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This commit is contained in:
James Betker 2021-07-30 23:07:35 -06:00
parent dadc54795c
commit 31ee9ae262
2 changed files with 86 additions and 12 deletions
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94
codes/models/gpt_voice/gpt_tts.py
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@ -1,6 +1,7 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
from models.arch_util import ConvGnSilu
from models.tacotron2.taco_utils import get_mask_from_lengths
@ -9,6 +10,18 @@ from models.gpt_voice.min_gpt import GPT, GPTConfig
from trainer.networks import register_model
# A Conv1d that masks out kernel elements ahead of the current location.
class CausalConv1d(nn.Conv1d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.kernel_mask = torch.ones_like(self.weight)
self.kernel_mask[:, :, -(self.kernel_size[0]//2):] = 0
def forward(self, input):
self.kernel_mask = self.kernel_mask.to(input.device)
return self._conv_forward(input, self.weight * self.kernel_mask, self.bias)
class GptTts(nn.Module):
def __init__(self):
super().__init__()
@ -18,21 +31,28 @@ class GptTts(nn.Module):
max_mel_frames = 900
mel_dim=80
self.model_dim = model_dim
self.max_mel_frames = max_mel_frames
self.text_embedding = nn.Embedding(number_symbols, model_dim)
self.mel_encoder = nn.Sequential(ConvGnSilu(mel_dim, model_dim//2, kernel_size=3, convnd=nn.Conv1d),
ConvGnSilu(model_dim//2, model_dim, kernel_size=3, stride=2, convnd=nn.Conv1d))
# Whenever we process MEL frames, we need to be careful to use casually masked convolutions to avoid adding bias
# into the model which we cannot provide in inference.
self.mel_encoder = nn.Sequential(ConvGnSilu(mel_dim, model_dim//2, kernel_size=5, convnd=CausalConv1d),
ConvGnSilu(model_dim//2, model_dim, kernel_size=5, stride=2, convnd=CausalConv1d))
# *_tags are additively applied to
self.text_tags = nn.Parameter(torch.randn(1, 1, model_dim)/256.0)
self.separator = nn.Parameter(torch.randn(1, 1, model_dim))
self.audio_tags = nn.Parameter(torch.randn(1, 1, model_dim)/256.0)
self.gpt = GPT(GPTConfig(max_symbols_per_phrase+max_mel_frames//2, n_embd=model_dim, n_head=8))
self.gpt = GPT(GPTConfig(1+max_symbols_per_phrase+max_mel_frames//2, n_embd=model_dim, n_head=8))
self.gate_head = nn.Sequential(ConvGnSilu(model_dim, model_dim, kernel_size=5, convnd=nn.Conv1d),
self.gate_head = nn.Sequential(ConvGnSilu(model_dim, model_dim, kernel_size=5, convnd=CausalConv1d),
nn.Upsample(scale_factor=2, mode='nearest'),
ConvGnSilu(model_dim, model_dim//2, kernel_size=5, convnd=nn.Conv1d),
ConvGnSilu(model_dim, model_dim//2, kernel_size=5, convnd=CausalConv1d),
# No need for causal convolutions when kernel_size=1
nn.Conv1d(model_dim//2, 1, kernel_size=1))
self.mel_head = nn.Sequential(ConvGnSilu(model_dim, model_dim, kernel_size=5, convnd=nn.Conv1d),
self.mel_head = nn.Sequential(ConvGnSilu(model_dim, model_dim, kernel_size=5, convnd=CausalConv1d),
nn.Upsample(scale_factor=2, mode='nearest'),
ConvGnSilu(model_dim, model_dim//2, kernel_size=5, convnd=nn.Conv1d),
ConvGnSilu(model_dim//2, model_dim//2, kernel_size=5, convnd=nn.Conv1d),
ConvGnSilu(model_dim, model_dim//2, kernel_size=5, convnd=CausalConv1d),
ConvGnSilu(model_dim//2, model_dim//2, kernel_size=5, convnd=CausalConv1d),
ConvGnSilu(model_dim//2, mel_dim, kernel_size=1, activation=False, norm=False, convnd=nn.Conv1d))
def forward(self, text_inputs, mel_targets, output_lengths):
@ -45,9 +65,11 @@ class GptTts(nn.Module):
text_emb = text_emb + self.text_tags
mel_emb = self.mel_encoder(mel_targets).permute(0,2,1)
mel_emb = mel_emb + self.audio_tags
emb = torch.cat([text_emb, mel_emb], dim=1)
emb = torch.cat([text_emb,
self.separator.repeat(text_emb.shape[0],1,1),
mel_emb], dim=1)
enc = self.gpt(emb)
mel_portion = enc[:, text_emb.shape[1]:].permute(0,2,1)
mel_portion = enc[:, text_emb.shape[1]+1:].permute(0,2,1)
gates = self.gate_head(mel_portion).squeeze(1)
mel_pred = self.mel_head(mel_portion)
@ -62,6 +84,53 @@ class GptTts(nn.Module):
gates = gates[:, :-1]
return mel_pred, gates
def test_guide(self, mel_guide, amount=50):
mel_guide = mel_guide[:,:,:amount]
mel_emb = self.mel_encoder(mel_guide).permute(0,2,1)
mel_emb = mel_emb + self.audio_tags
return mel_emb
def inference(self, text_inputs, mel_guide):
MEL_HEAD_EXPANSION = 2
GATE_THRESHOLD = .95
text_emb = self.text_embedding(text_inputs)
text_emb = text_emb + self.text_tags
b,s,c = text_emb.shape
emb = torch.cat([text_emb,
self.separator.repeat(text_emb.shape[0],1,1)], dim=1)
#self.test_guide(mel_guide)], dim=1)
completed = torch.zeros((b,), device=text_inputs.device, dtype=torch.bool)
output = None
for i in tqdm(range(self.max_mel_frames)):
enc = self.gpt(emb)
inferred = enc[:,s:,:].permute(0,2,1)
# Create output frames.
inferred_mel_frame = self.mel_head(inferred)[:,:,-MEL_HEAD_EXPANSION:]
inferred_mel_frame = inferred_mel_frame * (~completed).float().view(b,1,1)
if output is None:
output = inferred_mel_frame
else:
output = torch.cat([output, inferred_mel_frame], dim=2)
# Test termination condition
gate = F.sigmoid(self.gate_head(inferred)).max(dim=-1).values # TODO: accept single-frame terminations.
completed = completed.logical_or((gate > GATE_THRESHOLD).squeeze(1)) # This comprises a latch - but that may not be wise.
if torch.all(completed):
break
# Apply inferred mel_frames to emb for next pass.
mel_emb = self.mel_encoder(output).permute(0,2,1)
mel_emb = mel_emb + self.audio_tags
emb = torch.cat([text_emb,
self.separator.repeat(text_emb.shape[0],1,1),
mel_emb], dim=1)
if i == self.max_mel_frames//2:
print("Warning! Inference hit mel frame cap without encountering a stop token.")
break
return output
@register_model
def register_gpt_tts(opt_net, opt):
@ -75,3 +144,8 @@ if __name__ == '__main__':
torch.tensor([600,747]))
print(m.shape)
print(g.shape)
o = gpt.infer(torch.randint(high=24, size=(2,60)))
print(o.shape)

2
codes/scripts/audio/test_audio_gen.py
View File

@ -40,7 +40,7 @@ if __name__ == "__main__":
torch.backends.cudnn.benchmark = True
want_metrics = False
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_vqvae_audio_lj.yml')
parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_gpt_tts_lj.yml')
opt = option.parse(parser.parse_args().opt, is_train=False)
opt = option.dict_to_nonedict(opt)
utils.util.loaded_options = opt