134 lines
5.7 KiB
Python
134 lines
5.7 KiB
Python
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 GPT2Model, GPT2Config
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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=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 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=5, padding=2),
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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=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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ResBlock(channels//2),
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ResBlock(channels//2),
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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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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 GptAsrHf(nn.Module):
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NUMBER_SYMBOLS = len(symbols)
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NUMBER_TEXT_TOKENS = NUMBER_SYMBOLS+1
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def __init__(self, layers=8, model_dim=512, heads=8, max_symbols_per_phrase=200, max_mel_frames=1000, checkpointing=True):
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super().__init__()
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self.max_mel_frames = max_mel_frames // 4 # Mel frames are reduced by a factor of 4 during encoding.
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self.max_symbols_per_phrase = max_symbols_per_phrase
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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.mel_encoder = MelEncoder(model_dim)
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self.text_pos_embedding = nn.Embedding(self.max_symbols_per_phrase + 1, model_dim)
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self.mel_pos_embedding = nn.Embedding(self.max_mel_frames, model_dim)
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seq_length = 2+self.max_symbols_per_phrase+self.max_mel_frames
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self.gpt = GPT2Model(GPT2Config(vocab_size=self.NUMBER_TEXT_TOKENS,
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n_positions=seq_length,
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n_ctx=seq_length,
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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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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 get_logits(self, mel_inputs, text_targets):
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# Pad front and back. Pad at front is the "START" token.
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text_targets = F.pad(text_targets, (1,0), value=self.NUMBER_SYMBOLS)
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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.gpt.get_input_embeddings()(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(inputs_embeds=emb, return_dict=True).last_hidden_state
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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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return text_logits
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def forward(self, mel_inputs, text_targets):
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text_logits = self.get_logits(mel_inputs, text_targets)
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loss_text = F.cross_entropy(text_logits[:,:,:-1], text_targets[:,1:].long())
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return loss_text.mean(), text_logits
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@register_model
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def register_gpt_asr_hf(opt_net, opt):
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return GptAsrHf(**opt_get(opt_net, ['kwargs'], {}))
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# Quick script that loads a model and halves the number of layers, then saves that model.
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def distill():
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gpt = GptAsrHf(max_symbols_per_phrase=250, max_mel_frames=1400, layers=12, model_dim=768, heads=12)
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gpt.load_state_dict(torch.load('../experiments/train_gpt_asr_mass/models/21500_mel_gen.pth'))
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rc = 0
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i = 0
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while i < len(gpt.gpt.layers.layers):
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if rc % 2 != 0:
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del gpt.gpt.layers.layers[i]
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else:
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i += 1
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rc += 1
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torch.save(gpt.state_dict(), '../experiments/train_gpt_asr_mass/models/21500_mel_gen_distilled.pth')
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if __name__ == '__main__':
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gpt = GptAsrHf(max_symbols_per_phrase=100, max_mel_frames=200, layers=6, model_dim=256, heads=2)
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l = gpt(torch.randn(2,80,800), torch.randint(high=len(symbols), size=(2,100)))
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'''
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with torch.no_grad():
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t = torch.randn(1,80,800).cuda()
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start = time()
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s = gpt.inference_beam_topk(t)
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print(time()-start)
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start = time()
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o = gpt.inference_beam_topk(t, fn='inference_beam_opt')
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print(time()-start)
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'''
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