108 lines
4.1 KiB
Python
108 lines
4.1 KiB
Python
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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 munch import munchify
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from models.gpt_voice.lucidrains_gpt import Transformer
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from models.tacotron2.taco_utils import get_mask_from_lengths
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from models.tacotron2.text import symbols
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from trainer.networks import register_model
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from utils.util import opt_get
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class ResBlock(nn.Module):
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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=5, padding = 2),
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nn.BatchNorm1d(chan),
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nn.ReLU(),
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nn.Conv1d(chan, chan, kernel_size=5, padding = 2),
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nn.BatchNorm1d(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 MelEncoder(nn.Module):
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def __init__(self, channels, mel_channels=80):
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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=7, padding=3),
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ResBlock(channels//4),
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ResBlock(channels//4),
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nn.Conv1d(channels//4, channels//2, kernel_size=5, stride=2, padding=2),
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nn.BatchNorm1d(channels//2),
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nn.ReLU(),
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ResBlock(channels//2),
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ResBlock(channels//2),
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ResBlock(channels//2),
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nn.Conv1d(channels//2, channels, kernel_size=5, stride=2, padding=2),
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ResBlock(channels),
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ResBlock(channels),
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ResBlock(channels)
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)
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def forward(self, x):
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return self.encoder(x)
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class GptAsr(nn.Module):
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MAX_SYMBOLS_PER_PHRASE = 200
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MAX_MEL_FRAMES = 1000 // 4
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NUMBER_SYMBOLS = len(symbols)
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NUMBER_TEXT_TOKENS = NUMBER_SYMBOLS
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def __init__(self, layers=8, model_dim=512, heads=8):
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super().__init__()
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self.model_dim = model_dim
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self.max_mel_frames = self.MAX_MEL_FRAMES
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self.text_embedding = nn.Embedding(self.NUMBER_TEXT_TOKENS, model_dim)
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self.mel_encoder = MelEncoder(model_dim)
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self.text_pos_embedding = nn.Embedding(self.MAX_SYMBOLS_PER_PHRASE, model_dim)
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self.mel_pos_embedding = nn.Embedding(self.MAX_MEL_FRAMES, model_dim)
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self.gpt = Transformer(dim=model_dim, depth=layers, seq_len=1+self.MAX_SYMBOLS_PER_PHRASE+self.MAX_MEL_FRAMES, heads=heads,
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attn_dropout=.1, ff_dropout=.1, non_causal_sequence_partition=self.MAX_MEL_FRAMES)
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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)
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def forward(self, mel_inputs, text_targets):
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text_targets = F.pad(text_targets, (0, self.MAX_SYMBOLS_PER_PHRASE-text_targets.shape[1]))
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text_emb = self.text_embedding(text_targets)
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text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_targets.device))
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mel_emb = self.mel_encoder(mel_inputs)
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mel_emb = F.pad(mel_emb, (0, self.MAX_MEL_FRAMES-mel_emb.shape[-1]))
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mel_emb = mel_emb.permute(0,2,1).contiguous()
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mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
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emb = torch.cat([mel_emb, text_emb], dim=1)
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enc = self.gpt(emb)
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# Compute loss
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text_logits = self.final_norm(enc[:, self.MAX_MEL_FRAMES:])
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text_logits = self.text_head(text_logits)
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text_logits = text_logits.permute(0,2,1)
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loss_text = F.cross_entropy(text_logits[:,:,1:], text_targets[:,:-1].long())
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return loss_text.mean()
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@register_model
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def register_gpt_asr(opt_net, opt):
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return GptAsr(**opt_get(opt_net, ['kwargs'], {}))
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if __name__ == '__main__':
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gpt = GptAsr()
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l = gpt(torch.randn(2,80,800),
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torch.randint(high=len(symbols), size=(2,180)))
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print(l.shape)
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#o = gpt.infer(torch.randint(high=24, size=(2,60)))
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#print(o.shape)
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