Add gpt_segmentor model

The idea is to specifically train a model that extracts phrases from
audio clips.

codes/models/gpt_voice/gpt_audio_segmentor.py

@@ -0,0 +1,101 @@
+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 GptSegmentor(nn.Module):
+    MAX_SYMBOLS_PER_PHRASE = 200
+    MAX_MEL_FRAMES = 2000 // 4
+
+    def __init__(self, layers=8, model_dim=512, heads=8):
+        super().__init__()
+
+        self.model_dim = model_dim
+        self.max_mel_frames = self.MAX_MEL_FRAMES
+        self.mel_encoder = MelEncoder(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.stop_head = nn.Linear(model_dim, 1)
+
+    def forward(self, mel_inputs, mel_lengths):
+        mel_emb = self.mel_encoder(mel_inputs)
+        mel_lengths = mel_lengths // 4  # The encoder decimates the mel by a factor of 4.
+        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))
+
+        enc = self.gpt(mel_emb)
+
+        # Compute loss
+        b, s, _ = enc.shape
+        mel_pad_mask = ~get_mask_from_lengths(mel_lengths-1, s)
+        targets = torch.zeros((b,s), device=enc.device).masked_fill_(mel_pad_mask, 1)
+        stop_logits = self.final_norm(enc)
+        stop_logits = self.stop_head(stop_logits)
+        loss = F.binary_cross_entropy_with_logits(stop_logits.squeeze(-1), targets)
+
+        return loss.mean()
+
+
+@register_model
+def register_gpt_segmentor(opt_net, opt):
+    return GptSegmentor(**opt_get(opt_net, ['kwargs'], {}))
+
+
+if __name__ == '__main__':
+    gpt = GptSegmentor()
+    l = gpt(torch.randn(3,80,94),
+            torch.tensor([18,42,93]))
+    print(l.shape)
+
+    #o = gpt.infer(torch.randint(high=24, size=(2,60)))
+    #print(o.shape)
+
+