forked from mrq/DL-Art-School
184 lines
7.9 KiB
Python
184 lines
7.9 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from munch import munchify
|
|
|
|
from models.gpt_voice.lucidrains_gpt import Transformer
|
|
from models.tacotron2.taco_utils import get_mask_from_lengths
|
|
from models.tacotron2.text import symbols, sequence_to_text
|
|
from trainer.networks import register_model
|
|
from utils.util import opt_get
|
|
|
|
|
|
class ResBlock(nn.Module):
|
|
def __init__(self, chan):
|
|
super().__init__()
|
|
self.net = nn.Sequential(
|
|
nn.Conv1d(chan, chan, kernel_size=5, padding = 2),
|
|
nn.BatchNorm1d(chan),
|
|
nn.ReLU(),
|
|
nn.Conv1d(chan, chan, kernel_size=5, padding = 2),
|
|
nn.BatchNorm1d(chan)
|
|
)
|
|
|
|
def forward(self, x):
|
|
return F.relu(self.net(x) + x)
|
|
|
|
|
|
class MelEncoder(nn.Module):
|
|
def __init__(self, channels, mel_channels=80):
|
|
super().__init__()
|
|
self.channels = channels
|
|
self.encoder = nn.Sequential(nn.Conv1d(mel_channels, channels//4, kernel_size=7, padding=3),
|
|
ResBlock(channels//4),
|
|
ResBlock(channels//4),
|
|
nn.Conv1d(channels//4, channels//2, kernel_size=5, stride=2, padding=2),
|
|
nn.BatchNorm1d(channels//2),
|
|
nn.ReLU(),
|
|
ResBlock(channels//2),
|
|
ResBlock(channels//2),
|
|
ResBlock(channels//2),
|
|
nn.Conv1d(channels//2, channels, kernel_size=5, stride=2, padding=2),
|
|
ResBlock(channels),
|
|
ResBlock(channels),
|
|
ResBlock(channels)
|
|
)
|
|
|
|
def forward(self, x):
|
|
return self.encoder(x)
|
|
|
|
|
|
class GptAsr(nn.Module):
|
|
NUMBER_SYMBOLS = len(symbols)
|
|
NUMBER_TEXT_TOKENS = NUMBER_SYMBOLS+1
|
|
|
|
def __init__(self, layers=8, model_dim=512, heads=8, max_symbols_per_phrase=200, max_mel_frames=1000):
|
|
super().__init__()
|
|
self.max_mel_frames = max_mel_frames // 4 # Mel frames are reduced by a factor of 4 during encoding.
|
|
self.max_symbols_per_phrase = max_symbols_per_phrase
|
|
|
|
self.model_dim = model_dim
|
|
self.max_mel_frames = self.max_mel_frames
|
|
self.text_embedding = nn.Embedding(self.NUMBER_TEXT_TOKENS, model_dim)
|
|
self.mel_encoder = MelEncoder(model_dim)
|
|
self.text_pos_embedding = nn.Embedding(self.max_symbols_per_phrase + 1, model_dim)
|
|
self.mel_pos_embedding = nn.Embedding(self.max_mel_frames, model_dim)
|
|
self.gpt = Transformer(dim=model_dim, depth=layers, seq_len=2 + self.max_symbols_per_phrase + self.max_mel_frames, heads=heads,
|
|
attn_dropout=.1, ff_dropout=.1, non_causal_sequence_partition=self.max_mel_frames)
|
|
|
|
self.final_norm = nn.LayerNorm(model_dim)
|
|
self.text_head = nn.Linear(model_dim, self.NUMBER_TEXT_TOKENS)
|
|
|
|
def get_logits(self, mel_inputs, text_targets):
|
|
# Pad front and back. Pad at front is the "START" token.
|
|
text_targets = F.pad(text_targets, (1,0), value=self.NUMBER_SYMBOLS)
|
|
text_targets = F.pad(text_targets, (0, self.max_symbols_per_phrase - text_targets.shape[1]))
|
|
text_emb = self.text_embedding(text_targets)
|
|
text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_targets.device))
|
|
mel_emb = self.mel_encoder(mel_inputs)
|
|
mel_emb = F.pad(mel_emb, (0, self.max_mel_frames - mel_emb.shape[-1]))
|
|
mel_emb = mel_emb.permute(0,2,1).contiguous()
|
|
mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
|
|
emb = torch.cat([mel_emb, text_emb], dim=1)
|
|
enc = self.gpt(emb)
|
|
|
|
text_logits = self.final_norm(enc[:, self.max_mel_frames:])
|
|
text_logits = self.text_head(text_logits)
|
|
text_logits = text_logits.permute(0,2,1)
|
|
return text_logits
|
|
|
|
def forward(self, mel_inputs, text_targets):
|
|
text_logits = self.get_logits(mel_inputs, text_targets)
|
|
loss_text = F.cross_entropy(text_logits[:,:,:-1], text_targets[:,1:].long())
|
|
return loss_text.mean(), text_logits
|
|
|
|
def inference_beam_topk(self, mel):
|
|
def topk_sampler(distribution, k):
|
|
return torch.topk(distribution, k=k, dim=-1)
|
|
return self.inference_beam(mel, topk_sampler)
|
|
|
|
def inference_beam_sampled(self, mel):
|
|
def multinomial_sampler(distribution, k):
|
|
indices = torch.multinomial(distribution, num_samples=k, replacement=False)
|
|
values = torch.gather(distribution, dim=1, index=indices)
|
|
class container:
|
|
def __init__(self, i, v):
|
|
self.indices = i
|
|
self.values = v
|
|
return container(indices, values)
|
|
return self.inference_beam(mel, multinomial_sampler)
|
|
|
|
def inference_beam(self, mel_inputs, sampler_fn):
|
|
beam_width = 16
|
|
temperature = .8
|
|
|
|
b, _, s = mel_inputs.shape
|
|
assert b == 1 # Beam search only works on batches of one.
|
|
mel_emb = self.mel_encoder(mel_inputs)
|
|
mel_emb = F.pad(mel_emb, (0, self.max_mel_frames - mel_emb.shape[-1]))
|
|
mel_emb = mel_emb.permute(0,2,1).contiguous()
|
|
mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
|
|
|
|
text_seq = torch.full((b,1), fill_value=self.NUMBER_SYMBOLS, device=mel_emb.device)
|
|
probabilities = torch.ones((b,), device=mel_emb.device)
|
|
while text_seq.shape[-1] < self.max_symbols_per_phrase:
|
|
text_emb = self.text_embedding(text_seq)
|
|
text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=mel_emb.device))
|
|
if text_emb.shape[0] != mel_emb.shape[0]:
|
|
mel_emb = mel_emb.repeat(text_emb.shape[0], 1, 1)
|
|
emb = torch.cat([mel_emb, text_emb], dim=1)
|
|
enc = self.gpt(emb)
|
|
text_logits = self.final_norm(enc[:, mel_emb.shape[1]:])
|
|
text_logits = self.text_head(text_logits)
|
|
topk = sampler_fn(F.softmax(temperature * text_logits[:, -1], dim=-1), k=beam_width)
|
|
probabilities = (probabilities.repeat_interleave(beam_width, dim=0) * topk.values.flatten())
|
|
probabilities, sort_indices = torch.sort(probabilities, descending=True)
|
|
probabilities = probabilities[:beam_width]
|
|
|
|
text_seq = text_seq.repeat_interleave(beam_width, dim=0)
|
|
codes = topk.indices.flatten()
|
|
text_seq = torch.cat([text_seq, codes.unsqueeze(1)], dim=1)
|
|
text_seq = text_seq[sort_indices]
|
|
text_seq = text_seq[:beam_width]
|
|
|
|
# PAD doubles as a stop token. PAD=0.
|
|
if torch.all(torch.any(text_seq == 0, dim=1)):
|
|
break
|
|
|
|
if text_seq.shape[1] >= self.max_mel_frames:
|
|
print("Warning! Encountered frame limit before a pad token. Output is likely wrong.")
|
|
|
|
return text_seq
|
|
|
|
|
|
@register_model
|
|
def register_gpt_asr(opt_net, opt):
|
|
return GptAsr(**opt_get(opt_net, ['kwargs'], {}))
|
|
|
|
|
|
# Halves the number of layers in the provided model.
|
|
def distill(model):
|
|
rc = 0
|
|
i = 0
|
|
while i < len(model.gpt.layers.layers):
|
|
if rc % 2 != 0:
|
|
del model.gpt.layers.layers[i]
|
|
else:
|
|
i += 1
|
|
rc += 1
|
|
return model
|
|
|
|
|
|
if __name__ == '__main__':
|
|
gpt = GptAsr(max_symbols_per_phrase=250, max_mel_frames=1400, layers=12, model_dim=768, heads=12)
|
|
gpt.load_state_dict(torch.load('../experiments/train_gpt_asr_mass/models/21500_mel_gen.pth'))
|
|
student = distill(gpt)
|
|
torch.save(student.state_dict(), '../experiments/train_gpt_asr_mass/models/21500_mel_gen_distilled.pth')
|
|
#l = gpt(torch.randn(2,80,800),
|
|
# torch.randint(high=len(symbols), size=(2,180)))
|
|
|
|
#o = gpt.infer(torch.randint(high=24, size=(2,60)))
|
|
#print(o.shape)
|
|
|
|
|