168 lines
7.2 KiB
Python
168 lines
7.2 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 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, sequence_to_text
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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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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):
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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.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 + 1, 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=2 + 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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# 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.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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def inference_beam_topk(self, mel):
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def topk_sampler(distribution, k):
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return torch.topk(distribution, k=k, dim=-1)
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return self.inference_beam(mel, topk_sampler)
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def inference_beam_sampled(self, mel):
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def multinomial_sampler(distribution, k):
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indices = torch.multinomial(distribution, num_samples=k, replacement=False)
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values = torch.gather(distribution, dim=1, index=indices)
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class container:
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def __init__(self, i, v):
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self.indices = i
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self.values = v
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return container(indices, values)
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return self.inference_beam(mel, multinomial_sampler)
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def inference_beam(self, mel_inputs, sampler_fn):
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beam_width = 16
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temperature = .8
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b, _, s = mel_inputs.shape
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assert b == 1 # Beam search only works on batches of one.
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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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text_seq = torch.full((b,1), fill_value=self.NUMBER_SYMBOLS, device=mel_emb.device)
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probabilities = torch.ones((b,), device=mel_emb.device)
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while text_seq.shape[-1] < self.max_symbols_per_phrase:
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text_emb = self.text_embedding(text_seq)
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text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=mel_emb.device))
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if text_emb.shape[0] != mel_emb.shape[0]:
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mel_emb = mel_emb.repeat(text_emb.shape[0], 1, 1)
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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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text_logits = self.final_norm(enc[:, mel_emb.shape[1]:])
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text_logits = self.text_head(text_logits)
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topk = sampler_fn(F.softmax(temperature * text_logits[:, -1], dim=-1), k=beam_width)
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probabilities = (probabilities.repeat_interleave(beam_width, dim=0) * topk.values.flatten())
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probabilities, sort_indices = torch.sort(probabilities, descending=True)
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probabilities = probabilities[:beam_width]
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text_seq = text_seq.repeat_interleave(beam_width, dim=0)
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codes = topk.indices.flatten()
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text_seq = torch.cat([text_seq, codes.unsqueeze(1)], dim=1)
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text_seq = text_seq[sort_indices]
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text_seq = text_seq[:beam_width]
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# PAD doubles as a stop token. PAD=0.
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if torch.all(torch.any(text_seq == 0, dim=1)):
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break
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if text_seq.shape[1] >= self.max_mel_frames:
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print("Warning! Encountered frame limit before a pad token. Output is likely wrong.")
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return text_seq
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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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