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 forward(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) # Compute loss 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) loss_text = F.cross_entropy(text_logits[:,:,:-1], text_targets[:,1:].long()) return loss_text.mean() 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'], {})) if __name__ == '__main__': gpt = GptAsr() l = gpt(torch.randn(2,80,800), torch.randint(high=len(symbols), size=(2,180))) print(l.shape) #o = gpt.infer(torch.randint(high=24, size=(2,60))) #print(o.shape)