorm(channels//8, channels),
|
|
nn.ReLU(),
|
|
nn.Sequential(*[ResBlock(channels) for _ in range(resblocks_per_reduction)]),
|
|
)
|
|
self.reduction = 4
|
|
|
|
|
|
def forward(self, x):
|
|
for e in self.encoder:
|
|
x = e(x)
|
|
return x.permute(0,2,1)
|
|
|
|
|
|
class UnifiedVoice(nn.Module):
|
|
def __init__(self, layers=8, model_dim=512, heads=8, max_text_tokens=120, max_mel_tokens=250, max_conditioning_inputs=1,
|
|
mel_length_compression=1024, number_text_tokens=256, number_mel_codes=8194, start_mel_token=8192,
|
|
stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True,
|
|
checkpointing=True, average_conditioning_embeddings=False, freeze_everything_but_position_embeddings=False,
|
|
types=1):
|
|
"""
|
|
Args:
|
|
layers: Number of layers in transformer stack.
|
|
model_dim: Operating dimensions of the transformer
|
|
heads: Number of transformer heads. Must be divisible by model_dim. Recommend model_dim//64
|
|
max_text_tokens: Maximum number of text tokens that will be encountered by model.
|
|
max_mel_tokens: Maximum number of MEL tokens that will be encountered by model.
|
|
max_conditioning_inputs: Maximum number of conditioning inputs provided to the model. If (1), conditioning input can be of format (b,80,s), otherwise (b,n,80,s).
|
|
mel_length_compression: The factor between <number_input_samples> and <mel_tokens>. Used to compute MEL code padding given wav input length.
|
|
number_text_tokens:
|
|
stop_text_token:
|
|
number_mel_codes:
|
|
start_mel_token:
|
|
stop_mel_token:
|
|
train_solo_embeddings:
|
|
use_mel_codes_as_input:
|
|
checkpointing:
|
|
average_conditioning_embeddings: Whether or not conditioning embeddings should be averaged, instead of fed piecewise into the model.
|
|
"""
|
|
super().__init__()
|
|
|
|
self.number_text_tokens = number_text_tokens
|
|
self.start_text_token = number_text_tokens * types
|
|
self.stop_text_token = 0
|
|
self.number_mel_codes = number_mel_codes
|
|
self.start_mel_token = start_mel_token
|
|
self.stop_mel_token = stop_mel_token
|
|
self.layers = layers
|
|
self.heads = heads
|
|
self.max_conditioning_inputs = max_conditioning_inputs
|
|
self.max_mel_tokens = -1 if max_mel_tokens == -1 else max_mel_tokens+2+self.max_conditioning_inputs
|
|
self.max_text_tokens = -1 if max_text_tokens == -1 else max_text_tokens+2
|
|
self.model_dim = model_dim
|
|
self.mel_length_compression = mel_length_compression
|
|
self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads)
|
|
self.average_conditioning_embeddings = average_conditioning_embeddings
|
|
self.text_embedding = nn.Embedding(self.number_text_tokens*types+1, model_dim)
|
|
if use_mel_codes_as_input:
|
|
self.mel_embedding = nn.Embedding(self.number_mel_codes, model_dim)
|
|
else:
|
|
self.mel_embedding = MelEncoder(model_dim, resblocks_per_reduction=1)
|
|
self.gpt, self.mel_pos_embedding, self.text_pos_embedding, self.mel_layer_pos_embedding, self.text_layer_pos_embedding = \
|
|
build_hf_gpt_transformer(layers, model_dim, heads, self.max_mel_tokens, self.max_text_tokens, checkpointing)
|
|
if train_solo_embeddings:
|
|
self.mel_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * .02, requires_grad=True)
|
|
self.text_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * .02, requires_grad=True)
|
|
else:
|
|
self.mel_solo_embedding = 0
|
|
self.text_solo_embedding = 0
|
|
|
|
self.final_norm = nn.LayerNorm(model_dim)
|
|
self.text_head = nn.Linear(model_dim, self.number_text_tokens*types+1)
|
|
self.mel_head = nn.Linear(model_dim, self.number_mel_codes)
|
|
|
|
# Initialize the embeddings per the GPT-2 scheme
|
|
embeddings = [self.text_embedding]
|
|
if use_mel_codes_as_input:
|
|
embeddings.append(self.mel_embedding)
|
|
for module in embeddings:
|
|
module.weight.data.normal_(mean=0.0, std=.02)
|
|
|
|
if freeze_everything_but_position_embeddings:
|
|
for p in self.parameters():
|
|
p.requires_grad = False
|
|
p.DO_NOT_TRAIN = True
|
|
for m in [self.mel_pos_embedding, self.text_pos_embedding]:
|
|
for p in m.parameters():
|
|
del p.DO_NOT_TRAIN
|
|
p.requires_grad = True
|
|
|
|
def get_grad_norm_parameter_groups(self):
|
|
return {
|
|
'conditioning_encoder': list(self.conditioning_encoder.parameters()),
|
|
'gpt': list(self.gpt.parameters()),
|
|
'heads': list(self.text_head.parameters()) + list(self.mel_head.parameters()),
|
|
}
|
|
|
|
def build_aligned_inputs_and_targets(self, input, start_token, stop_token):
|
|
inp = F.pad(input, (1,0), value=start_token)
|
|
tar = F.pad(input, (0,1), value=stop_token)
|
|
return inp, tar
|
|
|
|
def set_mel_padding(self, mel_input_tokens, wav_lengths):
|
|
"""
|
|
Given mel tokens that are derived from a padded audio clip and the actual lengths of each batch element in
|
|
that audio clip, reformats the tokens with STOP_MEL_TOKEN in place of the zero padding. This is required
|
|
preformatting to create a working TTS model.
|
|
"""
|
|
# Set padding areas within MEL (currently it is coded with the MEL code for <zero>).
|
|
mel_lengths = wav_lengths // self.mel_length_compression
|
|
for b in range(len(mel_lengths)):
|
|
actual_end = mel_lengths[b] + 1 # Due to the convolutional nature of how these tokens are generated, it would be best if the model predicts a token past the actual last token.
|
|
if actual_end < mel_input_tokens.shape[-1]:
|
|
mel_input_tokens[b, actual_end:] = self.stop_mel_token
|
|
return mel_input_tokens
|
|
|
|
def get_logits(self, speech_conditioning_inputs, first_inputs, first_head, second_inputs=None, second_head=None, 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
|
|
|
|
enc = gpt_out.last_hidden_state[:, 1:] # The first logit is tied to the speech_conditioning_input
|
|
enc = self.final_norm(enc)
|
|
|
|
if return_latent:
|
|
return enc[:, speech_conditioning_inputs.shape[1]:speech_conditioning_inputs.shape[1]+first_inputs.shape[1]], enc[:, -second_inputs.shape[1]:]
|
|
|
|
first_logits = enc[:, :first_inputs.shape[1]]
|
|
first_logits = first_head(first_logits)
|
|
first_logits = first_logits.permute(0,2,1)
|
|
if second_inputs is not None:
|
|
second_logits = enc[:, -second_inputs.shape[1]:]
|
|
second_logits = second_head(second_logits)
|
|
second_logits = second_logits.permute(0,2,1)
|
|
return first_logits, second_logits
|
|
else:
|
|
return first_logits
|
|
|
|
def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, types=None, text_first=True, raw_mels=None, return_attentions=False,
|
|
return_latent=False, clip_inputs=True):
|
|
"""
|
|
Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
|
|
(actuated by `text_first`).
|
|
|
|
speech_conditioning_input: MEL float tensor, (b,80,s)
|
|
text_inputs: long tensor, (b,t)
|
|
text_lengths: long tensor, (b,)
|
|
mel_inputs: long tensor, (b,m)
|
|
wav_lengths: long tensor, (b,)
|
|
raw_mels: MEL float tensor (b,80,s)
|
|
|
|
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.
|
|
"""
|
|
# 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]
|
|
mel_codes = self.set_mel_padding(mel_codes, wav_lengths)
|
|
text_inputs = F.pad(text_inputs, (0,1), value=self.stop_text_token)
|
|
mel_codes = F.pad(mel_codes, (0,1), value=self.stop_mel_token)
|
|
|
|
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)
|
|
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:
|
|
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 text_forward(self, speech_conditioning_input, text_inputs, text_lengths):
|
|
"""
|
|
Performs autoregressive modeling on only text. Still requires a speech_conditioning_input due to the way the
|
|
model inputs are formatted. Just provide any audio clip (arguably, zeros could be provided).
|
|
"""
|
|
# 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_input, text_inputs, return_attentions=False, **hf_generate_kwargs):
|
|
if self.max_mel_tokens == -1: # Assume if this is the case, max_mel_tokens=-1 also
|
|
seq_length = 2002 # Arbitrary default.
|
|
else:
|
|
seq_length = self.max_mel_tokens + self.max_text_tokens + 2
|
|
if not hasattr(self, 'inference_model'):
|
|
# TODO: Decouple gpt_config from this inference model.
|
|
gpt_config = GPT2Config(vocab_size=self.max_mel_tokens,
|
|
n_positions=seq_length,
|
|
n_ctx=seq_length,
|
|
n_embd=self.model_dim,
|
|
n_layer=self.layers,
|
|
n_head=self.heads,
|
|
gradient_checkpointing=False,
|
|
use_cache=True)
|
|
self.inference_model = GPT2InferenceModel(gpt_config, self.gpt, self.mel_pos_embedding, self.mel_embedding, self.final_norm, self.mel_head)
|
|
self.gpt.wte = self.mel_embedding
|
|
|
|
text_inputs = F.pad(text_inputs, (0, 1), value=self.stop_text_token)
|
|
text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
|
|
text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs)
|
|
|
|
speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(speech_conditioning_input.shape) == 3 else speech_conditioning_input
|
|
conds = []
|
|
for j in range(speech_conditioning_input.shape[1]):
|
|
conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
|
|
conds = torch.stack(conds, dim=1)
|
|
if self.average_conditioning_embeddings:
|
|
conds = conds.mean(dim=1).unsqueeze(1)
|
|
|
|
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
|
|
|
|
gen = self.inference_model.generate(fake_inputs, bos_token_id=self.start_mel_token, pad_token_id=self.stop_mel_token, eos_token_id=self.stop_mel_token,
|
|
max_length=seq_length, output_attentions=return_attentions, return_dict_in_generate=True, **hf_generate_kwargs)
|
|
if return_attentions:
|
|
return gen.sequences[:, fake_inputs.shape[1]:], gen.attentions
|
|
else:
|
|
return gen.sequences[:, fake_inputs.shape[1]:]
|
|
|
|
|
|
# Turns the (utterly insane) output of HF.generate() into a far more sane output:
|
|
# [tensors(B,H,S,S)]. Outer=layers, B=batch,H=head,S=sequence
|
|
def make_hf_generate_attentions_sane(self, attentions):
|
|
layers = [[] for _ in range(len(attentions[0]))]
|
|
full_attention_size = attentions[-1][0].shape[-1]
|
|
for i, gen in enumerate(attentions):
|
|
for j, lyr in enumerate(gen):
|
|
layers[j].append(F.pad(lyr, (0, full_attention_size - lyr.shape[-1])))
|
|
catted = []
|
|
for lyr in layers:
|
|
catted.append(torch.cat(lyr, dim=2))
|
|
return catted
|
|
|
|
def convert_attentions_to_aligned_codes(self, text, attentions, codes, num_conds):
|
|
"""
|
|
This was an attempt to make some sense out of the attention matrix retrieved from the unified_voice model. Unfortunately, I can't use it for aligning text & voice.
|
|
"""
|
|
text_padding = num_conds+2
|
|
num_text = text.shape[-1]
|
|
num_context = num_text + text_padding
|
|
assert num_context + 1 == attentions[0][0].shape[-1]
|
|
attentions = self.make_hf_generate_attentions_sane(attentions)
|
|
results = [torch.empty_like(codes) for _ in range(len(attentions))]
|
|
for l, layer in enumerate(attentions):
|
|
dec_context = layer[:, :, num_context:, :]
|
|
# Mask out everything that isn't text (including the start token, which gets a LOT of attention)
|
|
dec_context[:,:,:,:text_padding+1] = 0
|
|
dec_context[:,:,:,num_context:] = 0
|
|
for h in range(dec_context.shape[1]):
|
|
dec_context_indices = torch.argmax(dec_context[0,h], dim=-1)
|
|
print(f'layer_{l};head_{h}: ' + str(dec_context_indices))
|
|
for t, att_tok in enumerate(attentions):
|
|
combined_attention_weights = torch.zeros((codes.shape[0], num_text), device=codes.device)
|
|
for lyr in att_tok:
|
|
token_to_text_attentions = lyr[:, :, -1, text_padding:(text_padding + num_text)].sum(dim=1)
|
|
combined_attention_weights = combined_attention_weights + token_to_text_attentions
|
|
break
|
|
most_attended_text_token = combined_attention_weights.argmax(dim=-1)
|
|
results[:, t] = most_attended_text_token
|
|
eos_token_mask = (codes != self.stop_mel_token)
|
|
return results * eos_token_mask
|
|
|
|
|
|
@register_model
|
|
def register_unified_voice2(opt_net, opt):
|
|
return UnifiedVoice(**opt_get(opt_net, ['kwargs'], {}))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
gpt = UnifiedVoice(model_dim=256, heads=4, train_solo_embeddings=True, use_mel_codes_as_input=True, max_conditioning_inputs=4, freeze_everything_but_position_embeddings=True, types=2)
|
|
l = gpt(torch.randn(2, 3, 80, 800),
|
|
torch.randint(high=256, size=(2,120)),
|
|
torch.tensor([32, 120]),
|
|
torch.randint(high=8192, size=(2,250)),
|
|
torch.tensor([250*256,195*256]),
|
|
types=torch.tensor([0, 1]))
|
|
#gpt.text_forward(torch.randn(2,80,800), torch.randint(high=50, size=(2,80)), torch.tensor([32, 80]))
|